test_cnep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device : cuda:0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from models.cnep import CNEP\n",
    "from data.data_generators import *\n",
    "\n",
    "torch.set_float32_matmul_precision('high')\n",
    "\n",
    "def get_free_gpu():\n",
    "    gpu_util = []\n",
    "    for i in range(torch.cuda.device_count()):\n",
    "        torch.cuda.set_device(i)  # Switch GPU\n",
    "#        gpu_util.append((i, torch.cuda.memory_stats()['reserved_bytes.all.current'] / (1024 ** 2)))\n",
    "        gpu_util.append((i, torch.cuda.utilization()))\n",
    "    gpu_util.sort(key=lambda x: x[1])\n",
    "    return gpu_util[0][0]\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    available_gpu = get_free_gpu()\n",
    "    if available_gpu == 0:\n",
    "        device = torch.device(\"cuda:0\")\n",
    "    else:\n",
    "        device = torch.device(f\"cuda:{available_gpu}\")\n",
    "else:\n",
    "    device = torch.device(\"cpu\")\n",
    "\n",
    "print(\"Device :\", device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters\n",
    "batch_size = 32\n",
    "n_max, m_max = 6, 6\n",
    "\n",
    "t_steps = 200\n",
    "num_demos = 128\n",
    "num_classes = 4\n",
    "num_indiv = num_demos//num_classes  # number of demos per class\n",
    "noise_clip = 0.0\n",
    "dx, dy = 1, 1\n",
    "\n",
    "num_val = 32\n",
    "num_val_indiv = num_val//num_classes\n",
    "\n",
    "colors = ['r', 'g', 'b', 'm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X: torch.Size([128, 200, 1]) Y: torch.Size([128, 200, 1]) VX: torch.Size([32, 200, 1]) VY: torch.Size([32, 200, 1])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating the data\n",
    "x = torch.linspace(0, 1, 200).repeat(num_indiv, 1)\n",
    "y = torch.zeros(num_demos, t_steps, dy)\n",
    "\n",
    "vx = torch.linspace(0, 1, 200).repeat(num_val_indiv, 1)\n",
    "vy = torch.zeros(num_val, t_steps, dy)\n",
    "\n",
    "generator_functions = [generate_sin, generate_cos, generate_cx_sigm, generate_reverse_cx_sigm]\n",
    "\n",
    "for i in range(num_classes):\n",
    "    noise = torch.clamp(torch.randn(x.shape)*1e-4**0.5, min=0) - noise_clip\n",
    "    y[i*num_indiv:(i+1)*num_indiv] = torch.unsqueeze(generator_functions[i](x) + noise, 2)\n",
    "    noise = torch.clamp(torch.randn(vx.shape)*1e-4**0.5, min=0) - noise_clip\n",
    "    vy[i*num_val_indiv:(i+1)*num_val_indiv] = torch.unsqueeze(generator_functions[i](vx) + noise, 2)\n",
    "\n",
    "x = torch.unsqueeze(x.repeat(num_classes, 1), 2)  # since dx = 1\n",
    "vx = torch.unsqueeze(vx.repeat(num_classes, 1), 2)\n",
    "print(\"X:\", x.shape, \"Y:\", y.shape, \"VX:\", vx.shape, \"VY:\", vy.shape)\n",
    "\n",
    "for i in range(num_indiv):\n",
    "    plt.plot(x[i, :, 0], y[i, :, 0], 'r', alpha=0.3)\n",
    "    plt.plot(x[i+num_indiv, :, 0], y[i+num_indiv, :, 0], 'g', alpha=0.3)\n",
    "    plt.plot(x[i+2*num_indiv, :, 0], y[i+2*num_indiv, :, 0], 'b', alpha=0.3)\n",
    "    plt.plot(x[i+3*num_indiv, :, 0], y[i+3*num_indiv, :, 0], 'magenta', alpha=0.3)\n",
    "plt.show()\n",
    "\n",
    "x, y = x.to(device), y.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = torch.zeros((batch_size, n_max, dx+dy), dtype=torch.float32, device=device)\n",
    "tar_x = torch.zeros((batch_size, m_max, dx), dtype=torch.float32, device=device)\n",
    "tar_y = torch.zeros((batch_size, m_max, dy), dtype=torch.float32, device=device)\n",
    "obs_mask = torch.zeros((batch_size, n_max), dtype=torch.bool, device=device)\n",
    "tar_mask = torch.zeros((batch_size, m_max), dtype=torch.bool, device=device)\n",
    "\n",
    "def prepare_masked_batch(t: list, traj_ids: list):\n",
    "    obs.fill_(0)\n",
    "    tar_x.fill_(0)\n",
    "    tar_y.fill_(0)\n",
    "    obs_mask.fill_(False)\n",
    "    tar_mask.fill_(False)\n",
    "\n",
    "    for i, traj_id in enumerate(traj_ids):\n",
    "        traj = t[traj_id]\n",
    "\n",
    "        n = torch.randint(1, n_max, (1,)).item()\n",
    "        m = torch.randint(1, m_max, (1,)).item()\n",
    "\n",
    "        permuted_ids = torch.randperm(t_steps)\n",
    "        n_ids = permuted_ids[:n]\n",
    "        m_ids = permuted_ids[n:n+m]\n",
    "        \n",
    "        obs[i, :n, :dx] = traj[n_ids]\n",
    "        obs[i, :n, dx:] = (n_ids/t_steps).unsqueeze(1)\n",
    "        obs_mask[i, :n] = True\n",
    "        \n",
    "        tar_x[i, :m] = traj[m_ids]\n",
    "        tar_y[i, :m] = (m_ids/t_steps).unsqueeze(1)\n",
    "        tar_mask[i, :m] = True\n",
    "\n",
    "val_obs = torch.zeros((batch_size, n_max, dx+dy), dtype=torch.float32, device=device)\n",
    "val_tar_x = torch.zeros((batch_size, t_steps, dx), dtype=torch.float32, device=device)\n",
    "val_tar_y = torch.zeros((batch_size, t_steps, dy), dtype=torch.float32, device=device)\n",
    "val_obs_mask = torch.zeros((batch_size, n_max), dtype=torch.bool, device=device)\n",
    "\n",
    "def prepare_masked_val_batch(t: list, traj_ids: list):\n",
    "    val_obs.fill_(0)\n",
    "    val_tar_x.fill_(0)\n",
    "    val_tar_y.fill_(0)\n",
    "    val_obs_mask.fill_(False)\n",
    "\n",
    "    for i, traj_id in enumerate(traj_ids):\n",
    "        traj = t[traj_id]\n",
    "\n",
    "        n = torch.randint(1, n_max, (1,)).item()\n",
    "\n",
    "        permuted_ids = torch.randperm(t_steps)\n",
    "        n_ids = permuted_ids[:n]\n",
    "        m_ids = torch.arange(t_steps)\n",
    "        \n",
    "        val_obs[i, :n, :dx] = traj[n_ids]\n",
    "        val_obs[i, :n, dx:] = (n_ids/t_steps).unsqueeze(1)\n",
    "        val_obs_mask[i, :n] = True\n",
    "        \n",
    "        val_tar_x[i] = traj[m_ids]\n",
    "        val_tar_y[i] = (m_ids/t_steps).unsqueeze(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "def model_train():\n",
    "    # run = wandb.init()\n",
    "    # nll_coeff = wandb.config.nll_coeff\n",
