zB2_threadup_captions_using_model_largedata.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3bb6130d-e340-42eb-8669-a9082b144e35",
   "metadata": {},
   "source": [
    "# Fashion Image Captioner\n",
    "\n",
    "This notebook finetunes a general visual language model (VLM) that can caption images to be able to generate more specific search-relevant descriptions of images from thrifted fashioin item inventories"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "347e8705-8b8c-4fe8-94cc-ca8c33473996",
   "metadata": {},
   "source": [
    "# Prepare Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "07a56973-6f74-4f49-9d69-4f1e420e5e1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Libraries\n",
    "\n",
    "#General\n",
    "import os\n",
    "import gc\n",
    "\n",
    "#Standard ML\n",
    "from pandas import read_csv\n",
    "from pandas import DataFrame\n",
    "from sklearn.model_selection import train_test_split\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "#Data Visualization\n",
    "from torchvision.transforms import ToPILImage\n",
    "from PIL import Image as pilImage\n",
    "from IPython.display import display\n",
    "\n",
    "#HuggingFace Libraries\n",
    "from datasets import Dataset as hfDataset\n",
    "from datasets import Image\n",
    "from accelerate import Accelerator\n",
    "\n",
    "#The Model\n",
    "from transformers import Blip2ForConditionalGeneration #visual language model (VLM) can do multimodal tasks like captioning or answering questions about images.\n",
    "from transformers import Blip2Processor\n",
    "\n",
    "#Extras\n",
    "from tqdm import tqdm #cute loading percent bar\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a6c4231e-f9b2-4e26-82ca-c505bf5b75bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1 cuda GPUs available for use\n"
     ]
    }
   ],
   "source": [
    "# Check GPU Availability\n",
    "# Device will be reset automatically later by accelerator, but this is a good sanity check to know what we should be working with\n",
    "\n",
    "# Check for MPS availability\n",
    "if torch.backends.mps.is_available():\n",
    "    device = torch.device(\"mps\")  # Use Apple's MPS\n",
    "    print(f\"Using torch version {torch.__version__}\")\n",
    "    print(\"Found MPS is available for use\") # Is MPS even available? macOS 12.3+\n",
    "    print(f\"Current version on Pytorch was built with MPS activation: {torch.backends.mps.is_built()}\") # Was the current version of PyTorch built with MPS activated?\n",
    "elif torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")\n",
    "    nGPUs = torch.cuda.device_count()\n",
    "    print(f\"Found {nGPUs} cuda GPUs available for use\")\n",
    "else:\n",
    "    device = torch.device(\"cpu\")\n",
    "    print(\"No GPU device found, running on cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b1f11cf-b5c2-48c4-9948-452298fd995a",
   "metadata": {},
   "source": [
    "# Import Data\n",
    "\n",
    "To use as-is, a root project folder should contain\n",
    "* a subproject folder containing this notebook (can have any name)\n",
    "* a data folder, called Data, containing\n",
    "  *  DB_FILENAME: a csv file with at least two columns\n",
    "     *   IMAGE_PATH_COL: contains image names\n",
    "     *   CAPTION_COL: contains image cpations\n",
    "  * IMAGE_FOLDERNAME: A folder containing images with the names referenced in IMAGE_PATH_COL\n",
    " \n",
    "The logic for this sturcture is that there can be multiple subprojects that need to access the same dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "281e6f4f-4ef2-4926-a598-f0edc2fe4807",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SET IMAGE LOCATION INFO\n",
    "\n",
    "#Specify Data Locations\n",
    "CSV_FILENAME_g = 'item_descriptions.csv' #'large_fashion_descriptions' #'ThredupData.csv'\n",
    "CAPTION_COL_g = \"description\"\n",
    "IMAGE_PATH_COL_g = \"filename\"\n",
    "IMAGE_FOLDERNAME_g = 'StanfordJpegs\\dedupped_images' #'Extracted_Images'  #'StanfordJpegs'\n",
    "\n",
    "DATA_FOLDER_g = os.path.join('data') #'..' means the folder one up from current notebook's folder, which happens to be where I have my data folder\n",
    "IMAGE_FOLDER_g = os.path.join(DATA_FOLDER_g, IMAGE_FOLDERNAME_g)\n",
    "CSV_FILEPATH_g = os.path.join(DATA_FOLDER_g, CSV_FILENAME_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1a14ccd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data\\\\StanfordJpegs\\\\dedupped_images'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMAGE_FOLDER_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "60c7b9af-17b6-48ff-a717-9db07a860731",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>item108153495.jpg</td>\n",
       "      <td>This is a burgundy women's blouse featuring a ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>item112414117.jpg</td>\n",
       "      <td>The item is a classic black suede pump featuri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>item112851743.jpg</td>\n",
       "      <td>These are women's coral pink straight-leg capr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>item111933518.jpg</td>\n",
       "      <td>This is a black suede peep-toe ankle boot feat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>item110548869.jpg</td>\n",
       "      <td>The item is a pair of straight-leg, red pants ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>item106874647.jpg</td>\n",
       "      <td>This is a classic, long-sleeved white button-u...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>item111414733.jpg</td>\n",
       "      <td>This is a classic black leather knee-high boot...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>item110695025.jpg</td>\n",
       "      <td>This is an off-white, cozy, cable knit sweater...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>item112262250.jpg</td>\n",
       "      <td>This fashion item is a black ankle boot constr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>item112315277.jpg</td>\n",
       "      <td>This is a classic black leather Chelsea boot f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>item105067431.jpg</td>\n",
       "      <td>These are red, straight-cut, mid-rise trousers...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>item111847207.jpg</td>\n",
       "      <td>This is a contemporary, purple, possibly synth...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>item103003446.jpg</td>\n",
       "      <td>This is a long-sleeved, deep plum blouse featu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>item112546508.jpg</td>\n",
       "      <td>This is a pair of black leather Chelsea boots ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>item111918601.jpg</td>\n",
       "      <td>This is a maroon, long-sleeved, round-neck blo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>item112313915.jpg</td>\n",
       "      <td>This is a classic black leather knee-high boot...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>item104830431.jpg</td>\n",
       "      <td>This is a women's white, long-sleeve blouse ma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>item106514323.jpg</td>\n",
       "      <td>This is a classic, tailored-fit white dress sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>item109150596.jpg</td>\n",
       "      <td>This is a men's classic white button-up shirt ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>item98918987.jpg</td>\n",
       "      <td>This is a burgundy crinkled-texture blouse wit...</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                filename                                        description\n",
       "df_id                                                                      \n",
       "0      item108153495.jpg  This is a burgundy women's blouse featuring a ...\n",
       "1      item112414117.jpg  The item is a classic black suede pump featuri...\n",
       "2      item112851743.jpg  These are women's coral pink straight-leg capr...\n",
       "3      item111933518.jpg  This is a black suede peep-toe ankle boot feat...\n",
       "4      item110548869.jpg  The item is a pair of straight-leg, red pants ...\n",
       "5      item106874647.jpg  This is a classic, long-sleeved white button-u...\n",
       "6      item111414733.jpg  This is a classic black leather knee-high boot...\n",
       "7      item110695025.jpg  This is an off-white, cozy, cable knit sweater...\n",
       "8      item112262250.jpg  This fashion item is a black ankle boot constr...\n",
       "9      item112315277.jpg  This is a classic black leather Chelsea boot f...\n",
       "10     item105067431.jpg  These are red, straight-cut, mid-rise trousers...\n",
       "11     item111847207.jpg  This is a contemporary, purple, possibly synth...\n",
       "12     item103003446.jpg  This is a long-sleeved, deep plum blouse featu...\n",
       "13     item112546508.jpg  This is a pair of black leather Chelsea boots ...\n",
       "14     item111918601.jpg  This is a maroon, long-sleeved, round-neck blo...\n",
       "15     item112313915.jpg  This is a classic black leather knee-high boot...\n",
       "16     item104830431.jpg  This is a women's white, long-sleeve blouse ma...\n",
       "17     item106514323.jpg  This is a classic, tailored-fit white dress sh...\n",
       "18     item109150596.jpg  This is a men's classic white button-up shirt ...\n",
       "19      item98918987.jpg  This is a burgundy crinkled-texture blouse wit..."
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#IMPORT RAW DATA TO DATAFRAME\n",
    "\n",
    "def getDatasetDF(csv_filepath = CSV_FILEPATH_g, image_folder = IMAGE_FOLDER_g):\n",
    "    \n",
    "    \"\"\"\n",
    "    Create dataframe based on csv file\n",
    "    returns data_df which has infomration to eventually create two columns: captions (string descriptions of images) and images (paths to image files) and c\n",
    "    \"\"\"\n",
    "    \n",
    "    if len(csv_filepath) == 0:\n",
    "        return\n",
    "    data_df = read_csv(csv_filepath)\n",
    "    data_df.index.name = \"df_id\" #name index to be used for matching datapoints across imported df, hugging face train/test sets and torch dataloaders\n",
    "    \n",
    "    return data_df # Should have \n",
    "    \n",
    "\n",
    "data_df_g = getDatasetDF()\n",
    "data_df_g.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "dfe7414d-5f32-4804-a1cf-e195e920b62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Optionally manually edit dataframe to explore effect of individual changes\n",
    "\n",
    "changeDataManually_g = False\n",
    "\n",
    "def changeRows(data_df = data_df_g, colToChange = CAPTION_COL_g, should_change=changeDataManually_g):\n",
    "    if not should_change: return data_df\n",
    "    else:\n",
    "        colToChange = \"Captions\"\n",
    "        changeDict = {\n",
    "            846: \"a black ankleboot with fringe detailing on the side\",\n",
    "            4815: \"a yellow beanie on a white background\",\n",
    "            5093: \"a cage-style heel with floral print\",\n",
    "            2835: \"a black leather heeled boot with a pointed toe\"   \n",
    "            }\n",
    "        \n",
    "        for itemId, content in changeDict.items():\n",
    "            data_df.loc[itemId,colToChange] = content\n",
    "        return data_df\n",
    "\n",
    "\n",
    "data_df_g = changeRows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "31a2936c-58be-4a75-a6ce-c44d7d571d44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['filename', 'description'], dtype='object')\n",
      "Original samples in dataframe: 151\n",
      "Samples with invalid image paths in dataframe: 123\n",
      "Samples with valid image paths in dataframe: 28\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>captions</th>\n",
       "      <th>image_paths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df_id</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The item is a pair of straight-leg, red pants ...</td>\n",
       "      <td>data\\StanfordJpegs\\dedupped_images\\item1105488...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>This is a classic black leather knee-high boot...</td>\n",
       "      <td>data\\StanfordJpegs\\dedupped_images\\item1114147...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>This is an off-white, cozy, cable knit sweater...</td>\n",
       "      <td>data\\StanfordJpegs\\dedupped_images\\item1106950...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>These are red, straight-cut, mid-rise trousers...</td>\n",
       "      <td>data\\StanfordJpegs\\dedupped_images\\item1050674...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>This is a long-sleeved, deep plum blouse featu...</td>\n",
       "      <td>data\\StanfordJpegs\\dedupped_images\\item1030034...</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                                                captions  \\\n",
       "df_id                                                      \n",
       "4      The item is a pair of straight-leg, red pants ...   \n",
       "6      This is a classic black leather knee-high boot...   \n",
       "7      This is an off-white, cozy, cable knit sweater...   \n",
       "10     These are red, straight-cut, mid-rise trousers...   \n",
       "12     This is a long-sleeved, deep plum blouse featu...   \n",
       "\n",
       "                                             image_paths  \n",
       "df_id                                                     \n",
       "4      data\\StanfordJpegs\\dedupped_images\\item1105488...  \n",
       "6      data\\StanfordJpegs\\dedupped_images\\item1114147...  \n",
       "7      data\\StanfordJpegs\\dedupped_images\\item1106950...  \n",
       "10     data\\StanfordJpegs\\dedupped_images\\item1050674...  \n",
       "12     data\\StanfordJpegs\\dedupped_images\\item1030034...  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#FILTER DATAFRAME TO JUST 3 COLUMNS: df_id, image_paths, captions\n",
    "\n",
    "def imagePathColCreator(data_df, image_folder, image_path_col):\n",
    "    #image_paths = list(os.path.join(image_folder,('item'+str(fileId))) for fileId in data_df[\"Item_Id\"]) #From original ThredupData.csv Use ItemId column to create list of paths to image files\n",
    "    print(data_df.columns)\n",
    "    image_paths = list(os.path.join(image_folder,filePath) for filePath in data_df[image_path_col]); #From Esra's LLM generated caption dataset (files in folder and spreadhseet adjusted to include .jpg in name)\n",
    "    return image_paths\n",
    "\n",
    "def captionColCreator(data_df, caption_col):\n",
    "    #captions = list(data_df[\"Category_Type\"] + \", \" + data_df[\"Description\"]) #If using original ThredupData.csv, use Description column to create list of captions for each image  \n",
    "    captions = data_df[caption_col] #From Esra's LLM generated caption dataset\n",
    "    return captions\n",
    "\n",
    "def createImageAndCaptionDf(data_df = data_df_g, image_folder = IMAGE_FOLDER_g, image_path_col= IMAGE_PATH_COL_g, caption_col = CAPTION_COL_g ):\n",
    "\n",
    "    #Create image paths\n",
    "    data_df[\"image_paths\"] = imagePathColCreator(data_df, image_folder, image_path_col)\n",
    "\n",
    "    #Create caption paths\n",
    "    data_df[\"captions\"] = captionColCreator(data_df, caption_col)\n",
    "     \n",
    "    #Discard rows without valid image paths\n",
    "    print(f\"Original samples in dataframe: {len(data_df)}\")\n",
    "    valid_rows = [ind for ind, image_path in enumerate(data_df[\"image_paths\"]) if os.path.exists(image_path)] #Get rid of any image paths that do not point to actual image files\n",
    "    invalid_rows = [ind for ind in data_df.index if ind not in valid_rows]\n",
    "\n",
    "    invalid_data_df = data_df.iloc[invalid_rows]\n",
    "    print(f\"Samples with invalid image paths in dataframe: {len(invalid_data_df)}\")\n",
    "    data_df = data_df.iloc[valid_rows]\n",
    "    print(f\"Samples with valid image paths in dataframe: {len(data_df)}\")\n",
    "\n",
    "    #Discard all other columns\n",
    "    data_df = data_df[[\"captions\", \"image_paths\"]]\n",
    "\n",
    "    return data_df, invalid_data_df\n",
    "\n",
    "data_df_g, invalid_paths_df_g = createImageAndCaptionDf()\n",
    "\n",
    "data_df_g.head()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ee6ecd-eaae-44ee-add6-b766d53c1b56",
   "metadata": {},
   "source": [
    "# Separate Train/Valid/Test Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "722e8780-966c-4a2d-9865-051cc6ff2594",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training Toggles\n",
    "\n",
    "VALIDATION_PORTION_g = 0.2\n",
    "TEST_PORTION_g = .1\n",
    "SEED_g = 22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5e3772b3-1b6e-4e09-9b92-024d3b2797ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize variables for train/validation/test dataframes, huggingface datasets, and torch data loaders\n",
    "train_df_g = None\n",
    "train_hfset_g = None\n",
    "train_dataloader_g = None\n",
    "\n",
    "validation_df_g = None\n",
    "validation_hfset_g = None\n",
    "validation_dataloader_g = None\n",
    "\n",
    "test_df_g = None\n",
    "test_hfset_g = None\n",
    "test_dataloader_g = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "dc58c676-beef-43a1-a0f6-e3f8179963f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train Size: 19,\n",
      "Validation Size: 8,\n",
      "Test Size: 1 \n",
      "\n"
     ]
    },
    {
     "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>captions</th>\n",
       "      <th>image_paths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>This item is a pair of vibrant red, straight-l...</td>\n",
       "      <td>data\\StanfordJpegs\\dedupped_images\\item1000016...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>This is a classic black suede pump with a poin...</td>\n",
       "      <td>data\\StanfordJpegs\\dedupped_images\\item1097147...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>This is an off-white, cozy, cable knit sweater...</td>\n",
       "      <td>data\\StanfordJpegs\\dedupped_images\\item1106950...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>This is a classic-fit white dress shirt made f...</td>\n",
       "      <td>data\\StanfordJpegs\\dedupped_images\\item1064935...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>This is a contemporary-style black pebbled lea...</td>\n",
       "      <td>data\\StanfordJpegs\\dedupped_images\\item1093901...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                captions  \\\n",
       "df_id                                                      \n",
       "67     This item is a pair of vibrant red, straight-l...   \n",
       "43     This is a classic black suede pump with a poin...   \n",
       "7      This is an off-white, cozy, cable knit sweater...   \n",
       "33     This is a classic-fit white dress shirt made f...   \n",
       "68     This is a contemporary-style black pebbled lea...   \n",
       "\n",
       "                                             image_paths  \n",
       "df_id                                                     \n",
       "67     data\\StanfordJpegs\\dedupped_images\\item1000016...  \n",
       "43     data\\StanfordJpegs\\dedupped_images\\item1097147...  \n",
       "7      data\\StanfordJpegs\\dedupped_images\\item1106950...  \n",
       "33     data\\StanfordJpegs\\dedupped_images\\item1064935...  \n",