    "    # other_loss_coeff = wandb.config.other_loss_coeff\n",
    "\n",
    "    model = CNEP(1, 1, 6, 6, [128, 128, 128], num_decoders=4, decoder_hidden_dims=[128, 128, 128], batch_size=batch_size, scale_coefs=True).to(device)\n",
    "    optimizer = torch.optim.Adam(lr=1e-4, params=model.parameters())\n",
    "\n",
    "    file_name = int(time.time())\n",
    "\n",
    "    epochs = 1_500_000\n",
    "    epoch_iter = num_demos//batch_size  # number of batches per epoch (e.g. 100//32 = 3)\n",
    "    v_epoch_iter = num_val//batch_size  # number of batches per validation (e.g. 100//32 = 3)\n",
    "    avg_loss = 0\n",
    "\n",
    "    val_per_epoch = 1000\n",
    "    min_val_error = 1000000\n",
    "\n",
    "    mse_loss = torch.nn.MSELoss()\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        epoch_loss = 0\n",
    "\n",
    "        traj_ids = torch.randperm(x.shape[0])[:batch_size*epoch_iter].chunk(epoch_iter)  # [:batch_size*epoch_iter] because nof_trajectories may be indivisible by batch_size\n",
    "\n",
    "        for i in range(epoch_iter):\n",
    "            optimizer.zero_grad()\n",
    "            prepare_masked_batch(x, traj_ids[i])\n",
    "            torch.autograd.set_detect_anomaly(True)\n",
    "            pred, gate = model(obs, tar_x, obs_mask)\n",
    "            loss, nll = model.loss(pred, gate, tar_y, tar_mask)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            epoch_loss += nll.item()\n",
    "\n",
    "        if epoch % val_per_epoch == 0:\n",
    "            with torch.no_grad():\n",
    "                v_traj_ids = torch.randperm(vx.shape[0])[:batch_size*v_epoch_iter].chunk(v_epoch_iter)\n",
    "                \n",
    "                val_epoch_err = 0\n",
    "\n",
    "                for j in range(v_epoch_iter):\n",
    "                    prepare_masked_val_batch(vx, v_traj_ids[j])\n",
    "                    p, g = model(val_obs, val_tar_x, val_obs_mask)\n",
    "                    dec_id = torch.argmax(g.squeeze(1), dim=-1)\n",
    "                    vp_means = p[dec_id, torch.arange(batch_size), :, :dy]\n",
    "                    val_epoch_err += mse_loss(vp_means, val_tar_y).item()\n",
    "\n",
    "                if val_epoch_err < min_val_error:\n",
    "                    min_val_error = val_epoch_err\n",
    "                    print(f'New best: {min_val_error}')\n",
    "                    torch.save(model.state_dict(), f'saved_models/cnep_synth_{file_name}.pt')\n",
    "\n",
    "        avg_loss += epoch_loss\n",
    "\n",
    "        if epoch % 100 == 0:\n",
    "            print(\"Epoch: {}, Loss: {}\".format(epoch, avg_loss/100))\n",
    "            avg_loss = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New best: 0.3170870840549469\n",
      "Epoch: 0, Loss: 0.024690332412719725\n",
      "Epoch: 100, Loss: -2.4513040256057868\n",
      "Epoch: 200, Loss: -12.071495911031962\n",
      "Epoch: 300, Loss: -13.762822594046593\n",
      "Epoch: 400, Loss: -14.996154035925866\n",
      "Epoch: 500, Loss: -14.354745261669159\n",
      "Epoch: 600, Loss: -16.027153805494308\n",
      "Epoch: 700, Loss: -13.238712769150734\n",
      "Epoch: 800, Loss: -13.856750338673592\n",
      "Epoch: 900, Loss: -14.947204568386077\n",
      "New best: 1.9227006475830422e-07\n",
      "Epoch: 1000, Loss: -15.310103200227022\n",
      "Epoch: 1100, Loss: -16.42003773331642\n",
      "Epoch: 1200, Loss: -16.548766746520997\n",
      "Epoch: 1300, Loss: -13.472772986888886\n",
      "Epoch: 1400, Loss: -16.760206696540116\n",
      "Epoch: 1500, Loss: -15.121107676811516\n",
      "Epoch: 1600, Loss: -16.99849509179592\n",
      "Epoch: 1700, Loss: -15.018031885623932\n",
      "Epoch: 1800, Loss: -17.116322848796845\n",
      "Epoch: 1900, Loss: -16.546393889188767\n",
      "New best: 9.654641530687513e-08\n",
      "Epoch: 2000, Loss: -15.898731758594513\n",
      "Epoch: 2100, Loss: -14.96726350903511\n",
      "Epoch: 2200, Loss: -17.13362059354782\n",
      "Epoch: 2300, Loss: -15.304041694402695\n",
      "Epoch: 2400, Loss: -17.933751784563064\n",
      "Epoch: 2500, Loss: -16.17928740784526\n",
      "Epoch: 2600, Loss: -17.97133402109146\n",
      "Epoch: 2700, Loss: -16.65220442056656\n",
      "Epoch: 2800, Loss: -16.397065894603728\n",
      "Epoch: 2900, Loss: -17.948205245137213\n",
      "Epoch: 3000, Loss: -17.65543560028076\n",
      "Epoch: 3100, Loss: -17.604164214134215\n",
      "Epoch: 3200, Loss: -18.900014360845088\n",
      "Epoch: 3300, Loss: -15.355403518676757\n",
      "Epoch: 3400, Loss: -17.922952529489994\n",
      "Epoch: 3500, Loss: -15.862609990835189\n",
      "Epoch: 3600, Loss: -17.344563241004945\n",
      "Epoch: 3700, Loss: -19.094205050468446\n",
      "Epoch: 3800, Loss: -16.06783118337393\n",
      "Epoch: 3900, Loss: -17.999496186971665\n",
      "New best: 8.225541137107939e-08\n",
      "Epoch: 4000, Loss: -16.210759303569795\n",
      "Epoch: 4100, Loss: -17.559868294596672\n",
      "Epoch: 4200, Loss: -17.71162468254566\n",
      "Epoch: 4300, Loss: -16.648781651854517\n",
      "Epoch: 4400, Loss: -17.556869860887527\n",
      "Epoch: 4500, Loss: -18.642983944416045\n",
      "Epoch: 4600, Loss: -16.674058522582055\n",
      "Epoch: 4700, Loss: -17.981640198230743\n",
      "Epoch: 4800, Loss: -19.21851416707039\n",
      "Epoch: 4900, Loss: -17.18874980211258\n",
      "New best: 3.670348647233368e-08\n",
      "Epoch: 5000, Loss: -18.248386837244034\n",
      "Epoch: 5100, Loss: -17.228019635677338\n",
      "Epoch: 5200, Loss: -19.066319781839848\n",
      "Epoch: 5300, Loss: -18.802479063272475\n",
      "Epoch: 5400, Loss: -17.82080960392952\n",
      "Epoch: 5500, Loss: -17.81639841556549\n",
      "Epoch: 5600, Loss: -17.94419541195035\n",
      "Epoch: 5700, Loss: -18.291643890142442\n",
      "Epoch: 5800, Loss: -19.77378254055977\n",
      "Epoch: 5900, Loss: -16.764643641710283\n",
      "Epoch: 6000, Loss: -18.852082214951515\n",
      "Epoch: 6100, Loss: -18.425457297563554\n",
      "Epoch: 6200, Loss: -18.729173151254653\n",
      "Epoch: 6300, Loss: -17.03979445695877\n",
      "Epoch: 6400, Loss: -18.11776249885559\n",
      "Epoch: 6500, Loss: -19.427841314226388\n",
      "Epoch: 6600, Loss: -19.379897468090057\n",
      "Epoch: 6700, Loss: -18.225636065602302\n",
      "Epoch: 6800, Loss: -17.683266561031342\n",
      "Epoch: 6900, Loss: -19.360596564412116\n",
      "Epoch: 7000, Loss: -20.154563800692557\n",
      "Epoch: 7100, Loss: -17.14187797665596\n",
      "Epoch: 7200, Loss: -20.17414137482643\n",
      "Epoch: 7300, Loss: -17.994972719848157\n",
      "Epoch: 7400, Loss: -18.93236193060875\n",
      "Epoch: 7500, Loss: -19.234973615705968\n",
      "Epoch: 7600, Loss: -19.487380820065738\n",
      "Epoch: 7700, Loss: -18.804519611597062\n",
      "Epoch: 7800, Loss: -19.366234498023985\n",
      "Epoch: 7900, Loss: -18.689808474779127\n",
      "Epoch: 8000, Loss: -18.82280918084085\n",
      "Epoch: 8100, Loss: -17.771361830830575\n",
      "Epoch: 8200, Loss: -20.6315420460701\n",
      "Epoch: 8300, Loss: -20.852399708628653\n",
      "Epoch: 8400, Loss: -17.607842438220977\n",
      "Epoch: 8500, Loss: -21.154148287773133\n",
      "Epoch: 8600, Loss: -19.221507083177567\n",
      "Epoch: 8700, Loss: -20.52274809449911\n",
      "Epoch: 8800, Loss: -20.197731429338454\n",
      "Epoch: 8900, Loss: -20.35620061516762\n",
      "Epoch: 9000, Loss: -17.32284435581416\n",
      "Epoch: 9100, Loss: -19.09154068470001\n",
      "Epoch: 9200, Loss: -18.818979843854905\n",
      "Epoch: 9300, Loss: -19.912328696250917\n",
      "Epoch: 9400, Loss: -20.952114959359168\n",
      "Epoch: 9500, Loss: -20.559020879268648\n",
      "Epoch: 9600, Loss: -18.839046317338944\n",
      "Epoch: 9700, Loss: -21.267358663082124\n",
      "Epoch: 9800, Loss: -20.725330359190703\n",
      "Epoch: 9900, Loss: -19.441210016012192\n",
      "Epoch: 10000, Loss: -20.471712312698365\n",
      "Epoch: 10100, Loss: -20.433876218795778\n",
      "Epoch: 10200, Loss: -19.768260335326193\n",
      "Epoch: 10300, Loss: -21.572653279304504\n",
      "Epoch: 10400, Loss: -19.212232978343962\n",
      "Epoch: 10500, Loss: -19.521995377540588\n",
      "Epoch: 10600, Loss: -21.665219876766205\n",
      "Epoch: 10700, Loss: -19.212284256219863\n",
      "Epoch: 10800, Loss: -19.833033518791197\n",
      "Epoch: 10900, Loss: -18.736374032720924\n",
      "Epoch: 11000, Loss: -21.584149589538573\n",
      "Epoch: 11100, Loss: -21.359860671758653\n",
      "Epoch: 11200, Loss: -17.91353371143341\n",
      "Epoch: 11300, Loss: -20.75630083680153\n",
      "Epoch: 11400, Loss: -20.111826993227005\n",
      "Epoch: 11500, Loss: -20.758353581428526\n",
      "Epoch: 11600, Loss: -20.981988579034805\n",
      "Epoch: 11700, Loss: -20.35238731265068\n",
      "Epoch: 11800, Loss: -17.98763501599431\n",
      "Epoch: 11900, Loss: -21.67322879910469\n",
      "Epoch: 12000, Loss: -19.2655185264349\n",
      "Epoch: 12100, Loss: -21.353921726346016\n",
      "Epoch: 12200, Loss: -21.32138710618019\n",
      "Epoch: 12300, Loss: -20.244258716106415\n",
      "Epoch: 12400, Loss: -18.92979093194008\n",
      "Epoch: 12500, Loss: -20.771785697937013\n",
      "Epoch: 12600, Loss: -21.067654114961623\n",
      "Epoch: 12700, Loss: -21.563076114058493\n",
      "Epoch: 12800, Loss: -21.31971289694309\n",
      "Epoch: 12900, Loss: -20.98873857319355\n",
      "Epoch: 13000, Loss: -21.074458540678023\n",
      "Epoch: 13100, Loss: -19.69401015728712\n",
      "Epoch: 13200, Loss: -19.933317125663162\n",
      "Epoch: 13300, Loss: -20.7175776553154\n",
      "Epoch: 13400, Loss: -19.969539796113967\n",
      "Epoch: 13500, Loss: -19.01979033112526\n",
      "Epoch: 13600, Loss: -22.60298228263855\n",
      "Epoch: 13700, Loss: -20.049484652280807\n",
      "Epoch: 13800, Loss: -21.66741778612137\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel_train\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[5], line 30\u001b[0m, in \u001b[0;36mmodel_train\u001b[0;34m()\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(epoch_iter):\n\u001b[1;32m     29\u001b[0m     optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m---> 30\u001b[0m     \u001b[43mprepare_masked_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraj_ids\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     31\u001b[0m     torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mset_detect_anomaly(\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m     32\u001b[0m     pred, gate \u001b[38;5;241m=\u001b[39m model(obs, tar_x, obs_mask)\n",
      "Cell \u001b[0;32mIn[4], line 28\u001b[0m, in \u001b[0;36mprepare_masked_batch\u001b[0;34m(t, traj_ids)\u001b[0m\n\u001b[1;32m     25\u001b[0m obs[i, :n, dx:] \u001b[38;5;241m=\u001b[39m (n_ids\u001b[38;5;241m/\u001b[39mt_steps)\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     26\u001b[0m obs_mask[i, :n] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 28\u001b[0m \u001b[43mtar_x\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43mm\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m traj[m_ids]\n\u001b[1;32m     29\u001b[0m tar_y[i, :m] \u001b[38;5;241m=\u001b[39m (m_ids\u001b[38;5;241m/\u001b[39mt_steps)\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     30\u001b[0m tar_mask[i, :m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model_train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_validation_batch(o_ids=[0, -1]):\n",
    "    obs = torch.cat((vx[:, o_ids, :], vy[:, o_ids, :]), dim=-1)\n",
    "    tar = vx[:, torch.arange(t_steps)]\n",
    "    tar_val= vy[:, torch.arange(t_steps)]\n",
    "\n",
    "    return obs, tar, tar_val\n",
    "\n",
    "\n",
    "# Testing the best model\n",
    "\n",
    "model = WTA_CNP(1, 1, 6, 6, [128, 128, 256], 4, [256, 128, 128], batch_size)\n",
    "model.load_state_dict(torch.load(f'saved_models/wtacnp_synth_1703250812.pt'))\n",
    "model.eval()\n",
    "\n",