       "68     data\\StanfordJpegs\\dedupped_images\\item1093901...  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Populate train/validation/test dataframes\n",
    "\n",
    "# Split off training data\n",
    "train_df_g, test_and_validation_df_g = train_test_split(data_df_g, test_size=(VALIDATION_PORTION_g + TEST_PORTION_g), random_state=SEED_g)\n",
    "\n",
    "#Split remaining into validation and test dats\n",
    "validation_df_g, test_df_g = train_test_split(test_and_validation_df_g, test_size=TEST_PORTION_g, random_state=SEED_g)\n",
    "\n",
    "print(f\"\"\"\n",
    "Train Size: {len(train_df_g)},\n",
    "Validation Size: {len(validation_df_g)},\n",
    "Test Size: {len(test_df_g)} \n",
    "\"\"\")\n",
    "train_df_g.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4808be69-12da-4575-aa49-1b0c6dd39a24",
   "metadata": {},
   "source": [
    "# Create Hugging Face Datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2d09e04d-6c31-4333-9028-5589f1df705f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training hugging face dataset created\n",
      "validation hugging face dataset created\n",
      "test hugging face dataset created\n"
     ]
    }
   ],
   "source": [
    "#Create train, valid, test huggingface datasets\n",
    "\n",
    "def createHuggingfaceDataset(data_df):\n",
    "    image_paths = list(data_df[\"image_paths\"])\n",
    "    captions = list(data_df[\"captions\"])\n",
    "    dataframe_ids = data_df.index.tolist()\n",
    "    \n",
    "    #Create HuggingFace Dataset from python dict containing two lists, image paths and captions\n",
    "    dataset_hf = hfDataset.from_dict(\n",
    "                                {\n",
    "                                    \"image\": image_paths,\n",
    "                                    \"text\": captions,\n",
    "                                    \"df_id\": dataframe_ids\n",
    "                                }\n",
    "                            )\n",
    "    dataset_hf = dataset_hf.cast_column(\"image\", Image()) #Cast the image column to image data type so can be linked files can be accessed as images\n",
    "\n",
    "    #Delete variables to free up memory (may not be necessary)\n",
    "    del [image_paths, captions, data_df, dataframe_ids]\n",
    "    gc.collect() #collect garbage \n",
    "    \n",
    "    return dataset_hf\n",
    "\n",
    "train_hfset_g = createHuggingfaceDataset(train_df_g)\n",
    "print(\"training hugging face dataset created\")\n",
    "\n",
    "validation_hfset_g = createHuggingfaceDataset(validation_df_g)\n",
    "print(\"validation hugging face dataset created\")\n",
    "\n",
    "test_hfset_g = createHuggingfaceDataset(test_df_g)\n",
    "print(\"test hugging face dataset created\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c118200a-464e-4797-a285-93bd6f10172a",
   "metadata": {},
   "source": [
    "# Make Functions to Go Btwn df and hf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "87997b9f-b792-4a75-8e0b-983a8c16db03",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create function for selecting train/test/valid dataset\n",
    "\n",
    "def select_df_hf(segment,\n",
    "                   train_df = train_df_g,\n",
    "                   train_hfset = train_hfset_g,\n",
    "                   validation_df = validation_df_g,\n",
    "                   validation_hfset = validation_hfset_g,\n",
    "                   test_df = test_df_g,\n",
    "                   test_hfset = test_hfset_g):\n",
    "\n",
    "    if segment == \"train\":\n",
    "        return  train_df, train_hfset\n",
    "    elif segment == \"validation\":\n",
    "        return  validation_df, validation_hfset\n",
    "    elif segment == \"test\":\n",
    "        return  test_df, test_hfset\n",
    "\n",
    "#train_df, train_hfset = select_df_hf(\"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "29434e8e-713f-4531-8ba1-251314bab836",
   "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>df_id</th>\n",
       "      <th>segment</th>\n",
       "      <th>hf_segment_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>67</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>train</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>train</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>train</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>68</td>\n",
       "      <td>train</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   df_id segment hf_segment_id\n",
       "67    67   train             0\n",
       "43    43   train             1\n",
       "7      7   train             2\n",
       "33    33   train             3\n",
       "68    68   train             4"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create table with df_id, hf_segment_id for conversion\n",
    "\n",
    "def create_uid_df(train_hfset, validation_hfset, test_hfset):\n",
    "\n",
    "    uid_df = DataFrame(columns = [\"df_id\", \"segment\", \"hf_segment_id\"])\n",
    "    \n",
    "    hf_segments = {\n",
    "        \"train\": train_hfset,\n",
    "        \"validation\": validation_hfset,\n",
    "        \"test\": test_hfset\n",
    "    }\n",
    "    \n",
    "    for hf_segment_name, hf_segment in hf_segments.items():\n",
    "        for row_id, hf_item in enumerate(hf_segment):\n",
    "            item_id = hf_item[\"df_id\"]\n",
    "            uid_df.loc[item_id, \"df_id\"] = item_id\n",
    "            uid_df.loc[item_id, \"segment\"] = hf_segment_name\n",
    "            uid_df.loc[item_id,\"hf_segment_id\"] = row_id\n",
    "\n",
    "    return uid_df\n",
    "\n",
    "uid_df_g = create_uid_df(train_hfset_g, validation_hfset_g, test_hfset_g)\n",
    "uid_df_g.head()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a9288843-b8e1-444f-a650-cf6fe3ba83c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The uid of hf train, ex 0:\n",
      "67\n"
     ]
    }
   ],
   "source": [
    "# Function to convert from hf_id to uid\n",
    "\n",
    "def get_uid_from_hf_segment_id(segment, hf_segment_id, uid_df = uid_df_g):\n",
    "    df_id = uid_df.loc[(uid_df[\"segment\"] == segment) & (uid_df[\"hf_segment_id\"] == hf_segment_id), \"df_id\"]\n",
    "    assert len(df_id) == 1\n",
    "    uid = df_id.values[0]\n",
    "    return uid\n",
    "\n",
    "#Example \n",
    "segment_g = \"train\"\n",
    "hf_id_g = 0\n",
    "print(f\"The uid of hf {segment_g}, ex {hf_id_g}:\")\n",
    "print(get_uid_from_hf_segment_id(segment_g, hf_id_g))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd65ac6b-2638-474b-b97e-4cc64f150b2e",
   "metadata": {},
   "source": [
    "# View Labeled Data Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b32fce5b-32ad-4895-827f-daae680333aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HUGGING FACE IMAGE:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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wpnswwoPhqarxcAwlXv+BB4SRp3FwQVuYpzbNlzmmEDxuZb1XF6ewwwIR6EcUdGwaLaN0S/cyeGMXgm5pWyz7rBrNGcgWp+++h20/fPhoNB7hxZ2vDx48fMCINp1OGUkQEZJGhtUZCsrUDtEabUoOiaeT3acJw/0AfUmcW+49TZWOLCr3KARNRD/O17KGWJo2Fs2RBChabVZFdFA6ndl8fnV5hUU8ZGFkNnv+9L1Jv8u7CQB60EGnci2SNXZ8hJoxQgjHAEgxXSVPLOvB2dnZe2/peT9+iNvdYcpYUKDzATSmp3QPvAn4wz8aHj36E7/2z/2/v/NXHx2N5tPVfDm8ni0O2pNf+83f7HWomTX7QUaRwHJOu9asUTo7BqULFuYNg8GYJnPSYM7G+gQ6JpxZB8CteCqnHI2hIcLeuwR04BgF1jptnm4PTg+b+69ZLrq66Pc6FxcXGoQTsgtr3CGIWNbqKU6kEZE5lGFviUR0nJAL5b0Rd29wv+ugz0CNuNezi4v11TW3e7BkkT87W7Snhfnc53EDO0BAvQzXalv3uQuYdANarHC3WN/Wb1mtcDDmywWqfXB8jPuCPcYwsjJoIRRsVjf38ouCqYd7S6aZejC7nj599y2eY5oMDju9EbYYg4455IVN/HCTiJwMPTrNcbEoBmje/JVf+94//d3pO/902FlPL5fsq+8cvf7gwSPsdx4bWVCK0YP9wwfrOfhZs2ktlhtE0c/odLQBjF1Occocf9y2DG9rZh+X7a6rN0QRQxD0ENMD0vnRCVoxi8ggwwSA5nBvDFdKEM/ai/bV2RntfP3Jk5ItjDBiUgMZOKGkNxr4x64HNn7WYGJfWq1mC+4s4DdFTdZ3/PBhNoe65Whnw66DHsGpQ0A/w8/WUHGZmMQKUC20qkUtaEllMbzH6rQOuN/DSzboAICxFrZxXsWvW3/bb7311lffeKI95pFtEf0+0pTSeKVG62B9cb04Ozm9PHnRb63d7tIbgiN2feEBMDdgNIAXbpRiKO1ZFIyFBLG4zNT52//aX/qbf/2/bS2fd7hBdX76jV/5Sofd8IAQjJKXQAXzOXWB9/ZAYNN/WcsnggDmMNjjQWfebdEo+ju1soSKWLpMgYN4WpCcAb3t8dFb4ZuewJHpAfVgsHV0uAVw0D56cMx9MXoSxr48mW5Xi042RAQ5qWhUGkOukB0K3OBMsJkhTr968ewFRY4fPbazlTwtvHNh10GPyIU4MBj0V9O5lgZBE+HsDbmicUdz1NAW/0Qbj4sef3c5tau4QQblXF5dotvjwwn++8HBEPUPhj1uox5PHrDNi4LsKUDTFI8eHeU5922SIJgzHOvWGuRPJgNsIC+dbA8G6B64c6AInccOiXlf4GjbS1xkAWeeA99lb/jwz/4rf+nv/2//3VEX96D1J3/1N1ghgvx8vui22Kxj7dDRbYit5Rw8Y1hrrgppGKIL9Pvd8bDLzJZmMgoMdEXsNNSuHBKUmnF4aMYyGm2bRlOIZL8DS1Sj8SFrTZfX84vL60evdOghUFi4WahaUiur9hniORqv72SMNaRGGTOViYctJcMuIx72dh30ylj5r7nTCGb1RoNsQY8JFeOqGUWQacGbA+bMR9VBdLRicc6zjAJHk0NwAoAX14sfv/32l7/2ddYvrhibD46rFnIyRnAEH7rmmisVa3ycHZZBxqPRdecAH4Y/XA67nr6DOSljxUYAN2PxTwI8ocOsl8TXvvKNb//mn/+9//v/fPKNb3dbnen5yeRowGLRurOasnhJH+rqtTnArGRbygYoVg+XI+rB76DBiILphO76fAWL8GNWyhIQAuDTRRGpxMODVEzW/DNsuIA5GDBWkEjNpNnnMPAKtKRqQUppNQS4pe0xcpMUjEE4S7kVRDZDhJUn9y4e7gHolR8bxrud0WTCMgVDcKfdU4VKX68EBS8XUxwDkIZd51YO59hbTHcNCAAIAKMYFMVb8SZHD1+cv8XgPh6MTnmNMP0GF5QMTFgt00BfbIg9gy4qWGuvMJlH3WMQYSw4KBTJBnOJgoLKFrCyBQ7ws10IYolQF385+xO/8Vu/+/t/9KUvf5NJKuQBNoPSaNBZXC3W8+nRsAeI04tgSZ5Aq+Tx3/gnQ0EuRfOLvw1LGG7dr4wncEIp7IHzCgxE03PLWPDULu9kQD48s0h/6HLvivtXp2dvHY4nc27AIQUEYG9nbYlZEHQcJzn08KDyJBctR45Mrazf/xkBHCa5p8A6wZSGI8gdxvw9sfRlLnCdV6sx6vdWJpMwXAQ8CZxT/qMellAwX+xgZKHGeZhyp1/wT99US8wbJOdAYjKeABQ8IJwfbBoa1akpBGd0ju4tAmg4lhmjYHYA2DfQM9SKK+KbPGA0faFizMOIzzZ5rLKuC/BgMop9Hf653/6XoXV1dT1ky8CqPRwOmEuPR52Fr1FYUhCCeNxUDI7TVwUrU8emRilbmZbZlSUHPJ0bGU5+LbXnRsUwlyRAK5f4ZURfXtq1jh48eO+998YTHgBgHLCDM9bYNPpIim/bictP6oBHgLm566CxkZg9RLKwQWWUtfmKZHfDPbD0t4SHwgbcPNJnn881nAjbsRlrimczw6Tq9RBTjj4/jvgubpKhrDNL3ReXl7wVHgN2fnH2y7/0DXpDZgrCBbMVjWllMVii307ms9pEEAoKEFS16Q91IpPxi7gkVDbsPfd2O9wiBaUuNDmMwMs3vvnL77z1FtTYatDrD5kGMwm4uDiHIiOVm+pzC4KeGaouWDUQCw/CG1aztZO6tt2PaOolS5AHstMP0oWkk2J4RPBAnasTVloe0O0BMWtZSIPqJM/KjrNYHHRzMgvCpjDQDXoDbgq4K26+cOK+mSTAgOykLhYbIMDljoeMTTvO4y32AMTF6YlzStfcOwds5lqvWaAB7to7EY56sewkutIcZeAcZHbJIMCwvlj0h4MrNu8ul7w/29fpcVcL1wFlAxac/ljrFBU+ECkESy0a5VjYIqYCijfJyQZOBS9yEtzYQ8jxMAgrHf3hIe9BIFbwsrbOC1yvrubXZ0xBhpPj3mBEv71ipZy7uFhvugLo13+DPC3S9POPMQP2iGkqpQtp3otJOYs3FN6Ip9uykIQVgAIlhOKmO2OV/dxD6/TimkWl4wcPyYdPBdtYiRiKHkXopTQKKSExRkiuYYkcmgBXgRQvl2HRJQTGqBfPn1Hx61/+qt7Yhs/idqeO98vSq2EW14ZsI4n1ZdMjuBThXdZIcF8zqMekq2vsp4uBTjFxuAX6QZuFeZa0ebb10cOHB6upyyi5zwU+SlWBmIse2GsQhIIJBeufOJYiK4PnQA0/OS4HKMMjthfgHONu9emmdADr0kcHdtwYumxdnp/ilnWHh7RiObtiIoh/T7102kxUvblm7VLXv7eFQlgMO3iYKkXy6EZFAHQaq01aOTgBPC68WT2nKLddZ62ri8vHr77K/Sc8ROa0pEMHhNu1UpGdGE55H2enB/a5r4cgmToz86GTgH7r18W3X7pRYt2aHB0Xh9azq+GegR4kDicTbg+JJkdkbJK+hFgI4rVwatdQ9hFbNZ1eY67IzCuEWdvmnEj2IU4GzAI0oQRHc0IghUUVH3WDRkuaBQ3uEuSEXAIxOVMiKEylxGuhk0E2wuTc2iDIPNdnZSkMHaafvqByuTo9PR34SBT9l7vCY4qnKal94y9REJhSTVVaDSyMVgxH9ilTLyE1lATsutQmI5xZXqFFXO3nZ2d88kR7gW3v9JkckZF1TFJhL7kZeBCEPjoBl4Ykdijw6nBkyB0TJRRWTD7gmykzVj8Pj44p43XEWSzt2vGegR6cD4bji4vL0dDdVahLgatPgiArNfCTCPwZpgA4mllkxHXNoJzlSO9JLlk1ZO09ekXT4INSgiJmz0WQ4Izr1BKXI4CWeELFcwoXELgdCSfQpEYxz26bPttmBBAYq2zcHpstlkeTIW4XD3pPxqOi1hzJRB+LbZYtYVgos5Zqn9EBV7B3O94IquLHladAEFcl5qGNxX7nnXeOHr16dHQIz/BGAxHmMncjQirVuQhsVUoXwbbYh8MG6Q7gdl3I+YnxYcb3VzHdejA5onUUKn2YtpPhnoFe0HS6Dx89xrlHJbWMk2E5WnYB2iv+BJrD8SLGDG9iyL3ToEUjyoo4U7R5G/+GbY2ufkQ7lKSoB5RHHP2BeFLV/kaV8EAInrbKNQYTT2SFQNQbqARXivAYMpkmG3Q4QH80HJ93eg8ePuJuE5sC2EdPLeAvnFR93oHmT/8Bq+9wsQ0s3riFrLm2Pi7knCPGO5ORFKOocwk2YNhM5hLPnj4/fviYWax2HNoOIdSifa728EudcF5jqFVII8NEhrJqqpnl2CT8oMOjyZadhqtd/blnoFf+3FHzkzYO+a4kcFclQVhguByQBT1rftGGowEzV8DkSM3yIZtaDnisrs9m297hgAUHsgMsoJk3dagokQAMVKbdQRAERFEyqY3DW92goEJkwbUQk0sgm3ltr88GY3oWXSmcghTv87Py7U6Aha/7oFFyGNSQJzzIia3Vaw5PDSZlgMxE19hSNDNhZROGHYQN/owmzMzliaGMO8QpcvIsT5l85RvjyTG3c4NzcKu4KFUIpsl2s1RO/8Pr4VQfyNptuP6bsrGb2GPkjpkxfmaylFBSfGcPN3ZlZ1n8UMZwGxD3sxfPL6+vdHLIVODFTcasAgejRFJ/4DMTZC46oBnAmc70YMgEgX3BbElg5wzTM3NVzjoWnrR5os+k2wHiXBZZTgoplQFq9JOil4VU5azhTM8IqMzIJdiBH04Enx6N5nyDOk4kXxCso1yY2TwNNsNYssq+ddAfTLNSuyvmmc+/nZz8+J133vjyV8eTCU5NtSZENPNV75Z5+CdQHGKcaBHkPPTNVOIIbyGRVC/vRbh/lh6xKvWDA1bcHz58CGJQKnbOlXt0GSR4CKRQmCsQG8SyhlNOAlEsdHJ/iusiWEUaMEWrAkgoCzaOBMaJqppzTjiSRKhzClVEIQC0aMVzwQZdbCG72sJJ8vtoCA8zDb3F1l0xLpHakNvQzG/oN7iHI7sWramcoPsmD0m5KK5YdRHsuigwvWI39dN3f8xTs+PDY75ei1lIUyxOfovg8Tu6+YwVzUFum0aluxbf1msTk99WQDwLPXQMs92XcC9BvxXugBe0Y7Sx3OUncIFp8yYlnz9gZ4z7Uxy3UWaN2qyB504nSmVCht7cYOydXHyCICAIVt+WCyByQnE0XbgSdgl1AjNJIqrhS0wY3CHAln1y8156AesU1i8skEYWq2h32Ab3eHLE226KppWmcB02lMObZMm2rc5eB34p6Ek5K1KubOSrTuIq7+nz51w8fuUVlhUtInfmzGYkq1IA7LTPwpa2Pfeq7LCShWFLUQc/lGrceEYzCq6xPj5ydY/C/QY9cys0zheZWKRB6KqEKFTmE0okBfTRhrDAyNsjyJYnrdE4z+MNB9yU0uxRXMVnpWQD4CDTBII50DATSAd6gRv8uZpZsCNCkFRmCokVVzwYT9h43u/yeqbMUysHR+6Mjsa8cft1dicM3FghXv2zOg/BpRd2QBgTcOZJTM0sqB04GmFtNMRTm8lKetgFyLwyH8H0xpPR5JClJPLw7C1bOOwUGStoSCRnYRuDpBAVQ1DCxuqb14h4YhyRNd1nNBpGdjbHuu9DuO+g1wnmfWPAgymcCxB4Fdyo0h/VdKHC0oJgQbHeeoyPwTaSQf/kvWevvPqKOEfd4h3Nqdet4jgvkHE0PuvsSaWM2SoDR4IkCpBNeTsgOcA9Ly7TrWJ2fZDVJPYOpauMx+OL8zMgJlhT7eZXEkUMorAG+QJ3KrKm9D3ZI2Zz6ZhXldt3aK87kVhi5OmnxRuvfTX8CnR6T9GhDuhgJeiiNH1TlvUfl6JIgjKM4EMiWW40EKoPcKP28uLqwaPXMnwp+Cp7L47ah3sdGJBBgwpD7iyRcN+EP9dK9Eq3TRMxrNK4YdiA+o6Oj7kvqwrZuoOKmce6RqGlI0MdC1Lby5jT6gZgoZkM3K5F0sE+BROEIORAT/yFUb8/YlH+xdN3ri9OgSNuPTCd6gI1PaY4pqw82k+17qFBPfSUhj3IJsVKrNJK2TPjVf4gA0VuC3MD4JqWDw4nw8mhgvKuBJueLcU5+ZxHY/9zw66qCylplWyrI1VF9A9OrpkOXbs3jq5sPY5pxXhY2fnD/bb0IsVxVYlr471fzgteqic7egfewFToiHs8GaJETmt0OPnxO29dzWYs77jUp2cEPAzkkSSgEUwb876xhOkXAWKDt2hctJkdTiyH9eWEqSnLh8bw+rEZD3It5xez6xc9nhvh1QTtr/OEIaDjCdfx4FFc5vBcfMMAnNoU9ylDnTMCvzlNsswaXxJIumwQ5fyeN0/xZgcmMevVoydPSKhstL5xg8ItpewMQbOF00VDpDGI9DTrzX/2gNA9gDtvkcKFZMG3P7x/L45tGlbyundHQMqGP77Cx3ZvIc8OEiBvm/AHoibuSnKhDkE9vg0TRjDC7hcW+zuPH7/y1ts/7vg4CE+YcpvdeAL2lOw5sX8Q6Z9zAXqG5lF3mXTOGf+NyTQYhz45G8QDFIcfug+xGUjIuZp2Wkv3e/XH7EOTKpafzyY7y3BoarwM7xNbEX/U5NQk7FiZtcGVjMEhR1sWs1/nNoAopqi8voePUpGr3RmPxjw8E9wjHvbaO/kgVBuFtYMA4sQoKKiIQUpUgD1nUytPw/ptcz6nxWZo66SwOc2SjPfocL8tPeJG0yz8sSaIwhqbFF2qi8ANRz64cAQXqNESvszVOV+oXPBQqtMxUYsGOWm2jDWFg7VSp0nYynQpsU2RjYcjScHXaL9GCTwbgUkJbuSwsMcl94RHj1a9I+748JYbtkST0Ov1WTyFAplBJ1gKKV4KomqAoV0LbmAyBooMGmZnm/pOlb9OxGHMdvLw6TfenNZjKHv86mvyC+fBKVXdFhSZCcRwpDgB4hDGdeHk2jdHpDda1vV+jqldisiU/I1UU/ZeHO456GPt8IlBDw9oozWgU1pRGZma6vyiQCDAFw4wp/r7bKVaXJ5fYNufX/HhpivfqeTjfD71D0ndCbsHlBocUNAY8OcYEnDbTazEjAmAoEkJD97aAR/2BB5R5aWVgJ++Nux0sZQdJiLY+H4KUAl7jPHL4JNCoU8prbo47wTfPtvV8FMAJRvXdUxVxU0yaehlnf0wQ/Y7+Jyk0xZiAnfxXa2gvQyN9B9Claz+wFjHbIdtlWmyDXEay1zXStJmyvsYGgwTpSQaKZi+6+F+gx41ClEfKFmgpD6PQSDwQlKjRYfsGEd95jbvzvbOC09sXNdKh9sQCN4TBUNRcUyzoMOXgZTuRLTIKe6N2i2Ta6QDQoL15tyeZqBsk80+RzKAns9BvZCFBPVBx4kgv1phll29s2ya5VNxkcL7D7Q2jBRkbVh1Th9M4Yo6cJZkGzeeFsUJ6hweT4Ss7ckgZmtdbNHZc86Q1d1u7/qar3NCuBJddIqpMI/93Ibm1OZaE0dOXdfPfbdGCsbfg3C/QY9GUJNL0gcHrP2x09uXahinyvDleUJES4/lY1Wanchtvl3MBgFf6Mdqnl916vZxi+gvfKM16tJ8Rq+gdqNqu5bKB6ocQCXdA9WLy0QnkoSYXtIlIQOY+U2H4cR7VeSkGP8EEaVdB+QJvb6jEBtdfEhVINI3pO4wYc6ClLPPlKmyRDJm0ZYwDDVxD9Y18s7XXXZsedd3jCAQAuwolth491TKevLY/VqMdbwYAqaKe5iN62LfgG1dHWcBMgIFNvTQPKRBi/H22QpF/D0K9xv0IIk5FopgXQY8np2ePnj8yOfoRLzAd4HZZAMAyPRPzelGOx/lxTi8VAnDVvjFcooobS2n2H5tomO3P6JUtQdkRljt7QAANb0gXXSaoVZRMu/jomLqWCSpGKITt/gyK+Ej41pkznUlsPruFrObEVyDbShIKbXwenxBShuBH8WJJI9M6sBzm7nFfmZ6OnEs44BQnp6RlsUPeCMsAiInWTlIKiw7BaIAr2fIJJXGJlDCPmrp5mhd9E+MfSLv0+F+gx4Fs/YBPpiDqru2O2rYgIBhR2+gB4WhDYdgHPkEfYVmzSEPgPJwCc+bF5xVHEWAsnc0hQ8Rgj2m3lSB2/gfpjUdIJXYH9J3sLiV4MSR7BrIJFg0obiyuJvUfeaLySJHedYZt2KHJrmyGpJYeXTvfwBHhtTFVRPs4ywg8g7KNNmbD+Rod0eTI5w5329FnwmtKiA/jHwd7zRBnArpYkypeeUhPVlOwroGPjfFwnkarAQoa80wxpEl/rTpPh3uN+hBlO64plzDjApRnu9AWvCmRbccoxVMKMfSLv3CBwdx7vUf8rCPd7LsLq5jBheNboGIK/ekuegZDatqdVsGntwBe7QtCrWVxjSIMZ5Y6tZ+O3fcdsIUEVb8e/DgISxDLFAT3xKRCamlEn0lTiAWsG35MZeRiWX7HeR4HMz+n7f9sJ0NK06Hp147TvAtDYNMVUijrIqtlBgHmaokV4yqA5hRp8bammC/cxtph29VhOZN0ibL7v7eb9Drwmb9LprTsuK9Y6d5v+N6NcDXzJoMStRXCKo0d/YQbVuHN8I+5L1eedhHnyZTPZaxa+IHiFEzkBJXcQTUbq6NCAxVdTCnVSZzDLNdqIkHbETj3/gWGiOFW+FD2NGlmHRi5T0Lncoj9dRaJ4CvaHKbFKxVHnb/m8feYm+DMNvvGLV4Ox9zYu7SkdVVWn5orEOJfk7gS/a0pwaxkIuP7iPsLHOl6tA2CW4p5Foq3baYTwscmCBA3dTC1g/z3pMQ9dwTXj/IJmYMewyeCkeYc0DAkyXAurRLEbDOOXaOA7Heg1nzrDZOUfxRbrbzgJWoUregL2oONI3aKF09+zhd8uFrM9WUYLFEwQRKNX+B16bqeCUONZsBh/OYXTjTAPNtQH70q+KcCK70kGTzwGyEI1VwTNeVPbJV7aLR0HTN4+Pjw6Mj95264AjbmRnH4cml2ShYdRFTJ0G2gnL6G4vu6LLJRhEpQWtTKTkRFpHMKLaR5rgP4X5bemyMkNaYClYAwS1D1FDgcBbr4pvICvKd9nENdsEEq+dk8wn/vNA9Wsw6UJAby42qVXyUqiW3cKAfMhLT2Dn59BRDLy/CQohA0HM9YMr6cp4weYMbrTSIdLrJCtISXEOjBoTqqCFlzyEeJjkhVCTEC23EWLUh9bhOteRZ4E73+MXpKSY/nTOcpi9RsHIXe0WNY6IVICbfVwYmFMUivS1IpGw4XJqCZKt7pMT9OGxFdj/Y/Qku0SPAdeFC3wC1gxkgjYK44RKQ4rQAfJDhy7jLN1b50EFV5OCtRWgOLVKmbJcgZh+ig3dTW5LqQhzrLghXjpKl5qqJbBSoY0YMPqBGDPCHuGBNsSZDSAs1GL68Yq2wM2Wtnf2hNYLIjjVQKKsoDjDwWAyFWxfYqxdZRf2RKX9BYYsvrb04OQkdBreq/8bAN22rvrK5wAvSw7E3GprVKM6gkgBbBGhhRioOyqz/Vup9Od5j0MdyZZdfY6iAOaAHwVifOLEgKIAjAsgEg3Gdk5/iqDgvpw4aUavKFjeCjUOs9VaRahuYB0QAkl9jIEuOJBRUAIQZiTGT+ZwCZl9NsqULWFJqzpVbrbPzM9yK04urC9677VScYgKPHOA7sKQSOmpBUY7oDyJb7VkTGTkT2SRRo02AcJs3LLz33ruScnJiXdW96Wlc1pETgoRMxbvxu1SIkktiyW9FCcZwnkv6gwsCWn0XEyrDfTneY9Bj4AvUyBrpRyWFEo9c8sw421tAUXSDFisPR5fq0j3YJzjjZqR6jS6BG9SquKgMFCBVJ16bSgRROVRUjiFwc+144aQaTDDlcNZhoaYes4UG+95cZWILGlsjZleX+FzcZ6APBLYw5aDUsHaLtg23PxQP0A5IvaIK6+CHgYEnWyej8Xe/811kxcTghkDOyEzVBNBfHaAyYAikEznUiiRCIRsxxqczUGEY8OhIe6/CfQU9GsBhRfXlBEfRqiaKYXKof+y5q5ncYeImlG91JRm1ESOsxL23hMqtR6ml6dJuFa9jxdQxThNENNjS39jObaktEa04kAD68sUdH+3iB8kCGgNI4q2RvGecqaHEnbZizWPgScQ7ipVtGkiDrL1CcEh2wyYuHJp5PRz2X3n86Pt//F36tnLJhJgKN2XtPATKwh4irakCuK/Iit/SVZhK1YanQzZ2ZFvvvTi5l6BHPQ6peR1XHnEodaMGteNr51SMFixW1kVK3hR5eX7OCeoqvQUAefsx+9WAo965qGygKcgCsdJxjiQBdYwvFeF/+KvnYEUBoXwRqMLY/HFaIgbyhZXAQntJjWQCbtxSoAE8U8IDHxSmKhjnGN+M3us5f+CLhRKdCqPK/8L34JzSOkVWTb5Av3oFxVjF4TsU8Hh6cnL6/BnbjbMCpAcIrG9jVCoxFiAbSvg5SeXUDZ6VZE8wNh0ty7GcqYtUfZvaLp+/r9m7zGh4i7rrjJuFGuw2S/WFCXQGHEuRbj/Isw7ObH1VI+/dbQ/7A07Aj/gzM3vxh2Kl2a1VqNq4E8EPmQvE/hSkqB2cCbo40vYAN7qARsEZVior/UNgBpPcKBDHwNOqcfHTBnsFjo03Y2GYze8cNepiKrT01OGUY0pSLGDz1eTcNzWLMfYTO5SB3HVMea5S4ODgtVdfmTNdnvGBw3dPnj6jIhyqJN8U5BLpxRZkiuznnofWSPWOUqkvnJHN1V8bpEFxdpwRQA7uQ7hfoG8kinrcDqXjoDXXkUAF3jpptI6GYq5UFOf4DeRCU3GFO6zquRptEc2vsCVjLqsCow1Ndc1lruqcYwXwFquM8oNAj5tim/z8kpkE7hExLl1evLg4e3F9eXZ1/oybBOxA4J4QyZhk0A8v2FJRnJ6UgoKKCkD59i9WHaJ0eIs6AFiFFdXJplIFggXgmwvDbv/i/IJ3X0GOj0NRzlEmQPcnYVuWKwZMt+74hkNalApDn6T8ShkC1IoWRP/9CfcS9OqZd3M7OrsDVjQLEZdWcq4CsVj+JAAaLkWF+vH1xa7nuN2c7YYs0vVEakBGJAiqUhDnkvOCUp17aRAE5IQmMf6U0pvUZNlENgVECKtLvldvOecDKKfz6dnZ6TPcd2aMdAaqWfCgE6W0mhCMF5YZgfxAMvyDsuI119kjRLelN1Q1qdm80CnmMx9FSocPj9lkdsXepA477MfcqTg/P8eJRzgIjVD5rSvBFbAOn+fhVZvUfEOdRGjDkCeRETntlvcn3C/QI3qlDz5wW4CLToWPO5Vjw4sthH20AiTxfn32R5znlq37Bzut6dUl4EJv9gvfmD3g9XpkI5fKxFnirdrqT1urA5sBBBLG5BiAu46f12mreIIErclTEWFR9rEQ5/2BxIJDbtd3J4cPeM0efLIsPxodkxs+eV0wT5owlTUn+LNicQWeIWEeachSyMupsMZliocvc2Yw2JbNCXnqklTaPjyc8Eog6tAj4UMsfJe82+UdJGcvXnD7tmRX6He+ERsyHAwy/ghziDimxFGTOWcSJHYRb21eqHp3/3gv78g2ZoxnkKZ8H7OCKsvcjyeEsiEnt5xIQ0eYIk6wcNisywu+wKw+ieVGDO+9YaUCLGK3Na58MCcmmfwx5cBJQyfgNsAincxkgw3NpCArn1po5Y8IXW7BamLKWwEbfgZtvsD58BDT6weDWFIlknthKx7t655fXNJVAT3+GrD37ZvUnHqpBFL6csExjQV+AZ7vGNMUQyihUM7RrE1muzR/w9GI94bj7PFmP6azPkJ7dTlyJ/30/JRPvh05POL42eUSspY6HPEaNp+0rDpsV+qAJAwwRkUCmyJNyZ3+uV+WvhElOigVgDnkj9BROR6LvyBS/aBigp6C6MoIAExZnuSPvSwYQHbt9EeD3lDcoz2zg8TNMC1F6/A/KblsYERUmUMKCC2RST67nEVSuyfhAbpFSoAyPNjdmFDgV/ldifJheDEO9YJ7uGAFNQadpKLRHG1Mg+BQrm4WZFcOMyTUSfFM1XVZPFAjcAemROLo04pnT5/x2caGw2SGD+RgZSzadBym2HKMvYA22ZpxwKYpeZrNf35LI1VXsbHLx3sJenYmImjUciNrNWAI8APVgBXrSmRQyjMTB3x7A8WrG27c+CFZntTmodXaQkisqjY/OQjoXvUXBZWIykvrlR6QaGUb0JuXK4+e5LLOOWq5WSx1E6gka40zvIU+tIzHT/Azy2QnDxTIsKFjoxK2MVzJSBHhYnvCefG5jTEfwZcKGrwjtloxxDG9AeVkFushZQszR9IQUCcMaPsdkZoQokg6BI0jou6WbKvbZN3R33vm3ogM1mGAJui2w+IH5N6janahTdWBVUYAh+YuD8/F83C/F0v1uOzZZ89rW9hG38Wx6Pawc3ygmNWPBQv/8aeZG+BJ8A6FxreBoDhE/XYHbbUxBV57CEzk0ruu2HtSo2zL3ATizJixI4ODro/XvowECIYccd7SMienENPcugsuFMkfXFIEiIo1A1XQn2CMOb3ZquPnxD5RHERY2mmKFeh5IQ5E2OXGu10V1AxfSsna2xq6m3awSN/rzXk2jYiaHSl2mZO+DHDG9ief96Jow2sSqvZdO94nS480lbNwxKUONrRSgY6LD4pfEMVSiwJUEg3QQ5hp4cCyNh3ctEeDEXM41idUXmxqPA0dlcBP/VZt1BdgSVtUSdwsWzylWrDKjk5GEbUOB5Ua8IQrSxNdXcJG1F/9+ESfeHG5Kds/wxEVpUPBSLqbFAhWLPRtWU5kyDqcpJiB0zDP7yaDFsEkbxfw0ageW2v4qiF7DTAFXe6IybCuHfMZmU9JKqPr208YHPgaLvOfYkA67JlwrhPpkkkTwd0QBrH7Ee4T6As+qA1VYK50PuPT84NSUQ8iL51nsEYlak2gtvmo2IV3YxLn2I+l51O/3rsVDaBHgMRSqTeBFNRvjlwG8ZDPAFDKNYuBVEgCAv4kFzYAUCUli8i4fel5YSbxEC8Hg9tGpARIEKJFUZAujGFDyhMuqfSGTyxs/tKaDSCraRZLWYcIQM9LpixPcTwcKNCOkifRXm5aTYZktGqcQQp4ln0cJUgvqFaxUohK70eITO8Hq8EIwMpcE2nDNVrgiI4CX/VYlwFQXADWbVo+PMrjPcnr1FNL1e/zThgUpdaiLRQfbfMr5EOnOVY2KgxwGvyQoQGSa5+NiSwipOhGhIjZtiHVFefEbehBdd0fyAyRPOjIb/oD8ZClRU2jSK08dbxF1YLhTaxT1r5MzA3iLYidYFWewBBHUk2HWElSdIYi3/S9bUWVgEkhmWZCf1MXv3VhQ+HTWw1hwoTdDjbmvgREGlemMOUXlAVZtMs6QxkcXBA0nmccdIWxoGCdp8dZHbSZLO3jRaQnAFUXEQWJe38xr6CQsvwRny0uhVsI4L/aWXRX8Xagg5olh+3z1hK3uYQEV0YmaPwMBRpOgBGzPVdGPK1DzHsGK6hhdOHg+vIizjJVFbGGTIi975C+KsrLHpMbIhnxGChcCNqwl1bINjsdpvDDxN219oVLjTxE7+qNoPUgZ5kOZcwI0/GdpGUP5ME0u3d20elTWakSNoQfpcJ/f3Y43Khph5m8YQ2VoBdXJ3G+AyJhVVKP6JX3xnID7kDVVXDwpLkiqssroeprPNz+77G1uBkuVDyE9JPip4AaYF7+g/0BVapL4dpYcVJLxQHG+xQdXt4XI6Q2NIBpVURdBuiIba46fP2TN0dy02eLHEil/QLS+hPoPhV/c7TT1lTEDkwFyWuv4JRs7GdDetwNEN9pS25GM6XnhrRrWTBSo6jMbLplWpz65U9CJKW8FfBnRYlJE5qcO/5zz1ZvwLwgAXXKNQflHmUTC3jUG9YcuGOWVDd7Fzk2asiWEtWc4P2gqytfASsKWP2Adu0MQ6+QEWaiyl9IaPG3pIyPaQz0t+Q1ll6Q+oFAAfEN2yHnMRZftDkmWT1HtjrzTLu4A0ypAkobqpK1ao9mqEpyIj+Vs6m7Mdd0D8giF6ba3hmzKVSsV6ZjU7U4EU+V6RJSlabdo/gwhqX9lLOabdVWGj6TGpGbd6fDPbP0qA5VIWjkjqUvlKhEESSEvIlJKsH9un7cr541IZE4bBuhKZXegqXHjcbfhYa+iibWeQPUt5otWBjfYCynxJar41UDAuM2QEx0cVqnQc9tEtvonODeYDXxubk/2qRsfkLzVt2JJ5LuX9XZXqWgG5aGqlY2l2aIc1ChU7FQw7OzAXxDCjlYMOjXcKQ7FUGOnrhEmelpbDkdhzFWlH8gQISbJx+I3tGI+2Tpowfslgt8wAyNllBRXp2gJreva27QLrtfeZMZr5Zm1sYHxFix6foQKi6t9xch4cRrNZvjwrPeRqEMIRIOdVRLP4nKAZJ+PqStiJOYQwypr/aO8bZ+KMoXttOrJggd4gXQJrp+GxdYsFITAHVjA5gCqLyaE6tMMSmZmxygjoaEWlK00DAD2MJh8pFQzkZq9HOY9l4dJZ4f464zpr0+Ekhub45BjRo0ATDBDueQo0LmM9ClYtwdSpmN4NIw78kyOo1RIPLGfxllYHSYIqMRux0auOw2kw13UXk0E00IBDWn2rY5NHVCAfywap4dyHEbsoMXY8eCs0tvIeWBHfZ5xZ8vmgQTggdPw9daQBJ9e0S9dgo9ZgPU62g9MZC6JoHdTxxN34Bge1pYCY+VKjlKY+nZn8Bs11eY5G3Am6LCenPeNDQxTUubqOIqjTeGeTMfkbP/AFx6eo9VWqhYU0LQyln1akTiKjDHgBtsV6aGdujpIilqe5/DQgXyFfAZVD2/D2EDl/vAK15jFgfh1WeLMvIWqNQZsaWKFW/5a7sdfHZ1hVPqH94LH24A8aXX4KM0REHuzoAE4BsQl1VXkdEggAxx6omDSy1VkGPZtO3l9qR42krUnMHsT0DC/GIQvhmddOh9S1neSllOBRkSmhorElJFregTSZ5tXZxUKqR55KBSQasmYFOw8lOOrA24w2HSg34XAAyVkyO84XfZZzZ1OSqmLrJV7fTY4vA2M7t5fm9Aj7iRKfaoEXtmfqWYjdjLRrNerOt5dXkJ6Ht8hMoN8x4wdyiPgGKjDLVF0uXlJTa+ME0tBOL1OqRLTo5UymJRk2SqQ0m86JyHmtAkbFiyOAwTOKkiAbjxlX9zbJb8iGZiTbdkF6QIi/NQBS0iRAtzTRciieoqQ2puKqrIfELK5S0YFN+5/WTbE8Kn5flLHtLT4KSSrWY+VSNHUq948Cqdqy6T0VKE1M6BG2v346bsvfHpo0tAr76wsVr9TOPoBupLbzY7CBzND66vpswH2RPrHhuMXP601dE6elZP2b+bhZM5WkZl6wOdafxgCOOiiglHAFSuh81JPGYGhPLjGVea4SC86WUVgMhZMSlbwBAZEglNfhuwMK9wLtHmDVWspDLd6PV5oaTPDeJQMQ+RC59mtIAOOEf+a6GtejM4mRheTSTgafhkmXFILP5IOjHsyUZATFt8slVGdP0x5qRWrwirzAO4x+wubaQH4lezKRsSaDoFrIs/6EgAepzZLePxy8COh3tk6RtLVgL19oo7FhG5JpegqmLI+MImZp694zziqZH3dV/vs/GNbRMqwg+AZB6rCpsZZCCEA6U2E/wtBTus130q81dqHYssNCoQyUkdpcKpsOW0kfmWNlAjJ1yAcMYkPGvWlKpj0jMlbnmp2T8yTAVmOicVTxpEoWJHJJJ+wjS/Bhk23m0WeW5zSBGRmkZrABwTDNZy02xvdTPwcPfAkW1TXWq3syr+NAMCWJTt0kKR2tnj/QB9YQJ7XXIsxRQWlHo0VwrDOM3ms8PDwxGgB0G+k559Vn5cTKtGyQQzbyDDOXaxEgV0VHujzuS3DvpVkAK+AEiVpixI2tI02yYQX5ccg1wS7J+hz0HHIxksDZ/2PYcvA8usYFZ7LjPC0xGh4JU7u9CilOWNNA+Y41dnJc+5IwQSIEVabtLpa1URjlVcTrMoSYxpoVlJvq2HPpreOL261vnTrFTHSBvTfRMpNYrSb4pyLnf6cG/cG6SICkuWCJfld46x7CqeoFIDQRwezDzvLiOV/GIdhUVTOQiXUAuMLKrGtHlSr9HfKBNiaD2RhraNZIoTY+Yyw5ZqQiVxQQEyVEW5pDAUcL3Akq9n4FV4rKvizmzcM6uQr9DnnBsMPiNuRfAmPZgMI3pyW8rh2bK+ydYmkMLyp5whLjmHqAe7QzWEzF5HCBVDZyMrn8UKtfCBqB3TDpjl0w91/WTNGKhKqkaPNCukSDTeZptrp8NnDPqtZEtAn1XToyCIIU7+MLWqUHzgs/IISKabaATTiGUbjvrtnqY9vUBVGTasRN0eYgWxikH2klv0ZY+lWXkopAaDsCIQc0sBecgR8BAXWOqh0CfzX5RmCwQQSc1FEB8aXq8uL3rd6cXpOzwgezltHx49ITcFoUUtzB54BgC3ni8p4N+X/ZYP/oJCSIm7lNEGyH7TK+zZoJ3rxbIHB5p+blGwt2zVHZBXb9DilvIbacrEUYTRx86I9BCkyVIntHET6XvMLHz8hfVfDUEYJUdDxnvJ8iItHkab059rYDFiV0NjOz8r9nBGG7F+VhRDZ9uXblNFLSCl/PiKpytggFh7Z/O8QGqUcatQEPwT1DCL+bJAKTrao0TK81t49eh5gQZPQnTEkOZIoaQFCmascuEwfciopp9AmQ7Lt6DoIGypBKLAiwmiPjiTC/cq4lt4JxkD3LAKHEMVLnCyw6iHMNXwaAXlaOkUyT8hm+b5Mo8r9hVXsrA5MJw8HDm3BSlu0x3S2lcX53N2cCjjdAZnHbo3FNocPbkd2HJH5spwO37Xzj9j0PNJ12ZU/awbCllIIv9oByecm94qDWVHC0EF6uOJOF7ycXJydXYee6hiouLmAIwIxR3ENJMZxCHFKo+WlfzxDfgt/W2PoDPDDNVAFtsuGdFzkzOFQwK+TLj1B5+QGg4n3d7x5PCNdvf4wcMn/cEIfqBRuUEeFF19EfJ5m4Np0LmpxVoJVX3qr7ZJB0qBnbDt+phIbjhBcPsEfTG8KS0ZQ8rxW6QPLs/Pri7O3JshSTNv1So71U82R8q4pGXtkimJN4R28uczdm9+YW3UGxEX0QrCRboAEJhqJR1coxnl7aaC3kH7+uLCp31G7Jtt1Cxv6tdQfJJUt1TmMw2h6fkXpZoZ0FR++xXazJ32dL/0tuKGYqq/8ZihkE4iK9G/yWGu2HBfO3fLiOTeQJIqo7hOhxP0rr0wl+XmUrfe2CN3zigkxLm2ees4WY+jh5xKK6D3hkYQSxNw+ZBduKrikZcM86FF+z9UqTDtkgKrXxenJwMgz0OAtXfaaIKCj13QnlR1RRYSNhgPhy2iHT9o59Wuhs/Y0n/WzYzifIAV3XCu76huM+hqzFo67wIwUNCS+pZJHdtBr8Nz4/PrKVYTxZABVEABp5NuIZ+uQ6Mb0LOaXl+CEBYucS5AZWALYhu9mjehTigiP2WZccFdSWd532VvPHKmePECBBId0FwEJ5kxh9YoICDhMozPlTop5HldP/3KJetInDBNx9Dj4fAeQiqnjfa3VOlY5J0CIqzAALW8nJArl25kLaMc7bDjLd0jdOMUFUwz/8FwkKX+6AP2N9cnL896dAkrQAJhONadfpXGhH3VoVis3hOBROt5X5X7EbjY4bDjoC9wYOYFHRf+gZhmEqaso3Uja3AvUaOpMoSUIt53SUtAtMUvkmC0BtJduEDRvD8V2mayLoG1pUznSrVF22N4Id0BPUUEz1bTxBQUyOmJnZQhpWBhVCgETeETZqyLVyvngzmcwrMuviiTQNGRLXgLnDk3NkfPN3VbJDY/TbAcqZaxCVWj1/Z//tJlIEQS78OZ2cWWfJaU0T+RGQSqeiJyAlNVFURkYBPodKmCDuz67yZ6R393GvSlcBSZkVdEKkVUxQZjFBmMau4TSGkAos5I5MqCbECwUOx9lS5qAkJdCkdSefMRd0QJbjsLam/rNecFVnmoim7XC5PGB3BWn1CgTJLXZCA0SWTKuVEaZh/24lTU5uWbdFT6AF3UcnabpriF6r8lFQZHGnuToWJyTF0pEHFsuoY8VEWcIA2mD1fXM7YnjSdH7FKCfWQasua0GmqQlkzfCDlJlQFRp9fTCIh502OXw06DvgSKeuoEOSJNOUajUWoD341lRfRlZtRkrCJHNE1xkZT3NsZhUHNE0jNQEopCfWxCDuFYP2LQ8gaXlRkqydAgh0gCMQQoUVGdYqFxTJqkqqUpaHqVqWJEpxlSKQ7BixkI2GuowFyWcMyvqTWjVGyORYjimmyha4tko4xAjslrNHmrihApGkZxaaUHrVdefXJ09JC+xw09YkO2uRlikTSBijmHVkMkzeAcykln/DSjIqVlOxx2GvRIG5FGBVF2gaY2+pakMfdNigZbgOsRMFfkcRB2zABeZK9WuOahEr8EbwSthixZXX6GAvl5XQK2VWi4LmPXAlMM1kRAmEN8cmuFp6S6I8dVellkkZt3ZzO1tjS1zUGv4NziQx4aGKRVojesw66oFS3kdt8MbznjqyRsq8dRcG+7bj0VhB7E5SslNxMGpwb8I481e4PC53yb6mDRqqmgQWpOBLpPluTFna32g4evDMcTiFIH3+BVdFCUaGYNzk6aAKs1tsQoyJR8KUjVxIIqMwFvZe32zjPUt7uhQCOCbywHumPGqBY2ahVMN/DatAZdGJtMIrFc6ri2JJBIBmgZ7BW8uj1fC0t+k6QjtkgPmZAKxCG7jSwmuEzvSP9IRfB8Y+/DiOyk3oZazhNpl+P5LTAGaAMq623cm9h780rbR8bkK72jeAOaFdIz7Z+eaHKTkYO97ybAKgEeyIX/RHJtOt7kSHJaTx7KBuJWZcev2t+XVVKbCGunPKUZpjaRu/i706BHYGg0YlWyUQiWhrgYP8EaiStp941t5U98Sb8yCOIELgno70YVxleXwMI6A9NGa9Y1YZySn8jCq6WCeyOtU7JoOvFVY9NDiGnik0eYxnJWES1zihcRILKx9xIkj40M3AV03pBKvGi6FeqicjXUkiroNyGghaVNKxJPLpokLhlEFq7fU1kiSXZTsWSq0TIvqwlGc1IaqRqI4ZJIS2xsQQpJsOI52bWw66BHnkFPVKzsHaiBBIos7SrQAJFLFMBVo4ONabIkeIl54wdkZ7tL8mUpAm0li095onXyNz5pKdZOgu0kj9WU3yAf/BeHBglIo1G81FJdEjc9M5Bt0LHJHTCmpIXDacpAkD4s4NlCIMwYBPCdbIhMpTaiycMdBg07Hc/ShmpO8Z6rZunJPBlJmNs4rOkQ4bUzbIL5Zr2FUmZrKkkvc9pTdkdjH+70ILd1VZGqtGqPWTLjNrIy785xp0EPstRAlK1A+e9SOqoC3C6SbdSDlhAy8EB5rNhUiegPlKgjFQBe6xE6fHfGdB8gdMVQuwiC+4P+1fkFGwDUO/5/rD12nE7HIjeGC8NYupYdiQm+Ct5HoANsB4GwRdKtLHXVFCGeAG3nE1TgFcR9XSE0bUe5cG7uZ17MJ+Ko05S8t0e3nrUess5XS9i2Q5Z/b3FpERhpfGDM+0QsdLFI7+oQcOe7ne5dpgX2WACNH5XHvVOj+HcfRPWT8GazkJ/8wwYl6WgIY2vfwxgVqov8yQHvA6dDhe2S0G4ddxv0aFHDU8IU1ugp+NLiEc2VPwkkoxv2JqIAJnQc2VLcqAsC5lfNJPBQ7NnZGZ8EMY92ksQW32ZgfTA3FJ0SSzKui2mpS5Wmb1lNEyzPKUcYuhW/Sd/8SqAYDSmiZXYTigI4Mt7BpxLEO31Jw5lO5RgXUHNiAq+Y4jkBAZqxIMIpJlOdWe1JJIagxPMkLn2Bloaq0rN0Vo3IkLYWoxEWqSkNwQ3j5DJwSb11vmFAtpO0eSVRJe/ecddBj2gboWGWYrCVLP9KvipSVdUlehfxmk4Mm/GW3VLYXNJmnpa9vmSHzovCorBvsytmyK0ZII+KU2lhQOJUTUUcKopUTxgEXL7Bbmo6iUup9x2qCIXFGbjeeupxAooO/gPdDbMskqp0KnKNBWwG9cUhdcZ+NqMD9SW9uLNkw2S48xyesqblO24c3ViRZP9lbk6Vuc5yTVVq7XH/0rO4soUc0vRGikRBJPLhtGnv7ZPKQBGYr6bIVVWwvb7rk10GvVpjw9QWS8jaKO9VlcQbjJRWIsmoCjVjDPHct3Oy0hz5FL9lwR9fIOHbMuzhxbbylB7aPDw+uri6xN/1ubiq45ZeLRY6daIm7XtBliw2CEglxJJkqBOAK3qcPeaeE+uChmW8ax4VXPotW34ESlNOkx8jzMHIwDdn8SxCWUBzYglxD2Oceyy2ASfScywq4LJ/n87Ou5o58BoVcsqdvMVxL1aJiXQtQqCb2nWaBjYdoJybqngrEzJtA5FZDbPkDobdAj1C8q8JIJM1Y7Ra47N65o/NIe7wBpQ45WC7Ubr7zLS3Lbey4B0PhiN0ryEGbfzDjHZ8lRIFUTn2EmTP2T57ft4G/JPjvvtDO4dHx6enJzycKp64VQknURqOApjNeRRfdppvA/LWdpxzFv5i/ciLvgl1sj3GOtquMrd0MBk3kJ/5N5gKNFn694pFdOrC6vL0E8Y+6/BFEr/fGwIkKheb7wTGm/+Nu5K5pvVm6OEXMZGHVRq2keHXsWADPXBfWzli9Nnt07SNgt7cCsYVtjt6WBPzA1vhmZaxfNzUD2nboVTqj3PbyNG1Vce0xtjL+y6F3QL9bckgPq1FGa0ySipbrxfZk+oiTESMTP1NKnrgF2x5WWWLaAbuGWsdvtSI7ezd05MXz56+N5lMDg+PsPrgEtwcHR3RMS4vLgB9lSNeOgWE2FAOpVqOdaIHkurMnFC4355XaiJ1NPAxeMsNbyomcEYPI2e708uWogwxbiAFiq4pxhKLJAJ0bgHIBoYzos3gMRmrdo4V4IpGkcpHwzPA8BFZ9l9r5KsA2WxCeixOUFGDHjTpHlUxFW1bZ0xVp1zkIvVriZJiDASLZsXs1LHhcqd4KlXBUrQYNYY/hWvcZiuO4lZDaiseaswSCnA7bjTRjNSm4liwk2XNazYGmKC3f/Sj64vzhw8fjidjnxxPfgCBnh4+fPD86VOy+0IO7sm7x15oUYt1EYIG6MNU1dKcNzgw8kPlSTyw2CIj5OS8vv/TH44AfZwaR5WqDH6kJsGbnlb1QidJUGg42dYLj5xbJgGOkYxSiteBU8cuIyiHrCuSeFtQow9YEz2QotWhbihw7ZyqqqaIZ7dCeNz2lgb9yFzOdi/s6H561XbQwhDelph4835/I/rSGVpCB4QGFI1xirRTIIfm1hXLHadnZzwnhUUfDQe8DUe1Ch4/qQB5nGx3nPV6Zycnjx4/xroRTZ6tjosa1EEARzpYPBPBIiZEWghxrHNy4qkLIz0E+EwuyAbYuBqAz9It/DGmF30+FiLA0rEA61wPwdU/ZzJwKxXRmbEu0GQnM31b7ILbbaXwCaidlPM6A3atHfAScLZPywMuHW+LoKfBWXUYhpQCaJv92GTa0oczrsMfR+S0nZwSDw+KLMymUfY06iVgi0j0ju8w5XfssKOgR3ziOGhQYkoS6KAOp3pEKHGDUfyhP90BdM88DAjoblo6ORq9UI7x/fj4eDh8lXgBYdYECYtfIAmFBw8fnbz344uLs8nxYyiCgkpxoCleXP33o1SyEUWXHwst4cyPPDq75CeXgpXLiqBp+C3uken6soY2lJar7qTvi27mUNSj97/41smma/gdLHir8tUumJK58jqsyJAaIhwKMx3nu4Wti9NznrbteBODVfnZxTUfmaJrMXGAgsblenp9wBNc3X46BYI0XunJtR8cxx1KH6UAEXDFPCJdk+oFelVs5grKiVw8o6jW8PCtRfZ2I+wo6NGdNjHiLEEFXfYELxFtpM8pAheoHFGvti5z3NigrZglFFRg2/106vkZaj4+OuaNCdDi48lsP2aay/QV9UCAu1+jo+Oz589Gw3GHh1lxVd2FVZi3futEsWqYc/6HnySYxh9xtzBqRqYgTBtcG1ldXb2YTc/YxdzrTwbDBxTB4PuwYuYVZk6fFzfus2QbnFWlBU0dVE+/BpO4YRVFBqvWypbLysKXDwuevHiBVWC7PL2InkY3jygiMTiFqJTXk/GkChYdjrYgBOEKykRohqzEUAIlsk4qsuLFt3v42lRKoJm3Od/mvMOTEtAdMvDhVSNNFLbRn8opwyLoc1aaqMLiL5osjVA2dr6hzIUW0onsvNteY7J41+UhX1obdNu8LGM+W1zTDy54iRdvsufmFKsO4GU4OaKui5PnAA6TCGK3oJOWPVJO8B4EZOw7Ha6BW+ACrARNBh+zEjScIAC7Puusp63l1WJ6Xd9TMfGAb1zqzoFmjhz4y/o+LztJdcQGfNRPoxSJJDk6OZZD4dsEabRW9G1fxxdGak4s2xSK7bAb50SpWp1gxbuzgRGpv5ucJidUYnFCxKZC00kyRtZkhHO67Jalbc47P9lR0CMXxnQEGH3nEOnrempyEG7jmaiDYALh8kdXQdoUJ0edKGKntjgJPHXKZkbe1N5h0QQVsW4y4mvhbZyH5Wp2ha+P+rmHBEUc+9F4cn52ygNwUgCJG/1SSw1C9AX8C1PoF+jbiswrPxDSNRFKLIC4BpLiNIh29HuTQf+42510u2M+fwWRwLXF63oAW9NDAh1RzLjj2kvqTGtTYa7jy0FeBpvFytScbNiFy4tzuWFPEX6Gn2VmREGAuftBAsVgj6EHsbKtOiGdvlLkGDHzT/5S4dbWUIJUSMBMhSpeR7JZjv0IG7K3U+/8fLdAj5j5U9G+pCUAQO34mBnf1Z8zP/Jo1bTBMbPxxHm/i7hDh8IdBGgwC0tCQLBRkrduAB7MrbruYPTQMchk/gb6eVk9j0SzhwuQQX90fMhDhNPLC4DhVBEbHXcXNNt1qA6ke0+Wz1zlT+4DlNh36y9MGG0z+GP0wonqDQ4Hh28MD78yOnzM8+G0tMdun3WLTe24aGmFJfkPJT4ORS/fujGFGAgqFC444l7bRBpmW+LoO73h8b/r6ymSGvYHS94T6IIo5bLQrpyRIy2iDS7DT698SthbIoyD9qVI2IZQCajn0skSFHCa0ksb4/ITCJYRpW3InEGFSmOXwm6BfiuZyA2MoQ7nTAQ1nLV2RV8ur5ozBM0IWpVyefuYdFXACWvV4tCuYR5PgH7WoQGMNgxEdDosoJOTm1aD4fjw6Oj89AVbCMwvBOPAhJPSaNFvapG2vHJJfiQrp3Y319sFmFcmbQIFUjQgsRVpGiYyNUiNNrP2Qs06XcAojacM57ZCyDY0iCS1rkoMl5dXpI3Go0G/x8so2VopYz4eyQKtetcYazn4jDgfYMyA5ss0m28TEQ+fDSeiv+G6eJBbuagGQOwmJEk7bzLhJmVXznYU9EGzehXKzV+kJz7QQJxRTkR8dENsVE0J1AOWI+AqYhmBl2V4VIfacIEpLKo2SmG8Bg2lJDbhcPdqOp2NJxNeeHR9eQ5V6yE5XyBvbmc2nU44/IQ+CyAItwwmBe2asi63hZvk0S0hSNgtkwMXDaFm/6p2Ne1w2fEDtTDekA8KNjoh9kGg4aPNpws6MKW4h+1cNP2EhpOVHgvuC9GcI514UMqz6UgRi4TSl2RoE4qN6t5wlBNGHUNlSSspalkj0+RN6Z343VHQIxulthGjOMC1wKdQaBFolhQ8dxFTj0cBo0gWswOjbcNK/PaEjAnRevAUVG3xx4nlNGngc8k2FUCK3SeShc7SOmoEizWvoC7O4cZCzTHcgWAoVK3mN4Mxm8ycmwjjCTrHUEhPgPvBYEQTnAZUA7N8yKb5pkyQVNWlWqhDV8kUTbJxLWmAfHDAjQ4+I1cdhk+sEQeLNtQJgJxBqkLYYdleW2DnTJtIKk6No2Du09Ec76ZVShq1oZGWStf6uWDY5MZI8bbNswsnu7hkicCRHAHpY5dAaM7xv7nJEnultVLEUQ8a8Vk7FIleSEZjKjRqUXnKv/Y/seeE1YkWX2oAugCr9CEoVZVGDvQ57dOtYvgmcEeoB0HI+MacBqg4wZpwelfyNAfhFlbjyEiTZEETMnKra86cRMqWpIC9lO0Py9lyceSuitV4fLS8nqZ/e1/C+wNMrdmbNmPXzZoJQZWldU4qoEMQwY5sUE4yHaU9Xy3cau+eAgHbZQ2e7ot5ZzdyPnJvsQTalOYy0jDdZ9rt57rgD2HolsOC/QpOGYLKm3IevJ7zylheqqUvBBlbmbZzuQlSJ56lAT5quIncid9dBL2CESw1vqNZYUMA4siRE/4r0Ryd2GrAVI5/0Za5A7YcipiAxfZwBGdlrZNLmoRUyAEHnv5AFX5Tu8esrqFjuspP7RgwLoVDOKW4ZN2+b11EWldGDrPJV55CdPW6/J0wCiDANd84AfXYe0CG5086BtX797TJSS12mjUQoEtwQh1WSaYezmk+RwKZcJHw0MLSitte9FfyZMRIMRiix2dIUZLFHnWHFrzwoVlvWbBDAaYbkTfNURzmiyrs01D1lpmfaCEnfzAPu/CmUixlA7KoUAK07M4ETcMOhmCozJYaVQuRYwNBJWuk0sWOkaToI/AYMJNEjWVtnfsLXJDhFG2RCvjMEARwSTBVYqKBSxhgMwI+CSv3bMIRcXoMTgxSTQO+ItIc5UeOCNRS1UGNVDNYnZSr0lwCbxgxL2RJpjq6Ext+cAsyLSEvGfkC4jVeHJ/lobikEkK1aUKysSjpYiuMVobmJKyQj/wSbxruhh8FxGCF3wjD3Eua51OesOHKWcQbQwNFuQX2epEQcmM21KgLdqouW85fkjdJtlSRhJ/idkeOOwp6xBk1Kdr8ITuUuhBKhGAXrIBCFyVEAgLPfwSNRSSLGjEhmonoU4rSIW72OgEVnFfA6lILKSCm3+1cnJ0B/cPJIX6JA4pL+CugSmVqOKxRKzV5hB72MGg3LcFqSEvQJakTeSK3O4KoyAwpry2u7UDUEZtd/hDZ4YrhwElnWtV0fqgIQhFMj4IO8XYYa7HSNLHBqh55xdr3rJAijgzVDVvcpvON0wTLOShJUIh7qOePK2s5P3ZgeNEUbIZZhZJBrxpFOlVybsZdCjf63iWuEGTGX2QoOlQ1jBKLTFGJUg52gajOQLsbxWiexBylnHcF7mpECiwaUrzsKmZ1qxVgh0qgpoNhb2Hdg9s0wmHGFq2z8wePX7WubA3nDo5jCj6PuKAa6g5onP8ZE1ijaawnSAkPEStVwAchXj2126k4kGgTKLic00ALsdufJReqZzqbMYcjDFM1e/d15PgnEwiFpupnKxHmGOxYzq4KMcbip368yuWKaUlst4h0HPPXsvQrjwx+WvsD9yrYr0yGK7s31chnLRREjpG8E2FYlXkE7y0Rzv0vuEOcIj72lU086dUk7k5QLjsYkGAJsUFLlKf8xQ0YLbbVdrxYERWMiSyknsLlzqoELkFYxnk/Gg4Vmgw1kkyNooxBa3n9KmhgBH/x/OTRq6+yWq+5ZSLpp0F8T7IzQaBndwyQqbIJ1APUi3fJErY824/TFYkUlRXi5QNAuIIT3Buwi6kWit49Ep+ih0lmPW1I39synG6fBqQu4Mxj4K4ptWZz3sw5B9QkUNwTxEL/qBAPhzgCjIcfKyIGY0/VLs4kkGSm6sHJ6eWmm0FM0Ff5Tf4iWEmSDoWKzNVOHHYR9OIj5qhEppwbeCli0qJ3EFEKE1fmVKmNp05xslUsCvBN3OkwZCM391+waaBLBZby+X4BN2XYhXtwwL726Wx28uLklSevs+2MKlk9wb+IrQXx9dY71zW2NCErFu0HMcMyWLbfHlCMwJ84lg2zNwE+E0FhDX9sLtNNXH3vf3KkkHn8ojI9j0tqjTykx1X91277HcUezHPCF+YoN7vmETMGIjdbwoNum9C3IJeFVyopTuAYRpj54AMSU1Vwoo3Y9FXrMqiFKphLD+TfFtme65KlIjinyO6EHVy9EbHCeoOOkmypSqMT6JDMKTgP1HjKDturqggoT3vbeOoaMwKK89UXgTizuel0Ss6tGkAASZJary4uLlD6a6+/jnvBF01SltuiDPdoWr+4SmGGU1tj8I0UtFronHpMGq5D5REoFQIq89FJPIJjkqCer+Jg6jlPTp8dhG+qpssx0RywnGpfrVZb2PKwpENlUCwuTLY7C+z9/Orqkse0cHPoRtj+cewCLbKycGVRNskrJE5xatyBQ29br2pS6wjZ61ItH+HRoBTnxRvndnsGXnsOPEqUHNBCgwqTc+P5s5/sTthBS48kcXPxW5FULBlCiyhjnLSAUbWyjHrK2qk1NRCJa9CigABC9XPCsG1qtADuY3S9LJtHFbq3ByuWKY+PJhD1Tfa6NO5Bw63AyWaZ3Mq0ymjUChrDnpM8Jmt/k9nmr8FlzRzTE/Wkq++JO9ErUDhyhS+Sc7L4ATb+ABSfcaJVcOKMIo+TWGtEgPIYUARm0FdVS5aa2Mg2GjNDuLqeDkbjGbsY7JWwVe2VAPnoZ1r2sMCeUxaKIAUPZEoreLWlO/yooni0jLwSbC58wFymRRSyzQTS7UGC3kR+86ock3Yk7BroIzYxo/CQUa6VlfBUioIPvUS+Gigha0btqD8/GSAAZpzI6TuICEtxQsE6d2QgUmS5GZOnZsnkjssMymRmdCYz7lDRlo8UiV6b+qBAqIs6qSOZPeH/5mjxBLFczQhLNDWZzSumwioZqXoTfGh4W9z8lihi2xOjDNj73nB0eITDQ3PZRIejAllSoJYyIpOr8OW2BAHKav302kdyCvpSb/LDlWRT1rYE3JAyfqMl0iueZM5ptj4V3ch30O5Q2DX3Bj2oYlSukCJEZR1VcUTU4kELCTK5x+QjFMQj5SCr8S44j4zVQZY6GbUFdGmLSNRfBUVAVEiHghJLf7whgedocSLQu270BiVVNjxYvCpNLZvKgsKKgSaUqU/mwl5ss+cF1tRud7Pe4MnRJC2mIpoP69RCS5drH/aDk1p0skk2VqAVZUsZE2ElNQl0VHqI72p+/u47x4cTbtDC0qYVVk0hGkcdtBKgI1Q8J54tYOJsStOleF3Zksfmt6Kz0nTLDSk5ogNnikRcSspTTMmSJVSf0iq+duS4c5a+xKNC3i8htC6GiI99At08+cZSH52Ef8TSC1BhFSKP4k/+GHp31ZKkJqWgYiq1MnlE59BxE+IBm8yAoICQqvVt7W5yNnrVUtuprLXOq3arSUVGagjtT/HZLAjOSdQDsE7Is6WZKuoZKIcg66RnM7DkhNyusFKGNRzmGLj1Te+CsO3kyglNaqw2OjDhzzAP4c0fF6d9vPIBCzv0IuqW37TpRhT2LqpZgW/2GF9Rn1kiQzLR20iHcnXL8J1DuqXjoUR9/6d/YYNqPNUtUobcV7OrbzhspHR3P7sG+g3UVaJaIXiiT5unRxGhgWjk6bOlJKkdMR3TEjSLYK79RzYsNnnywHXgDsGgSPqc1DF6bc+njAnQ8c3dWcpwFwD1AUFqJmeF4g1omjV/nAeB5pHBMC9SM7GOW84kICnkkAE7FdDkyZbsGibJ6JTOMOK0Fo9NN4w2UBFrUAwfYJBGyYbNs0dZn7QtRcA6ZADEXVEozF64o+wZ2ZUlI54XhWNOKuiht7ustrDYWa2iVtmNzafYRnI1qEoJCjUgWyuBKOkbIhO7pAtG/tphJbYbYdfcG5GAZJRbBNQIMbokQjQrOxN5sJVVOTJwcUuFpamIH/iy4JhXvgBanSIpJENZKQG1wtklHjrAg0vMKrUwKLNwucz+A7wNUF/LNeQkFBGrFhUywznUORLqUmVvkAJ74J55Qrc/uGbl6PKi3+MFfWz9nfR6I7v0gUuovIqDI+9ZFYHpRR4RRe49N+5NVYAEyuAHYKnT6uCfo8uvrmDyds6ZHacBXrWxtsvYCoL85wjEndF6QGgwy8ydOUzSm1akQJpILIwROIEdWFGOOqV0VAmSq5LqBLKkVv6icrfHXQN9hKqShA7BMzuCgUtlCrzeP6fE4piAyoEyP05SIeFcrtBQhAqc0tzEcoImVFupKvc19SXoCe3+eDRauKcXh8fRXdVuRv3bRCSeAJ0tKc8cRlzOK9cIGvSk3/mdv/n2j3/8F/71f+n8/ASL2+/P+IysGcqsx3enIKXknx8HCu6MyqTrN0ypJV1W1a6fxpaZSHw6M6UIMEVDdGdcrL9pY6VWDEf4IhvrZfR0hKsU6Tl0RI6ZWmSlS+IJW83cdBhF4/yq2e5W0qB6ZgpyyHX1jY2QK8MdHncO9OgI4SAlLQ4SU2lIL2t53CjFIGVMtzPwD7WwEBlf3I9JtpytmgDagJFDMz4nLzLOCk8whOYKEJygpxI95znRTGJrGeR5WLyfZ4gEq5hXeWLQTlfdj3qMJ5BBeMV/BWWcEQlNkvFifNYXULhgevCVr37l8auPqZdPj7DZnSwAmfVJN1SKHeYVwRyOPjKQtG9s5d0gtGjJnBZ0ZhMO/URg8k+BENhU3M5bJ7PWpLE+4MkCaqRe2IO3SFWSolnaVVA+qctpRLvHLTBe+8ckQwFmNSxNrsxW7s3ajSUp00NWDXx1V5uYoSfUabQC0FqhBZ7McpM2DMh4TmT8LsLOgR7AZkhXKop+jVfNy03Rkw4ucQIoeFBZ5lKODs52FOQr6JKF5Re16ys0ALpzOHNuA8pG/bWQF8hSCoquNkxnU1bK604+GVBbwToYR61FCJqJCIwK4qSlm7qq39Rnr5MrNgHzisdvf+tb9oLF/PHjR/Qdt7d3B9eXF/jrdA94EyZiVduZroI71waI4N5lKGcA3jXguQCkQaU0gv8iV3LyDwf0XO7Ntlg2qfsMue8BSXNKmZASXnlJNB8VBIqYEGSUTitFztNhkAxdxYUsRQHKW91wCI8oRZPjhMY7aIsWL1OBZtWiZGKtqCZv3bG+HQiN2naAE1lA+oHsVidqSSPnwM5tV7lVY+qpLKPYSEE1RJJmzEz8N5ANgpxUUuJuDhubp2olKyy2Rc3m/DWhyogvOUK/qr8GilSh7d8kBSiYN+FgFwXXuclKFtx1njoHPdQEvjTnTCK4yLbeLN3gvofhogYKqb8BXL47Qt2KiDLp0tAnkDlSyUn2Driuq+hgrJFFNbbyRx7adnlKdeby0W/WRtOvFJvN3eYvu1DNLN5IggJHiUQsCMQicYqQDvHIPUeViNA834GwW6AvlJZ0mmPkjvCymxzJCy8VhDQDe44MzcgaiRIZ3XBCu4wjYO0YiEEPxcRKCCYFfatXCQY3myRsK7psekJyRlvRMZmbGL2jhh/RAwmIhOAmQ8VZY+AFuxpj2uV6jTcBNNMQoS5AzzzVFiWEJQ5yGHdLkpVUu+oVTvHs0IcPQ3uFF9mIZ6mEYNuommKptagRQwTHZDY/bYU9WPF1Ha01b8qpR8SC+UY4EIYTjpDaUoMOZaGzDbS+cA/dcn7o8OSnBowWkducd3uyW6B3NERe6AXdlQrRboKqUeCNlNEWQdVjCMu/xfIZQ+COlXDUhHEbnyehVPwa3xaletepPCiNdXm3KdSgSq960O3Dg75wek8mmRCI1sNA8E9PyogEFTSa3kiF1osnU4iUd6oLX9xLZk+LAWcJJPMEny8vZj2ReqgINNNmSoCsqjmZJUEANDSeW5tsAEYEdJqsRZFSxJUWF5DFuWefDLxBBAoywhvA9U/IQDbsAvQdQKpI1QJ96mWrmvxppI2u/NZBjWSIn5aEhivkQHPNkLlQasHIsLiMjbF6fCZ2fHKJQqUXUv7cadgt0AcQiA6FRpcbb6cki8REodpK0MZ3QK65xaT7v0njFBWjJxDAFRYLHACC4FDzxwkEqYuwpZZkIUsGUeuLMXRwm0ApaOk0q2DzgCH+oXLO3ferC0Zxi1TfhKe0IuBzQinQ3S3Pe2P55S2Wrsz4D1JOX+0qBqcDVKRJpixHSMbDwRXijfhCnxiqFYyBks1veKMEZUiXNxiCR2ojA6fOUmOMZTH+OsStVzLr0XjMiaTSfFGa5kLP9pa7EuCasoGwtW01Ej4hSMWOYMUELNCIdBsidiFsALQLvCgyTa8ibsTKmbj0IdEELCScNpJHO9ym1WrVw/mN8ihLHvEiejTPG/1VI0lVHXXXqSBVxKmdkxzc116RVSZHbpryS1l7FoCsJLJRF5yYZrQwDew9ViTUiBMNkA/IZKryx0baI/i/QQ+4T0Xmhn5yYlPd+ktFCU1jSapgjRFdNcFqsjwle7JaHangClnnFSQl1QdoePsDdCgEH3ZEgusBNCCE84b7ylDVkc4JTaUDV1soa7sVt3OOjH3CnXo0BlFElb3z486BXolEoPxG48o/cRobIMVlaZcjwgTyiBWAbwpZvPLQOUB2nOZSB0fBRGqIixtCsvtOpSpVoK0C21RKBAT6BuLANwlbLgQarBSKZCn9gctKrWNltiPmz0gLeKgu3Rh4I81bDNhz8dcFng4GYKQcZhTUcmJ7GpxZCmBtY8hS3RcC8rthRvhlNmJUJFknvX6P8pzDHyKzCmGaYuQjzaUbK5XpVO355kSxhhNiqCE5fWidrPxRmKHNRqXInR92C/RRg3ut1K7CRWpl0hAUMisfo8wg0OdZQUowsvcwtmSmiKJHRRmgowC0rPWq3SmSVPLg3hMKE6wLaPhEFa6zPgAeMRiPIkVbDGizBwv65A8WrVvqGYs4Bym5E0X3w9GCmWIe/smCR84JrXHGoqHlBzsZxwzHCJ4xmYDN1qQJ8AyHNIQ1FZw1scqvz/TxQEzelyZflEtLbLTkiyVFoTTYJp13FqQvOKMwzpy2WXbof7zBmztxen9VNX2S1XpyuMVDxNr/lD7dnSvaZJyRWalhnHLzKkK1d8HxZrQhhlYSrduFAHytg5v1dyHsEOhVGPZFoRq4rBDbVNLMvScTCxgCMRLHPGWB2dmeoQqSKzm3PzUaiHiQjQmlazCpY1stVHC6qZubM04oRB5khEb+1DOK48gfSKljcNmo2Uqdbno/F4oC3m7A6AEFi3idu2wMKah/xs1eDtyCyOAfyEFKDgFKOqaektQTbFWWZaoKq8v8AaJVNankqEtO7edBIVisSCREXc6SAGMwLE1qarU189bbtEt0Yj4QgrWk7TTDk+RIjYwDaZG07TD2ArIbUl3ME9mdvsuVHk4oVJ67Pe7KzSnkQkBu6gMRbiRK5EaOSlPhel3iV8A5j3osm8LgP1BTbSKnoJPiJKQPhRQfY/OxOl10NO/Wlytc62DamKrMIxfRnDXnkpMKZd6KGjCDOPNn+U/xba+Ixo3GKZ/ViwEz1bZWbyHRew2pB268QevT3FQKZHRvhCCBwcTJKOtTvJMnN4yLn2Jgy5KRwpR1lEWHVz4VKDXeSkhKDB15Vz7Y7fn5Zfs5DXcVNRJFKp5RUBly21gjndZhCajftQEuSXayQnGpaewrTzXfy40HpbNUpJuu0QjwTn52BfSICSUhptgbRBH9eIj0I5sor9GEsU2qYHJ6G62opJyHgHoiGzNd4iiOfjhB5xwBTac3QhUME6gNw5sMHsjCX3pg0bI2L+PY5NwuQU6OVAG4OQGKRAgFN6KAABkJNQtybjW8JZzHrzR64rkyEUkG0B04kdOCrvb0Bitfwor7IfigzFTX0WPTb+VVbm8CdPCR0tnI5caxqlupJq8V8Z8FJ27rClfvCUMZZnWu2P6ua0ehMBHClCOJ5pCNwPNjaY0UaYLSh6ZXgl76EQtFU0/t5zNS3O9GEAS7ELQpeqJ5MV/JS5mJlYvLy3feeYcT9Js/otGCt9ixgHQVMYpQ3WtiyVIarhKQ1/21VMHJDkBGKmG3I3fqqZHM8WfX8+srFszReI0SaFeGRHCDqjDDgRgY1S2qYFSTTcpEWpaXjWXFJqXCXaAgtUC/7zpixiDHfyIdniga+lQNWJc8uMgDrqTG2FfT8nKEgAzKxWGq84pQLCEd3kKsjNja6bPe0qVe/pyfKut8LiHLskKWVKSImQffop0c9AInFVAE1JAKcfIVcAU6xXT8pWcwIkeq45wiXNZ58VX3nsNhI9Kc38Fho7o7qPonq4wYkZciI1QygmP057to6A9MciQIEuXbgAkRkhvTqH6SoaHiuesGmadRQluNY4Btg1TVkNgDnpEDesDQgtEZP6lI9ViwYquDpTNJ2t3IDu+mGjSc/NAPsZfAlwyyUEGvRFw4msG8fQp2rRBry1yi6b22KnhiACEhrjlx1EYpbylBgOKhRuGiZt03bMhKmlwnjnKUibD09KAGI17KBtXmXil1YqctEfOTXuKlQsvCkZ4bBTnarPynB9Aemt9ICWbCjxmTWZ6tJf1BSrX+Q9qdhl1xb9ACcAQyCIiATEpY6IQXZ6MaIkEYMo24kLKCB7xYLiJJQn9EuhKiftUupShQr6suIUMXr0HgxfhFIz4OhzmkJ1hv9El8GLAQRLgsBjzRuqlpQzwFai+bCrA4oUgxCbVkaiJFUTb3c0+VCSV9AhZjP/F2QHySOQScYAYW7FEOPrYu+/zNVNUxzlVdHGWkKW0f4Nyj7rUi7XbGkSkHm0MqgbtktIgAu2TmBK/cdUb7BvWK/nK60sFwe2wXdVVxCSWQqqMWf33LBik0x0+zRQIciQmfLIYuen4j8Y7D3XOwFQBSA8mgHR2UpEhCoITKU5HbI8AE4mwFtiBo5gZKbBLaUhmANV+TBB58hgOywAc/PoBHD/yKD8jjzXMOBTEVZFR1eAecVLaqIjaveeik8kDA/tYEu0fF10lFF6oEHbE1gXCFlR4jwsEfH57SXFbdgbUXwQo7Fa4oZTcVmga6yByHnBnth7yxnnRyEqBLBWwX3ZCyJxBC1sETitRJfjKI7BgIPlfCN3UjHTx0tgr5Plnddv4cADRF1SgKMu4QRWaLh0mSqCI12kkqZ1IcD6gR49LrDYknUmp3FHYE9KDNcda3MCLYjSyQDlclO4/EK0JUhe1hCwqaC+LViLJnQuB281X7xY9fDPlEZI+nh1qD8UiCfj2NNyhFdVKVEgasXF6oWCe723OLl+SChXGpteGBGYJ23acrHPWbOwrgngd2WYkmr71Lz8UuJsKsL5zz/hnm25ymk+YF70EbbECMCQq7L7NvAoTZGqumQR2mH3xHZCpBZsCdNZ+DG7gFZ7Ge4lzwyj6nv6yV2qRIB/aQ5Zq9nDwIRruJho2kQdX5KzZB8i6tWEsgzZhC82vtFo7oj3bmlGJh1RakDGIhjsuoAYhLmNoqMtmFfWYTjRdqN2HuBAu8TGo6XQ3H0Lf+uwu7AfpG+LVOrzBQGxr0RFuMdpSyFsUIPRuS0ApSJoJz8mSnLtpfzS74zP1Fr9ufjMdOdMkcR4hpHRAyM7DK13goJp2yf6mIClO5913pAKTGOmo+OS+W/I3itXbpgWHNyNADH3AqbnAY+FQn3OYugsrHiQK98GOnAxywZovc58MoRLTNi8nn1xzMudmZ1u8zM+Rlf6YxLeE5xuupDUYgK54vYZnGSkktPmWswyuXV3lORcSnGoaHkipNpTsFu9UzAW+He1JLKrMHRuCQTTR7QPJlHpCb5sGB9Mqoh9k650ggCfHABpkjOnPAgJdG+zL+zXujkvcuDrsBehfFRDbAaSTOeYQYd1ZoBl5rPjNiLlTlXSD2INAXmr6BdPFfuJ/Y63QZ+s/PT3UhkDSrhIMB2SHinC4K8CdVULzArdMZ9eB30g24DOpUHoxwtCa7GOdaa2JgqmiUwZP/xIAwypC5FkAoZjYrpLsxBfW1JWx0tutSsvjXzOa5sAxa0KHB3BL1A7d6QD0+AqRXI9MHfCWTYry7jzp6674PLsmTPgYZII5toO1UxxIRVZbDYXymwvQfciG1ao/CzGKLJfEYYZ3xcMMx7PpoSBrDUaKKHUFWdolUfEo3h8CdjHYbSSUgA1pFK7qdO/5Gw26AHgli7ViFZgTknmj0ESwqMWQXwaFoNRkRVxY0q9km0rHYjelkEUd4AtPZ1YsXeJB9vhvFpyr54gATYnKiDwEdOiiEElwShA1TS9Vjh/KHvwbrIcxlAhXAEhlSxBkz/GJkSdfAw7EWuiIpIqE0YOPppBlgS9iHfhx03u6BQcVdaAykVUoHsfQX7TkQnx1cUyWlMewHWEvf7NHn5R6MEtrOdC3ZJpCNtSxubwXfMkEqA46G1lFOGTXIdhZlW4jkGyS6f2amSNpAXp+Hsg3e8vZONt8lso0lKou+L8B0NYoTsiFXqGF5zBTwI+r3FbiLizsHfRSCAWDVhbdRZ33GNYdaS1EBOo5gOghA7gALfGFDmNApPiSaXoHaAAS6Y1e6hsbHkn1ScH51eTkaTfRtY2rQgZAN3NGe5wGKyz65vQhD1og/I3pcmLCW/EeP0KYIgToEB8dQgAY4putAk7temDTXQCRgv6I4BBl01H+Gp8ahFhB8NJPpuJ9NZms92fnEFbwLFIDKHCP7hhDIYs6UgMpI6rPYkh7CB3CpyfWdTT2QOWDAA8I8luLqjxUqHOZBBLw+uQJ58+ZtNvBGFjuGm20APV+jGFBcudAHmDtBjFkDFUibLP5KS+jfhO1lmhvhlZS07wjKCvjVhfWDPHcJvLus+0ZgCFHPG8hoHgIVgcUJTia/nGdTuN6Pb6MPziJBMBn7j0URqQp86fthvN/J3iwAc3W1fP78aX8wwmnmdZR8XJJ6SS2Tw0SWMnWErOVJ4owJAD0NivClttC0OjY+tg6kcW5frVJOJvWJdUcyVyUzpQoKEhXe0OPBarufqfErbJs0sdcdWCJ/GKMF6aY2zp2k+ETMy3l/Mvzw7iQWORk+eLuJsAapzXjY1AhBWlqNgm9IKVQ8qKx+OncPYxgXWVX4Ts3J4vl6PRlP+JS5LK4P+MSiMwe7VPqwkLU7bIOENy1NNmgQlJi2x1Q1mEdwbSu9MMfkuqPDzoAeF0XhIhCUjjKQF4YHBOC6uNTFERGVbkpWCBRtgiVgF1SyOAPWcfdnfAb8ejklCXI4TZeXFyDm7PyUnVXidLEY9vtUg1Zw97GsHAlkhnIA4p5HQRpkl/LsDvKjry927ZMBb4BSOPZekt3DvsEJGeoYhgGxIAA1UIJzRypHrYPZ9bXjAEMQyaHJkWtOqQQrTmALmCCCHxjQ3CumDt85a7p8RoZUUxRoo7mVjcGOhlnxcSZ5kEmo51YdMdCnG/DrpuLFksdc5rNrGunrpXyTRPVRV/3JKZ9iFzKhE8RHBjEZ9hryW4ScBE60GlzZOQneJPbDb3cXdgL0SAglcsOdozerxRaTJ+XGf7TiWYkvEuU80nOBko8dR8JKEy2oFZ/3611xk9WNtbyijif0exDstnp8EhlLCcJY3nnw4AEqjgJcHtnWQnHO0Ssq9QjdrMDpKGgTrZkaycMJ/HlMzlJiFdmybW5BIP/8kqr2cymssb7pZt5AcPEn1W1MaYinHxJPthbGvsuaoy4XWcmWYQGRZTZU9btA5BnuzfW1+XlEWCMd1yXM6DpaWAtCFQjB9vrjaInQ8AyzlIQazAXXsIYfVQ3h2Mg/7apaQ9hmEqAKv+TJFa1mGHFcJV4JEmzNXYY7B72y8bXoGLAue301pQiaZW/FnfUHRKWUuW7EqB641H4wIBCJPQMITGTxErAhegq8o7rDfBQkSbDVPZjOx5NjttyAO+igY6a2agDtB76YsaJf2iLFrtZY3KgQ5bO0IixRrAj2fwJR1Q+EYc4hwrn8JktIgUONqh2Z7fQgQ8aBQdZGyd9MKZtODiVKA8KFj/ym8UIf7xpsLmbLWe9gBPO0jqoBJ99NS90FVFDqGyR5rsmZBwlsmo9XRuVAvJmpb+BLz3cYoRZkCH3XgTqzaX3JQrHbOrfjpJ8jE1ijhSFMIVvstQQ2kfxaJqnkcCVKtfHIAbOFu95YL6N3HrwBHtmpnSyQRY6qB3kiPwLA3+aJtY2HqkOLNB0owC9DMxn98it+93AcJJrM+MH9HVb6JpNDMqjjanOAVaaRGquiSikMVI0xi0KT4QJoZoXQEamY5KjVZXyIWoEAPIWtxlMqgs2R3IJZRFDEsjY576Y1SR7ya6YGxU3JwAq2bDI9Gdz6XhQyA3xKkZkCxMMl9HVW8saeKs3mC3KSh4ZzUk0mqXigOGwgFk0A2PS5RPdyRiDmIaT57lUmW+WEDrVYa4I85PyGX68pHhWZ3AxlxIbJTcbP/XcnQI93iy4jVnSJ3Bs5ljQQl6N/ZFpJzVH7g0AbABuZcbMzHLIa8qWvfXPd7mHq0DVFMf+nL577FifslXPcPN7hFks9mdJBobdUyBGCJHHCkQD5GENOQRXxN3xWZkgJFM2zVVQvhcj2HFY3xCVb9GkDSAWgNBAhcEySjSKTEE8ZLo0JyqFjNmkIYvqfJ5GPIx3DXNFBHv65jkWeokBOaYYrjlxKtsE3rxn0C+lwTArDJaEoR2bFGy+w5xYt62L1oIkqYD4B68VA1UIVFCSoHvvjprHpUbwcdw96jaioChaRHUrM0WiATRKOQdkqxacwFeIW7wUmKZjI6iDztc5ofPSlr3x9ND50+NfTwOYsz05PVT9VaA4lICEqs9KEQLzoUz3xm3QRyXnw1HQGLotzsoEPj0kGtvKRMYo8NAdUciBwSaDSqqLQ4nUiOUCkHC0sNXka+pBNgAmIECk5aCo3vQ5CaFa1GZTYblBFbB+LjjBl7Q2TJHmDyUs5iaWv6uQhzJPKlKl4JltYlh+z8eRD4Ltt0faE1GSWMqHi5dIWp51Q2sRTxZ2EO/bpA0fAx6+jIKMnGioPB3llbNSEsBqJpMiE3UNmSEoJEh/NlaCVr8k+fUof4fLxa09wRabz+fXF2dFkpMHzJSwzXjbDTf85fYmna3u8oc7BHZo80qTrEOJUSgzx/gQcsNNcMTOL+goB4KcyFxviL9QAeVrnLQjzuPhJCTYz2l4yOSgAMHtLu9/TJVcCencyT9vIqKl2qQYkwYVHSME5pLmUMW9i2BvTt2HEgLHHni9Zv4JmBMuPu2vSaS3FX2b8NXRQNa9NximcXs7oQxRhK6qtg91kZiwiC11HleA9HvR9Cw/fufBgW6BZzeeEoBBk0g6QX5pmt8vdCcXiE4kHPLZ2N0FB3GFQKfkAMjKsB+Gyz6SxcFgaBlPRgESdY2k2vUywLyhTk4kQWIgeI8Tdf6aJ3mxqPX785NVHr/AVDp7S6/dHFBkM+peXlw7crI6jCneBgbWl6+dBK7QaNNeYLYqsOb+e2LEIVmy9Fc+xjH1FJod8oXJLJCdlyMOQhQ2mmHGyKhIywcVjMWfcJ371mK0uxbHpObN2MgdJFic3jTL3TS1eZRBQYtSmoJJqfNjmUnGlxc3RpgDgg+lqPZMkbyYzE62yCRklYM4226ISFRFhqn48bVrq2UZoZCnWyUVpUphS3yph3s853DHoaS2CVO4KRHeTwMuQcOLxTgE9n9NAFVrWOkZPlZOygZRyrIKounrFoMfrV6eqjUkbMl/MGAMWMyd/TNEgDu4pBnpK5ejSqWGUShGohbhOF4E8XFYIqwKd4hzJWamFeOOjTxuTUKVkj7SYvfxyF8uVpipFH6PDx7UJfIEVDyRhfKHA/7ROOno+fHaNEQs0O5dFdIlOQ4NmuKUqyyVQl1ykFRyIqyRaum1m+LGFeix+h7B1fvKMm1PeeWPRx1HQGi25IUthCiSC1KauqrGORpKDQdtaYVOXcivFm7PbZT7H8zsGPe1nQK+7pJjAQX8AAlwrZiOIL73rsyMF4TsKbywrwkHMG60qKrRaEkNzwJo/Rufp9aXxKn3eXk2BvJ4tK/dmEMfe7acnoNJYWN/CsXFmPFOX5AqeNt2grjlWGkAWzRvcQ5e22HNQdApyAh3zb8nZnSRDsLjIsIpen9vMYphqU8T2mUm3wBlIVWMzpVW5rIh8FVF1IT3WqWqmaDe+ERrVbZz4UIYzyW96FOYCUov5tN9e/e7f+1tvf+87dEhGEvsYKeEq8wjhziWUw4wnsmPcTSDKGNpNNN0jPJocM0GRqvqmwOd7dsc+fWS1PDk5aT96hDjFelYMkAv6G40nLLOoDjf6Lg6WbCqJ5iimWJUqOmiMLYJj2R5Lx6r8modEfecjOsWK9rtzHgry4zVsUYAanj533+crbt8cHh6WDgpIvjfP7bUxlo0xK1CpOG/ElsX24eh4ya6WuusQPoLBjepTCNVKtoE56+tOQcRA/tPdRA4ePJe+08a8RtA7Cy30n5pMVzfTGJsJinZczT1NbNHPvF3h0wGywDCJCxWxVEWWISsdkZplhn/BOpkhRmMRNQ6QU1QWQ+erb/7Kty5OTmHdr6/EOcEu0H4KcsmZ0qEHwD1i1FMzOX0h+aXJLJy5CNMAew03yuUZr15+jcrjzLblTtB/x5YeISEOWu7AHSvFGjAqCRwOZvMZuwPsCqixbjQqW/+iL55iBjG9Vbs3P+AmDh8twDCyvxJbx2d5mBEKIu56Mz5zfwo/Fdnz/jravLia8WQ4RhHbXBAvBRRM1WkCl5rYBM6CgYYDwK/maxCI3xIIqkiRtQlQIIgUWIBphgKWwFlMpzMnUI/t4X+8NYpXCAHLwqFGl1TX1/MeMrqL02EYEHvURxIxZI5bqKyUWSQmNBWZmVONGpelHGkZ8Vw6aV2x6QBPcP3akzc73TFkYZri6MI8KcCBQFai0RRag4JRaYNVFHFKGsimuwV9LsyJoMKGHfvuwl3WbatdsfE9lcilHFxllRslyIsXDk9Zb5mxwWrN4jDqRZdkJDtLmK5veI+P5/0Yx3NvhSVOsrEVFpdoPkUxKM5vYKy44zsbsjlhOKJbjIbjfm+wnGF62JZzWWpDa9SrenJSGgkviYw6gQ+p5CGzY78qzowwRaBTocoml6ecgMCGatAG2cqTJXU8LpZwfL2ZAMua4LYPlEyAVyplBBBcwLiYhI6w8hpAy3/1I/oJcKxUqveEtaIEEFhVcyS+gqD20g/XrWaz46OHk+MHPEVpX6IGU5Mxp1C2xsRQHXerIJyuJ2HOZSgOJw2yWPW6sEGqLdI0afvvKtwx6Gn/VbaIgGaNVoLqz7DOESvhOeLJm5IKF16RG2vHcntUxYKf2tTq6QOo5+gG5dBr9OSnz+aXzzD3o8m4Pxo+ePwYPwAnfDa75lvyKNibVVFq4ShKpY6GJempQyqw5uT1MWqVlwsqhzc0Cr5pS6mTZAER9XKOD0MXcarInIOseYpqiwNzxmaTM47bprPZ1Rs6cGBv9+6R95XJT72FKgWEELahVmzgNP2APPoZYYWKYI+eSArnDAtoIaaEm8qIYTY5Ymux3gmcwCRNojqKQowVBkTAnIF5FlXRBqpVboql2gkxWXEgYxCTAO6WgVHINq7XDLCMdDJxR+HOfXrRpdxxS/XeUQ4eak8XE4MdlSFTdI4xV8PBNQrFBKEJNBJtskJPQxyDUaiWcHbgAs50Nh73eCyU/dvdg6vr5+eX5ycXncHowatHr77x5GtfPXn2jKc2wREaY6jusGafKSiXhTwcbpTEMKGbo6MMbjFRxGlZgbfq1SEXRD7XlA3zeLAWT7ckI0wCYvhyfk7DLCqGWGkijtZdXVy++vghSXQfFnTc22g72T4jzsSQLbO/zPiMPZ+yX0zHffoVifz5RCS4SzZzQlC4YRN44MNtvXyTAr+CJFJAXYKemgNmj63zLmKxUIajQi10jAPuYww6y8uT9zr9cW8wZqMPwkFBZKbTUeWA+9wYFtd7/XGuS/+na9g92SAdrg54yf9YEbFaMJ2y249eWl0SxPNJr8PDI1i5E9jfPeiBpivXgAPRl9JW7PLrly+LvcBCIBrkRQx6QlAZB8BNtrsIJ0OyoWkf6ETNOPv6OQEoqRg6tl92V+cHB9PnP/jxj77zu4PxcWf0mH32xjN/7vedGfqt8LyXT3xCP7jEH81qd4h5BxSusHmdtvtjza+WfRQA9HBZjaBSTgjyEw/Hve9smhepcp4DMa2Ly/M8pJH7A8CXRmrarY2yHLkAxthOqs5EvMUCjV00oZBDkfgiGGD7FgvsDgUOfQDLMQRK/Af4dsIiSzeytxLYsNTH4F9fntNiHjS5up5xB+/64nrAG82Iv75iM9/0enb88BFtr0Anwq8c81Z7jL03o+zZxVLI0t/IaGPTD2mBfa6OLJ2Fn5cS9EgEmCBXzBIrlEh2tsQXb2Y/aiNwQZTqycfV0JkWlyslbAfI1+2ieXGReTCPYw/7vaur68kR5kRNQCrTWezb4uGwO8GeXj+dn/3w4qBz2h0e9A8HkweHhw9ZMKK/dXt9+iCvzAlctNBBD3Ydd0kMMaozW17On8fHwPRTheuV0SWWsm9bEswO1HCVp9wXPqOr8FySu0F7/dGY9zX08RSOHz5odwfcjQar2H6e9gIrsE1RuhDzcSggJR8EQDT0FWwEndCRwXaBLB70E3RKycyc6NSzzp5mOEYkOoBzbEGE9C07Fm5NVqtctbm6WF6ddVpjJk8vzq7e/MqXHbzcZD/HNUFLcYRkibFoMB7Tdmq3urSfKmRO6NuXNkHQk43MFIRViJAEDwT65ybb5/p7x5YerCOAwWhUNgFtAgcUhgzQnHIhKhtOhBCeC2hAwQyuQJl/MR5kISdFmBNz9K0YvoWC59zypWxvWFpEXwLXyXTcC1dzVjxbHbwul+eLpy+ev/X/OWSjM3MKi6wBShttqhYiEjhF2+2ZEwEoAEd52/gbYtfbS2x1dnMBTUj1ZrSb0oBWd9XqDh+/9uiV1x+/+sarjzCfdmYGq2B3Ned5JSDCnIW7Q+DbdwSye67FNJxGc6ql4EucOnUYANBPNKeKyNo5eoVHpvScAri1Ny+6ogdASzEwGOA3wtryxenz2dl7nfkJb2PqDg5p2eT4Ee9TmAx4VstVXHpsKLOXb0iXwNtBDKlILVEltdnnnH40XTEKtDVolnMyE1w6s347Elru66TdQbhj0AMXzRoqi7OLjBAK6gdoBKToqB2xMFQKOr+PqbeDlNENUm7wpKH1XU/6BY6hbmlZrllnQD9kRi3GEmkxzJdoAKYVnBOvB/3Rok+l1mh3My6Ds8vpxkDd+ggZo7W+uLbCGML0BqiRISjLUBQoWsw68RooOwA6ZJQKNztPf/j82T979w+ZKPgQ92A06fSGw8Pjh6+8fnj8Snc0pJ06CAxfWAGmEMv5aLEcDsZcYk6HQwclbmX0BwMGFvbPwzJA5oLlUObxAwcc+lEvklSYhUhYury64p7r9eXZ9OLFenrJPQucPF6beTZfd0ZDnKhf/bVf/cH3/vh4NAIfPGkb9whjQV9tjQ/HcIQwEKHtYuTRrCB1Rw5VRvYEYmSJlWKUK+4hpksm7tu8wJlNUD4ZjAwVkAr9nMIdg37bVATRgCFCEXyKUbGSFMQrUDDAscTKkVQMKjLNzUdwhQARNDNjrEl3fjmFJirBLpFEMn8qhCNWhzcAa6dBoMZUmGODYzEhyxXxaNd4KNoNNN7k57R5lwi5U57cMCohulO4MlcYS1cJ4LZNLc3SPXqrcc7hannARriL9axzcfrWxdMftgaHGPL69iqFqQYhCH39B+7Scd3qtfvD4Wg4GvPsACjCrOuAtFnX56udmObr+vgzXSK8WxNPDOuizadQ7qx5+RkfQlOKmoADkNBhXfhBpgo48ZPDQ2BMqyDra6OQelsvhQ6GvZ/xOUQ/2tOs3jio2rsb4JaCEJmVammQuqbD3hExEePbHyZp/+d+uGPQ64TEMKBU7VCwjmC4UDz8D8RLLJWTYVE3QOHF2mM7tYf6rz7I7RoZj5E4mwT5QA8iqAK0UAAPBFuLKjQ9ZNXuqgTUkzE/gE5lsEA8bECZCLKRJ4/qcUqRlIII5175sFFZ9EpGw3xhWFVbu10XxPhDoEianIUiKTHbcL12MOJwSFdZXx9cXRANSEi2YbQc5xvU6DG7fx3vnhrnl/QW3ReAZef2V1STmfUvrql0deHEFtiaymIpaQiBNzDQNBsOZ1TOkCV3ujJKhXmCN8Iy5GLTWUtQdvaBuJqIhRsdaQwc2nBlghy2wTilV56qVdbwFrPFGEoSd0gePnplW+LzPLlj0DvbYsk3M1ehmIAE+UWBOOaccalSEavYFXyckCHZHBrJzkCPCdRiGq/OUDCSn/OkLP49eYhPktpBj6KHP4Sf8kxVRWcwBWmRqYUFNfQc7xp4r0D2WBPaqkfrByeCPiqXkm4MJWXeaAcWcSXD9k5PuUiSUUIcmOp2Y7/rNTXWa3+BDKd5LRSUwGSaip8GvnmPh2RSNX1csKtJjQUejnXAbtbfjSHQUiPxgcCu3iOvBbFXpcVUzGtDmPQysDFmKMnIn7FD27xgaxCDLY6foKcaoCxJztKQIgL5kkIxFt7Jbv465xcbx+3DXMoMJxF2CH2OhzsGPVpHX1gw8KcVRnLioxFHiQzAcUJAcdwWEYzKUfDp65AfABRQxTbI5XMD+o6z60tWYpgGxMfA6rBS4eK0s0PuEZHV9+TFgDXSx8yFBxhi3QKqrsQJFu4jpC4Nf6kKBgji1rL2GU/Ii0NlMyqBxXiZpWO4vJ/3YNRmfDLTh5I3ORiXdO3dx1vtQxicUA4EZnKYV65C15baaTHfTju4IEoj7kaYyAGPAuQLfk190Ek5Zu90SHsCfIpsOConUs/IztfmWyzYYIcerD1EpAgHOOJ2Br2UJtimsGlLpesLmD3LxBQCGoiGtVgqs7PCqaKhrlHoZari27hCQyF8buGOQY+fCmLQNA1GoAgScAdMihfpcMSPDObRVwvQRzQKlGBmUIgKmrHChRRWBVEjXi2I8F66MBKhHvVDwIGdg2tvtGjvDZByCI9KNpeqFhcA/asnMJK8xR6lcRCIJ5CfI/yEf0GpIqkRDQew9AlWhbgkG9XEItrepq9IAyIaRZ/KDin6JQQUTEOExjZJFMSFs64NDuFadDuJliQZHBu1y9K0InunlIp5GSWeWIWXZC7cnMfzVowyLBBZIilWa9dNGyELtQoynYDTaJPIZyeiICdUIBu2NWKhIUYlne5KwA2lNKmff7hj0CMcNIsUaD/SQgjoCcUgXD2K3HKq9WAylBYi5+ZgZuLLnXCu5KwLr/56ymZ61zcpi/MUBHKFiUd3rt6Iv2jBHqBWDCSpTnGPckSYVap0lRoMkzd4MrPcVtFSG9lJLT3akMCudE931a5uchN5q4j3pGhI1aj30VDWEEAQLDObCHw1xpTd8iwS03G26OcaPEFegvlXDNCO8FZA9BxeQt1mcmrnxkjg4vBBWUYFTTWDXfpZPYnGrHeD+C23Ut3SsvVSgp5WPsLkh2uHR028jeA/dMA8lp7bGrWAU3Q+t+Ndg94PxcxHvmVSJwBZISOEDxIBK3JitxlHZMeRLMPhwE+VYdfRtogXnWgX6HO3iBW6iPIcxTMnYwtl94D8S26ygHgnotQROrH50WhjnBwrtMWEIB82IKsCy2K5LRiVasyMDC7tJbGdYkPGvQyrXEAQdKLH0ISMOAoWAgk6No0MzvKJhIDCPJkMULGtxhzgh1gb8uAn/2TJiskhiAyynCm8ZjjdR5mkJfJlZjutPT4cVEQKplNLjGem3NcHKVd3faM3kTr3ELbCWHFlDl2bSp/I6ArB6p2Qo8kKzdrScnnjIstr9iDZi5jkX4lD/y7CHYOeO6ZYUd7GxGietRF+26zcKxMkDahdKufMHTSR5CrLZG7/Yu2MbSG66a2DK3Z38KQV0Pc2pOj0HlR/ODp8MD1+bX5ycHn+gh1rdgYtvys7aiNKEgk5ET16oCy8CCYuTBJknOlncEVssG4cs+UAS0ymq6JRMiUbdCTG1n4DVEKSbFKzc7nxAoB5bWTSK9VeFZgL8gSdowik6UghIjqhIMp1afBtYKa6H5GbgvEnzKchsf/kHfn2kjh44lQiSIC3KQPMDpKhs/LsAiueLDHQBwGrBGo9bVNjLI58qD54kLJE0xhrh5sYCIvKWEer0QzgiMsVJO9C0Kzw2jBM7s8h3DHox4eHk8MjdE5T1Tz/6f/AUkufZXFcFO7FxL660LOcM2iLazwivRTAqIYHbKEB0d3eyCXLrGbE9AHD48Hkve8fvPfjt0hFyDFbTBhd0vG80OSIzrhBnNIHQwSlTwVkMQ/xmTsTqRLlWL1qxrKg4ldphbzQE0jOJjXkwEKLiIuCD0NJqZmP4pLNi8OKYswscay1i/tY0y3UuHSJhV5mYYuTJ79ewgPB2IY7UzlFVqIxPRKcuS6fADWbnlBESOPhAvZecwMDbEKU5f9s3Man4grywfOGvpXKSyq0q6VVGgJ3hXAVjZFuFjLD27Y6Tur+B25PMoTvMPb5HO4Y9IrehtrsmJ8Mp1HNlrPRT0rCfqEkLRNxKtkPEZxdR3XRDYbMDtik5ZpGQm4Cc0tVS8dBvIhQYCTM7U4GaIoMSoQFGBTXVa0xYT0nAVFKEkn+FLdgEC95gKFZ3BAUJFYkh1JKZXRC+jNRwMyBIklVe7WQOBtgiiCqI/gGkgiF1hUpj/LpGj9J6XtGlNxgIwxaqX0j61FYhPPLi17/dd7MQBF6LORdssSqxBYLajJXSSrwEUc2Ds2gkRXOSA3xpi+nYTJZ0M8RK+DaVIpCiZpvVqLTpkr5PI5baH0elX2wDjTzyYM4ppRACSzAwC0iWyQaa5fCOrKxdTSYnpzpFcUtYM0I085CG2YJKIIwVnxCpuDHkZJbUtLH7XHlxWoFHWpjUdAasNCUTXSxEStbOaVATs26pcxmZmO1w2peNHGZlohM62qINa0zHZttGU+t3YbDQ52nM4E3Lim8KYtDmNxOjbTT+C8ishmu3KaGo2f9BLDYaZ2dX77ypMfnwxlG2QY3YtOdDEBWhOLy0YXqYU5cSmLckdcf0CdkqUGwyOaCBlM4nY2U5kkDeGb0ToeiH7LOxmMurPg3W9Ak8nmFOwb9z9NMFf8h4SYyZ9y46rBPihf6vXj69pQ7QECNl7xgqHAkRIhmnhUf4hykg6UMurFv0ShGLEYebAg7zgW6bv4mxshYZ+2vFxwtJYfCrIqBDtK0+MEJWADIoIfroMs1zZTXEaEKGpd4fjHYeio6ZJtIwRiUG+UQAVvWapfgGg6d5VOAaBCmD00Jnw4IjtmYjz9GThu1WvXW7eur2fjoiK88MNoORxNmCZJp679l84+7NuTLT4O5wjZn//FsxlfXEQUbnzTkQF9G/QviIx8ZwsazLsTWD4q6esPgSwNzzsLdR2jSZv1Cwj0G/cfKAx2jam5U9UaHo8mjbm9yPT3BEeDeCw4Mxm2uSrFhqtbdugEjGtAwom9N50YhGi83Jwui/KFgvotJBoHk7s3cxzJ/gGlCYKkrXWekwY7WXaSLdQKP+QIP0StGKUVwUmDv4A/q9EkqYB5fHcDOAPeyaN+CYJEimrmsDGVJhLozSzFCllxLp1NZKZZXyfgGEdK4Z0dK+/pyOZq88sqTN9hgwLDyta9/g3zWIHcJXov5BLmKR+56azWq5KYFYeOyArTrEYiP+a+rDzkW/x+S8AuLakT2C6N/x4SjODV3eXby7lvf/6e/9w8vTk95XKLvpkLmbAl0C/7Yl1JQii2Gb+CEVUZnOfcoxrS2opIEljjULGgzjQINkM1oupEU54JxQLPKHSUhK/acprsJgBzlx7tAExfFqOCLjEG+ddBxwhAGM/yEFU136Jvq8g9JlElODuktXhBnv8xokPfm6MbPpldsTOPetG9Q7vSOX/nym7/8a0++/JXOnb45XuZ/8eELDfoAAMzwC3AI2LfT05Pry4uTF895xmp2dc6L6r3141786WJ2RfcARmy1Eu2+yE4jR18ANywcYYwxWmIQBJMeM8wJwTq8HU+c6BSEZsEcYr+De++winU3LC9Xk9GoN3TbnKuy2dYyv1rw1AvGlyFHgnKrmc1p4RkYC+s0y6LkIQtt8x9ZMkDZc+GNPhBfAkRjjJnL4OLDNYu4TOxZmeEZqINO7+j4+PjBo8OjYzZsprPRP22YofnJeSO/Or/3xy806D+JdrCE7r0XZjoCHJlkcQQxgIXgK9MCL3C5ISwGAUmhjnPONv0rJ3aBBqLGA7s83cId6P/mv/6v/t7f/r8mw6H01ytuDP2ZP/vn/8u//JfNkn5lv0k1VF02W0xDDsw3aUw9GvqJwJO2Q9otqYAf3B02FfCUV3HJBT3AVc/QfYkPX2Sf/hOpVU9GA99AjZPaO/uJiPwsmYEyxC+uV3/we9957bXXKIK15ob8t39j9aWvfK2A/ulgWWUhWFV8FBG6TY0kPwu3X8g8e9DfqLVQkmODnxhVMZpA5BZXN6U++mxT7v05NLsH7UGHV8nyMXDyZPG/1attXT+1inj2N9R+gr5LMRVy4qT2Vrix7y854pHJHvS3gNGcFlo4ApTbyBGgH8y9jbmddRv5ISc6RCyLe7vf1wvi9/tEFPeDm2WfFLmpqFz41Hw7AxE3eT6kFnm9zZHN+dBsL2HkHvQfVHqB4xND5OMLBIQ11WWhBk8dhxtng4UblvmIgBVmEwHnDbEbE/2JUXtD5IONfJlj9qD/3LW/gSI2nqklM+fcJ3UKfYuVvWG+JYzP+vR9I+ZnTXxP7yMloGPNhFJPRzeeo6tGhHSJxqX5yNL7hJ9LAntL/3OJ75MULmiz+i3QfXXecMibbrhwHT1Pnd5QC/RvLvdnn6kE9qD/TMX50cTKdxHM8VyOHxw9ef3Jq6+96t2p3IXiY84pTZbK+9G09ik/nwT2oP/55PcpSseK8/rSJ1964+HDhxBwN8tydQzo0x8+Bcl9kU8kgT3oP5G4PrPMDx4+ePLaaw82oMfDsQM0A8FnVsue0IdKYA/6DxXLZx/ZLKv7I7SPHjx89fXXjw6POMfQM4t98Oh4e6+0yfzZc7GnqAT2oP9ccbD11nlI8rXXnvCwJOs1Af1a98YQ7+dzZeqlq2wP+s9b5dycwpCzvfG1J2/wcgfWLp3JHgj6vUv/+ShjD/rPR843tZTrwpbeV5+8zpvsMezelD1Yjx8c7438jZh+kWd70P8ipfsB2ltYT46OX319xvvaawkTn563bn8g+z7iFyKBPeh/IWL9WKKHR0fueOd5j2TF1vNVEk63c9mPpbDP8KklsH+I5FOL7ucqmCdUnNbeLNSw9XLzzNTPRXpf+OMksAf9x0lon/6Fk8B+w9kXTqX7Bn2cBPag/zgJ7dO/cBLYg/4Lp9J9gz5OAnvQf5yE9ulfOAnsQf+FU+m+QR8ngT3oP05C+/QvnAT2oP/CqXTfoI+TwB70HyehffoXTgJ70H/hVLpv0MdJYA/6j5PQPv0LJ4E96L9wKt036OMksAf9x0lon/6Fk8Ae9F84le4b9HES2IP+4yS0T//CSWAP+i+cSvcN+jgJ7EH/cRLap3/hJPD/AwiwsbVLFRGqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HUGGING FACE CAPTION:\n",
      "This is a classic women's white cotton blouse featuring a structured shape with dramatic ruffle detailing on the shoulders, a standard collar, button-up front, and long sleeves, potentially suiting a business-casual style or a vintage-inspired look.\n",
      "\n",
      "ORIGINAL DF INFO:\n",
      "captions       This is a classic women's white cotton blouse ...\n",
      "image_paths    data\\StanfordJpegs\\dedupped_images\\item1002463...\n",
      "Name: 75, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Select image to view huggingface and original dataframe data\n",
    "\n",
    "segment_to_view_g = \"train\"\n",
    "segment_ind_g = 6 #index of image to look at in train/validation/test segment\n",
    "\n",
    "def view_labeled_data_example(segment = segment_to_view_g, hf_id = segment_ind_g):\n",
    "\n",
    "        dfset, hfset = select_df_hf(segment)\n",
    "        df_id = get_uid_from_hf_segment_id(segment, hf_id)\n",
    " \n",
    "        #hf image\n",
    "        image = hfset[hf_id][\"image\"].resize((252,252))\n",
    "        print(\"HUGGING FACE IMAGE:\")\n",
    "        display(image)\n",
    "        \n",
    "        #hf caption\n",
    "        print(f'HUGGING FACE CAPTION:\\n{hfset[hf_id][\"text\"]}\\n')\n",
    "        \n",
    "        #original dataframe info\n",
    "        print(f'ORIGINAL DF INFO:\\n{dfset.loc[df_id]}')\n",
    "\n",
    "view_labeled_data_example()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9314f84a-dee9-49e6-88ca-720e6df6e956",
   "metadata": {},
   "source": [
    "# Import Datasets to Dataloaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3a104297-402e-4474-93fd-713e85baf5d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create Pytorch Dataset Class for input o Dataloaders\n",
    "\n",
    "class ImageCaptioningDataset(Dataset): #this class inherits functionalities and structure of the pytorch Dataset class\n",
    "    \n",
    "    def __init__(self, huggingFaceDataset, processor):       \n",
    "        self.dataset = huggingFaceDataset\n",
    "        self.processor = processor\n",
    "        #self.new_size = (256,256)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.dataset)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        \n",
    "        item = self.dataset[idx] #each item in self.dataset has an image and text\n",
    "        \n",
    "        #process image (returns a dictionary with a single item containing the processed image)\n",
    "        encoding = self.processor(images = item[\"image\"], #get image\n",
    "                                  #.resize(self.new_size), #resize\n",
    "                                  #padding = \"max_length\", #set padding\n",
    "                                  return_tensors = \"pt\" #return as pytorch tensor dtype\n",
    "                                 )\n",
    "        \n",
    "        # remove batch dimension\n",
    "        encoding = {key: value.squeeze() for key, value in encoding.items()} #\n",
    "\n",
    "        #add text info\n",
    "        encoding[\"text\"] = item[\"text\"]\n",
    "\n",
    "        # Include the original index (from hugging face) in the returned data\n",
    "        encoding['hf_idx'] = idx\n",
    "        \n",
    "        return encoding\n",
    "\n",
    "    def get_original_image(self, idx):\n",
    "        #This method gets the original image from the Hugging Face dataset for a given index.\n",
    "        return self.dataset[idx][\"image\"]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "16a07083-a044-4135-97b4-0f3e82bd50e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create Processor which does image and caption pre-processing\n",
    "# (used in creation of pytorch dataset and collate functions)\n",
    "\n",
    "def getProcessor():\n",
    "    pretraining = 'Salesforce/blip2-opt-2.7b'\n",
    "    processor = Blip2Processor.from_pretrained(pretraining)\n",
    "    return processor\n",
    "\n",
    "processor_g = getProcessor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e9d13a8a-da66-4585-8361-ea4bb6f13585",
   "metadata": {},
   "outputs": [],
   "source": [
    "def collate_fn(batch, processor = processor_g):\n",
    "\n",
    "    #batch is a list of examples (dictionaries), where each example has the same keys (image and text)\n",
    "    \n",
    "    # pad the tokenized_captions and attention_mask\n",
    "    processed_batch = {}\n",
    "    \n",
    "    item_keys = batch[0].keys()\n",
    "    \n",
    "    for key in item_keys: #only have to iterate over keys from first example in batch, bc all examples have same keys\n",
    "        if key == \"pixel_values\":\n",
    "            processed_batch[key] = torch.stack([item[key] for item in batch]) #stack images from all examples in batch\n",
    "        elif key == \"text\":\n",
    "            #tokenize the caption (ie break up caption into chunks and convert each chunk to a number using look up table)\n",
    "            text_inputs = processor.tokenizer(\n",
    "                                            [item[\"text\"] for item in batch], \n",
    "                                            padding = \"max_length\" ,#True, \n",
    "                                            max_length = 50, #9, #16\n",
    "                                            return_tensors = \"pt\",\n",
    "                                            truncation = True,\n",
    "                                            )\n",
    "            processed_batch[\"tokenized_captions\"] = text_inputs[\"input_ids\"] #input_ids is conventionally used in the Hugging Face Transformers library and other NLP frameworks to refer to the tokenized representation of text inputs. This naming convention is primarily for consistency and clarity within the context of language models and their input processing\n",
    "            processed_batch[\"attention_mask\"] = text_inputs[\"attention_mask\"]\n",
    "        elif key == \"hf_idx\":\n",
    "            processed_batch[key] = [item[key] for item in batch]\n",
    "        else:\n",
    "            print(f\"{key} KEY NOT ACOUNTED FOR !!\")\n",
    "    \n",
    "    return processed_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c3743e72-dfd2-465a-b1bc-6ab0bb6d463f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#CREATE PYTORCH DATA LOADERS\n",
    "\n",
    "BATCH_SIZE_g = 16 #(32+ Too Big on my Vast.AI instance) #56 #64 #128 #512 #1024\n",
    "processor_g = getProcessor()\n",
    "\n",
    "train_dataloader_g = DataLoader(dataset = ImageCaptioningDataset(train_hfset_g, processor_g), \n",
    "                              shuffle = True, \n",
    "                              batch_size = BATCH_SIZE_g, \n",
    "                              collate_fn = collate_fn,\n",
    "                             )\n",
    "\n",
    "validation_dataloader_g = DataLoader(dataset = ImageCaptioningDataset(validation_hfset_g, processor_g), \n",
    "                               batch_size = BATCH_SIZE_g, \n",
    "                               collate_fn = collate_fn,\n",
    "                              )\n",
    "\n",
    "test_dataloader_g = DataLoader(dataset = ImageCaptioningDataset(validation_hfset_g, processor_g), \n",
    "                               batch_size = BATCH_SIZE_g, \n",
    "                               collate_fn = collate_fn,\n",
    "                              )\n",
    "\n",
    "# del train_hfset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "74c5226d-9d22-40ec-bac9-47f7cc9e0506",
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_dataset(segment,\n",
    "                   train_dataloader = train_dataloader_g,\n",
    "                   validation_dataloader = validation_dataloader_g,\n",
    "                   test_dataloader = test_dataloader_g):\n",
    "\n",
    "    selected_df, selected_hfset = select_df_hf(segment)\n",
    "    if segment == \"train\":\n",
    "        return  selected_df, selected_hfset, train_dataloader\n",
    "    elif segment == \"validation\":\n",
    "        return  selected_df, selected_hfset, validation_dataloader\n",
    "    elif segment == \"test\":\n",
    "        return  selected_df, selected_hfset, test_dataloader\n",
    "\n",
    "#train_df, train_hfset, train_dataloader = select_dataset(\"train\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9792db4f-bfcf-495e-ba92-76e325f4ab10",
   "metadata": {},
   "source": [
    "# Get Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "6879c4a8-031d-404a-98d6-f28e5fc657aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading checkpoint shards: 100%|██████████| 8/8 [00:06<00:00,  1.20it/s]\n"
     ]
    }
   ],
   "source": [
    "# LOAD MODEL\n",
    "model_g = Blip2ForConditionalGeneration.from_pretrained(\"ybelkada/blip2-opt-2.7b-fp16-sharded\", \n",
    "                                                      torch_dtype = torch.float32\n",
    "                                                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e5bcb7ba-b9ec-47ac-98b3-e17b0d9a08ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 3,276,800 || all params: 3,747,956,736 || trainable%: 0.08742897079162015\n"
     ]
    }
   ],
   "source": [
    "# LOAD LoRA CONFIG\n",
    "\n",
    "#maybe it would be better to save weights from LoRA\n",
    "\n",
    "from peft import LoraConfig, get_peft_model   \n",
    "    \n",
    "config_g = LoraConfig(\n",
    "    r = 10, #32, #10, #18, #16, # Rank size determined number of trainable parameters.\n",
    "    lora_alpha = 32, # scaling factor sets magnitude LoRA updates have on the original weights\n",
    "    lora_dropout = 0.05, #percent of cells not enabled at any time\n",
    "    bias = \"none\",\n",
    "    target_modules = [\"q_proj\", \"k_proj\"] #focus model's adaptation on the attention mechanism\n",
    ")\n",
    "\n",
    "model_g = get_peft_model(model_g, config_g) #peft model is the hugging face name for their library that uses LORA \"parameter efficient fine-tuning\"\n",
    "    \n",
    "model_g.print_trainable_parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "cfb2196f-69d0-4b35-ac70-7a10950d528e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # OPTIMIZER\n",
    "lr_g = 1e-4 #2e-4 #5e-4 #8e-4\n",
    "optimizer_g = torch.optim.Adam(model_g.parameters(), lr = lr_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "24ce774c-6fbb-4201-b0d0-ab7a569e98fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: cuda\n"
     ]
    }
   ],
   "source": [
    "# # ACCELERATOR\n",
    "# # optimizes training and evaluation loops for the available hardware in a device agnostic way,\n",
    "# # eliminating need to use .to(device) or wrap model with torch.nn.DataParallel for multi-GPU training\n",
    "\n",
    "accelerator_g = Accelerator() #hugging face optimization handles device placement and optimizes training speed\n",
    "model_g, optimizer_g, train_dataloader_g, validation_dataloader_g  = accelerator_g.prepare(model_g, optimizer_g, train_dataloader_g, validation_dataloader_g)\n",
    "\n",
    "device_g = accelerator_g.device\n",
    "print(f'Device: {device_g}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "67c221dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model_g.load_state_dict(torch.load('large_fashion_dataset.pt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9770396-0757-4ade-815c-7e2bc0ad0c9d",
   "metadata": {},
   "source": [
    "# Set up to Compare Before and After Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c128614d-23b9-48bc-97a7-527ee041900e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SUBFUNCTIONS TO COMPARE LABELS, UNTUNED AND TUNED CAPTIONS\n",
    "\n",
    "def getSubBatch(dataloader, nSamples):\n",
    "    nSamples = min(nSamples, len(dataloader.dataset))\n",
    "    batch_iterator = iter(dataloader) # Create an iterator from the DataLoader\n",
    "    batch = next(batch_iterator) # Fetch full batch\n",
    "    subset_batch = {key: value[:nSamples] for key, value in batch.items()}\n",
    "    n_samples = len(subset_batch)\n",
    "    return subset_batch, n_samples \n",
    "\n",
    "def unpackBatch(batch):\n",
    "    tokenized_captions = batch[\"tokenized_captions\"]\n",
    "    pixel_values = batch[\"pixel_values\"].to(torch.float32).to(device_g)\n",
    "    hf_indices = batch['hf_idx']\n",
    "    return hf_indices,tokenized_captions, pixel_values\n",
    "\n",
    "def getModelPredictions(model, processor, pixel_values):\n",
    "    model.eval()\n",
    "    predictions = model.generate(pixel_values, max_new_tokens = 100)#9 #16 #Here the predictions are text captions written in number encoded words (LUT)\n",
    "    predictions = processor.batch_decode(predictions, skip_special_tokens = True) #now we're in english\n",
    "    return predictions\n",
    "\n",
    "def decipherLabels(processor, tokenized_captions):\n",
    "    labels = processor.batch_decode(tokenized_captions, skip_special_tokens = True)\n",
    "    return labels\n",
    "\n",
    "def getImage(hf_dataset, hf_ind):\n",
    "    return hf_dataset[hf_ind][\"image\"].resize((252, 252))\n",
    "\n",
    "def showImage(image):\n",
    "    display(image.resize((252,252)))\n",
    "\n",
    "def showCaptionComparison(df_id, hf_ind, image, prediction_untuned, prediction_tuned, label):\n",
    "    print(f\"DF Index:{df_id}, Segment index: {hf_ind}\")\n",
    "    showImage(image)\n",
    "    print(f\"UNTUNED CAPTION: {prediction_untuned}\\n\")\n",
    "    print(f\"TUNED CAPTION: {prediction_tuned}\\n\")\n",
    "    print(f\"DESIRED CAPTION: {label}\\n\")\n",
    "    print('==========\\n\\n') \n",
    "\n",
    "def showCaptionComparisons(segment, hfset, hf_indices, predictions_untuned, predictions_tuned, labels):\n",
    "    for demo_ind,label in enumerate(predictions_untuned):\n",
    "        hf_ind = hf_indices[demo_ind]\n",
    "        df_id = get_uid_from_hf_segment_id(segment, hf_ind)\n",
    "        image = getImage(hfset, hf_ind)\n",
    "        showCaptionComparison(df_id, hf_ind, image, predictions_untuned[demo_ind], predictions_tuned[demo_ind], labels[demo_ind])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f2f0a402-a6f3-4476-909a-9451a91a13f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compareCaptionsBefore(segment, maxSamples, model=model_g, processor=processor_g ):\n",
    "    #Get dataset segment (as df, hfset and dataloader)\n",
    "    df, hfset, dataloader = select_dataset(segment)\n",
    "\n",
    "    #Get images and labels from segment dataloader\n",
    "    subset_batch, n_samples = getSubBatch(dataloader, maxSamples)\n",
    "    hf_indices, tokenized_captions, pixel_value_inputs = unpackBatch(subset_batch)\n",
    "\n",
    "    #Get predicted captions by applying model\n",
    "    predictions_untuned = getModelPredictions(model, processor, pixel_value_inputs)\n",
    "    predictions_tuned = predictions_untuned\n",
    "\n",
    "    #Get desired captions from labels\n",
    "    labels = decipherLabels(processor, tokenized_captions)\n",
    "\n",
    "    #Show comparison\n",
    "    showCaptionComparisons(segment, hfset, hf_indices, predictions_untuned, predictions_tuned, labels)\n",
    "    \n",
    "    return segment, hf_indices, pixel_value_inputs, predictions_untuned, labels\n",
    "\n",
    "def compareCaptionsAfter(comparison_segment,\n",
    "                         comparison_hf_indices,\n",
    "                         comparison_pixel_value_inputs,\n",
    "                         comparison_predictions_untuned,\n",
    "                         comparison_labels,\n",
    "                         model=model_g,\n",
    "                         processor=processor_g):\n",
    "\n",
    "    #Get hfset for displaying images\n",
    "    _, hfset, _ = select_dataset(comparison_segment)\n",
    "\n",
    "    #Get new predictions\n",
    "    predictions_tuned = getModelPredictions(model, processor, comparison_pixel_value_inputs)\n",
    "    \n",
    "    #Show comparison\n",
    "    showCaptionComparisons(comparison_segment, hfset, comparison_hf_indices, comparison_predictions_untuned, predictions_tuned, comparison_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d490efcf-a555-4f87-959a-18a6730f7f57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DF Index:102, Segment index: 0\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a cozy knit sweater with a flattering scoop neck and a flattering cowl neck make this a perfect layering piece for a cool day at the beach or in the city\n",
      "\n",
      "TUNED CAPTION: a cozy knit sweater with a flattering scoop neck and a flattering cowl neck make this a perfect layering piece for a cool day at the beach or in the city\n",
      "\n",
      "DESIRED CAPTION: This is a light grey, cable knit poncho-style sweater with ribbed turtleneck, wide arm openings, and a relaxed, draped fit, likely made from wool or acrylic blend, representing a casual and cozy style possibly from the\n",
      "\n",
      "==========\n",
      "\n",
      "\n",
      "DF Index:6, Segment index: 1\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a classic boot with a pointed toe and a high heel are crafted from supple leather with a soft lining and a cushioned footbed for comfort\n",
      "\n",
      "TUNED CAPTION: a classic boot with a pointed toe and a high heel are crafted from supple leather with a soft lining and a cushioned footbed for comfort\n",
      "\n",
      "DESIRED CAPTION: This is a classic black leather knee-high boot featuring a slouchy shaft, a stacked heel, rounded toe, and a simple, timeless style that could be versatile for both casual and more dressed-up looks.\n",
      "\n",
      "==========\n",
      "\n",
      "\n",
      "DF Index:137, Segment index: 2\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a flattering scoop neck and a flattering fit make this top a must have for your wardrobe\n",
      "\n",
      "TUNED CAPTION: a flattering scoop neck and a flattering fit make this top a must have for your wardrobe\n",
      "\n",
      "DESIRED CAPTION: This is a contemporary, burgundy-colored women's blouse with long sleeves, made from a light, possibly synthetic fabric, featuring a relaxed fit, a round neckline, and a high-low hemline that adds a modern touch to\n",
      "\n",
      "==========\n",
      "\n",
      "\n",
      "DF Index:10, Segment index: 3\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a flattering scalloped hem and a flattering fit make this relaxed pant a go-to for a casual day or weekend look\n",
      "\n",
      "TUNED CAPTION: a flattering scalloped hem and a flattering fit make this relaxed pant a go-to for a casual day or weekend look\n",
      "\n",
      "DESIRED CAPTION: These are red, straight-cut, mid-rise trousers likely made from a smooth, non-stretch fabric, featuring a clean waistband without belt loops, and finished with a simple hem, giving off a sleek, modern look suitable for\n",
      "\n",
      "==========\n",
      "\n",
      "\n",
      "DF Index:128, Segment index: 4\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a classic chelsea boot with a pointed toe and a pointed heel are made from a soft leather with a soft hand and a smooth finish\n",
      "\n",
      "TUNED CAPTION: a classic chelsea boot with a pointed toe and a pointed heel are made from a soft leather with a soft hand and a smooth finish\n",
      "\n",
      "DESIRED CAPTION: This is a classic black leather Chelsea boot featuring a sleek design with elastic side panels, a modest block heel, a pointed toe, and a pull tab at the back for ease of slipping on, suitable for both casual and semi-formal styles\n",
      "\n",
      "==========\n",
      "\n",
      "\n",
      "DF Index:23, Segment index: 5\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a classic buckle add a touch of class to a boot crafted from supple leather with a high heel and a low toe cut for a comfortable fit\n",
      "\n",
      "TUNED CAPTION: a classic buckle add a touch of class to a boot crafted from supple leather with a high heel and a low toe cut for a comfortable fit\n",
      "\n",
      "DESIRED CAPTION: This is a classic black leather knee-high boot featuring a sleek, rounded-toe design with minimal detailing except for a strap with a buckle at the top, suitable for a streamlined look that could complement both casual and slightly more formal attire.\n",
      "\n",
      "==========\n",
      "\n",
      "\n",
      "DF Index:147, Segment index: 6\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a flattering v neckline and a flattering asymmetrical hemline make this sleeveless blouse a must have for your wardrobe\n",
      "\n",
      "TUNED CAPTION: a flattering v neckline and a flattering asymmetrical hemline make this sleeveless blouse a must have for your wardrobe\n",
      "\n",
      "DESIRED CAPTION: This is a women's deep burgundy blouse with sheer long sleeves, featuring a v-neckline and a loose, flowy fit, likely made from a synthetic blend, presenting a simple yet elegant style suitable for various occasions.\n",
      "\n",
      "==========\n",
      "\n",
      "\n",
      "DF Index:74, Segment index: 7\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a pointed toe and a pointed heel make this boot a must have for any season\n",
      "\n",
      "TUNED CAPTION: a pointed toe and a pointed heel make this boot a must have for any season\n",
      "\n",
      "DESIRED CAPTION: This is a pair of classic black leather knee-high boots featuring a sleek, fitted silhouette with a pointed toe, mid-height block heel, with a full-length zipper on the shaft, likely from a contemporary era and versatile for various styles\n",
      "\n",
      "==========\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Look at predictions before training\n",
    "segment_g = \"validation\"\n",
    "n_samples_g = 10\n",
    "\n",
    "comparison_segment_g, comparison_hf_indices_g, comparison_pixel_value_inputs_g, comparison_predictions_untuned_g, comparison_labels_g  = compareCaptionsBefore(segment_g, n_samples_g)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63f05192-5952-4c3b-a9e7-09516d87c9ae",
   "metadata": {},
   "source": [
    "# Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "16a157d0-1af8-48d6-945d-291b9400d869",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define default values\n",
    "\n",
    "n_epochs_g = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2ebdf79e-8a18-460e-b26e-a87b8e600488",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, n_epochs = n_epochs_g, train_dataloader = train_dataloader_g, optimizer = optimizer_g, accelerator = accelerator_g): \n",
    "    for epoch in range(n_epochs):\n",
    "        print(f\"{epoch= }\")\n",
    "        \n",
    "        epoch_accumulated_loss = 0\n",
    "        nBatches = 0        \n",
    "            \n",
    "        model.train()\n",
    "        for batch in tqdm(train_dataloader): #batch is a dictionary\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            #Get X and y\n",
    "            tokenized_captions = batch.pop(\"tokenized_captions\") #!!!*** pops instead of indexing to save memory (on old hardware)\n",
    "            pixel_values = batch.pop(\"pixel_values\").to(torch.float32)\n",
    "\n",
    "            #Get predictions\n",
    "            outputs = model(input_ids = tokenized_captions, #input_ids is conventional HF name for tokenized text\n",
    "                            pixel_values = pixel_values,\n",
    "                            labels = tokenized_captions)\n",
    "            \n",
    "            #Calculate loss and train\n",
    "            loss = outputs.loss\n",
    "            accelerator.backward(loss)\n",
    "            optimizer.step()   \n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            epoch_accumulated_loss += loss.item()\n",
    "            nBatches+=1\n",
    "    \n",
    "        print(f\"Average Loss: {epoch_accumulated_loss/nBatches}\")\n",
    "        \n",
    "        print(\"!!!***!!! UNCOMMENT VALIDATION CHECK ONCE IMPLEMENTED\")        \n",
    "        # model.eval()\n",
    "        \n",
    "        # rouge_score_aggregator = scoring.BootstrapAggregator()\n",
    "        # bleu_score_aggregator = scoring.BootstrapAggregator()\n",
    "    \n",
    "        # for batch in tqdm(validation_dataloader):\n",
    "    \n",
    "        #     tokenized_captions = batch.pop(\"tokenized_captions\")#.to(device)\n",
    "        #     pixel_values = batch.pop(\"pixel_values\").to(torch.float32)\n",
    "        \n",
    "        #     predictions = model.generate(pixel_values, max_length = 10)#9 #16 #Here the predictions are text captions written in number encoded words (LUT)\n",
    "        #     predictions = processor.batch_decode(predictions, skip_special_tokens = True) #now we're in english\n",
    "        #     labels = processor.batch_decode(tokenized_captions, skip_special_tokens = True) #these are the pre-set captions (decoded from numbers)\n",
    "    \n",
    "        #     eval_metric = compute_metrics(predictions, labels) \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c51da2df-9160-407b-a3d1-03794e55309f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# n_epochs_g = n_epochs_g #Set this to a new number if don't want old n_epochs_g\n",
    "# train_model(model_g, n_epochs_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "cc371cd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch.save(model_g.state_dict(), 'large_fashion_dataset.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "e3572f82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeftModel(\n",
       "  (base_model): LoraModel(\n",
       "    (model): Blip2ForConditionalGeneration(\n",
       "      (vision_model): Blip2VisionModel(\n",
       "        (embeddings): Blip2VisionEmbeddings(\n",
       "          (patch_embedding): Conv2d(3, 1408, kernel_size=(14, 14), stride=(14, 14))\n",
       "        )\n",
       "        (encoder): Blip2Encoder(\n",
       "          (layers): ModuleList(\n",
       "            (0-38): 39 x Blip2EncoderLayer(\n",
       "              (self_attn): Blip2Attention(\n",
       "                (dropout): Dropout(p=0.0, inplace=False)\n",
       "                (qkv): Linear(in_features=1408, out_features=4224, bias=True)\n",
       "                (projection): Linear(in_features=1408, out_features=1408, bias=True)\n",
       "              )\n",
       "              (layer_norm1): LayerNorm((1408,), eps=1e-05, elementwise_affine=True)\n",
       "              (mlp): Blip2MLP(\n",
       "                (activation_fn): GELUActivation()\n",
       "                (fc1): Linear(in_features=1408, out_features=6144, bias=True)\n",
       "                (fc2): Linear(in_features=6144, out_features=1408, bias=True)\n",
       "              )\n",
       "              (layer_norm2): LayerNorm((1408,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (post_layernorm): LayerNorm((1408,), eps=1e-05, elementwise_affine=True)\n",
       "      )\n",
       "      (qformer): Blip2QFormerModel(\n",
       "        (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "        (encoder): Blip2QFormerEncoder(\n",
       "          (layer): ModuleList(\n",
       "            (0): Blip2QFormerLayer(\n",
       "              (attention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (crossattention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=1408, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=1408, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (intermediate_query): Blip2QFormerIntermediate(\n",
       "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output_query): Blip2QFormerOutput(\n",
       "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (1): Blip2QFormerLayer(\n",
       "              (attention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (intermediate_query): Blip2QFormerIntermediate(\n",
       "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output_query): Blip2QFormerOutput(\n",
       "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (2): Blip2QFormerLayer(\n",
       "              (attention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (crossattention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=1408, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=1408, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (intermediate_query): Blip2QFormerIntermediate(\n",
       "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output_query): Blip2QFormerOutput(\n",
       "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (3): Blip2QFormerLayer(\n",
       "              (attention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (intermediate_query): Blip2QFormerIntermediate(\n",
       "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output_query): Blip2QFormerOutput(\n",
       "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (4): Blip2QFormerLayer(\n",
       "              (attention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (crossattention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=1408, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=1408, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (intermediate_query): Blip2QFormerIntermediate(\n",
       "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output_query): Blip2QFormerOutput(\n",
       "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (5): Blip2QFormerLayer(\n",
       "              (attention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (intermediate_query): Blip2QFormerIntermediate(\n",
       "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output_query): Blip2QFormerOutput(\n",
       "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (6): Blip2QFormerLayer(\n",
       "              (attention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (crossattention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=1408, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=1408, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (intermediate_query): Blip2QFormerIntermediate(\n",
       "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output_query): Blip2QFormerOutput(\n",
       "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (7): Blip2QFormerLayer(\n",
       "              (attention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (intermediate_query): Blip2QFormerIntermediate(\n",
       "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output_query): Blip2QFormerOutput(\n",
       "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (8): Blip2QFormerLayer(\n",
       "              (attention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (crossattention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=1408, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=1408, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (intermediate_query): Blip2QFormerIntermediate(\n",
       "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output_query): Blip2QFormerOutput(\n",
       "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (9): Blip2QFormerLayer(\n",
       "              (attention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (intermediate_query): Blip2QFormerIntermediate(\n",
       "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output_query): Blip2QFormerOutput(\n",
       "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (10): Blip2QFormerLayer(\n",
       "              (attention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (crossattention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=1408, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=1408, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (intermediate_query): Blip2QFormerIntermediate(\n",
       "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output_query): Blip2QFormerOutput(\n",
       "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (11): Blip2QFormerLayer(\n",
       "              (attention): Blip2QFormerAttention(\n",
       "                (attention): Blip2QFormerMultiHeadAttention(\n",
       "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "                (output): Blip2QFormerSelfOutput(\n",
       "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                  (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                  (dropout): Dropout(p=0.1, inplace=False)\n",
       "                )\n",
       "              )\n",
       "              (intermediate_query): Blip2QFormerIntermediate(\n",
       "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "                (intermediate_act_fn): GELUActivation()\n",
       "              )\n",
       "              (output_query): Blip2QFormerOutput(\n",
       "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (language_projection): Linear(in_features=768, out_features=2560, bias=True)\n",
       "      (language_model): OPTForCausalLM(\n",
       "        (model): OPTModel(\n",
       "          (decoder): OPTDecoder(\n",
       "            (embed_tokens): Embedding(50272, 2560, padding_idx=1)\n",
       "            (embed_positions): OPTLearnedPositionalEmbedding(2050, 2560)\n",
       "            (final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
       "            (layers): ModuleList(\n",
       "              (0-31): 32 x OPTDecoderLayer(\n",
       "                (self_attn): OPTAttention(\n",
       "                  (k_proj): lora.Linear(\n",
       "                    (base_layer): Linear(in_features=2560, out_features=2560, bias=True)\n",
       "                    (lora_dropout): ModuleDict(\n",
       "                      (default): Dropout(p=0.05, inplace=False)\n",
       "                    )\n",
       "                    (lora_A): ModuleDict(\n",
       "                      (default): Linear(in_features=2560, out_features=10, bias=False)\n",
       "                    )\n",
       "                    (lora_B): ModuleDict(\n",
       "                      (default): Linear(in_features=10, out_features=2560, bias=False)\n",
       "                    )\n",
       "                    (lora_embedding_A): ParameterDict()\n",
       "                    (lora_embedding_B): ParameterDict()\n",
       "                  )\n",
       "                  (v_proj): Linear(in_features=2560, out_features=2560, bias=True)\n",
       "                  (q_proj): lora.Linear(\n",
       "                    (base_layer): Linear(in_features=2560, out_features=2560, bias=True)\n",
       "                    (lora_dropout): ModuleDict(\n",
       "                      (default): Dropout(p=0.05, inplace=False)\n",
       "                    )\n",
       "                    (lora_A): ModuleDict(\n",
       "                      (default): Linear(in_features=2560, out_features=10, bias=False)\n",
       "                    )\n",
       "                    (lora_B): ModuleDict(\n",
       "                      (default): Linear(in_features=10, out_features=2560, bias=False)\n",
       "                    )\n",
       "                    (lora_embedding_A): ParameterDict()\n",
       "                    (lora_embedding_B): ParameterDict()\n",
       "                  )\n",
       "                  (out_proj): Linear(in_features=2560, out_features=2560, bias=True)\n",
       "                )\n",
       "                (activation_fn): ReLU()\n",
       "                (self_attn_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
       "                (fc1): Linear(in_features=2560, out_features=10240, bias=True)\n",
       "                (fc2): Linear(in_features=10240, out_features=2560, bias=True)\n",
       "                (final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (lm_head): Linear(in_features=2560, out_features=50272, bias=False)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_g"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b3ccc3e-09ee-47e2-a55a-34851a68b1be",
   "metadata": {},
   "source": [
    "# Look at Before and After"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84a2838c",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ec3be3aa-8d22-4aa1-82f3-1fc31d997367",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DF Index:102, Segment index: 0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a cozy knit sweater with a flattering scoop neck and a flattering cowl neck make this a perfect layering piece for a cool day at the beach or in the city\n",
      "\n",
      "TUNED CAPTION: a cozy knit sweater with a flattering scoop neck and a flattering cowl neck make this a perfect layering piece for a cool day at the beach or in the city\n",
      "\n",
      "DESIRED CAPTION: This is a light grey, cable knit poncho-style sweater with ribbed turtleneck, wide arm openings, and a relaxed, draped fit, likely made from wool or acrylic blend, representing a casual and cozy style possibly from the\n",
      "\n",
      "==========\n",
      "\n",
      "\n",
      "DF Index:6, Segment index: 1\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a classic boot with a pointed toe and a high heel are crafted from supple leather with a soft lining and a cushioned footbed for comfort\n",
      "\n",
      "TUNED CAPTION: a classic boot with a pointed toe and a high heel are crafted from supple leather with a soft lining and a cushioned footbed for comfort\n",
      "\n",
      "DESIRED CAPTION: This is a classic black leather knee-high boot featuring a slouchy shaft, a stacked heel, rounded toe, and a simple, timeless style that could be versatile for both casual and more dressed-up looks.\n",
      "\n",
      "==========\n",
      "\n",
      "\n",
      "DF Index:137, Segment index: 2\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a flattering scoop neck and a flattering fit make this top a must have for your wardrobe\n",
      "\n",
      "TUNED CAPTION: a flattering scoop neck and a flattering fit make this top a must have for your wardrobe\n",
      "\n",
      "DESIRED CAPTION: This is a contemporary, burgundy-colored women's blouse with long sleeves, made from a light, possibly synthetic fabric, featuring a relaxed fit, a round neckline, and a high-low hemline that adds a modern touch to\n",
      "\n",
      "==========\n",
      "\n",
      "\n",
      "DF Index:10, Segment index: 3\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a flattering scalloped hem and a flattering fit make this relaxed pant a go-to for a casual day or weekend look\n",
      "\n",
      "TUNED CAPTION: a flattering scalloped hem and a flattering fit make this relaxed pant a go-to for a casual day or weekend look\n",
      "\n",
      "DESIRED CAPTION: These are red, straight-cut, mid-rise trousers likely made from a smooth, non-stretch fabric, featuring a clean waistband without belt loops, and finished with a simple hem, giving off a sleek, modern look suitable for\n",
      "\n",
      "==========\n",
      "\n",
      "\n",
      "DF Index:128, Segment index: 4\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a classic chelsea boot with a pointed toe and a pointed heel are made from a soft leather with a soft hand and a smooth finish\n",
      "\n",
      "TUNED CAPTION: a classic chelsea boot with a pointed toe and a pointed heel are made from a soft leather with a soft hand and a smooth finish\n",
      "\n",
      "DESIRED CAPTION: This is a classic black leather Chelsea boot featuring a sleek design with elastic side panels, a modest block heel, a pointed toe, and a pull tab at the back for ease of slipping on, suitable for both casual and semi-formal styles\n",
      "\n",
      "==========\n",
      "\n",
      "\n",
      "DF Index:23, Segment index: 5\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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9e/78xYkXXzv37NdeItRvrK9Ojk285c1vPnrP8Vdee/X1k6fY4terJHisEmM4sOPgr8IjZIP40CAl4wTEB8GlYfEUGwZN9bEK6zow1H+g+LFMSEkwjRTJQipP5a/C9KircwvkGVmNjo4bag4fOtyz3mvVjHjTEC+fJl/xmEObiwO9YURBtynkbn6zbjbozeViQjPl79KlK+aXNOWWhGamZ2OUMjl55MhhCsTxseOTUxPmquMjox/92A+++c1veumFF3rWe/bt7ltZOHzulRenzy/tuue+0cnRiaHlL/zWV1965PH/6u/9vd0jY/c98AjjGSg+efLUKy+/GuGkHOEZ5obI7uUgTEVI/pDH7osXFVD2EEt4GtZ55IM+wRq9mddevnjF5in5wfTQME1939QEFj5PMJ+rnYm2bvnHZgFRSLJvz146H4+IW2YSJuTqg5IJ9mzze/usdm2wKlXcoUN3VY1a7brXa9wDNxv0Hcz1U66b9k1PXx4eGQaje+65hxBCjI62vn+Ays+88MR9x3/0Ez+yujz/f/1P/k+/89k/OHDk6FsfeOtTX/nqaydfXd3oGZ3avTB98f/xt//TyT0v3N3H4nFkYXb+xZdeurf3XupGrNfGWTkrEMQhHkaBuHF6gR41cGPBZBJw54QBXy3QkkOyi5yfQxu0+PP9GYBMA0xb1xdXhwYyM2FnRuiXLfnEaq4ZOr8kxoGenjHiEztn+lYzldn5OdWqwSTNbIODZSlDHPGmOqZ7uS49sFPG0Fq9j87lqaeehozGTScmJmEL4rNlZG6OleWTT37+v/i//K1nvvpyX//40RNvecuj77y4sPTKmcuXLi8f2X/00N0P/O4XX588sOfyuVcO7t1P1JYP+mG4i4TAmoN41/QlJUuJ1Lz1JBe4h1FXtOYWBOGVp9GGcH6k0hSLbskopBGSjGwtJ6ETMUUgIGUkQRADAwYHalbEY1EWxPfs2W2x1oR5atdU8h+kgYp1nRzEZ8Ipc4ZA1+VtdzOtHtgpoA9W1tZ279pFrgVQpmbwwQgH/4OezAYHB3/j137jl3/pV4am9vTu2n1hbuGBBx6YHB1lm0lluRYDm9mRgaVnn3ruyOGjuyd2HzpyZGndGm0/EwNYBCOQkid4NYRtXeWPc28SQAf0uLX+EQfKjUKuIrhFkDwyJLirJMQ3P+qyqqAIDWnwBXQ825qU+M2A57xVrVrGIrKbjh87egw9lO1NRh6ZS474+dlydvF5/XrgZoO+eK7mgYrZ6vPPv3DgwF19PX1QwijXjNY61MzcLLCyNWdjueRsj8tXelFCZA+q8WGGjLC5OD/7pnuPfuLjP/q2Nx2hHL9y5fwKKxrcc6BPJlkizTSVXpOtQcSbdqXEZ2iQ426I2O0whCweqZNuiaYSMYApZDco07J4mpXZFYsJA5Qzs9MzQE9XYxcuASq7a2nrQyCrysLD5YVg1B9VEN0HKGcnxscmxlRBnLGJUVpWlVQhu3n37NllNVr8rurm+iFezjcb9JuNw8kJsqdOnV5dXn3o4YcdnhFNDslhdp52/IUXX/z8579Aw/OJP/3xvvW5p774+2974MTLLzxv68XkxAhDNcaUX332aWL94MSe3uHxxfmFR972FmjGffF44CczmBvArmz9QCHoFyuPJENHiQCIGJnCltmCOGSTmrwyfVuASpMK/yWXFXrLTpdSjJoqyPbyxcuKI9aMjY+Tx/bv3yeyW/ocINZKRQrhJ8xkimzRobaKi6NeTZkzPjYqc/ye4edmx3R/r30P7IiJrGaZapIU9u7FGoM+UDDzMymEdZjDmBnw0m0/+NYH/tYv/Nd/97/775566rnnn37qS0eO7t+9/4Wel5bX185dnP07//3feenkyR/6pb97eP/k7MAIq0vGDDgxpCrCDo/lpUXMm99VnhAeICL97LvNBDphtRcWjvH5AD31cQjCKiYulVoBpWjkkKHh0YHB4XPnzs/Nzh04eEBB/bQ5A8Ox9FyNIpKo1vIkC5kZD492Bg1EJ7nlK7TH2VouQ1K+ZSziE2KQg5Cuu049sFNAT2Q3sYQbkOIwPKCBPAYKoGiV3t3i0vo/+cV/8aUvfuF/93/4Pz73zLN/8Pkvj44PP/zwA6NDa1cuXnIijR0Y97zv3aPul2bPnZvG1ek9K7/hifF12/egH4NHXrBY6PeLykxtY6jDTy4haOnrErLx5hi3ufUIKK2CocZmZAb9fYMDFndNJuztHewfwvIjKdXhUEA/PjE+xF6ZibIIY2OrqxoYCwtkwR4Tsl955VVcnwmxo9fUihzVmtyu1+l9d7PVAzcb9OG3+JytRpO7p3a5yXDf32+qR6bHYskgprNwQI2dyezQ0Oee/PLLr5768Z/4iZ/+mZ9hWra6eGFp+vVTL794/tzFvsGhAwcmX3z68wNTdOIrw4PD5qN2VBGv5Ym/ArSckIGCyPGMCxSneEYDnmLG5ByFugXB8H90N0BBuUpmV5YsSs7ZUNt4ZdgzPDQ16Vg/s/CVtXVU11cn7UztniTET01OqLwRw5QXrEGfQDQ2OsIEZ2N1PaNC//C+fcMmA6HAXotWc2jsnnuOqy8yU7x6dt0174GbDfpOg8JHwaLE5pgkAD0BoKlKRAH5xWz/m6tdSsNr6/2/8Ru/9fxzL5hJYvkrC8sLC0swcvbiybXlxZUZGp3VUxdnDtx1lxwbX5cJ7FoqDcrLKY6crRR8F+7pEwkqEXu29lVt9IwMZxcsUhNZIqA3AqhVzBAsts7O4/egTPIho8dK06y6p0cEG17pbYjmBBsuoptBbGSECdDE1ARrivXeHptKdu1aJgKVjBMNqek7gjRlT5P977rr0wM7BPQ9XjZMj4/3DvdklMdo4R4gtBrUCLvYIcPGodGR8RwtZptSH6Xi6IgjZg6eWjp3eWXGetHFOds+eleWB6dn59cHeu2rctaHlaOIKCW6gJfcIBv04RiC8WDqHeHz8+NEDvIPjDarB1wfRbTKqJ5UoqkJnOPiVogpHIWYtkrFIzdTW4hXeRHcmSxYjnVwA9CbTCtLc6y5xrSyj65mT5OdFAfxUikR4XdnsdcH6ldzjfy6ExxMe/dZ9mQotkjDuA5SmSzWxhHgIE0Iv3Tp4uLywtz8jIMAnRLc2z80NjW20btMO9m71D+yPjrUi2WaUPaOoI+hnDspK3li6q4QDF48MAr0CY+YkqVZiwM2lHBoz+LRmx54AAs3da4dWDnnDCTLZYcK+M7PsTpOOK4vN4tKCmqjk2xT4f5+WNcoqXLyq1HC5nSH8NhDtbCUrVUVR+l33XVXW8BCJ0pnViSHqqbfrrv2PXDTOX0QiSNCCQkCn4MV9wDEMz4xNnAhAjlQAlbP6vrK4vLslVnS/8R4FozsFrSbaaCHAqRnBZ/NfsBleBkdGi5jYLx5qMQbD0TuB3lSTst/S35QUHNCpL9w/vyZ06dxfUchTE5N2emHbdtDQvQa6x1lHGmtCgmxIABxUGZXQC9khrpr9xQpHVwBd2x80q6T/gni0O6x8dHl1aW5hYGVpdWZubnpWSdy7nKAg4ZNToxbaWZUh/6m56YPHT701S992QiQLum669YDO4XTkxA4zXQFQUgl26AEMAJuU1gcEVsVgcAOf2WomHMTxCEwMP8iZFtWDU/vp3QnugTBBJmaDUtIXZMjAYkZ6UyZUttsmiEIVqgiyBiYLuZNSjl77tzTTz99+fIlm5jI7ssrMTcQk/wjg2RVqlUxGZCpBrvlIUtcaxsDJt3mybHNXMfpjVhO7g5d1RwXCcmHWkfl1EFLyVdmBU5ksNkK/bCbKC1Qqtl116MHdgroAa5hDkeHR35X3BQNlIoQKrKfo+zgexyIYM4HUkJEA6PMFhcWoFAfZXm15PWCJXRGnm+IhzMxKjxkILJsW7fKjWsh6kC2kS1oPvvss7/+67/OFhIlqAwRy1M5KJfcoiDHqlG+WKuCb7D2VOVbleTmxDI6J7RkZS0tWlmhlWonjCtO3cRHZjLcs2evDQDOxVd0TWPTlq67Hj2