    "o, t, tr = get_validation_batch([15])\n",
    "\n",
    "with torch.no_grad():\n",
    "    p, g = model(o, t)\n",
    "\n",
    "p, g = p.cpu().numpy(), g.cpu()\n",
    "t, tr = t.cpu().numpy(), tr.cpu().numpy()\n",
    "\n",
    "for i in range(batch_size):\n",
    "    dec_id = torch.argmax(g[i, :, :]).item()\n",
    "    print(dec_id)\n",
    "    plt.plot(range(t_steps), p[dec_id, i, :, 0], colors[dec_id], alpha=0.3)\n",
    "\n",
    "# for i in range(4):\n",
    "#     plt.plot(range(t_steps), p[i, 6, :, 0], colors[i], alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'cpu'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[15], line 38\u001b[0m\n\u001b[1;32m     35\u001b[0m     plt\u001b[38;5;241m.\u001b[39mscatter(o[i, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m200\u001b[39m, o[i, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m], color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m'\u001b[39m, marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m32\u001b[39m):\n\u001b[0;32m---> 38\u001b[0m     plt\u001b[38;5;241m.\u001b[39mplot(\u001b[38;5;28mrange\u001b[39m(t_steps), \u001b[43mtr\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcpu\u001b[49m()\u001b[38;5;241m.\u001b[39mnumpy(), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m'\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdashed\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     40\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'cpu'"
     ]
    },
    {
     "data": {
      "image/png": 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Q7qmVelhlI+QE9Lzcg56XesREXx4wnWiC+Wdm6so8SVEwQipWLpRD8Jkg+t8Tm65xcg6NP2iE+TQzNT8rE8YYIn+OoPuxboAB2m9o0Xp9K+RG+n2XWy6UQ9dvu5DpzIBTcHBc6kDD4Q1SD2tcDVZo9j/qRz4oLsnq9tXBfoEdapd6F88mtYyCEVLx0pvEZL/BAlu8lkfzKc0wnWialOvr5SIUBPgX+9H3tz4AYgdm+0V26s0xgYrpIjx3eUrLFs2nNMMy21ITSxZZfxb+R/xIrBHfx/ImOWzn2WA8hNpEEApGSBWJfxpH4IkAMh0ZAOLJzHKGBQ3fa6AktzEqxApwL3Ij+cVAouo5Vph+SImqUmCMIfhMED0v9gAADAcb4LzKOa7djydSMVNEz4s9CP8pDFZg4OQcTD8yofm05lFVRiW1iYIRUlUYY+h/rx/BZ4LI94jTvCqXCtazrWLmPV08RyzjzqDz5k7kg3nwGh7Oa52oO4DeP1Lre68Pvvt8YHkGdZsarTe0TkjxqfHCGEPsoxi6H+su7ZLTf0sP+y/sUDmkrZBKKg8FI6QqCTkBkb9E0PPHnlJhJN0+OljnWKHdo3YT/8ZbbHUMnjs8ENIClFYlWm9shdpJa/eVIrU+ha5bulDoK0BWJ4Nrvgv6fSq/0WTGk0H3I92lpVWFWQHb+TbUHVxHNwxkuygYIVWtkCig56UeRF6PgOUHdt7MMsIy2yJ5f4pKxhhD+NUwAk8EACYGcq75LsjrKFG10uTCObgXupHemAYn52C/yI7GoxqlHtZ2FdNFhJ4PIfx6GCiKZcWbT25G8ynNlN9FdoqCEVITcj05BJcF0f+PfrGcsQxoOrYJ5p+aaSfI1wh5Af6H/Oj7u5io2nhMI2wX2ChRtYIJWQHee7yIrhR7OjX9sAm2ObaKabjHGEP0gyi6H+9GoVfsIWM4yADb+TaorHRTQHaNghFSU9KdAztvBjL2ebW486bph02ULAexe3LXwi6x6ykH2M63oen4Jpo6rwKMMYReCCG0LARAzL9wXeuSvNFeujON7oe7xeRnAEqbUlySOZDOwWT4KBghNSnxeQKBJwJIb0wDAOSNcph/ZkbjUY0Vczc50dKdaXT9tgv5UB68jofrOhcM+1ODtmoT/SgKz90esCyD0qFE241tUNknfvahmCwi+FwQkTcigABwSg7m08ww/chEzS7JiFEwQmrW4NRx4OlAqcCSqmVg581Bk2vnTWxVDJ47PRAyApS2gUTVFkpUrVbpzQOBZTgPmVGG9lvaJ6xCLmMM/f/oR/cT3ShGxeTxupl1sJ1ng7K5enb7kMpCwcgoFKIFykOoIkJeQO9bvQj9IYRiXDx5avfSwnqOFbppOolHV16MMfS83IPg00ExUXW6Dq7rXFTBtgbk+/Lo/E0nMpsykBlkaP9te9l7OKU3peFf4kfqfykAgNKhhH2uHYb9aIaNjA0FIyMwuGbb82IPpiyaQltIq0wxWUTPyz0IvxYW+2FAvKOzzrbWZN0DISfA94AP/e+K5fQbj2uE7XxKVK0lxWQRHb/uQHpdGryOR/vN7WU5LxXiBQSfDaL3rV6AiblY5p+Z0XRiEx1PZFxQMDJC7jvdiL4fhapFhd3v3Z3WRqtQPpJHcFlQ3E3CAPBA4/cbYf6ZGYoGhdTDGxf5vjy6FnYhvS4N8ID9AjuajmuSelikDIqpIjpv6kRqbQq8hkfbb9qg22t8ZvyYwNC3vA+BpwKlWUXjYUbYzrFB0VQb7xVSGSgYGaFCvIANl2xAobcA00km2M6xleV1SPll3BkEngwgvlrsA8KreZhOMsF0kqlqS28DX8sn0Mvg+qUL+umVXyiLjF4xU0TXzV1I/jcJXs2j9detYy6Ollqfgn+JH+kNYhK4qlUF+1x7VRRdI2PHGJvQvDoKRkbzOqtj6Lq5C+CAKb+bAt03ajvvoNYlvhjYebNePOnKjDJYTreg4eiGqpuCrpSdFmTiCVkBXbd0IfFZApyKQ9tNbaMKHArRAgJPBdC3XKxDw2t5WM6woPG4xqp7P5DR8z3oAwBY51gh05b/5oyCkVHy3ONB/zv9UNqU2P2+3amGRZVjjCH6YRTBp4PIdecAAEq7EtbZVtTNrPwS1owx9PyxB8FngwAA/f56uK6TvgYFmVhCTkDXwi4k/j0QkNzYBv2+wwtIWJGh9+1eBJ4JQEgKAID679XDerYVinpakplMEv9JoONXHQCA9kXtEzIbRsHIKBWTRWy4ZAPy4Tyajm+C/QJ7WV+PTAyhIKDvr30IPh8sbVvU7KmBbY4Nur0rcwZMyAnw3utFdMVAdc4TmmA7t3Kqc5KJJeQEdN3ahcSaBDglh7Zf7zogSa5Nwr/YX+qIrZ6ihn2ufdxyT0j1KGaK2HDxBuRDeTQe1wjHhY4JeV0KRsYg/mkcnTd2AgDaF7YP+w6EVL5iqojwK2H0vNIDlhUPfcNBBljPtlZUI7l8bx5dt3SJ6/oywD7XjqZjKFF1shNyAtyL3Ih/Egen5NB6Q+t2t9/me/MIPBko7biS6WWwnGlB4zGN4HgKZicj3xIfev/SC4VZgakPTp2wWf/hXr9poXA7DPsb0HiM2LDKe68XxUxR4hGR8SLTymA5w4I9H9lT/D/mgfi/4thw6QZE3oygEmLz1IYUNl6xEekN6VKdCQpECADwSh6u610wHGgAyzF0/bYL8c/ipc8LBQHh18JYP3e9GIhwYo+iPR7eA03HNVEgMkkl/ptA7196AQAt81oqMv2AZkZ2oJgeWK4J5dF0QhPsv6DlmlqU8WYQWLpl503D9xtgn2uXLKGvf2U/vL/3guUYVE4VWm9opS7FZBtCXoD7d27E/xUHp+DQ+qtWcHIO/of9yLqzAADNHhrY59qhnUp1kyazYqaIjZduRC6QQ+MxjXBcPDHLM4NomWYclJZrOGDKbVNonbVGfb2aqeFgA1y/dE1oQMIYQ+j5EELPi83SDAcY4LzGOSHZ7qQ6CQUxIIl+EEXWl4WiUQG5UQ5ZnQzWs61oOLKh4hO0Sfn5H/Uj8noECtPA8swEn1NomWYcGPY3oP579QATl2uEnCD1kEgZcBwH8ylmtN7QCk7BIb4qDu9dXrDixMTpQlaA+zZ3KRAxnWRC6w2tFIiQXdJM1SDryyLfk0d6Qxrab2qxx8N7iI0jKRCZ9JJrk4j8OQIAcFzqqOhzCgUju2A7zwZ5gxw5Xw6hP4SkHg4po7oD6+C63gVOziG6MgrvvV4wobwBSS6cw6brNiH2YQycnINjngO2c2y0tk92Kt2ZxoZLNiD0bAjqNjW0e2qh3UuL9No0st6s1MMjFWBwNx4Y0HBkAwzfquw+QxSM7IJcL4f9QjFfpOflHqQ3pSUeESmnugPq4LzOCciA/nf74XvAV7ak1tS6FDZduUlsiFYndmhtPKqxLK9FakdsVQybr9mMnC8Heb0czquc2PedfVF/RD1YgcF9qxv5SF7qYRKJBZ8NisdIoxy28yq/ojgFI8Ng/I4RdYfUAQLgvc8LoUDLNbXM+G0jnFc7AQ7oW94H/xL/uAck/e/3Y/P8zSj0FaBqVWH33+9esfVOSOXoe6cPXQu7IGQE6PfTY+pDU9FweANkChmcVzuhblOj0FdA161dtKw8iaXWpRB+NQwAcFziqIoiiRSMDJN9rh0ygwyZzRmE/xSWejikzOpn1aPlihaAA3rf7EX3493jEpAwxhB4JgDPnR6wPIPhIAN2u2M3KM3KcRg1qWXpzWmxlDcDGo9tROuvWyE3yEufl6llcC1wQWaQIb0+Dd9D5ZvVI5Vr6+WZ+sPrUXfgxG36GAsKRoZJUa+A7Xxxqiv0fAgZT0biEZFyazi8AY5LxW1wkdciCD4dHNPJvZgpwr3IjZ4/9gAAmk9pRuuC1qpu3kcmRjFZhPt3bjGAPdAA+4Xb336usqrgvFac1et/px+RNyISjJZIKfSHELKeLOT18tI1qxqMKhh56KGH0N7eDrVajRkzZuCDDz7Y6dcvW7YM06dPh1arhc1mw5w5cxCJVN+bpP7/6qGfoQcrMPju85U9uZFIr/Goxi05Qy/1lHa8jFSuJ4fN125G7J9iomrLFS2wnmWlRFWyS4wxeO/zItedg6JZgZYrWna6U8awnwG2c8WLUPdj3Uh8npiooRKJpTam0POyeLNjv8g+ZOas0o04GHnhhRdw+eWXY8GCBfj0009x6KGH4thjj4Xb7d7u169cuRKzZ8/Gueeeiy+//BIvvvgiVq9ejfPOO2/Mg59oHMfBcYkDvJpH6n8pRP5SfQEVGbmm45pKCWCh50MIvTiygCS5NomNV2xEpiMDmVGG9lvb0XBEQzmGSmpQ5I0IYh+JQazrl65hXWCaTmxC/eH1gAC4f+dGLpSbgJESKQkFAb57fYAAGA81wvgdo9