wg0APgxDAyGxLMoZIgm+oIVu2bUFi2ZLZJ+gYB/B7KMQpRYNmmKPOJxkhBLAsg4OAmGhUc8rMHRvEga/h3lWEhnWeRipuledW5moSYWNj48knn3z2mWeI5maxylIopCIzFXZroFH6wPDQ4sqy7bMIq89qbun4DSfyUa4ZsDxbPXF6RIt+5GPBuNXKQHP61ClWN6lV6LHrrlcP3GzQb6hA6sDUamzcXDCr9Pgi2AFrKU+C8uLK5BMEEEgAPZHgsu0gG6ET8nSDPpMYudn/Whyc6SJtt7ylgaiBfJSh7oEet2+TZhKOx0LalYAkRRIVPSSZqcX4uKO0lfiZ3/6M41pRgurh5abZK8tLA32xQ7YIdvb0aSrLhdk5h9k4LxPPV56NI402SsjZr/Zw76QTZ5TYyY4wrkxfQgsHDth8mAM93/rWt+ZV966XPUS8XXfNe+Bmg36zQZANfyRa/A+kSA4YZLDYbN/rWyA1nY3kLZzgkQMr17Krg1DRWD4wJ59i5DyViUFCJhapWIN5Egd5ri0f/hBVKTS3JB8FcaqmSjAqJtUNNm9HyL//9/8eU1fJcOsr8ErdPjY1tYv4hUzsjpWzEElQCxISDUHKTYhBA1NXXBupzp49N33FIrEdBPbZ5lw0MQ8dOrTZJd3f69UDN11702kY7t58mCVIwSJk8ASRJYU3jyvc4O5QjtdCCU2ItAJBvNCF2efzO0BWwMXkGR6TK1ZguvboRXqRDzTLGUbblQe5NE7cKEEFsP/2VIkC8XtV+r3PfvbEPffc98D9xhr7eicmdw+tDFqOtfdJnpw01qII6NQytkplkl2my6R5FeNwdOKZEi17GdlGnY+Qz5nMsK2nvlRi113XHtgpoKcihIys8cQIMjK6ZkNnQyG/wOCyQM8P0BcvXpibuwviG2cVE4Yl5JrlAmkHQ5eTGSN9TowiA+yOk2fLvBKmk0MVNbk0OtRdEUSeJBxRqR0ak/9zzz1nZnzivhNI1DYoG3r3H9hNTGNrY4RSN1Wy8oBstMtAJDdyP9N5ZRiaELIM6ZxsYjTBjdXQ0jLdPxGou32k+vv6Xm62eBNOGgdSNklBcJv2kYPBgoN7IAUjV0/BTuR2BUkegbumdpkIVDTnS+ZQS4gsG7Xk3JahXAnKTfXZspUWkw/iSTvi8ZmAhnDylzXTHvvKUUukcM7Y4Nw9Z36cOHb0Q9/zgddefeVLX/xK5tO9G+fPn3vxhVfoUh0tv5oTKjN7ILPQPjmRk+iiUUYJ+30JQWhPErvFkbBagT6rNVKQr3/6ypDmpNJddz174GaDfrNtwB0Usi7Y1DbSf4MaKG/xeI84t6QaMKIYwU3dxkK+VOCeArtUcnWFYiE5x6aGCKNGIx4EloyKnAL3cuJwsC9cAD/a4KnBJqQpIITR18s87J4TJ/7Mj/0YxH/2s581tVAfVGqmS7uqCMpNtaJocgiI4xF8vdNQ4VCPI4ePPHD//aYHtSE4MaQCd9d8V+LCxWPHjrfSq0bdy/XqgZ0i3gAKGd22aWD14sPkS5LhRwxQ5RaeijAyByUZl07cvLaXiQy/CJDqkRw4+CyIU1nGIsDtwkLHSJjU0bpTIBewlzQP2CYAVXqYPSe8lnI7pGjejOZsdLLdmxzy5//8n/+H//Af/tanf+v9H3i/Gvp4zsG7Dpqt5lzl6WnqJWc6kHHMHowwxJi7Dh64+8jdPg7nuAdTXkxdTSRELSbHyr377iPX6z13893WAzsF9MeOHTNHJCFg8IAIb20ttvypLw8Q+6WibDKAzz7h+/6E4PoiwDroi7xFMIaC3BJ3QkvUKRkE0ECjCsUAcYcGEEzRBjHGceEICDkprjIL1zdimJ6ybXZWmevuPXsmJqd+6qd+8l/+y1/61V/91fe/731OU8uqQn8/nq0VPk7uSA8OmcjntddOjo8PAz0FKEFffWAdDatJI2Y/b3rTm1Sv6653D+wU0IOCtXoLn2aKYMdMBujp153hDc0FaPDF9THEqG4C3I21HCYw6AB49pDRbyIVaYvjW97PmlRkaEhvC7EluhTz7hymp3M9C/RL1MHd/UMbUCj/4vZBbXsHJBsU5jtSu3dNJQn16Mbae554nHLm3/9v/79f/dSn3v3449OXZ4wVKC3GzWT7jVW32DzcG7d8gs1pU6attDqulpXHxyeL1G1H7B+dGCb0b77vnSJ2btbntvrdKaCnBScYXLh4iQKejReLMro8wB0ezqqnLsfBmdyXxJGDA2C9XVmqmynincPDp+nRQZyA7VorUxh0mK2YNUpEXOEqt44s1G5dxeF4UEijAX7Q5xcio9yqSllJCFHPfocnb6w//NDDrN9+69OftmprPVWSsdEJws+Zs6d2TeZLtIqT1tWQQnw3TPlojyxJaCLLp2V+9OgR+koxVUPkrrt+PbAjOIo3DcSHDx/y+pljObYSy7VRsGaEnYUqSAiE88WbTExhkcaG+o920mQAekrK72w4ElP8BjUxIVX+7VZXNig3bLWsBPI08InvtnIIGfBLy/FEoJfYtZdY7wtZg8Qm5sdGp4/9yMcfffTt586dUdbRY0fGJ0bsDVd/j1wBnRIz62j9/cwqUaCm0M8qRbbJsK/vyOHDmuC26653D+wI0LdGvu1tDzrW3VzWYTeW6aFHOLjwuEJGEzQos93CDYlI5NJCZkUWqhq4IRWGzF/ZRUIVh1TC+4WS02uWLOf4K2bztDo06MdfXF9aTyWm18x0AmnViSC+myIKxo+B79q9y3d1iDAf/NAH4ZvM88orL5uYml7LDY4pi0huxiI1bMRDoJcbuZ9sY9OAcKVYi2001mrSvV6/HthBoPftjUw411cpPRbml8xTs+90zTGrOSbAAagEXwAq9JcVFwxmndROv2xi4ofOzZ6KcJ8VqWLVRBPyPJyBqQRc0UIu/KApFU+L7Nb5w6QicwER3GLvhA5aGwGQitjEvzJ9hayCbQsxMFCbQvn7PvD+V197lcWl4wTtSrEZhRKT0NIs1ZCrhAYdGiGELZrMLcmO+kTo4ODuXbs97bob0AM7CPR0FyzMmEKSg6HOxcoOXEMGRqgvmn6dB7iD/jK9dIsMsFg8FW7dQqoISAXjLw0kkSTzX7AmVbty7dYVqCXnEuK/+WtFMI0l8RhPwub7OmrNOu+An8CjfKPP4NTYxL5de3yXYWpsXHnY+UMPPWRTFawjBpHa6Wj8GsXuX0GNkFybB3VqIHI6ce8JZXbdDeiBHQF6MNNU43vhgBc6KUDsLcpMkSOrQDDEgBHFCH/jy+AC3CKAJlGh9DSZhnJCiCR4NbQZLsQsbp6C4i+sQ7miga4J65lCFnUJ9ReayTlkMo+Qw2N2oPTZmfmZGdsIl9gSO/be8fcOLcsnpuoLgQ899LBRiXijtpacwL1JaPKmtDEitQpXHYtE6/QRZTnjMi2vruDpuuvXAzsC9ODCRRI4eBfGF+aaD3JkDR/XhBshYEH2BSAcHP6s6hTHzfyyYB0tDYmooJmZK1TVMU1+cfEwbpkAIk+TZqrMeBuvFQ9ZIQZkUaCXPBgUAez9IRQUiK6YBBPAfOd+fmmxb2hgbGrClQxkLmLAsav88ccfN0QQfpwra4biz2lQyauqoVy4d03NLCfXEfgqTARSVtfdgB7YEaBv7A1iJrK92irNikOKLeCDe8ElRlrMvWjn/ZXVSsBKz+OK93sK99Q4kIQqgNuNR1DmAsPGjSKGKC55FIctA3LDt1se/LxvvYdwY7Nf2eLYkUu10tuQ7pSnHJ/po8a+prm6PD07bReIk+YNJUNGoN6+BPv+5iJTswnE+Y53vCNnci8tMSOzQKtWjgRhAU2aL2VSh4Bh3SPlu9pjdQPed7cIPbAjQA+s7WWYywIlP4sUCmw8FZRwRLI4cDf+3a7oQSCRprHMlgM5pMFdHNAvnp5dVEBNwnHbQC9yi+8axJcWX3yGYuHzFlBJNUwSaoE2Fav45tg8KqNopYDy0tz82vKqw8wmR8eI9ZZjjQZqJSsKe6p661dqffjwXXv37rHlanFxXiE+5Oa8pz27WUQ7tJ5ilmKq99Dhg0wbWid0r9e7B3YE6BvyNNW5rYXOMGnaD9APFuu0blCDQmgW2ewQ+GhRCAjCw4wju2fpR3zRPBWt+i6ANXoIgHghdVvMvmaQ4rfSC9i5RJ/YksshR6x1shItT1Um+wMXLM0+89yzL778EnPiwZEhxw1PTu1q1VNhov8jjzxsMkK42b9/L6EMFa+trjgaipKfmT3QHz16t9M+fGDZmi6Pal/vl93Nv/XATlmRbbVxzlFEjnwz2dYkFjIxSfcI2poahM6EMEP4dos1Yv+e2q4B0MQeCd1KghLQQ+2jyknczvtGBxaFIBh5cDxcODc2Wxp3+s4mwTNlTi6eEngISG4rW4HIJLsJQ06Z4DK3vDIzs3/1ANGGRnN4aHDvnj1U+FQ+xgLHhxw/fuLKlekXXnjJaSKGB58YQo8kGQOFicaRo4ctMrz66iva9853vkOZXXdjemBHcPqtpjpiCbTMJ4n1hV2rSwCfOWigmgMIbIkKI/fFSShnNG9SyAG9KNmGVGbDOK4cMuvMYXs95gI8+LabNlzIlKOrkauUMjRjaKOACBz2rpig30qwLAhdgTuKcCX59y2vZIhYWV6dm553UL0lKN+ksiI7NztD6JIKs6fJUWlwd33LW97KzqLt+t29e8+hw4ftiDxz+uyF85cNYPfdd+9WJ3Q917sHdhbojx8/TokBsrALlBqPZ5dYEmUOMQWTzaSwFH+gadGqLXnyY5/4MFmGX0I+aXnED8GUk4PceOEV0HlaKQLRWAsU0gLdtt7nSeyaWiiFNXDRWE4jyxk29neXQgaOHdjNEpO4f/b0mahQS7g/c+aMmjg8R020y3SdctZCLLXm66+/ZoHLrZM/Wlnd6w3ogZ0l3ljChAkQGRwagUhMHUSJOg7N48EyIalQzixnUu9AKhSKab6LVPDXWswKnQAqhPHIDduGP9AkzMC9hA33DdaugXkxflcxC+ER5ZtzK5xoY0dKyrJgW0RCL2q2QCNpzk05MzIxdvDQIYpLnxq/YvtsaYSMSMDtE0BmIMynHRuodMZCCOCV11+xy9Y4c/ToMSMAOs2w0nXXvwd2FqcHsgcffJA2kIfQAmGEeJ1Qmj5z1pVLVy77+A3BwMfL8qWadXqeeUY2+Xq4E/yWltEJ3LczPyRECbKC0Qg5g0PZS1IOjglMgTLYFj0Idt+IQUJ+IZI3xCdRyfl+jBaRvcqppBkFN7/oaOIlawhZCB50rP5oO2ZZbd797ncjvzNnzxCaIBsNcGfPnZ1jbFEflGVRLPsa2K7/C++WsENUlttfxIc+9CG6beDDCzlyNR4JVWABZvTl4bnRDEYrzxKB2YIP0AI0EQRQDQjkc9CPAG4yWsp4mIsZTmakkdRdlehXKa1oWckf+qXxZ6oqxC1qEYcrwiD9xxYIuJuaSLhhRK18SQqPJs0zxYFyTB8Joj50Rbcjtyfe+95nnn7m+eefO3BgP9UNUnEe4fycw8cXUIID/bb3QNd/vXtgZ3F6raW1hFdo46fbBj+Qo64BNeoaCkXQi8DuQU0xxYRp+EMhdcSxJ56Ha/sJzANxZ5v11ZbtiC6eYeHWlEIVZbojjhzk6tpCPHLrKmKVFgoB/RaBFx2CO54trWihqxK3MGxUyjnPo+VP9EcJDmf93Oc+J8n+/QdQNfNpghlRTbXvuuugtF13w3pgZ4EepAi+FNj2mBJ8A9kw68xfwQtXv//ee8GLeQLhIaJ1eHEUkQ2vQA+ONOLYdEnU4dNwxoFdkN2zYRKKCAgoAJ5e3mTqcmhOkrB8Y4IJcZGB/IUJL7E7iUTgZIsUI9wsLjrTRrQCcW3mGhndt38fQEO2mkP50buP4vFOxjQdEMKKIXPilRXk0Q6kV2Ky7rrr3wM7C/StvU888R6LP/AMUARkwMOUAfr06TNOEsY48UnSw0a+T5bj7+ytLeP5ZXIPOolMbbshOOf0pfBvOEM8cmhayAZu+WcokHtBXNEN5alDxWh8GlFlgllzBclbDgIK8ehunagz46NRlJi0mNSm9S3Y/Qf2TU1N2jeClSMPWRox3vrWB+kxfWqKeEPvYyKg2khcNAWm3K67IT2wE0FPaT07Mw1zMAQxzdNEFHaL9lM7OGlxYQk0sVqgJ2eDLhSSsItB53t92Se4HvmkOHQU/wzGYmpcDDWoLobdOjnmM3wl2YvgoVtpxQFW1MMhIvoVuXEeCVG9aHJW1nyHJ0u39VksTxGPT+hMTLLzt4Xc1DYzcsbIFKxyJdkbxzD4OgNw3QkIItyQd90tpNMDOwv0wVZPD2stQz8M+YA4zgrNFexJRI42dyQwwBtVCZMY6CZgSMsezDcaYL0mo4zBiDAyNKnNLhCz4MZ0xYTa1gHBd01h220Ld+Wq3BBAY/xt0Gg1FIgYOITCMs7gw6mzp5xSXLF8yCbwiMYEzYhBf2+0sQMYDYigCDF99zwN22xhq0b3el17YCeC/k1verPFGmCiAKH7gzCTTJiDDECEGAgrq+N8IsFkV4g+gjlXCkMwj5kaRJZeMuByGAHgl/6xoU1M2crfLeTxbw93y7VwHhISVBbIM6lVGSUiP0sK/AYdMvqFC+dVSRI5gzsnAiX9FHsJXy6pSbMi1J+gb7oiH7eo4sEH3yZJ193IHthZoG8tt1LDJJ1N7vSVK5AK98QY80azSVCBM/hmyOWWGI1LihDJvXdjYCgHVlIswlNk8XX8OEfcBIsl2cfWYHPmCohUm+AraUSX/s5sWIj4gXV2CZo4BJ457qMsHdzBPNtJX/dGe4YddVafGBYreXWNCokUZC+gOqC0PYC/f9/efXscyIz6WulYPiFNQjutnJEmT7neyLd+h5e1E0HvlXzkIz8AUpcuX5q+Mg2dDRZwDIsQJsQiKP7aCAAvB8SoUlaWakgwDeiFXPJ7wfeqcWXLwTX4qxWoqOXNevH7fLwnyOPnhLoKkbNFU6B0706gCQAFDQavJm4D2I2ebGafnTOrsKXQqhmpRlaYOrOziUl7X1jdZ4aAtbeDouyu4s6ePU1OS01yCpXnXXcjemCHgv6JJ54gFYAxSQA3Lek5cCQekCKgTd/AvVsEANlh9oXIRhL8UFtiSL45BZbtPFcAlVA+zYG5GWvOwdy2Std5VMy++dmdSdbyl0ObdyrIRALhVWU2UMKSLzSsLNs5YrKhDPGJQfSaly9emp+dyyJCVmuzxKtFRCOVf+SRR376p39aKQJvxNvullE9sEP72rj/Iz/yI7SB0ABbDdkg1FArhBzdgAhzDUxwDz0aJQTgCC8Ea+INJaZBwCOByCNx/G+8vOQN4ekKlFESOa+nCmpluY1dQyL4H5WOqxJLskqI5OCrUI9MlcGaSZqFBKXbvuhr4mpgfkCRqnQxo6SfmTl//jyu/6f/9J9G2zJRYvLquhvSAzsU9Nr+Yz/2Y04sIxKDBKYZiZl9MF80lauXZ2adewlDQiAGkeCdQ4M56VtasokJbUk4/VT5ZBiQJaM3Bpw5LJZPoRmsdT68E2+J+IqJoINN+2eJAHn4jpWHzMuKzXtKIvcU10YdoiJO4w8TIPiWLQpwqLdSTAMkcbDf/OKyz5ET5c+cOVumB+H3tlNtM64E+i7ubwjkt43qN6i8P2YxkHTfffe9//3vhc2mJHG0ZcRsavnYxOdUgivTs24b+8TmSSkF5ujmIVgOrv29gygBYv0hmaKHzCY5jBvFl0wfyofO4K7sDjzdRGAkG+CHfApQXBzilWgpNWSTgy8zFTZJMAOxV5xYT3RhdSYEax8aGTbO+NoIgexSbZZ1lLGs5EPod/wBz2aHdBG/2RPX/3eHcnpg0vYf/4kff/31V++55zig+EqNQ58wco9ACpZjTlMH4JQYQ5AZwPg9AllggkXQXI2qcy0EU6up2LZfT8XJoR0RaqI6BEGOEMW1Pi9M86IxcTrSjswhvj1yrbvccoQWhvVmpSYhkoG+bE1kmx+Pf/2112yAVFUFIdGi5HUnn7Xiutcb2QNbnOZGFvrHLcs5wJNTE5DJOJEcD9NE+cZZ3Z4+dfrwXfnGpfDGO+EMVbhCHnQqBkzF50Eq5pqEbRiHUatcmd+ulTRf0cTB5kWWkBNfCpOIMP0kCeuXUIRGUeKE9sohlVaouapAYjrzetTlXJyljSXf52FGz146a7IOM6O8HIp8Hy3+1FQqo4yuu4E9sEM5feuBocGRD37PB5997pm77jpg2oc7jo4BDWZMK7Lk/+zMHP04CQTuoacdPAZV5B9QFwLXxoyyPQhbb7AOiM10m5RTi1MN6zUHsGBlCMmJCIaI9V4nYEZHb5IrORyLY42gNiKC9wrqwr+np3PSMssIp2/T2FtWY1rsD6m89NIrX/jCFxj9O/YAfbI5G3WG30CfTCw8bw0sN/CNd4vaGUeAfIv38D0f+B7b7WDaSj7QYI2llonpGPGE/OKvGYPhuflcwmB2SxGH3GLJlbM5ALZNyi8LtSIF2G2F4rg8yCBQr+P7cF2xExbWD/FGgw2zWNJ5GzRkIIQ/pFGUU1u6lpjBTRPrnQuCHKnrV1e/9vzXfKfkwgXn3jilNUSrUrXQ1jni4Vs0vPvo+vXAjhZvNPuBNz3g2AzaEto/gAZcMjFtCWSCOw9sOeyAagfKGTX6sj3hwYotTSPVTmQUOsSSsPkbamUbZl/m8gKbJ4F19IFHmQesOgqhJBrjCGrwVwZqRQ9veB2Sy1Y1KHMAWpUI9OQYU9tf/dVPGQpUuGoxEE0rJWxv79TklK2xmzT5hty6NzegB3Y06M0hDx6864E3vensmfN1AB6x3PyV0aIvNUAzcC/DFktGQjWccfg1NbmEEWaihAzQG7L1JnbeMJqBokAP4iLwexo9TRQ1kbAZLLgp0vAhoEFz4qh36pGr+Jz5MSFGHeTqiosz9lEfs1VryU8/9bSpLX+qsrFhjms5S1qVNA4gAAdC3YAX3C3iG3tgR8v0YDg8OPzEe564dPECHl/gzIJom0TGAIcJji+M18kchAez3iAy4ryWgnsO/AA4gQ39/OmCzRACEqCzKwjntxoQ8aeeJwfLW8G4HJQjT9TWnrbhQgqjAdaNcxOWWOeQwYheJJnz5y9+9ctP0dybsSpxaWHRYtX87Cw236QpQv/Urt0HDmbDVEruuhvbAzsd9HrjB3/gI/BHb0MkgCG8ExAhjHjD+SFSCy+OjoM77zI790odnnNS44rXBl/lGLHJIdgttWPj4ozFpILgohnUEPaPxzeIC1SQ1AKFJE+UE6E/LjPfjEQ+DDFu78jI8Oh9990fz0gWiZsMpqoQLxUqRavOfVC6EoW4dt2N7IEdDXqIAQr7pj/yke9/7fVXAQticHoOVlzBiNDsk8XCC4vZuCQcDkUgNEOka2PYrsH8pnTO21aaxIwzYy00JxKmnqgdS2bvg7/F6mRYQ0cL90gRyq1pdHBPwerbJJl5T4xDfKuGQUAOIhPxrR8rS1Ythxv5vrtl6YEdDfroxusE4b/8l/+yEyFxekwdboL6MjcAHagK7MsuLUCrT9mQmykukYQIWKx2NkC7bn/rcI6QuGLfOdYv8k7tlhIYbl9kAJ0KFeLaoM/THEA3TAfBNUkoFc2gQYDHkSRyI/mrg4RiQjzdKxJ120C/vT5d/43pgZ0N+vRBanjgwF3/4O//A8LI1NS4L1rS0gBQoZDF4kp9rodR2gDtItnGE1KDbz1JCMOYskuWoUoYaUEwhyQIJfmkfaQOwnsd4scXmScY5eEa4kFcbmigkUE9yeZz40gbEyJXRasZAclcdmZulhCDWBAhKYoExSSBtKOSZgiMc47efQQByLPrbnwP7HzQ65MY2NB1/J//s/+UuEMtuGfvHiv5kEdObvhjisPsMVyX3XrZxzMLgNdaqLJGy5uWilwT4hggACv7sEFAXdsYBH+ecp7IAsZFFtDydxXeCKCSJke7vqN0b8J6xafAYSQnMu7ujBxFqGfoMGsGZK1MhQlC2Viy31mcXdDrj5vgbgnQt9Winne/+z2MVbBhgAMvnF6HNQUOdEY33xuhguQDWBCGK4sJwPlfwHeNwFQIljYRPIxivk5G8LicCC2tONK6deXnQX6iEJ9cF3wN2daTyo4ghZejjMqzl1k96vLtNCYJJXONSahijm71kU11Pn/+ggy77qb0wK0BegjTO5CHRzLtYltGVGgQxODpyEGNoUsQ3dtLMUJuZkvf8CphwTJm8ZkKlMmaaHkK74MMMQc88ycEx2/xW4mNrvi3nBC5NYVM8t1yxiC59fX5WiyR3dkezG+UpWIyhHgRkSJqYUxv6dYJxsK77qb0wK0B+q2uwYIvX75IS0mMKYuvHP4B91lDJaX49mY+SjWI3wMg+YEfTJtDOLALha7QHZVj4CggWwHzqBDME4YtXzGKDKg4efIno9geJDIUt0lxg700pg4+LGIgQpYMhWgomRtTdLZtAMkpH/10KiAztWGG01uN6npucA/cYqDHRPF1/Jh6xhlJAWz0M5Grqb9BChwxVFcydKBZ0ghclieGNHBb4fFEQgJG94BexBAEb8rucvYyPOq8ko58H5oRYp7rWeE/yWWD8TeTHlxcNsQba8ZooOqYLeqiZa+JbzmsrtbmmBv8rrvFdXpgR5shfONbAuiICrXUym6RIqSBGDGAHZxBYzHXfGgWFzefBDtoQwPA5waMA9BCcyMJfp7tZYkJtZIILPjWGFBJwu/LzJgZpmfBe8dYDSX69kl2EsYyoiycEYVMQl3Z4JLlLR9xcPE5Kl9U3l5i138je+AW4/TkdUaXtPKg3A7NgznAImRz5AroL5CFzQN3E29EcIs3e6RzYRocoVCE4uyxNUiEzQmuOC2JmGAvWrkw+PJ05H5Y5ipVhhROfAMRj0ASTiurJTammL/SMrEu9uEJtONp192UHrjFQP/oo4/qJoZc8A338AS7bSLboCZQBFinK7Q4C8n+SrDPnimrXeKXkO3AArtX6SlBl12DRVyw76AflGUS4aVEH6lCFAlgZN8TREcd399Dvs+ecfbH/sfU3mTDlnWbuiLL1IRBDbF2uia5q6F8du+ePH/hDBJVRNfdlB64xUDvzAzn4PkocdNIMr3ZxjJjNQBk5rJkG6AvRtzRbAbRXKam8KjVgb8g94IL0RkaahBITPnkPqgPtAW0YSDYBfocjR9be2bzRB/xCUS+cbVIk7TuhPxQSSyM67PPsirBKkdBMUBoY5EkXXezeuBWAj2oUct84hMfP336FOjUuk/Mb6DKI3jF4HF3oG96G1cYLfh2ZrQQv/VHMofXlpZgkhewTbyRcNMSh7CedSUxSUHJrUgi5KHUOi8zS6yR1aOZMdOen1u0mcT+QM9b/p5xKiYVj3q2EelmvfU7vNxbCfSACEYf/egPHT58xH4Ms0L6eO8PH4Wt4qZrhGaocovNN/TXo8xEJSeTADYQY+6U/Rh/Y+3iBNrlRBIIlDyNCjzLYJDxIYQhFtQG+p1F3KQ2NGTSsLLuK8p0l2uUNEupRsuKUpVHQlekSB7rgv4mEt6tBHrdBHokhP/4P/6PTp963UdmiTGAhdmTcwAV0IDbIOAKlw3Erq1/Ac54gFuLX0nsjcpKExfUbuiKYLSiNYklVvI1BfDdwtXsJcwJf1SQSlv20atIRjbR5lMPa/l+g6j5hme0S/4MD77YwN4fvpGJP/nbdIKEksfmnvSb+O7v2KJvMdC39/ThD3/4gx/8oLkgsFrkB+9I66U6dAWyNs1taHaFe1wf2jyVgysH/aDtET8agHWXSPDh+G1/VkQmT+tRJPlKFU2l4iSJEWhYf3P5wmHEdt8TLw2SurXBgczFvyU6iYbZh8y67ib1wC3Z9cD3cz/3c0BPTmjGNkJINdDXprBN+G6IhLBCPDSnseK4brFzq1puQZArTBPfCSEw7dTiTBWaHOIpf8XKRRLFbRFDyzCqnCKhVjqKMaNWPRmjhA511ReyQm9l8yZh1934HrglQQ9/jC4/9KHvpZqHpy2IowGUALsckA2PDJe1giht7anUMZv2BCCLucO/3Ph0fZ1M5vhvhsDRe3Ietacy5Mm1whsNCKm7DB1oA5N3y4/wjD9m0zKiwCHJNDpRCqFL+ayOW4ktefd6g3vglgR966O3v/0RInNDUilpIqhwpAsgEx4cBtZxxBn8Oxx8zZH2UNiUN5HyC8+kF1r2ZSRABqpzX5NVy4cHvpGWxLL1318IqDaeyzqWllHa5zwpBOTKABPcY3RgMSEn38sfXfWS+Ik39E5WDVrFutcb3wO3cNffc889vtoQRQpbyYFcOTAVwvHgqbar1taNnOqK94fLlyoGSFFApegcFAXWIYA+yhdxc/UU0KVyz593UwnahbDUArOElaKQUed5aCO5ZaAgBXGSJk7WBoRmCcx4kgy77mb0wC0J+oY2dl1WZm2JbSy2TGIy6QQsCCPHm0H2h/nCbfaRhBuXvA6c/sQRU3QUAoXp/AA7YRZrKWj8ga/7xuAb1ts74hfOSdjmxrQz/G3YwePl2WoivoI8ovZJCbatDA21zY0tq+71xvfALQn61k3miEePHj158mRZHJSGxMa8zW8lEPezrSniOgknTF0qTLcYMBV7TIubI/XwoIzYJ2SjIMVkVJOw3uDuCt/Yd0BbcJcpj6tICYr+R+qcWwbxIqtG5JyBASsJrh4pXTTsXipxBN74l90tsfXALQx6HPR7vucDzz77jIUqc9YYChTrhSrgI89DmM/eRKjppZ6HuXUyi617rgaHrCXlIFfqmnbOq0+SMJoZMgaE58s967BJ1q6kl2jgy8CzmPqaXHjERVDUpsip0YqkUA7iRCv8n1+IW2l5rN6iBENBF4I3qwduYdDrso997GO4uOPEnARMnmlMFLcO0oO8qN4LpmGrZULjO5th29ApAsiSSsCxxRfAL4z6xUfuzRPkSQ/jrqG2RouoO+PkYhCJq9lxBPeMHTCN5GTYrCQU0dIiiTYNkI4WVQY365V3y721QX/o0KE/92f/7MnXTwKZ3ajgBUxgCPfQ5uAZG/QI9IT7rBCxpcT5+agyhQ171AyDw9lhkYNaTj4logevwffmbhK3CANoIFg0BMWPEiQUp+SfmOZzSm8eBIByFMSJiyLc0qu2RYMu/m5KD9zCoAc1mpC/8DM/46OCJSWHMcOWcFrLknCyawnuIdUVmAG0cevmETmsnoQTEUacHBTFA52Z/eYrUaX3LPzKvB6VHibAz6EJnCJURBRTiKC/NDatGI+aGFPjQs5xQI8i1CQk32voupvSA7cw6PFaqMLsf+Rjf+qpr355dNSOWECNYAPl+dxTz3ppMk0607cgWx7COeAZDWL1lfMrYyYc3QsH6dIy0/TRKnqXxvRzYt9Admx1dPwJxd6bMN/TUioS4pMhgPf00M84nUF9hNSRT9EXhUrrfJGiru5E9qYAPoXewqBX+wJzz0/91E85y5u6JUw9sklmjRyVI8VMifUR7UkUYC0V8MIfR8cClEaJZtFgcBAojsii8QfseHVp/bO3tSWsR3Jzm5iZEjhAkyrSkBLGL8NgvT4kGH08s7UyOqjSU7/UrcwflNJ1N74HbnHQxwxm4+jR448//tjTzzxTYn3gDq/FdInahd2gFmvu6A2Bb2tuKiYHiK6SkItAFlJdIqTHXH7DghVLSpm2nFGX9+Rhg28EnTLm2SpXclSkCMguQSvnpXkqmiSu7OEaXfF33Y3vgVsb9Fv99f3f/5EXX/waKwOT1y14FQotxEZMt06Vq0nsiB1V2XpinCjzzAAR8xVSuvno6TMTXlumrZc/9u/QMtMG1gQmBRlc+uwYRA4RjIJmxgV10FrNE4JsGcE0EuIvAanNJRroM/0lXWU60nU3qQducdAHSPn/rne9ywGXr7/+Gm0gcAsJroK5iPLEbirInAZV67KuRBLB4EfO9wf0rlnbDaozGSCzS7u8umRPrF2BQlBCA2qwnz/lZhUYguVT8+bgWXgmuD09m4Y3JhLpZNVRE7VCEgYTo4nArrspPXCLg36zzxyq4btlr7zyCqGiWas30ANbY8CgiEW3wGL5W/PPwBdKg8jORxzMTaO6geCWvVQcsFLvN0M1p7b6EkpGiGwejOMTv7kkJuCbDo/Gbl7aRpmiGQNc2xQiybruZvTA7QB6qIKz97znPdO+pzw764skoKYzs53KJtayiCzEh/27dRWBkyrjQK1kVQBOb1jIpFQstNDieySaK1EmiXsj3kS2qdUoATWuhLpCGDU34AnvX8meQ2lVxtMqIrThUclJN+OFd8uMfHq7uHe/+92scewZJ01QuAOiP0tGBdygvGlvwJ60XfJMAC+4CSo8GDS4+6lJqqdNER+FULCLNpzZJN7QEEkl+syA3XQ2ADYSQLO+FFMu0ioX9IvGojPl90g1iDaiiXO7dPyt147bAfTABGEnTpx48MEHz5w9DeiEHDgTnt2otacVAiny66BL3N02K+tEDAcil29iNbbBmZaGPK46MM1bzfqrD9aytu93ys1Qjsoxs+132IiHGT9CSybHHS0QujKKSOtaa8SUoSGJ+FdXzS7Gx0aTbdfdjB64HUDf+o0M/X3/wfddunSRlpDEUrgPZ/WU33ZyZl5uOYjGd4v1JinaaBKOcE/bbXh+Fmg9SVYhgkorPgJzEU8OjukLcWH7pPzaKwj+pBeUJn7YOVGqpraF/ghHotlGSImUsrvuZvTAbQJ6SNV7xHqfO2BsDKbgJbBdm0rH9FEc6G1X0MWbxeHAt8E940IJ/eJU2sj9/J7KE7mI7FZC+EUsEfAdlCbSAKO0zlORZQL6jM9AP8+QTZ7q7SSXcwlgvF13E3rgNgF967ljdx/9vu/93pOvv24Kyx4BlIVDOU8df8kOLJpKroFPOOcWTHkgdesN8BNRIreUFhJMw9BzrqWkGw7zixyUQSK7ZvPQNLeOSAjUHXtmHlyDDMpQIrgnZnLLGQo85g9bZXU9N7gHbivQQ+WPfuITrj76RDEP7qC2Cd82lw3oLVI1IOpraAZucZq/oJkQq6pBaA4FyTjgqRWqmhWHt0Nv5smeFSUIYKIA6KJVzth6ChqbyA4S0+PoesLjYwohjqNn27oYf9fd+B64rUBPQfmWt7z1B37gB14/ebJ95kmHwi5oujYa2BJvoBEEcXlzXOAnnyOAcP5Nls+DbVO2FPqDewNCWH7Yeh/xJju+pWzgz4pWnLIaoBtBuLYM6yEqSj6KZjFx4192t8TWA7cV6DUJE/6Zn/kZc1kWMuGym7vyGtaBNVgs1aFZJmWOEKnCr0u10gwKILth1NNGKp7i3AX7aDndEdQBPBwc6gkzhX5DhLVWpNLGCgTDQ1+jRFkWwUB8FgJauSm16254D9xWoK+ZYs+xY8eeeM8T586dhVdOl0JeQT3rROaU5rVghx+XIB5FuyGiZqFIojMyiIlgCt/h65xhAHAb1bhGfhE9ahuSSzi+tMJRAKnepALijRlKL6xnVl2STxSdCKFp62/46+4WmB643UDfcP9DH/3opYvnrU0Ba4Bf1sIFysJvKRPD7nstN2G6EXMC41qdKnqIqpLS0kWCRjCuvMWwQZnFGWVlDsmRLW7vCGNwb6AXB+g9MF44X9Og0aiFZKQmxH0lTk/PdAF4s3rg9tzK4Ax7SDMZrR1R0d5AW+DZ41SPaGDaX8ExE9zW+2anjUjaLQ7dmHS4eLSQq9aXJPE0UI+DXqNEeDnKgGmSDGppcSgrletRK6Uj+ddkl/TlG4M365V3y709QW83ty/O+nLlxORUe8cQSrDBxbFaWIzQX5bDngbaHWgWkkMbnTmr+xolnG+ccQKEG/HAsQAop6fPzDbzgRASMvCo5KKODlTOSbrpDAVKtyjru5qbYd3fG90Dtyfop2dmTCe3GC2kEqN98Ngkcmm5x8cTShuZmSVEbnJiCG6mBGHkJdVkBNiMA9p4d9Q7eLY/WI/SvRabjCpoSUwFSSJOss2hUaIlicgN+a04Q5D13pa5q0DXrrthPXBbyfRbvXbh4oXZuVlaS7YJBVPY7ZmZmZudnWONwwiA2a8TPiAbKLH/JuGYgIYY6vT4WGgG03HSNo4OvQ3xriSbSo7l59AbLNwf+mlZJVXoJbJNS1K4F2HDFNZM+n/8H//5q6++Cu7iVCHdy43rgdsT9GfPniU0AxyeSnWTmWRcEC0QPybqNCFEKBRybTVKxzcgNpbc1C8CM/+MPieTXY8asl3bbb0uwQF9fgrK2WpYFp1FS5kJKJ0T+Nxzz33tha998pOfbMVV8u7lxvXA7Qn69tlNECzZHcPObBUWYRfcncCB1/JUSBg2jy5vnuaXFj3wc+BeYO1kIlpzwptzK2cxeeQjYSMhMpXhArGIJkOPnGT81a8+JZrIv/O7v/Piiy/y3Li33S2peuA2Az1RIdLClcuX5mZnePF7qIJCTLo8abTbtn7UMIpbN+wSh8okLJkUFJ0TwvhgadW3d4guOd0yfySf9re84tM6zroJeaARfL/JPMiIyrO61ywip6Pxuzpe8+WXX3LubBYKBoZee+3kL/2rfy1tq1LF715uRA/cZqAPfvw/sH9fgRO287UzV2ClvAFuIANb1w7QQb4+zuMaMZxNTW0aDO8mrkA5VbtPTC35jlW0785J8NfBfYzyOeKTmWtoqclRjczkQNOP+0O1CszPm1HMOHGWZkmVfI3Ko0996td+9/c+eyPec7eMbT1w+4Fe4zbe+9737dq1C6zx1KAytgC5QjajF84eq3ZIpTgw6upRgB6qMDJkxpmDbhqKa9Mh7DcRRQGBetQ40dVUILhntuB2E/E6FvnFwsxI48+WLgfql840ZghGFZFnZmf/8T/+xyxA+bvuhvXAbQn6HlL7rl27KS7hGwoBul0xY/oc9DA1NQX0wCdEX7uCeynyc6q9+WtmvELzP8fxIZjmCuuRVTisusSWwnfIpGl7mkIm2cqTbhM9yE8mNeCswTvXKI04/+STT/7yL/+y2xv2yrsF3WagB538zc8v2E1iFxW0YfZ1duRI8fK+ubk5s1t+8DWtdMWhA9hStjSYb0J6Az+uL8Ri15HYG3lAMSopMSosHPSLNFKw4aTJV0YW1CII4JXFGMEtrCtuZHSYIYK/iYlxdVP2P/3Ff/bsc893sXjDeuD2W5wKyyTD33fffbYIElDQQCC6QXMSNTzwtdltoF9cFxCJP3itpyKDZvW+qH2safBy/F4ejn/yNCxZXuX8YP8ZRsLPIxZBvAj1OLRXpyFIvm4iQNuJ04fY1pEBmcpw0b9v3z6bGF9++eW///f/wS/8wn9lxbjl3L1e1x64zTh9p6/oJHHrhcWcrXf6tK3iGC5Dy6Zrj3ghXvC7Kc2TQ4q7k1giuoiAB6MI4WDqoG/WlALlthVTNMkFBt0l5cjT0wwbpa3nF9J0lxLKx60xBwG4JWIpgiYHZXK/+Zu/+a9+6V+JIK1r113XHrg9QQ9MUOWLVGBuayxeDr179+7Zu3evKaxNVQR6mGuobXDXyzh0iekR8SWB6oZsnB1kgRjEeZrfbdPwJ2ENJfAqlavb5q9yE8sUQlluA/qVZf6x8XFY53FM1fHjx2X2P/wP/68vffGLIl/X993NXA/cXl0c+SKv9cqVK6QI53AvzC9MTU7OzsxAqHA4g/vJqQlWCAyHwToH0GcQCFDzT6p8Xy3aHjIS7AqJJX2gXOCWS0eAiSkCvwcVzUmAYfmALwrs+v6CtWBhnhpcnIa5urYytWtSFU2vx8B/bHh8YpRwf/CuAydOHL946fLf/C/+1oULFyQPFXXddeuB2wv0m91ktsrQBnBJF/TqZ8+dI8GjBOYG+/fvP3ToLgKGD4m7ksZL8sFzy9i9r49o1BF28unZyEVogyeC+zaHIJo2ZgvluHVy2VRoVlqXdVJRNKTjY5T9BgzDBQ2ScIQhCU2Smtx3/73Hjh77/Oe/8Au/8LdRS8tnszXd32vcA7ffRDYdxPaGbGOOiHeTc1544YVjx47DmXD4O3bs7smpSSeFwDAI2kFi/umpWxNTpNIcvxD4c4VOEfi5rZgEfTExexKMCEC8FQc9yK8dbgPiJCIS18LConPQ9u7eYzgaHRltkZGlevb0jL/twbfR9f+bf/PJEyfu/fmf/7lr/J672W3rgdsT9JcvX3bCPDBduniZwp7mBNzvuusuepunnnpqfn72/gfut6uQZH/u/Dni/vzcApgaDfQMT5sG8MM03j0/vwy1oO8KqcIbMZD5iYewjr0LaZqc9ojf9ACFtJkDkmDf/8xTTxttiFgz09N3HTw4O5+vistQDvIU4a1veysq/Uf/6B/t27f3z/7ZHxPYddejB25P0GOfgOszgbgz8fjuu488/fTTe/fuBj6Ky2eeee6FF16cnJwiV1sNXZiH9kX79xaX5gnwUAi4WDwkU87zDQ+308gy0W0ALcUMCb4TGW0I55IOqayuOhccLx/2dbdSGbG9R3tOMqb7HBufOHOWuJUjRkSHeyJ+yGljff+BfQ+//WGRf+G/+duTuyY/+gM/KALKaROVzBi67lr0wO0J+mCljkodH58g3+PxdOGnT528++gxMrp+IzefPXuOrgU/xpV9GoTRQZ022Tq118l7jakHc3G5Bn7bXHbMlp5HHB4R2qQ2og6s95GIfM4qdLK0SGMDur0XL146cOCg2yvTV6BcREWjGexfDmQtIv77P/D+r371q3/1r/5Vs4Uf/qEfTuFdd0174LYDfUEUp4ckaB4dGSfM7Dp44P7773/mmacPH7kbtmAULrF5XjFNViHSHzkl9jaldWzLVS1ywzTstp7fDnGam054rNOywqV85YJvTljo6V1cWlxZMzdI/gI9QkuMzy5cuDgxOeb04pL+1w04vhgnggGK0OV8QjX8a3/9r7NL+9Ef/dFQlLqWAuqavv07NLPbDvT1Hs+dO1e47wgPkPT2t7/9xRdfEE6Ub8gr/jq1e88REwASP2n7yhWmlJHj8XRYBDuZBcfFyCEv4NvUJ8J6aSyjsmyhiVoJ5E9oEdGkYn5+kcmB8QSgyzxz5LXXXlvwjfOFBepLWDcQSYTTm1jDPUFLcmqlRx99VH5/42/8DVn9+I//eBtqxOy6774Hbi+VZbF5aLNdEMNm+QuYtDc+nnbo8OGHH3nk1ddetkmwzuzONkISzquvvEaOZ6gzMjyaczwKwKXEjJ1ZNPRZp2KfjNWuxWLSQd1R25OFhAM86SWYZmvjvsGegA7iNDoLiz5cFYPk6P7Xc1r30vLizOw0jdHy8qJvGhqMMH6pEJzr+fPnTbVRpuM4za0fefgdj77zsf/bf/43/87f+bus1bzsNg347t/6HZ7DbcjpoerM2TPLS0AGxMEmxnnmzKmHH3nowsVzzz///BNPvG9meoYmEcRx3PPnL5T43osxO0Ch4R4sALzhGBw5mViPNYDwB+8BYLO9iY2+yE1d46lbDwlIJgJ2mRDcDRozM/PWYt/8ljfj5QzrrZ21rNQPaydryVO0qknPufPnX33t9ePH7/ngBz9I6Pq7f+//+eprr/61/+SvWmS4w/F6TZp/e3H66hLyA5YZTOZz4UPj46MHDux7/eRr7Fw+/OE/QVXy1a98Bbww44ZpGAVEfshr9gseNejLhJwDlADKw4eHj46NTe3axY4AFdTBgL4u2NlFLhMJJQf9MrTMflyOYHPu7DnCuu/doquiqExw5dyUoRIiP2yeR505SS5ePH/uwpnv/b4Pvetd7/jkv/03P//zP//Zz3Z2nDTauCYIuAMz6Qzot0fLAQ7afulf/Yt/8k9/kRLcQr/lfYudJo7MjK9cmT569JiWXr585eWXXgFBMJYE8sjNcA9JksMiiDfZWrg4DWFiNgQzpEE5KcvilLRik+B7e639ioASqGki4dTHDD1TuuzHx8eO33OclYHcPLVGNjKUdVnzAdSpDkiFfK9WMhEuSUUbf+yxx216+f3P/cHvfOZ3pPqLf/Ev/uRP/iT6vD1e2U1pxW0G+jDmv/Vf/pe/+elPW0SyP+qRtz/smzywC22Ug0YA2Dp8+MiCCebS0plTZ+AM/8ZiS+BeciXVO+IPx625Y9ZfJQfKEkPC9REGzFl1YhBfS7EZNCjmI7RkU+IQgpEk+sr+vpXF2FQ6MocZgs1aTD6HR0ZMM1AKVT5Ymzl48QaSev1WBvoZ3yMnc4hFx+MMDBy9+26qp127d8v/k//2kxcvXHjf+97/V/7KX3nkYRp9sXK42k2BzrUsNKqAb+muaRNvK9Drtueee/Y//7//zRdfehHojx09+h9++D+wSQ/zpp8BOFBzUIJZI3PJvXv3sZ+nPwFrmhPAnZ2bMVU9f+ECaQWyIVhCDojljAzIN9gwnEkiAtwXV97A+slFFD8llmSUaFKKaMzwfWGqb2jAdw8X5ubYVCIY4bJVQ4Zt8Zgg0NkP9CNFI0RbizLkoDNPmTco68iRI4+842EJf+3Xfs2isqHmz/+5P/czP/uzMWq4DVwX9F5iQ5WX/e2+0F/6pX/5//4n//T8hfOjo+P33HP8T/7JH8ZWYZEkjZ2fOnWKVA2U1JQYPx26R4TpknBoWjpHG1iKFWh9igEm+CIVNWHLgIM3IQeFyISfMyEGTatgo6O+TJg9h5xUUlJM0mg6WOryzJXxiQmbFLF2LVI08+aFhaWCfuR+gVQ6VPvoyjqu8QramWkKtxc9T4cGd+/d9di7HlM0Ev30pz+tLW9729t+7n//cx/5E9/fdtyKdms5vfcdvOLvvo07VHsDDd9B20jPT37+ySuXL+tN7JPaG0eEEiTkSlwmjvNzjAKAFeiJ47YzgTX7RxINJaRZKYPLCB7YMLmE3gYcY24ZRq5WEMkJ8cJkiy6h1vEeoDs6MEKyR0IGE1I+Hj81PiECk4SY8o+NoQQSS40PJPshUlDRSIjEsQmLizIhO8XM05xgfXaen8CFApV75vSZ3/7t37YjTFYf//jHP/e5zz3zzDN//a//tf/vJz/5Ez/5k+997xMGh++g03ZCkhvL6Ht2KOi/szfx3PPPvfLqqxgkuZy1+v59e0q7QjIh6wPYAmLAZe3zttJ55TLtZEfzjcYK90Orq4tWZqE5VBexfgNC4ZupDCjT4hw+cvjM6XN8oGzDuWhwLLYk7O+XLs2QcyD2yOGjygPlnjr9zzap4cFha1IWoTRNEhydUFP2lR0Vp7z27t6rGmMTE+rJkf5ZHStrdBz010cXhk0pvvb88+d37961Z8+73/2YA0WYV/zGb336d37vcx/60Ad+6qd+6p2PvlP+mZew6f/2x0mpboyz2EHHG61wx1WNM4vaCsl8JeNAI4htwZtJvvPf2wr0X/7KV86dOw/iQ0SEocEjdx8h5ASgvf0gRp1ItuFMSi9dumwKC82e4rsNZK4F99wK95SnXYEbsEk7IgPi/HyGDnGQkBDPmpDDT+wxpIS0VlZanByqlu2C2X4of2zbU5K9cYCnVDQTIk9OjCtrbGycuYFo3O7dEaLkJh/VHh8bR0VsM5V7iRZodfnQoSN3HTp07733fflLX/l3/9u/+43f/PWPfvSHP/HxTzz00MPD+Xz0reqy6z6GG9cU6ds64/YBPQA98/TToAwZdOXmrwcPHsoK0UYsavp7B/iJLqLNzS1cOH9Bv2KrYA2y0MbpFretc9zCVvOLwB8CiIlYPxQaK6qUrMKKIx/xXaETiMFaNH5QBncRcPSw8PER1TPHcFvjwOrE5Dj0i0khNIY+VdRosrpCNSmVEolnMuTZtXsXwUnk0blRkhvxSYlnzpyemMgc+rHHHtu3f7cTYX/lV37lU5/61Pve+4Ef+ugPPv74Y8Q5+bSmaQX/dXXq+UciVZzUIRy8mH2nQh2AB+wa7s+AW4+uB/JvH9Cb3tHMAA3OC1XUHRBio5Lji20owbWpBc1mqWsINnPzc+31NGSDBZi6Cmw4znvp7W2zVaBs/c9i0vyUn8yCAIR7hYX+UAgPlDeWz8M1cpKPClBZmj2rmFIyYViB7CnX2m61AbugTJqRz2j/mKs4buWJWkBfobakGAKMD6GNNsddWWnjlc1YNtref999L770stNh/9d/979+9rO/d9+9937P93zgB3/woyyrq/478bJJA29k6lBf9h45Nqhq/cbH321DrjXoWx2/Ra2ubfW3FcTQAKALext79+zbvWs3Hre0PO9r9XbEXro0h7tfvHB5+gobr3lIkhSwQBCweAq7EWag39NGAPzYthcDfwKzozAHFQe1xCU6SjtgzR5pXUycB+zA6oupsFQcrKMKuMfsjTGNfhZ6lxgw27biq5rmCZkAhNhUYHCjP303MjiC20soPie5irWqhggXFqwH0xHt3renvzdnKxgxzDbmF2cd2DA8OvrWN7/ZzsQvfeUrF85d/OznLn7hi1/+X/7Nv/3+7//I2972VnNiA4i9uYcPH1a6PL/ebX933/I1Balv5MBb0beDuLHt7WL6JiM3ZoaPU1q1sQG0NwtPTvJusvw35vD1df6O7q816Lda/x3V5rtJhNNDAJTAGxHi0KHDboki1DNQZYe49alahIrRQZNA4AnmvCchmKu0KhABfGgIBxUO+ji3OI2Re4qfC+S8FyYJhUiiP0BG+OmJFUJenwiNkBotKa6R2aLJrMPxh4eF7N23W6BoiqNRFSiVslRGYCMz5VJGzW6KTGrIIQkkrRHmHTmOqr/f8VHqOkPbMzZqALnn2D27p3ZpLy7wtReeffUfv3Tk8JFjx07I3L5gawX33XfvocNs8A7t2bNXQYinsPZtdH9h1EW6TW8QvCXepBdaVyRKuc7TzRQtMLQD2ptxtmK2bHVrC7m212sN+j9e7Vp3XMMmgYt3nG6tkwi8SBKIujSwzszMnj13Fs6AHs54GvsU36205TBUNepFKo0SGl47r6qeAZynMNoCWWXi/WQebw2B4PZ+FSphRPtSyfOTarBYMSQXODwSQzfhjZHLjUeFXVu24qg28iDMaBR6kFDVDBeqhxolIaw791g9EYCZivwpWUXAgRcXlvbv3W8dwJijVtYWlHX6zJnLl2dEY0Eh/OSpUyQra2pTU7vwBaCfmpxwRIqZgzDRPLAuQX1EeVWg7nD2hsF0U150545Pv6cTtsLyNL3p2hoV3i3OJlXE03DdybglbqnqQcs7AY0ott1X4Hdzucagbx30xgqlL9Lictey7tuKwdJY5OJ5XqH5Z3uRsALT09NOAJkhVZv8edNC9Lg6NaQLkY17ty0/qaBESF4tqJWwwSPQIzocj6DNJCE3UY2LKbd6w7Guz8vWZKQlQ5IMbY+5hFQxUBsavrh6icdmRYMPYWxkmA0OVelq8qrzkyWUl0wsIYc+11Pu3NzsXAlOaENVL1686FBkiHSsoNzyMS0C2Moy2MK0M31osWIZvW7P+2qRR1gA+sm6xMpKphY56U2nXSCx5TiUCGKRxww41ER2NvKaS6g8IvTMVau9SXN18UskG0B7SGb37j2OEZKnJqQzkX325Wy+8bYbIcFx+oFrBJNf4WJ2HrbnHbCI1smkEz3Jr4n7rkG/rUJXa56m5C7vrgg87So4YIYJ3+ySrSSth7ZuW9u+CYW8MUYbFkWbX1yYnV/w+uxnHR62lTUsHBoUCiL45fzCPOTlyO7CsyqQDapWVbGqkpgtvnfMzyJYBNofLHyQ0TybHIYxPixeS1hpgseFeKmCf4rR1fIQbyIFOfpAEaadOdIDnjQKZKFqackOqUU7A/HUvrv6zYKVtbqxip7oM1UZ1k27fe9TA9nxzIvdsechQ8Fb5iHLq6YlBHvMvl97s0O9fwALh83BXiG9C+HxUTopV5513WAeZ07jBcjG8CJcY/WVGjZk92xM9/SeS16ZbTPFi6zFmUjUqwsQgb7FdzXsGCggnhErwenuo0esHpC+Wk+qBg8nZquAK3/Lq6CRHQkiCG8ufboFkhpDW9rN59fg97sG/besQxvyXFtL0u7v3m3lcrWjegz6+BxW5B0UZPN1YlwKenA+PN5yphB6yDLw0snBDnYPq5HF815kp3uDhgZTIsWqKHVua0b5enNAwM4MY+7tySS4kYdUnvI3ZIOkCLKTPyzRk1ZxKRptwCsARShKHdZfeeUVIgWH71q6gvVirH0GK+xW5DD70guJzCMXoG+wcKszbFMxDvX3kEPg0Qdxe/ky36ixS5XUTc+kbeXkA+Wt5lra2uW1eJhWVJyQbG1aRxjSJrzIeuvK0xLycC0O0jBCONaBhdzjjz3OqhTLS/e+0bWELawVuv154XyTx29/cO381wX0hfJgs6GJp7UzsMr/q9XfiilCQosfJH7R+huiVqLN57mpOJ289Dso6HrvuAEL1nF6sz3rUBx5gL/xy0Jhvk1CT2L5Vr8HPRFUMhuVsxCVJCLX5u510cJLCxC4d/tjr4BLilYgCVxaQh58MXAOLZHvw+ZjhLy+KqWBw7cNCyd99I5Vn0XfTZFqaHiUJoZmsyDqE3EjEEl+kNpIxR8RKPAasP8E/DESZFsNR0WKt4k2NjzJvCctgnsdIsQ1YfUW2lVuAsmE2wPFSVUrh1S/+kE3Vlh7kqxUtUL8xrVUPFotAwtwamudmJXEQw899NEf/Oj+ffuWs/UsFfg6J1ULkWHzV87V/+0tlKzYAr8u7Xdze41B3wFrOixA9+Jb36WKaWA1si6t0vG2+U2737xu7wJhmzTAE5Q3pHvrHchb/Rkb9z70u5GXBIIA4GlmxjdIli9fvuQdR7PNeHIDuBnW5CBisJCdt6tP5VRsV3WSZV4tkT1ncOOugoA2bwWDdKwICBOTvODiYvXZZMk2mX1qa/OKathSWBYNKUhguWKgYf9wmC2EEVRWZwcHSAij6zlXGSmOjkXqwC+NWmShxmsNZeyiVWClR+vWDGtoEj7rTHxtCRANURFzgDKcN3DnoE0b1UH5W8jjR0ItDr9orgF6aKfDZUkd6Ye6DdXmTyfosWTVUpQnbS/o5yJcRHK/KZbFsi9/+cs/+7M/+9a3vLVqm/qInJ9K3zx147L5IK+gBP2rj6+x7xqDPuhJH+oavZcWZpJSfVM+vF/HeOvZOSGczlrkxPRTkcWt6J27pLoa0hc56apTVuQTjI1wzIZx+vIMTg/5MD49fdk78nFB0b1gwcqgZV+Hk1W7tkkFZGmaR2JxG4IVn6o3iPDU0dupRuKRFno2RobkvJ7lqQhFa70pPMSw/Zo3byq8Ed2/TCr/rFulsZE3UJdiQir9w9Q+awuLWTqbW5gfnJ6x60XyoZlIyfNzr7T1WnIOsSfzy/FxQxZ7BJnqOxMItEwPJFtYX+3JkLW6gv07d5z4ZNDLpyi2S8y5rU9UpGIqUDKPuimUa93emtMIVWD6ob3RekXuGt2as2iQTNpTlTCaVIa5YvpYPnKV/Bd/8RdtfLn/vvvhXvxW9NZ1M38P6pF7ZdQfT4vv9g2vveJ8N5drDPqtqrTW5bZQG3QEv532tJ4N5jf7VBdWwzwpoqlwr7ZwmGwqvPNsM0oFg+/GBq3bm9705tdeex0XN3mzYOOMDWxRTCwfg4T7qkIKaeJHX86ej2uVaZ7mbzzPK29JpPEi2w2eWRJUNDzygTu8FBAkb7gh/cM4fzJJ9knrGmgWhnhky1/VC/m5Bd/BATZxkXyg3PSDAt7WcUoesr51pUj9kxOg34QcBEBY809MNSkIRmGiF92YPaJ0FqSy9ifPTkP4yXJ4fJk2CG+PVCkV3ewKkfldZaVDkn3EvzSBugmnD95rMi2OHFrKpEocj1jgGWzyARirb5r2z//5P/9Lf+kvaUBivtF1ykrR6ZZUuAPxiJub/vJdu8v1Aj20qX97661h6lyYr2ZVA/SYIE8b1CpaZ2zt0IAeaGlag0M/wSgnoBKud9hsX98TTzzx5JO/f/nytLkeI8qLFx3XmmVXggGg1GtLQd5E83uF/CLwcDzeH49sK2dvofweoCvMnnhuelo2OTIUKHLqsVkfXgmBogXwC4F4ty1n5bY4FaHecQbGOFmhAQNUqx5dSakhBx3oIJvSe16enBoL4tbWCPqiLczND8/HsAdJI+yV2CtQTqUhRKYAsFWmaqUCynBtLg2v6rltNNma3K4CZdL6xLVcJv2K1nC3KoyzbEZmkq24jYnxsVj5DQ6aBtARebFiGpeU9fu///uf+tSnfvTjn8hSRufF1RssDi4fdY1kVLd+eDxOAJeIWzfl/64v1wv0KlaAuFpdPdVq29oTqvAw/1twITrvKo6uIREqixbiSs5uWaSXyuXFJpO+2dnp/+l/+uV9+/cssJFfzeEfDusLUjeoYoKmVozXqRpeg2u4XSHYVbabOG7ydyG5QN8wIb7X0pgeiMqhVaE1Kk+r4h4Btpdd0m1HYMhL3axwi6/ulWFK9j8zh/jCIDunVDHxX7WfcISuUmS8XyNEoL4sKd8Cau/a0Kp1LlMVuinSXYSdETRJxbloHpIcw//zD1Om1gmswobs5dJWq9R5ATpEhunW3hBPdSsGjyD1alDbrvUyU0WVaSHRYOaQw6ay3G2hl8rStgHrCdPTziU3fUpkzWT9gbI+//nPv+n+Bx5+5OGM3h4km85LzE3A0v7yLE+FXTf3XYN+W91481IrBAwQdfo5IVcjAbMmtR7hweqTqjW481Jkklbna071kloGkC3Dyq8zGoBKpe2lpPj1X//UpYunT5+7YKJHb7bWZyCGgvbJy4CfNN/b01mH8qZlHZxVx6sMB9x5q5TykfYhJpoIdNZ6XkSaHOwPEwTsDONr61YF8MlChgy1YZWsHjCJB8FRgUeNczWH4vrKSIRUHvfNZLQ1qtYD1Khmz/lQzzAAqSreSe/qj+2EblkaXJ6bmScqDI0kc9XatcdXhrQ4jihn9655NmV8Zhz9gfJgcfOo76vVYQSZBhsG6+WE4A0QkWQ444oLWlQtHSFJTehZFnVoADvvJUT2946Mjhhz7GjxESGzjjamqYOdmQvzRtrLxC9f4ZWPybhJ9r/+F/+fF7722A9/7GN6xOto3dKuBZptINn+7Grw9tDvyv+Gsr+rnCpxoSi+q6S6DfHCdWtF7FyC820uz4rqi+4b+oN/gR068UI2U4Buvaakd2bT4MAwpcap1087P2/f/gN6H+w9MihziZRaJQkkyY0fp0pgcdt6rnyBxuvoblLV4kotCfARcKRFTjIXn9+j5FN1yptT+ZBS1XlTQtgqNxlWD0gIIsYf+hkhkeCjdKzkqUzVMJJzuosTIXXODkSHmrSZ+jKSAX01CasmqpU9AiA6RZAeiOJHUTZAVt2cSuJFp1CVJwulGlUVGTK1DnEAu+lICXhVR0Rt2y5OEcljZNAe9n6Lx+YSRpXde3YfOOgMnn10ZW7lpHNUgw5UVZ0c6rvQu6b22KksW+Y9+/bs+b3P/PZ7H3v35z732Xe95z3WryS5ie4ag357SwqrUBBYbIV7hfx5vcKCsTzpgMYzSCv2I7Ae1jhQcfLyEzPx87Dgy9fSTk7u0q3vfs/jPRuff/XVU46t3Ls/plT9FmoK08AhZnNoIJ682yQP8OtlF69SAyeZDQA1+LbqyQFWXCFPfGAtAoghsSSczOEzuKzBQ9bK1EJJUlBVessjvgJlKBMeaZu4FdBHDo4CPsWkXjKInlRWlCHWxdIKpqNLi3PzWX7yiIkR0wbanqYb6R/sp9Fk7aAmxBsHCqIr8VUcp4dLAjdhyTwH6Zimmi0oKUWXMqdaYyyOBOjMEvE1Cs5RA4YuUEF6VZ6OEhKZR2cK1BWZaE+Ms2C1JKJdLFqlNQIgns9/4cmJ4d4De/e8dPLkpcuXjv0hoC8e03nBcrh+7nqBvtU9102c/pFtSO8ntkT1m2X/evFSFnoqvLIJCwyv7UTu6WFTObew9Ad/8HlnVr797Y9+6StffeHFF146dQogvAxvS9cTbaVr9YmouxFt5ZargolUmHfKyRaGgNbzIE+quNrYQBjgxawD9XqcJ0lVMStHzyP/fN1QUBFcKnlnlIBjKdpVFpVt4/GN7jJuqDbZncAB4rNWi6rh/YPkN3Y1A8uLywQJ5jO4OxCbcUO4PGFRj2ZNS7y1tbGx4YnxCfHNMgl+61npCiNIP2DpgzFfs4btgPFDdx2c2jWFqasqJak4xhhFkFJWV2fZQ4hmWQ8lFPGvIwS7txwrxDku7vKly2fPnXfCHHp46cUXLl68cPyeox//2A89+fkv9tjOdvhweq3ctu7fDLohv9ce9K0lXhUXJFQzoDNAdSdcUCfS1SbqXzdea1K1tBDYnncQDxmSJgKXbBp8At+NlbWVw3cfuXCeEfkfPPDAfe965zseevBtuM4XvvD5r73wAmMtKCcYMOkqQgqGpSJQR+VWdXUhfYJxmdyUEiZ0UlxQjeox+EFnkQDIsqTP5ERN8rQqFb9Vg0JSG0LaowrJNLhKc9eRj1sLcl/1qcyrH1BLBDpCllY77ztqLsp5gpUVr4GIZVTy8jMqJc+FnkW4ZZaGi7uFc9VhWweUSDRDFCFqfnl4YBmq52Zn3ZoXs+0J8fZGoQ7xBHTxeQwnRPrlJTOEhfPnLslKnnh5X98MPw4imt2OuhRdseJGqs88/RwG7zhaM2/rDlamzp87Z6nkxD3H3/P4Ow8cPPirv/E7IPAn/sSHJ8cntStIKKfhfot5da5uPWsd2uJc8+u1B/3XVXGz9gFHPznU4zCXjmvEsHmX33oW+SANr0sDRHABGduiuk38IDLKeD05NMjScOX5518+f/7KXQcPGaDf+eijH3j/++2o8hqcWnzhwvmZ6SuWM0uzoQyML/bocd6+2VmogL4wFixc7JPrFYUTw1+PFaUh2/Z8pk0w6KRRAWhiqQxwNE81pFNZge2Wp9VZblsxlbyVZLs/pN/qUKmQdgYWFj+k/5gEqUqyBXy5iRgSKelfJpUua0/Q3yJIztczuzG6OkIFJKYKcI6tNT7wSNIWNKTiYZfaBDCWP538a0TyVI8RexSrChaFzZvOrl1wQNzCwhymfunSBcsLaO/o8btOnHjv0buPfu1rL37xS181SvjiI2kHybR3q/KcmuQnqN9ESu474WpVd9f4cn1BX1X+xnoL6QDi61oTgbYx+3oO0GGoDdx6h2dbugoXITTEX4YG9pfSV9PTA/eMAxGwbRD34kV45O2PwkaOkentW1xw2tkFA/+ZM2fV4dLFi8t1ipm0ComiphBvVRMcCh42QA1nAX49a0jhkLBVL6thQrRUohi2DDuBm7PezqvdbK1ozSvnNGAzFf+WQ2+A1SKILEGQifMnScZNxQkXQVnJrVLyA2urMA+ZW+YCO1NhKho6mUj7luoQWyzPklHamFtJclNNbsQjLX5QIag7/KGVSEDiN3E9eer12RlbMq+YYDPQf/Btb8nMdd/+xeUle5G/9OWnMngOwryjHFbYVkhvip4Kl5NzazJPC0nLqjVbIZ2o1+7n+oL+G+t5tWXtWRF4tTPyj+bmWmBPd9QL0SnVfr/ebIJ5uK0f3QXWQmLUFXYM1Rv9g33Rz68sUSlwIs/P0S3Y+5dOpXOYMBCMjr39kUfsHDLiY0KMdrwYzN8RsNAv+bnzF711ybHTfCBqIYf1+acsswWTBBoP94Z7Q758a4ggegRHAUdtiqt6pp1msJU0bYiQVK3g51pblCXnzVtSTYgLFhMuRrCeMVDOwsTktFD85udxC4utN0Tj0VBQllgmzD3XBtdI21JkDKuFrUwMDFzsGsgwsR5VT1OC2A/zeKSn8XeaysiHRc8QP79gyJyWJ/HG1vb3vPtDu3ftqq51TtbixYt2IS9QmxoVrRkgGt9bef/73/uR7/+Br1ufUolW/9Yd6blqjMq3Rl2P640G/dbr2daYvMh604xyeArNwWYkooaGitx6Ix3biZPQTi9hXRHA+5zrhJGzWCR05iTUzNlWlhmQZ5i2LDVIYRzysLOEItk781K9aawPrFnlOFEmDLK+GO5c4ntP3OPEqJMnTyEJGXHqY48pvflLL71kGWjX1C4hJnC0IgZ+Bp2LtStFfqpUSCJqQy9XsrBfPJWkBM7VII1JM6ul5d1qso6IRFXdUAMJ6bBGBtkm/qY+SgU4aV1F2OLWLYSs0gCksWIxXNBVeZRlj2TYoqFRz1XRLQEGAfCnf/MBlXyJSDcuSbzoaJNlZ7+Rst7x8CMogYKSLsgOrHMXzmsGzkDSsTQc3X8mRdYuNg4eOvSJH/34Rz/6UcvM8vTSKuf8pObtPmHbfHV7nS43B/Rf1xgdnQbDQbU6ryG9EcYff7kWR7SEl+NJulpDYT/s/VEytGd5v+VaTI+ADRoAseDdYagC3cqRJyzNZqLZiLBzPUsbl6aTR0/PF7/8tFJkCB5GcE9ffPEl4bYguUrHGJgucP/4xJG777acRGCAA1ITK3mM37jx0ssvg8v0zNzlKzPLvtOQI457TCKpxVGXIQLGqskunX6okq9SQhPSt9reQCyO+OrTUrWWutUWfoHVAVG2tkctMrptESBbCL/JqAil8NnMSdr1NfiOyiiyUAwfdu2akNPjj73Pbp39B/ZfvjKNWdAQvH7ylILOX7ikaP2ll6ynGXTtP0E2D9x/34c//OHv/b7v3bN3T/V9U3xd5eKbRbY63qBrZ2C9QaV9O8W0F08v3ViapDqVHpwn5JDfeKjVHaT03/63/81TT32F/GIxUkQzLeKL8diLMCdjlIvhYUKNVdfryXAfWOdnCzdgLMcOwwnki/jyYor8qkShXMaj8qhVVo0EYGvYOYzg4XAJVTbRQYz9RKBw6NBBQLn78F3W6k+dOXvq1Eki7/zs3EWHT1GMkDZ6B4bYFQxnE4wSg+zANlVw23qjXRtQVFsFEqXQ365ChPO3R5WwE6thXbY8XCuCh/yjc9IIashyaq5fHA0L3IY4g+S9993LuI09n9qYLKW9lY8eloM8Ua5OMlyaJgk5eNfBhx5+6F3vete995yIekf9S2rySG2uNtDNpit+/wauv/nk2v/uXNC3tnaQFSRGn1hzoLzmuo0HJoD+f/mf/+df/tf/msUHecagintZKtG5GLsdslTI3hzQe68w0Zh9ZRJMBTwlVsFBmFVIq2grhdZNo4IgT5SQmwf1qxaNCFOfRgdFH+4ivieUip0orRCzUkk3eswlSDoTkxPWd9irEKnpue3vog75ype/mq8OxrIta8kgYjxAD1imtkR5U46/tTy5pytKXImnU7dECKmoQ0JLNsqjFl8PVCd0dhIKhN39+/eWUDcKw0eP3n3uHBtVa7cLBHfyinoYEA1KNiWa0KdWA1W9sthTCamsWpmn3nfvfY7ZOnL0iK3lDHJy0IKDbbOd7SqZqV7rPZ6qVC43EvTXWLypd5A3sb09Ww37DjztVTVsEYKzuTKwDD9jKGD10Usltz/xxHtffPHFz3zmd3FKgkp1a2QlapgY3RT/a1VqNdTpYCiH0vxBM72MoRxWOgUWgvIiaPYllyp4EyEo6qHNDpiKyQkp3upBlDuZ5oaUCqG1qkAmTqaDgaTIyI5VpEOnXn39NffYKhHHMQT2A9xzz7Hh4Qfuve+E1ARl57NC7Zmzp3FQlpRkDfSjFYoWnplCzIFSMdk3caW08qHqgF6oxx4VX+enj8fUHYOFFg7s348LHDiwn6BugEFfxsM683CVkhEFIjX7xaS1j0UR2eDYxP1sRtfSZC4VyYdBAgUxjY0ZkXUAQMdbVpaoBLTZ1KHXbhlzBavjqU85Hnl6ia5x6a2E+X8D3HXh9NqlIdej9tXVMs6vN2eu5M+qDwULXm6fzqd+9VOvn3wdawl3yffPVikTbAknfVqJ9AcR+lr/1hyL6ZgJnLfpJUb29aTVXAEQvNWItMgrKSB5BArGkPmFOWucOFmeZp2f6/CwDCZlPJOfeLIyxbPVJ8QXVONWJQPSzMSxXmA1twY3orZzBmyTHYJUgEYTNOEQ5nNUFkcNFFNT4ydfP6l1UopFTS7QyW2gduLEPTGoJmgZwjY2CCdWjky+Wcs899zzhherSIAoDoeKdAizS5NmZKz5+LqG6xP1diuCCuDUuIn68JOInBciH/XhV0NHcGaPlx3iqbWqj2P8hMyQYhOE6nif3CY/2KgfuTeYNBax1TvX2XNdQH+d67wt+wIjwFKkOCEDI1x0ouWzzzJkZYYwc2VmE3BE1nVKSGNCk1wDsvriXzZwR7Eoj2hmGrMpT6cU/gaLPCrQtxcl+bnz0fFzQoL3AgdYtKFGSN6pg/hyXFQU7BUrU2dTi04FagHB8CBtABaSCTXW7JGU3eHZ6gyExBx4asTsqCbjGVsx9AxzAtE8TFM0KZ3lhXzolPACsMaPm2inqkWWMR/Ihz4Rao0XaYLSi9GmC1JXqIzwPZDvag2brDOsAXEiPpTDtDJRu0eulqJGEsKb6mk/Kklrpa82o2KkaAulw05aR7VOc70p7tYA/RYKvcs3dFMJEgLDmPFpM6wsIMVIy+rgSy++/Owzz9ik7NOZBm4SRdZisa/O4ggJ3qw3UI6cH0YbkMkN55NJsg0ZXHWVd4K8THEuXroQZtiMzGrkCV7yNKxR8kgE/pV5cLFvooLDyOJCZQpyhjgVid3pmWxENkKMVV4b70ON4BeOm/xDNwTrmo63Oho61DGVUKbK46ruRaskIcXUVlXo+/VQClVsstWEpIyL30S6wVEOYIuQYlBpPGGEkyUNaxrCgmsQD8rD3XPVIg6kJUw+Rfx6VRv5nR6dn1KOCSkqeOMbvNq7N8739e/1xpV8HUrygr3YxrG2uhaMZmZiCOU7m6+//hpbKOzQt/58dWp6Jt8joTwBRLywuG9muiCodrDiRYIij9fmncFMIwNxDh484CNSeK0Pwho1xO/34alhUICOHETspReqUqlAK04JEa44RciYpxXqKnPazKY/STwuxFlFyojcUytbDbKKQ05eXvgySqunQXTuG0FF7nebHNqPyIkawUJzcOTkmiOowrNxbjUG6dhKcpPjrJQbyoP4HIKW/5zA4uaiB8SyatcCubCAvObfOZYiUtGma9XYCdfbDfT1jtOogkLDxDfpZw+ACjFQUIAh5UmTlaXCdXOY3pVpy7RACTquOSPNzghrsgsL3jFea3fie554jJAD6eBAoPW+JS+0Rv0nTkOwVOXc5csQHF9TofI3tZIQW75agnq+5LiSWhReAeHSlxsEbNJDCJi6ZWCzD9bE1jcjGhlO0qLMVUIMcF4UG6IoeSYdIlbWBpzjXKL5UHh0GDUwgzUsgza0g7Xb8YgwOUacE08KqVyIQ8H1FsDLkwqoQg1xys8rCEWGAtvCyzd5ATc16LYC/VZPdkC/dV8esKBgCQcvjigsr+U7cEQHqdBVs755Qw551Eabqyw682Ouw+MRA5LggJsf+uGeMz1Fe0Rwg0+uC74Zbl46v8iH3rLOYCJeSw1VATw2g0zZX6BM09bYvm2qRHhzolTpW0qWHgozHxra4tnjttz64GGJLkF54Tu8vSQW+A6yyeLsLpUUnGexNuBuaA7KSZXpCS7yZbk8rfZ3bjvBO+vn9gT9H9bHAeLVZ/X26lbgNyWArXe5mWh7xI4/4na9/MrhaoSEVQnbco7WSFZbVMcvQWqVH6JOW0bIEWuW840WbSgIPRRFhDjQBAqxEzzGQbXftY7WKO1fhHXwNBTQslg4okDMehcj+XDswnXmmkJ8DYgaHf8OnIG6mDVAF88Ouju1NpttnRaIb42frRHplM32tkcJuQXcnQb6IGzrtWyBrwVuZ13i5M1W3MbbcredZLZyeaOn0UDCOrDZBvHN5DKUVWKWa9XYvOsEfssf9SXKWK2uChbw5L3VnJa2JI6tXHm2/Ft5t65INs1nNDId3YyZsK/LcyvlLe2540DvbW3H/faXt/WCvy7CFlgKXQWRMLbSh2xLX7jZNuJvYqdFSSYNWVtJro40CepE2CpsK9pVz/b03zTeHxmhobsmPEF64jeSbn4zacOAftADnd74puVcrdIt6euC/tt7bQ0KZpNb7H8r/dfLQhFur7oOpq8GBG7trtFYacq3Pb7BaNte1221uOq9wfW5WvC1911jM4RrX8HrkOPW+/369+hB9H0dRtg487bbYpOV5hsRr5p2sxTu69Jkl210kELfWJ67zYD8bpLAH7/BW+1oSTYzu5rB9gjf+PRqvPj+qOdvjH1r391ZoPdmC9jO5PlGEHZe/DZAt+hXX/C2RwnsiAfw0kFMyEV47itkO+iu5vINvsStan3Dk28d0Cn1D4/0R0b4w5Pe1k/uLNB7lQ2NHcP9b3i13yDNRx/yhlib6OwguxZ8OqnKX9G39DZvgF1HjGlVeEOm3Zsb2gN3GOgLwBg2ML4Ry9+k0wPrRNommxNXNtk4BDdOL0Z8WfwPnBvyvzG7bywuNLBZn2+M3w25fj1wZ01kv0U/gl+HEkr514aEDiorWTHtupTSo8VPmpau7gv2m2LPppCOHppcVAivFJXhVvi3qFX30fXogTuM0//hXVhw7uC+Yfdq3IZW6G6Yrwe820T8N6TYFn41D75WxBuCujc3owduK06Pd1qqzIp5nZh3zfpzE/Rd2F6zLr2pGd1uoG+d+Yfx2m+7q7fg3sn3286gm2AH9sBtBfpvt3+NDBxzk283YTf+Ld0DdzTob+k31638d9wDXSb3HXddN+Gt2gNd0N+qb65b7++4B7qg/4677tZOaFcLq/1buw3fae27oP9Oe+7WT2czq3n8rd+Ob7sF3Ynst91l3QS3eg90Of2t/ga79f+2e6AL+m+7y7oJbvUe6IL+Vn+D3fp/2z3QBf0377I7c4b3zfvitgvtgv6bvNIYJ9yRao1v0he3Y1BXe3M7vtVum75lD3Q5/bfsnjqcrMv1/4g+utUedzn9rfbGuvX9rnugy+m/6y7sZnCr9cCduF1wS1y5ZntNbrW3fofXtyve3OEAuBOb3xVv7sS3foe3uQv6OxwAd2Lz//+mT3ZriLsaMwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a classic buckle add a touch of class to a boot crafted from supple leather with a high heel and a low toe cut for a comfortable fit\n",
      "\n",
      "TUNED CAPTION: a classic buckle add a touch of class to a boot crafted from supple leather with a high heel and a low toe cut for a comfortable fit\n",
      "\n",
      "DESIRED CAPTION: This is a classic black leather knee-high boot featuring a sleek, rounded-toe design with minimal detailing except for a strap with a buckle at the top, suitable for a streamlined look that could complement both casual and slightly more formal attire.\n",
      "\n",
      "==========\n",
      "\n",
      "\n",
      "DF Index:147, Segment index: 6\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a flattering v neckline and a flattering asymmetrical hemline make this sleeveless blouse a must have for your wardrobe\n",
      "\n",
      "TUNED CAPTION: a flattering v neckline and a flattering asymmetrical hemline make this sleeveless blouse a must have for your wardrobe\n",
      "\n",
      "DESIRED CAPTION: This is a women's deep burgundy blouse with sheer long sleeves, featuring a v-neckline and a loose, flowy fit, likely made from a synthetic blend, presenting a simple yet elegant style suitable for various occasions.\n",
      "\n",
      "==========\n",
      "\n",
      "\n",
      "DF Index:74, Segment index: 7\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=252x252>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNTUNED CAPTION: a pointed toe and a pointed heel make this boot a must have for any season\n",
      "\n",
      "TUNED CAPTION: a pointed toe and a pointed heel make this boot a must have for any season\n",
      "\n",
      "DESIRED CAPTION: This is a pair of classic black leather knee-high boots featuring a sleek, fitted silhouette with a pointed toe, mid-height block heel, with a full-length zipper on the shaft, likely from a contemporary era and versatile for various styles\n",
      "\n",
      "==========\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "compareCaptionsAfter(comparison_segment_g,\n",
    "                         comparison_hf_indices_g,\n",
    "                         comparison_pixel_value_inputs_g,\n",
    "                         comparison_predictions_untuned_g,\n",
    "                         comparison_labels_g)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14ce04cd-7ff6-4f29-8b29-cec5fc27614a",
   "metadata": {},
   "source": [
    "## KARINA TO DO:\n",
    "\n",
    "* commit that refactored for clarity and addressed bug when displaying before and after demo data\n",
    "* make a similar items benchmark csv of deduped data so can use this data as a test once have model trained\n",
    "* don't need a multi-GPU instance unless change code to be able to take advantage\n",
    "* implement Regi's nbdev approach to organize jupyter notebooks then delete Regis scratchpad from this notebook\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dad14c2-ebd1-4c12-b574-9945794ffd5f",
   "metadata": {},
   "source": [
    "## REGIS SCRATCHPAD\n",
    "\n",
    "Command line to get list of deduplicated files in a folder: ffor file in *; do md5sum \"$file\"; done | sort | awk '!seen[$1]++ {print $2}\n",
    "\n",
    "https://nbdev.fast.ai # from jeremy howard see youtube"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99aad966-816e-475f-bcdd-0b2d169cd682",
   "metadata": {},
   "source": [
    "### dataloader"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "281ff26f-f315-4cd4-aa5f-7f26c3972f03",
   "metadata": {},
   "source": [
    "### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "568a9f0a-3a11-4746-a7c5-0686be79c91f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # nbdev: EXPORT\n",
    "# def load_dataset(path):\n",
    "#     # return a dataset\n",
    "#     pass\n",
    "#     return x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef1e326f-576b-4790-ac89-53e77ff0af01",
   "metadata": {},
   "source": [
    "### tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "100cff0b-241f-497f-abc5-baf2a3aa1f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # create a folder\n",
    "# #create 3 images inside\n",
    "# ds = load_dataset('./test123')\n",
    "# assert len(ds) == 3\n",
    "# assert ds[0]['filename'] == 'first.jpg'\n",
    "# assert ds.isin(..) != ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1a56799-9e03-4f62-bcfe-8afe6ab16632",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}