RDGpERByN33303zj33XJx33nnYa6+9cM8998DpdGLx4sXb/fqPP/4YbW1tmDdvHtrb2zFr1ixccMEF+OSTT8Y8eCkoTUpY51gBAIGnAsgF6U0+GZh+aILlLAsAIPh0EOHXhpc31PePPnRc34FitAh1u1pMVKXieWSYUutSCCwNAACs51ih3WN41VQHb5w0UzUoxovouqWL2lrUuJ4XepDpFG94qrHB64iCkVwuhzVr1uDoo48e8vjRRx+Njz76aLvPmTlzJrxeL958800wxhAMBvHSSy/hBz/4wQ5fJ5vNIhaLDfmoJI3HNkK3jw4sy8q69ZNUFvMpZphPNwMQp793NjPGBIbuJ7vF0u4Fhrrv1GHK7VOgbKZEVTI8hXgB7tvcYAUG4ywjmo4fWX+iwT42MqMMmY6MuLRM56qalN6cLs3Y2ufaITdWz/LMoBEFI+FwGMViERaLZcjjFosFgUBgu8+ZOXMmli1bhtNOOw1KpRJWqxX19fW4//77d/g6ixYtgtFoLH04nc6RDLPsOI6D41IHOCWHxGcJ9P29T+ohkQli/qkZzT9pBgD4l/jR+7febb6mmC6ia2EXwi+LsyfNpzbDNd9Vkc2pSGVijMF7txf5njyUNqV4vhlFRVWlSYnW68W+NdEPoqV8AlI7hMLA7pkiUDezDsZDqmt5ZtCoEli//qZgjO3wjfLVV19h3rx5uPHGG7FmzRq8/fbb6OjowNy5c3f4/efPn49oNFr68Hg8oxlmWansKlh+LgZl3Y93I99LRYYmA47jYDnTgqYfinepvgd86PvHlmA0tjqGDZduKDUwc17thPVMK5XmJiPS83IP4p/EwSk5MZAdQxlv3Td0sF0g5jwFnw4i9kllzTSTsQm/HEZmcwYygwz2C+1Ve64Z0VyOyWSCTCbbZhYkFAptM1syaNGiRTjkkENwzTXXAAD23Xdf6HQ6HHroobjllltgs2279UilUkGlqvxOpaYTTYh+EEV6g7inv3VBa9UeCGT4OI6D7VwbWIGh9y+98N7jRT6SR2ptqtT9V2FSwPVLF7R7UsdUMjKJLxJi00aIU+6ads2Yv2fTMU3IbMqg9+1eeO70YPe7dofKUfnnWLJzma5MqU2J7Rc2KOoVEo9o9EY0M6JUKjFjxgwsX758yOPLly/HzJkzt/ucVCoFnh/6MjKZGOVX+/olJxN7iXBysbladGVU6iGRCcJxHOwX2GH4tgHpTWlsuHSDuGQjA0wnmzB18VQKRMiI5fvz8NzuEQtWHVGPhiPHb9eV7QIbtHtpISQFMaE1RQmt1YwVmdg/qyAWT6z/br3UQxqTES/TXHnllXjsscewdOlSrF27FldccQXcbndp2WX+/PmYPXt26etPOOEE/OlPf8LixYuxefNmfPjhh5g3bx4OOugg2O3Vl/H7dZo2zZYcgof9KMQKEo+ITIR8Xx7+h/2lmRAwoNBXgP18O2xn2yg/hIwYExg8d3jEFgEu1bhPufNyHq75Lsib5Mh6s/Dc5an6G8LJrOeVHqQ3pMHreDguHl1OUSUZccrtaaedhkgkgptvvhnd3d3YZ5998Oabb6K1tRUA0N3dPaTmyNlnn414PI4HHngAV111Ferr63HEEUfgtttuG7+fQmLNpzYj+lEU2a4s/I/44braJfWQSJkUk0X0vNyD8OthsKx4IjedZALLMaTWptD9WDeUdiUM+1d2h0xSebTumF0AACAASURBVELPh5D8PAlezcP1y/IkPCsaFGhd0IrN121G/F9xhJ4LwXLG9pfYSeXKeDMIPTewe+Z8OxSN1bs8M4hjVRAax2IxGI1GRKNR1NVVZp391PoUNl29CWBA642tVdMPgAyPkBUQ/nMY4ZfDKCbE6W3NnhpYZ1uh31cPoSDAc7tHrLCq5NB2Uxv039RLPGpSLeL/jqPzpk6AAc6rnWWfcu97tw/eu70AANd8F4wzq3MHxmTEBIZN125Cel0a+hl6tP26raJnRYZ7/abeNONEu4cWph+ZAAC+B30oJmk9thYIBQGRtyJY94t1CD4VRDFRhMqpgmuBC7vdsRv0+4oBBy/n4bzWCcOBBrAcQ9fNXUiuTUo8elINcuEcPHd6Sg3wJmLtv+HwhtKOMO/vvUh3psv+mmR8hF8PI70uDV7Lw3FJ9S/PDKJgZBxZzrBAaVOiECkg8OT2666Q6sAYQ//7/dhw0Qb4H/Kj0FuAwiz2BZn6wFQYv23c5iTAy8Xpdf1+eggZAZ2/7kRqfUqin4BUg8EZtWK8CPVu6lLbgYlgm2ODbroOQkaAe6EbhTjlu1W6rD+L4DPiTivbuTYoTbVTRJGCkXHEq3g45oldXnvf7qUGVVWIMYbY6hg2XrYRnjs9yHXnIDPKYPuFDXss2QMNRzTstLkdr+TR+qtW6L6pg5AW0HFjB9Kb6K6TbF/w6SBSa1PgdWIgyysn7pTMyTi4rnNBYVEgF8jBc7sHrFjxq/aT1mDDRJZj0O+nR8NRtdXfioKRcabfR4/GYxsBAL77fdQPoookv0xi83Wb0XVzFzIdGfBaHpYzLdjzsT1hOsEEXjG8twuv4tF6Y2tpG2XHDR00DU62Ef04ivArYpXelstboLJOfN0PuUGO1l+1glOJ1aRpRrdyRd6IIPVlCryaH3VF3kpGwUgZWM+2QmES7zZCy0bXcp5MnPTmNDp/04nNv9yM1NoUOCUH049N2POxPWE+1TyqXQ0ytQxtN7VBs4fYqKzjVx3IeDNlGD2pRtlAFt57xARS049MMH5bugRSTZsGzivElhvhV8Poe5faW1SabCCLwFMDDRPnWKE0187yzCAKRspAppXBfrFYQyX8WhipdZQ3UImy3Vm473Bj42UbEf8kDvBA4zGN2PORPWGbYxtWq/adkWllaPtNG9RT1ChGi+hY0IFsd3Z8Bk+qlpAT4P6dG0JSgHaattQNWkrGQ4xoPlWsl+R7wIfURjpnVQrGGHz3+8CyDLpv6koz77WGgpEyqTugDvWH1wMM8N7rhZAXpB4SGSDkBASfC2LDRRsQXSFWzTUeZsQei/eA42IHFE3jt2dfrpej/bftULWqUOgtoOP6DuRCuXH7/qT6dD/ejcwmsZeI8zoneHllnIYtP7eUdoO5F7qR76d+W5Wg9+1eJD9PglMNVPyuseWZQZXxLqhRtvNtkBllyHqy6PkjdcusBLmeHDbM24DQ8yGwAoN+fz12v3d3uK5xQWUvz5q9vE6O9lvaoWpRIR/OY/P8zciFKSCZjPrf70fvm70ABzivclbUbgiO48QxOZTIh/NwL3JDKNBNlJRyoRwCSweWZ86ySpJXNFEoGCkjuUEO+1xxuSb0YoiSGCUm5AS4b3Uj58tB3iCH8zon2n7TBs2UsTci2xVFvQLtC9uhtCmRD+XRcX0HdXqeZDLeDHwP+ACIVZsNMyqvSq9MJ0PbDW3gtTxSX6XQ/Ui31EOatAaXZ4SMAO03tGg6vknqIZUVBSNlZjzEiLpv1wFFwHevj7bOSYQxBt+DPqQ3piGrk2G3O3dD/az6CZ3yVDQq0H5rOxRmBXLdOXT8qgOFKNV2mAyKmaI405ARoNtXB8vp0ueJ7IjKoYLzaifAAb1v9SLydkTqIU1Kfcv7kPgsAU7JoeWylppdnhlEwUiZcRwH+4V28Doe6Y1phF8NSz2kSSnyRgT9/+gHeMB1nUuybHSlSYkpt06BwqRA1pMVAxIqNlXTGGPwL/Yj686KM3JXO3daq6YS1B1YB8vPxYCp++FuqiY8wXLhHLofF2elLD+3lG0JuZJQMDIBFI2KUmXF4HNBZH20o2IiJb5IoPsx8Y1tO8dWKuEuFaVFifaF7ZA3yJHpzKDjhg5qH1DD+v7eJwbCHOC81glFQ3U0NWv+STOMs4xgBSYub1Ke04RgjMH3gA9CSoBmTw1MPzRJPaQJQcHIBGn4XgP0++nBcgze+73UunuC5HpycP/ODQhA/f/Vo+nEylh3VdlVaF/YDplRhsymDDp+3YFiigKSWpPuSMO/xA8AsJxpgX6f6mmeyHEcHJc5oG5To9BfgPtWN4QcJbSWW/8/+pFYkwCnGFieqfBZtPFCwcgE4TgOjksd4NU8Ul+m0PtWr9RDqnmDCavFaBHqKeqKayqldqrRfks7ZAYZ0uvEwmtUsbd2FFNFuH/nBssxGA4woPmUZqmHNGIytQytv2oVj9ENafge9NGNVBnle/PwPyoGr+bTzVA71RKPaOJQMDKBlGYlLLPFddjAEwGqN1FGjDH4HhpIWDXI0LqgFbyq8g53TZsG7b9tB68Tdy90/bYLQpbuPqvd4E6InD8HRbMCLVdWbwKi0qKE6zoXwIt37ZHXKaG1HAbPWUJSgGaqBs0nVV/wOhaVd3aucU3HN4k9SzIC3WWUUeQvEfS/I67Tu34pXcLqcGh206D9N+3gNTySnyfRtbCLpsOrXOSNCKIro+DkYjO6sVbzlZp+uh62c8S8t+6l3Uj8h5qAjrfoiijiq+Lg5APLM7LqDF5Hi4KRCcZxAweagkPi3wkxsY2Mq60TVq3nWCVPWB0O7Z5atN3UBl7NI/FpQiwXTgWnqlJqfWpLoao5Vmj31Eo8ovHRdGIT6r9XDwiA+zY3sgFKxB8v+f48/A8PLM/81Ax16+RZnhlEwYgEVA4VzKebAQDdj3Uj30fFr8ZLLjyQsFoEjN81VlUmuu4bOrTe0ApOySG+Og7PHdTSvdoU4gW4b3ODFRjqZtah6YTKSJgeDxzHwXGRA5qpYvNH90I35TiNg8Gt38W4mNtmOrl6zlnjiYIRiZh+ZBIbqCWKpWx7MjZfT1htubT61un1++rRuqAVnJxD7KMYPHd7wAQKSKoBYwze33uRD+WhtCnRMq/6jr9d4ZU8XNe7IK8Xt6V776GdgWMV/TCK2EcxQAa0XNZSMb2KJtrk/KkrAC/n0XJZCyADYh/FEP0oKvWQqlopYXVDZSesDofhWwa4rncBMnEd2XsfnfCrQe/bvYivjoNTcHD90gWZTib1kMpCaVLCdb1LDJg/jKHnReq7NVqFaAH+xQPLM6eZJ6Q1RaWqzrN1jdBM0aD5ZDFj2r/YT5U4x6D3zd4tCasSVlgdL3UH1sF17cAOhnf6Kdm5wmX92VLFTOtZ1pq/qOj20sF2wUAhx2eDSHxOCa2j4VvsQzFWhLpdjeafTK7dM19HwYjEzD81Q+VUodBfKJ3MyMgkvkiU9uZb51ihn175CavDYZxphPMqsUdI31/70P1INwUkFYgVxeUZlmXQ7aurmMJ65dZ0TBMajmoAGOC5y4NCjG6mRqJ/ZT9iHw4sz1w+eZdnBk3un74C8AoejnkOgBPvgONr4lIPqaoMSVg9zAjTj2or+av+sHq0XN4CcOJ20cATAQpIKkzPyz1I/S8FXsuj5fLayxPZGdsvbFC1qFDoLVD+yAgMWZ75yeRenhlEwUgF0E3TlbLufQ/6UExThvpwDElYbVfXZMIgADQc0QDHxQ4AQPiVMILPBiUeERmU3pxG8Dnx/8N+gR3K5upeHhwpmVoG57VOcHJxB1jkDSqINhz+JX5xeaZNjebTJvfyzCAKRiqE5UwLFBYF8j15BJ4MSD2cije4Ha4WElaHo/H7jaU1+p4/9iD4BwpIpCbkBHju8gBFoG5mHeoPr5d6SJLQtGtgPccKAAgsDSDdkZZ4RJWtf2U/oiujAA+0XEHLM4Pot1AhZGoZWi5tASAmYya+oISwnel9qxd9f+8rdUJVWmr/jtR0vKl00g8tC6HnZdrFIKXgs0Fk3VnI6+VwXFRZfY8mWtPxTTAcZAArMHhu91D9kR0YsjxzKi3PbI2CkQqin65Hw9ENAADf/T4qCb4DyS+T8D+yJWHVsJ9B4hFNnOaTmmE5c6C/0ZMBhF8PSzyiySnxRQLhV8XfvWOeA3JjdZd7H6vBytLyRjmy3iy6H6Vk/O2h5Zkdo2CkwtjOsUHeKEfOnyutRZMtcuEcuhZ11WzC6nCYTzXD/NOBCr6PdiPyFq3TT6Riqgjv770AAxqObkDdgXVSD6kiyOvkW3Z//a0P0Q+pdtLW+ldsWZ5xXOag5Zmvod9GhZHpZHBcNJCs+KcwUhtSEo+ocgg5Ae5FAwmrbWo4Lp28U+Pm082lstH+h/zo/XuvxCOaPLof7UY+lIfCooDtPJvUw6ko+n31aD5FvOP33u9FLkydyQEgF8rB96APgHgzod29NvoVjScKRipQ3cF1MB5mBBjgu89HDdMwkLC6xI/0+oGE1V+1QqauzQqXw8FxHKxnWUs1LXz3+dD3Xp/Eo6p9sVWxLblKVzgh00zeY3BHzKeboZmqgZAU4L3LO+nbGbAig+dOD4SUAO00bWlWkwxFwUiFsv/CDlmdDJnODHpeokTF3rd60bd8ciWs7grHcbCdZ0PjsY0AA7x3e9G/krpAl0shWoD3fi8AwPRjE3R76yQeUWXi5Tyc1zjBq3kkv0hO+vNX6A8hpNaKdWicVzvBySbnbO6uUDBSoeRGOewX2AEAPS/0IOPOSDwi6SS/2iph9ezJlbC6KxzHwX6hfUslzDs9iK2KST2smsMYg/d+b2mJ0HKGReohVTSVTQX7heL5K7gsiNS6ybncnPwyidALIQCA42IH3UTtBAUjFcx4qBGGA8Xtct77Jud0Zy6Ug3vRQIXVQ40wnTT5ElZ3heM4OC5xoP7/6oEi4P6dmyr5jrO+d/oQXxUHJ+fQcmULeAWdOnel/vB6cblZANx3uFFMTq7tvoVEAZ47PQAD6o+oR/1hk7MOzXDRO6qCcRwHx8UO8Foe6XXpSbeNMxfOYfP1m1HoL4gJq/Mmb8LqrnA8h5bLW2CcZQQrMHQt7ELiP1SrZjzkQjl0PyJuVTWfYYamnWpDDAfHcXBc5IDCrEA+mId/iV/qIU0Yxhj8D/qRD+ehtClhn2uXekgVj4KRCqdoUsB2zkB3zGeCyHZnJR7RxMj35tGxoAP5oPhmbrupbVInrA4HJ+PQclULDAcbwPIMnTd3UvG8MWJMbIInpAVo99Ki+cdUG2IkZDoZnNc4xe7T7/Wj793JkWTd9/c+cRuvDHBeTYnOw0HBSBVoOLoBun11YDkG3/2130o+3y8GIjl/DgqzAu0L26FoUkg9rKrAy3m4rnNBP0MPlmPo+k0Xkv9LSj2sqhV+NYzkF0nwah4tV7SA42lmbqR003Qw/0zcQeJf7K/5G6qsLwv/w+IskOXnFmj3oG28w0HBSBUYzAnglByS/02i96+1W1OiECug41cdyHqzUJgUmHLrlEnXfGyseAWP1utbod9PDyEjoPPXnVSvZhQyXRkEnxYLD9rOs0FlU0k8ouplPtUM7d5aCGmxn0+tlisQ8gLct7vBsgy6fXVoPplm0oaLgpEqobKpYJ29pRlVLRYTYoKY65DtykLeKEf7re2UfT5KvJKHa4ELun10EFICOm/spAZmIyAUBHju9oAVGAwHGkptGsjocDwH51VO8Dox/y30fEjqIZVF8JkgMpszkBlkcF7ppBy3EaBgpIo0ndAEzZ4aCGkB/of8Nbdc0/NyD1JfpcBreLQvbKc70TGSqWVovbEV2mlaFBNFdPyqY1JvER+J0POh0kVlMlf6HU/KZiUcl4jVpXte7EHiv7WVzxT/NI7wK+Img5bLWmhpeYQoGKkiHC82o+LkHOKr44iuqJ3eD+nNaQSXiVPi9rl2qFvUEo+oNsg0MrTd1AbNVA2KsSI6FnQg66vtNfuxSq5NoudFsVCX4xIHFA10URkv9bPqt9TEucuDQrwg9ZDGRSFagPdusSBe43GNqDuY+hWNFAUjVUbtVJfKCfsf9qMQrf43s5ATxP34RaBuZh3qD6f9+ONJppOh7TdtULerUegvYPOCzTWfRDhaxcyWJnj1h9fDONMo9ZBqju0XNigdShQiBfgeqP6EfMYYvPd6UegvQOVUlXY/kpGhYKQKmU42Qd2mRjFeLGVtV7PA0wFkPVnIG+RwXExT4uUgN8jR/tt2qFwqFCIFcbdSqPbyjsYqsDSAXHcOCpOiVAGZjC+ZWiaWRZdziH0UQ9/fqnu7b+SNCOKr4+AUHJzXOsGr6LI6GvRbq0K8nIfjMgfAA9EPolVd/jvxnwQir0UAiOus8jq5xCOqXXKjHO23tEPpUCLfM1DHJZKXelgVI74mjt63xJ1qLZe3QKaj2hDlot1dC8tssaS+/xE/Mt7qzGVKd6YReCIAALDOsULTRgXxRouCkSql3X1LASbfQ76qLLVcSBTgvWfLOqthBvWcKTdFgwJTFk6B0qpELpDD5gWbke+jgKQQL8B7n3gsNp3YBP10vcQjqn2mH5mg30+sh+O5wwMhX13bfYWcAM/tHrC8uOOq6fgmqYdU1SgYqWLmn5nFtdfeArof75Z6OCPmXzJQLtmuhHWOVerhTBqKpoFCcs0K5Hw5dCzoqInco9FijMH/kB+F3gJULSpYz6JjcSJwHIeWK1rE7uSbt9R0qRbdj3eXlpdbLmuh5eUxomCkivFKHi2XtgAA+pb3If5Z9TRH61/Rj+j7UYAHnFc6qdT7BFOalWhf2A55kxxZTxYdN3TUzM6GkYquiJZKd7dc2QJeSafFiaJoVKDlMvEcFn41XDUNHmOrYuh9c2BJ74oWyI20vDxW9K6rcrq9dWj8QSMAwHe/D8VM5S/X5CN5+B7yAQDMp5mh3ZPKJUtBZVNhysIpkNfLkenIoPPXnVW53DcWuXAOvsUDx+JPzdBOpWNxotUdVFc6h3l+70G+v7KXDfORPLz3ikt6ppNMMOxPy8vjgYKRGmA9ywpFswL5UL7ipzoZY/De44WQFKCZqkHzqVQuWUoqhwrtC9shq5MhvSGNzps6UUxPjoCEMQbfvb4tx+IpdCxKxXaODapWFYrRIrz3eCt2uy8TGDx3e1CMF6HeTV1KwiVjR8FIDZBpxCqRgLjNLLm2chujRd6IIPFZApxyoDy0nA5BqaldarTf0g6ZXobU/1LourmrKmbYxiryFzoWKwWv5OG6xgVOwSGxJoHIGxGph7RdPX/qQfLzJDgVB9c1LjpmxhH9JmuEYX8D6r9XDzDAd58PQq7yMtMzngwCT4rb4Gzn2qByULn3SqFp16Dt5jbwWh7JL5LouqWrIo+h8ZL1ZYdsyaRjUXrqVjVs54oFwwJLA0h3VlYvpdT6FILPDlSJvsBOx8w4o2CkhtjOtUFeL0fWm0XohcpqRMWKDN7fe8FyDPpv6dF4bKPUQyJfo52qRdtv2sCreST/k0TXrV1Vt91yOFhRnGpnOQb9fno0/YC2ZFaKxuMaYTjQAFZg8NzugZCtjOOvmC6WqkQbZxnRcCQ1ThxvFIzUELlBDvuFYtXInpd6kN5cOXcWPa/0IL0hDV7H0za4CqabpkPrr1vBqcTpcvdt7ppr9x56MYT0evFYdFxGFX8rCceJ/bfkDeIur+6llVGywP+wX6zM26yA/WI7HTNlQMFIjTHONKLukDpAALz3eiviQpLxZBBaJs7U2H9hh6KRGo9VMv0+erTd0AZOySG+Kg7vXV6wYmUmFI5UakMKoT+Ix6LjQgeUJqXEIyJfJzfK0XKluN23981eRD+WtiFo/4p+9L/TD3CA8yon5HraxlsOFIzUIPtcO2QGsZDQYEtrqbCiuHuGFcQqhdQErzrop+vRen0rODmH6MooAs8EpB7SmAk5QeysOjDVbjyMmuBVKsN+BphOMgEQc+CkaluQC+bge3BLGQLd3jpJxjEZUDBSgxT1CtjOFxPBQs+HJO37EH41vGVKnJrgVRXDDANarhooSPVy9RSk2pHAkwFkvVnIG+WwX0RT7ZXOMtsC9W5iQ1DP3R4wYWJn51iRwXOnB0JKgHYvbalbOimPUQUjDz30ENrb26FWqzFjxgx88MEHO/36bDaLBQsWoLW1FSqVCrvtthuWLl06qgGT4an/v3roZ+jB8gy++3wT/kYGxOWZ4LKB7PPz7VA00fJMtamfVT+0IFVvZRek2pH4Z3FE/jzQkHFeC+QGmmqvdLx8YLuvikPy8yR6/tQzoa8f+kMIqf+lwOt4scuwjILXchpxMPLCCy/g8ssvx4IFC/Dpp5/i0EMPxbHHHgu3273D55x66ql455138Pjjj2PdunV4/vnnMW3atDENnOwcx3FwXOwAr+aRWptC5C8Tu2+fCQzee71geQb9DD3qj6DlmWplO8cGdbsaxWgRvgd8FVuQakcK8QK8v6eGjNVI5VDBfoGYlB98NojUhtSEvG7ii0RpR6LjIgeUZsotKrcRByN33303zj33XJx33nnYa6+9cM8998DpdGLx4sXb/fq3334b77//Pt58800ceeSRaGtrw0EHHYSZM2eOefBk55TNWxrQBZ4KIBfKTdhrh18LI70uDV7Lw3EJLc9UM17Jw3mNE5ycQ3x1HLGPY1IPadi2boKndChhO8cm9ZDICDUc2QDjLCNQBDx3eMpeIbiQKMB7lxdgQP336lF/GN1ITYQRBSO5XA5r1qzB0UcfPeTxo48+Gh999NF2n/P666/jgAMOwO233w6Hw4E99tgDV199NdLpHW87zWaziMViQz7I6DQe2wjdPjqwLIPv/om5q814Mwg+Iy7P2M6z0Y6FGqB2qmE6WUwo7H6ku2pKxve/119qgue8ygleRWly1YbjONgvtotdprtz8C/2l+21GGPwPeATu4nblKVZGVJ+I3pnhsNhFItFWCxD6/FbLBYEAtvPtt+8eTNWrlyJL774Aq+88gruuecevPTSS7j44ot3+DqLFi2C0WgsfTidzpEMk2yF4zg4LnWAU3JIfJZA7KPyBnZMEPt9sLxY3IyKA9UO86lmKK1K5MN5hJ6rrKJ625ML5eBfIl64LD+zUBO8KibXy+G8xglwQP+7/eh7t68sr9P39z7EPoyJwes1Tsg01E18oozqNuHrU+6MsR1OwwuCAI7jsGzZMhx00EE47rjjcPfdd+PJJ5/c4ezI/PnzEY1GSx8ej2c0wyQDVHYVmn8sNgHrXtpd1jLf4dfCYtKXhheDIFqeqRm8kod9rninGH49jHRH5RTV+zqhIMBz98BOiGlaaoJXA3R76WA+XdzR4n/Ij6w/O67fP+vLwv+wGLxaz7RS8DrBRhSMmEwmyGSybWZBQqHQNrMlg2w2GxwOB4zGLXv699prL7F7q9e73eeoVCrU1dUN+SBjYzrZBIVJ7OxbrtojWV+21LuBlmdqk2GGQVy/FwDfg5WZzMqYuCSZ+lIMiluubKGdEDXCfKoZun10EDICPHd4xq2o42A3cZZl0E3XwfRj07h8XzJ8IwpGlEolZsyYgeXLlw95fPny5TtMSD3kkEPg9/uRSCRKj61fvx48z6OlpWUUQyajIVPLSsmsoRdD415EqLR7ZqDfR8NRtDxTq2zn2cCreaTXpdH7116ph7ON0B9C6P9HP8ADrutcUNmooVmt4HgOLVe1QGaQIb0xjeDTwXH5vrFVMaT+lwKn4uC8wkkzuhIY8TLNlVdeicceewxLly7F2rVrccUVV8DtdmPu3LkAxCWW2bNnl77+9NNPR1NTE+bMmYOvvvoKK1aswDXXXINzzjkHGo1m/H4SskvGQ43Q7qUFyzIEnxufN/GgyJ8jSK0dWJ6ZR8sztUzRpIDlTHEmNPBkAIVoQeIRbdH3j75SPov9Qjtt461BSpMSLZcNFON7JYz4v8dWjI8VWSmoMf3QRPWQJDLiYOS0007DPffcg5tvvhn77bcfVqxYgTfffBOtra0AgO7u7iE1R/R6PZYvX47+/n4ccMABOOOMM3DCCSfgvvvuG7+fggwLx3Gl2ZG+5X3jVpk1688i8LS4dGc71wZlMy3P1LqmHzRBPUUNISlUTDOzxBcJ+O4XS3c3n9KMpmOoG2+tqju4bksxvrs9yPePfqa3790+ZD1ZyAyyUm4dmXgcq8RF36+JxWIwGo2IRqOUPzIOOm/pRHxVHHXfqUPr9a1j+l5MYNj8y81IrU1Bv58ebTe30azIJJFan8KmqzcBDGhf2A79vnrJxpL1ZbHp6k0oJoowzjLCeS1Ntdc6ISdg01WbkOnMQP8tPdpuGvm5RygIWH/+euTDeVjPsaL5JApGxttwr9+06X4Sss62AhwQ+2cMqXVjq2gYeWNgeUZNu2cmG+0eWjQeK96d+hf7JesQXYgV0PmbThQTRWj21KDlihY6DicBXsmLQaeSQ+LfCfS+OfL8pf53+5EP5yFvlKPpBzSTJiUKRiYhtUuNhu+JCaaBJwOj3hGR7c4i8JS4PGM910olkych62wrZEYZst4swn+a+A7RQk5A1y1dyHXnoDAr0HZDG3glndYmC7VTXaqqG3gmgEJs+PlLTGDoeUnsd2M6yUTHjcTotz9Jmc8wg1NwSH6RROLfiV0/4WsY27J7RrevDo3fbyzDKEmlk+lksJ8v1h4JvRBCNjC+tR92ZvAYTK0Vm5m13dQGuZEa4E02jcc2lvKXBis/D0f0oyhy/hxkBhkaj6Hzl9QoGJmklCYlmo4XpyUDT418diTyRkSs46Dm0XIZTYtPZsbDjNBN14HlGPxL/BNWeyT0XAjRFWKp99b5rVA71RPyuqSycDxXKtve+9depDftuhgfYww9fxRnRZpOaIJMTZVWpUbByCTW/JNm8FoemY4M+t/vH/bz8r350lY46xxanpnsOI6D40IHODmHxJrytxwABrbw/mGgT8cPvgAAIABJREFUq+rFDuinS5c8S6Sn+4YOxu8aAQb4H951QJz4dwKZjgx4NV+6KSPSomBkEpMb5Gg+WcweDz4bHHYCYuj5EISMAM2emlICI5ncVA5VqeS6/xF/WRvpZboy8D04sIX3J81oPIqOQQLY5ojF+FJrU7u8uQr9UQxkG49thNxAS3uVgIKRSa7pxCbIG+TIB/PofWvX2ehZXxa9fxO/zjbHRsszpKT5J81Q2pQo9BYQXDa+RfUGCVkB7tvcYqXfGfpS8TVCFE0KNJ8qBsSBJwI7DIiTXyaR+ioFTs7B9CMq+14pKBiZ5GRqGcw/E5tPhV4I7fKONvBUABAAw0EG6PbWTcQQSZXglTzsF4pr95HXI0hvHv9Gev5H/ch6spA3yKlsN9mG6YemUkA8mBPydaEXxVmRhiMboGikaquVgoIRgoajGqC0K1GMFnfaRC+5NonYP2MAB1jPtk7gCEm1MOxvgPFQce3e96APTBi/ZNb+lf3o+2sfwAHOq5y0c4Zsg1fysJ0nbvUNvxreprNvenMaiTUJgAM1w6swFIwQ8HK+NN0dfiW83V4jjDEEnhBrijQc2UA7F8gO2c6zgdfySK8fv0Z6uWAOvge25IlQwirZEcOBBuhn6MEKDN2PDW1V0POiOFtiPNRIDRQrDAUjBABgPMQIze4aCBkBoRdC23w+/q84UmtT4JQcLGfQOj3ZMUXjVo30ngqMqW8IIJbsdt/hhpAUoJ2mLS0rErI9HMeJtW9kQHx1HLHV4u6urC+L6IdRAGJASyoLBSMEwEATvYGll963eocUr2JFhsCT4qwIdbUkw9F0XJMY3CYFBJYGxvS9QstCSK9Lg9fxcF7jBC+n0xbZOZVDBdOJ4jJM92PdEPICel7uAZiY76Zpo47xlYbe1aREP10P/X7i9GZo2ZbZkb6/9yHrHehqeTLdUZBd43gO9ovsACf2/0h8PvIqvwAQ/ywuXkQAtFzaQjVtyLCZf2qGvEGOnD+H4LNB9L8rbvelWZHKRMEIGWJwdqT//X6kO9IQsgKCz4nbNM2nmSHTUaVCMjzaqdpS8zHfQz4I+ZE10sv35+G9ywswoPGYRhgPMZZjmKRGybSy0vnM96APxXQRum/qoJtGuwArEQUjZAjNbprSbojAUwGEXwuj0FuAwqxA43FUXIqMjOXnFvHu1JcrzXAMB2MM3t97UegvQNWqgu18WxlHSWpV/eH1ULWpkPPlkPVkaVakglEwQrZhOdMCyIDYqhi6l4rZ6NbZVvAKOlzIyMh0stJWy54/9iDbPbxGeuFXwkj8OwFOycF1rYs6qpJR4TgO6jY1GGMQ0gI4NdWlqVT0DifbUNlUaPx+I3L+HNIb0lBNUcF4GE2Rk9ExHmoUc5HyDP7Fu+4bklqfQuBpMenVdr4NahdtIyejU0wVEV8dh8KkgNKmRPCJ4IQ1ciQjQ8EI2a76w+uRD+dRTBRh+JaBKl2SUeM4DvYL7eAUHBKfJkrbK7enmCzCc4cHKALGWUY0fp+WBsno9b7dCyEpoO7AOigsCqTWphD/V1zqYZHtoGCEbFfkzxEozUrIjXLE/hkDK9LdBBk9lV1VWq/vfrQbxdS2bQcYY/A95EMukIPCrIDjEgcFwWTUhJyA8KtiRWnLzy1o/tFA35qnAnQ+q0AUjJBtpDamEF0RhdKmhHaaFjlfDn3v9Ek9LFLlmk9uhtI+0Ejv2W0b6fX9vQ/RFVGAB5zXOGnnFhmTvnf6UOgrQNGsgPG7RjSf3AyZQYasJ0vnswpEwQgZgrEtBc4ajmyA9Rxxa1zwuSCE3Mi2ZhKytSGN9N6IIL1pSyO9jCcD/8N+AGICNW2/JGPBigw9L4m7t0w/NoGX85DpZDCfJlbvDS4LQsjS+aySUDBChkj8O4Hkf5Lg5BwsP7eg6bgmKEwKFCIFRP4ckXp4pMoZ9jOIydBbNdITcgI8t3vAsgz6/fRUWI+MWf+KfuRDeciMMjQetSXvqPG4xv9n777j5KjLP4B/Zna2971td3t7LQkYQYgECMWAVCmCKE0QA4giKl2BRHoNVUAUfnQBKUHFgoAYDBAgiDESmhGSXNm9u623ve/OzO+PuewlIeXK3m173q/X/XO52/smmZt95vt9CuR2OUqREsJ/3v5QUDLzKBghZaIgwve4VMrbclwLFHYFWAULxxnSnJHg74IopT4/RI+QiWj9fitYLYvs+iwif4vA95gPuf4cZEYZ2i9ppzwRMiWiuNmuyDesYJVjb3OsfGwoaOgPIZQSdD+rFRSMkLLoiijyA3nIdDLYThl7OjUdYoKyUwkhLZR/yQmZLLlZDuci6fjP+wsvwn+UnlDdl7ght9DcIzI18bfiyHvyYDUsWo5p+dyfmw42QdWjgpAREHz+80NBSXVQMEIAQGr7/pSUVGg7xQZOx5X/jGEZOM+U3jxGXhxBIVyoyhpJ47AcZYHCpUBmXQZ5bx7Wb1qhn6+v9rJInROKAvxPSDlvthNt20yC3mIo6EsRFAJ0P6sFFIwQANii7XvL1z//NKHfWw/NbhqIBRHBZ+hpgkyRCECQEg2FnADtHpSwSqYu8nIExWARnIWD9RvW7X6d/sv68lDQbVV2kZlHwQhBKV4qH79sr+375k8T0deiyHlzM7pG0liCzwVR8BWgbFdCPVsN38M+qtYiU8KneQSXSQ9KjjMcW+SKbEt5KOgbMWR7szv8WjL9KBghCD4XhJAVoJ6t3mHbd+0XtDDsZwBEIPAkPU2QyUl9lCq/afTc0gNFqwKF4YkN0iNka6Hfh8AneSg7lDAfat7p16tnjd3v/I/7p3t5ZCcoGGly+aE8Rl6RSnadZzt3WsngWOQAGCDxzwTS69IzsUTSQEqJErx3egERMB9hhuVIS3kib+h3IeSHxzdIj5DNFUKFcqmu8ywnGNn4KrKci5xgOAaptSkk11Kb+GqiYKTJ+Z/0Azyg30cP3R66nX69yq2C+XDpqcP/Gz8NnSLjJooiBu8ZRClSgrJdidZzpSDEeKARur3GP0iPkK0Fng5ALIrQfkkL/d7jT4RWOBSwHCP1IfE/TvezaqJgpIml16WRWJUAmLHz0/Gwn24Ho2CQ+W8Gmf9mpnGFpJGMvDiC5OokGDkD9+VuyFRSpQPDMGg7b3SQ3toU4m9tf5AeIVvL9mURWxEDML7d3a3ZT7GDVbPI9eakcQSkKigYaVKiKJbPSc1HmCc0pl1hVcB0iAkAEP4LdTEkO5fdmC1fb63ntELdrd7iz5WtynKrbt8jPvDpzw/SI2Rb/L/xAyJgXGiEZo5mwt/PGTnYThodovekH0KREqmrgYKRJpV4N4HMugwYJQPHdxwT/n7r8dby61CdPtmR4kgRA7cMQCyJMOxnKG+Lb836LSsULgVK0W0P0iNka8m1SaT+k5LGV3x34vexTVqObwFn4VAMFhF5JVLBFZLxomCkCQmlzRoDfdM2qa6Xqg4VdF/WASIQfpF2R8i2lRIl9F3dh2KwCEWrAq4LXdvdRmflLFw/dgEARl4aQeYzOgIk27f57q7lGAuUrcpJv5ZMJYPj9NGxF88FaWeuCigYaULRV6MoDBcgM8pg/db2GwPtzKamQtG/R8Fn6JeXbInP8ei/rh95bx5cC4fum7rB6bkdfo9uD510BCgCQ/cNQSjRljnZttgbMeR6c2A1bPmIbyrMh5uhbFeCT/IIvUBl5jONgpEmw2d4BJ6VtsAdpzsgU3++XfJ46fbSQdmuhJAVEP1HtFJLJA1AFER47/Qiuz4LmV6G7hu7obArxvW9rd9vhcwoQ64/R7OQyDbxOb7c68h2sg2cYcdB7ngwMgaOM6XdkfCfwihGilN+TTJ+FIw0mdAfQuDjPBQuBcxH7rwx0I4wDIOW46XW8SN/GYEoUFkckfge8yH5nlQ503l1J1Tu8SdIcwYObT9sAwCEloWQG6Buv2RLoWUhFMNFyO3ycv5aJRgWGKCZK429CDxDeUsziYKRJlIcKSL8p7HGQCw39f9+0yEmyHQyFPwFJFdT0yAChP8axsifpUZ67Ze0Qzt34nNnjF8xQr9AD7EkwnO7B3yOjgGJJD+UL9/H2s5tA6uo3NsYwzBwnj069uLvNPZiJlEw0kQCTwcgFkRo5mpgWGCoyGvKVDJYjpKqIzbdIEjzSqxOwPeQD4DUrde00DSp12EYBq7zXeAsHPKePIZ/Tc3QiJS0OvzgMMSSCP3eeuj3rfykZ+1cLfQL9DT2YoZRMNIksv1ZRF+T8jpaz2mdcGOgHbEcawFkQPrjNA2camLZ3iy8t4+1et/Uu2Gy5CY5Oi7vAFgpWTHyNyq5bHbxlXGk3pdKeVvPrex9bHPOM5009mKGUTDSJDY1BjIcaIBm14k3BtoRhVUB44HSwCk6Z21OxZEi+m/oh5AToN1Ti7Yft1XkjUK7m7bcHdj3kA+Z9VTu26xKiRKGHxoGANhPs0+plHdnVG5VOaeO2sTPDApGmkDqgxRSa1KAbDTinwb2b9sBFki+l0Tq49S0/AxSm/gcj/4b+lEaKUHpVqJzSWdF8pE2sZ5ghWE/g5Q/cqsHpWSpYq9N6ofvER/4BA9Vl2pKLQnGy3G6Qxp7sS6D5L8oH266UTDS4ERRhO8x6Qy/5ZiWaXuaULlV5dwR3yM+epJoEqIgwnu7F7neHGRGGbqu7YJMO/ly8W1hGAbtF7dD0apAMVjE4C8G6fpqMsk1ScRejwEM4LrAVdFgd3vkFjmsJ0hBj/83fog8XXPTiYKRBlfpxkA74jjdIQ2c2phD7I3YtP4sUht8j/ik4XcKBl1Xd0HhGF8vkYmSaWXoWNIBRsEg+e8k5Y80ET7HY+jXQwCktu2aXSp7zLwjtm/ZINPLkB/Ml3PuyPSgYKSBCQUBgadGGwOdZANnnHpjoB3hjBxsp0hJi4EnAxDy1D2zkYVfDGPkRamE132pu+K5SFtTd6vhXCQdM/of8yPvy0/rzyO1IfBUAMWQ1FPEccbk589Mhkwrk46gIeXDUYn59KFgpIGN/HVE+iW2ysut26eb9Xgr5DY5iuEiwn+mUt9GlXgvAd/D0vGf8yxnOYF5urUc3wLt7loIOQGDdw9So70Gl/k0Uw54Xee7IFNV9ghwPCxHW6BwKlCKlBD+I93TpgsFIw2qlCwh+HwQAOA4w1HRxkA7wirYcvVD6HchFKPUUrnRZDdm4bnDI5Xwfs08I8mEm2zKH2FVLDLrMhh5aWTGfjaZWUJJwOAvBwERMB1qgv7Lle8pMh6snC23iQ/9IURt4qcJBSMNKrgsCCEtQNWlkgaPzSDjQiPUu6gh5AQEnwnO6M8m06sQLqD/hn6IeRG6eTq0nVeZEt6JUDgUcH5PCngDTwZQCBZm9OeTmRH6fQh5Tx4yowyt32+t6lqMBxqlNvF5EYHfUvuC6UDBSAPK+/OIvCQl+Dm/5wTDzuybBcMw5ZtH5NUIsv3UCK0R8FkeA9cPoBQpQdmhRMfijhmpatgWy1EWaHbTQMgJGLp/iKprGkzOm0NomTQkse3ctp1Oe55uDMOg9RzpnhZ9LYpsH93TKo2CkQYUeCoAsSQ9uVZra1M7VwvjV4yACPgeplLfeifyIjy3eZDrz4EzcdNSwjsRDMOg/YJ2MByD1JoUTY1uIKIoYui+Ianl+z56GBfOTD7Szmh21ZTvaf7HqBFapVEw0mAy6zOIr4wDDMoDn6rFebYTjJxB+sM0NQ2qY6IoYvihYaTWpMAoGHRe0wmFfXpKeCdC6VLC/h2p0mH4gWHkPDTUrBFEXokgsy4DVsVWrJNvpTjPckoB8NoUkmvonlZJFIw0EFEU4X/MD0CapqvuUVd1PQq7AtZvSsmNvkd9EIpU6luPRv4ygsjLEYAB3D9zQzNn5vo87IztWzbo5ukgFqTurFR6Wd8K4QL8j0v3MOdZTiis1Q96N6dwKNByXAuA0d0RaoRWMRSMNJDk6iTSH6fByBk4vjuz9fjbYzvZBs7MoeArYOSvVPlQb+L/jMP36GgJ79lOGPevjS3zTRiWgftnbmm6rzeP4ftpum+9EkURw/cPQ8gJ0MzVwHKMpdpL2ib7qXapEZo3j8hyar5XKRSMNAiRF6VheACs37DWzBOFTCUrz8MJPhdEKU5zRepFZn0G3jukKbyWoy3l1ti1hjNyY9N9X48hupzyR+pR/K241M2XY+C6wFVTxzObk2llsJ822gjttwHwGdqNqwQKRhpEZHkEeW8eMr1syqPbK810qAnq2WoIGYHK4upEIVTAwI0DEAsidPN1aPthbZ3db027m7a8Gzj84DBVO9SZUnJsIq/tVBtUblWVV7RjlqMtULQpwMd5hP4QqvZyGsKkgpH7778f3d3dUKlUmD9/Pt56661xfd8777wDjuMwb968yfxYsh18ji/387B/217VKodtYRgGrT/YrNSX3ihqGp/h0X99P0rRElRdKnRc3gFGVruByCa2E23Q760fyx+hJ9a64XvUBz7OQ9mhrLmHqW1hObZcIBD+UxiFMPW6maoJByPLli3DxRdfjCuvvBLvv/8+Fi5ciKOPPhoej2eH3xePx7Fo0SIcdthhk14s2bbwH8MoRUtQOBU1e86q/aJWKtGjUt+aJpQEeG71ID+QB2fh0HltJ2Sa2gput4dhGLRf2g65VY7CcAFDv6L+I/Ug+X4SsX9IE3nbL2yvWu+aiTIsMEC7uxZiQUTgSdrxnaoJ/6//4he/wDnnnIPvf//7mDt3Lu655x643W488MADO/y+H/7whzj99NOx//77T3qx5PNK8RLCL0jzEhxnOmr6F9l5lhOMgkH6ozQS/0xUezlkK6IowvegD6n3U2CUDDqv7qyZ3KPx4vQc3Fe4AZmUgxB5hRIMaxmf4zH0q9GJvF9vmfZhi5XEMEy5E3Ds9RgyGzJVXlF9m9A7V6FQwJo1a3DkkUdu8fkjjzwSq1at2u73Pf7449i4cSOuvfbacf2cfD6PRCKxxQfZttDvQxByAtSz1TM2rGyyNi/19T/mp1LfGiKUpMFzkb9JJbwdl3VAM7t+3hg2p/2Ctpw07XvYh+xGOhasVYHfBlAMFiG3yeFYVBsVgBOhmaMpj9vwP0qN0KZiQsFIOBwGz/NwOLa8aBwOB/x+/za/Z/369Vi8eDGefvppcNz4WvouXboURqOx/OF2uyeyzKZRHCmWB4U5znDUdILhJraTbOAsHAr+Akb+QqW+taCULGHg+gHEXo8BLNB+UTsMCwzVXtaUWE+wQr9AD7E0mj+SpvyRWpP5LFO+B7h+Up2JvJXgWOSQdnw/puaOUzGpPf2t3/REUdzmGyHP8zj99NNx/fXXY5dddhn36y9ZsgTxeLz84fV6J7PMhhd8PgixKELzRQ10e+mqvZxx2aLUd1kQxRhNwKym9CdpbLhwA1JrU2BVLLqu6YL5MHO1lzVlm6b7yu1yFPwFDP5ykJ5aa4hQEjB035A0kfcQE/TzqzO2ohIUVkW57N33uA9CiXZ8J2NCwYjVaoVMJvvcLkgwGPzcbgkAJJNJ/Pvf/8b5558PjuPAcRxuuOEGfPDBB+A4DitWrNjmz1EqlTAYDFt8kC0VAgVEXpXOwx3frY9dkU1Mh5ignqOGkBUQeIoSv6pB5EUEngmgd0kviuEiFK0K9NzWU9dvClvjdBw6FneA4RgkViUof6SGhF8II9efg8xQ/Ym8lWA7yQaZUYbCUIGus0maUDCiUCgwf/58LF++fIvPL1++HAcccMDnvt5gMOCjjz7C2rVryx/nnXcedt11V6xduxYLFiyY2uqbWODZAMADunk66Havj12RTTYv9Y0ujyLbS2f6M6kQKqD3yl4Enw1KT6aHmTD73tlVHx8wHTRzNHCeNZo/8pgP+aF8lVdEcoM56doD0PqDVnCG6k7krQSZWgbHd6QH8uCzQToWnIQJH9NceumleOSRR/DYY49h3bp1uOSSS+DxeHDeeecBkI5YFi1aJL04y2L33Xff4sNut0OlUmH33XeHVqut7N+mSeQGc4itiAFAzbR9nyjtXC2MB1Gp70yLr4pjw4UbkPkkA1bNov2n7XBf7IZMXZ/n9ePRcnwLtHtqIeZFeH/hpXkiVSSKolRyXZKa6ZkONlV7SRVjOdICpVsJPskj+Hyw2supOxMORk499VTcc889uOGGGzBv3jysXLkSL7/8Mjo7OwEAPp9vpz1HyNQEn5aeaPUL9NDsUp8VD8Bmpb4fp5F4lyqmppOQFzD06yF4lnrAp3io56gx+97ZMH+1/vNDdmZT/girZZH9LIvAM3Q0WC2Rv0WkQFjFwvXj2m35PhmMbKzUd+QvIygEqBHaRDBiHTySJhIJGI1GxOPxps8fyfZmseGiDQCA2ffNhrqrvrfWA08HEHwuCLlDjl3u3wWsonb7pNSr3EAOnts9yHukIwrbSTbYv2Ov6Z400yG2MibN2gHQsaQDxgNquxS+0RRHivjsR59ByApoPbcV1uNqc9bRVIiiiP5r+pFam4JxoVGamdTkxvv+3Vx3owYQeFp6qjMuNNZ9IAIA1hOt4CwcioEiwn8JV3s5DSf5fhIbLt2AvCcPzsyh68YuOM90Nl0gAgCmg0xo+YY0/n3w7kHkBnJVXlHzEEURQw8MQcgKUO+qRsuxLdVe0rQoN0JjpKZ7mU+pEdp4Nd8dqY5lPs1IdewMyslS9U6mkpUTDEPLQiiEaGuzUpJrkxi4aXTY3Twd5tw3B/p5jVMtMxmtZ7dCu6cWQk7AwE0DKCVpivRMSLybQPI9aSJv+wXtYNjGOZ7ZmrpbDfPh0vGn7xHKhxsvCkbqiP8pqaTafJgZSpeyyqupHNNXTdDM1UDICTRPpELS/0uXp+7q99Wj89pOcMb6r1qYKkbGoOOKjnL/Ee/tlNA63fg0j+H/G53Ie5INqs7anshbCY4zHGCUDDL/yyD+Trzay6kLFIzUidSHKaQ/SIPhGNhPs1d7ORXFMAzaL2oHI2eQ+k8K0dei1V5SXSvFS/Dc6pF2RObr0LG4oymPZbaH03PovKoTjJJBam0K/ie23T2aVIb/N35pkKdLAdvJtT+RtxLkFjls35L+roEnAjT6YhzoDlUHRFFE4LdSroj5SDMU9voaXjYeSpeyXKbse8RHI7knSRSl8tXSiHTz77iiA6ycfs23pu5Wo/3idgDS1OvEaqrmmg7pT9LSvCMArvNdTZWgbv2WdWz0xUs0+mJnmufKqGPJNUlk1mXAKBjYT22sXZHNWb9hhXpXNYSMgOFfD9NxzSSEfhdC6j8pMAoGHYs7Grp/yFSZvmJCy/GjCa33DtJoggoTikJ5Iq/5a+a6a844VTKVDI4zRhuhPRek/KSdoGCkxomiWG6Z3nJsC+QWeZVXNH0Yduy4JvnvZLmxGxmf1Iep8g5a24/aGqLaaro5z3RC1aUCH+cxeA/Nr6mk0PMh5AelKq7Ws+u/5ftkmA8zQ9WlgpAWEHyOGqHtCAUjNS7xbgK53hxYFQvbSY1/3qpyq2A/Xdr9GX54GMURelodj2K0KPXQGG3vbjncUu0l1QVWwcJ9mRuMgkFqTQojf6Xt9ErIeXII/T4EAGg9txUybXPu0DEsA+c5o43QXhpBfpjGEWwPBSM1TBTGckWsJ1gbYobDeNi+aZMG6aWlrqH0tLpjoiDCe6cXpVgJyk4lXD9yVXtJdUXVoYLzbOkNw/+4H9l+mpU0FZu3fNfvq4fxwOZuLqefp4duvg7gpWResm0UjNSw2Jsx5L15sFq2PKK6GTAyqX03wzFIrk4i9gYd1+xI8Nkg0h+mwapYqXJGSb/WE9VybAv0++ghFqXATihQ9cNkRV6JILNOavne9qO2hmr5Plmt32sFGGmnO/1JutrLqUl016pRQklA8BnpjNF2oq3ptjlVHapyCbPvIR+KETqu2Zbk+0kEl0nXiet8F1Ttjd/DYTpsKi/nTBzyA3l6gp2k4kix/G/nWOSAwtp4lX+ToepQwfI16ejU9yg1QtsWCkZqVPS1KAr+AmRGGVqOa8zWyTtjO9EG9Ww1+BSP4QeHq72cmlMIF+C9U8oTsRxlaagJqNXAGblyue/IiyNI/JvKfSdq+MHhhm/5Pln279jBqlhk12cRX0mN0LZGwUgNEgoCQsuk5C/7yXbIVM21K7IJI2PgusgFsEBiVQLJtclqL6lmCEUBnls94BM8VD0qtP6gOasVKk0/Xz9W7nsPlftORPzduDR9Wybt0jVyy/fJkJvk5aZv/if8dBS4FQpGalDkbxEUw0XIrXJYjm7uqgh119gTlu8hH4QS/QIDUmO47KdZsFoWHUs6mqqZ1HTbvNx36F5KoB6PLVq+n2ijsvLtsJ5ghdwqRzFEg0G3RnewGsPneASfH80VOdVGbzIA7KfbIdPLkPfmEXk5Uu3lVF309Wj538H9UzeUzsaZU1QLWAUL98/c5X43VO67c/4n/ShFSlC0KRq6MeNUsQoWjkVSI7TQ8yGU4tQIbRN6p6sxI38dAR/noXAqypMfmx2n48q/wIFnAk39C5ztz5a7Wtq/bYdhH0OVV9SYVJ0qaRQ8pHLf3ECuyiuqXel16XJw3Gwt3yfD9FUT1LPVELICAs8Eqr2cmkFXTQ3h0zzCf5C27uyn22m42WYsR1qg6pE6GW6aXtxs+DQPzy2jA/D20jXcwMRa03JsC/R7S+W+nts94HN8tZdUc4SigKH7Rlu+H2GG7kvN1fJ9MhhmrBFa5G8R5LwU6AIUjNSU4LIg+BQPpVtJlRFbYVgGbee1AQCif48isyFT5RXNLD7HY+CmARR8BcjtcukYgRIEpxXDSP1uODOHvCePwbupXfzWQn8IIe/NQ2aUlRvHkZ3T7a6DfoEeEKSdN0LBSM3ID+XLCU2t57TSG802aOdqYfqqCRAB34PNU6vP53gMXD+A9MdpsBoWnT/vBKdvjm681cYZOXQs6QDDMUisSiD0u1C1l1Qzct5cueqv7dw2uiYnqPXsVkAGJFcnkfowVe2XEdAcAAAgAElEQVTlVB0FIzVi+OFhgAf0++ihn6+v9nJqlvMsJ1gVi8z/Mk3RmZXPbhmIdN/QDfUsqlSYSdq52vKuXOC3Aeo/gq1avu+th3Fhc7d8nwylS4mWY0YrBR/1QRSa4+FqeygYqQGJfyeQWpMCwzFoPYf6ReyIvEUO2ymjtfq/8YPPNu45filVQt/VfVsEIppdNdVeVlOyfM0ildmLgPdOL/JDzT3wLPJqBJn/Usv3qbJ/2w5WyyLXm0Ps9cZ/uNoRCkaqTCgJ8D3iAwC0HNcCpYvKNHfG+g0rFK0KlCIlhJ5vzG3zUryEviv7kP00C5lehu6bKRCpttZzW6GZq4GQFjBw8wD4TOMGwjtSjGzW8v0MBxR2avk+WZyBg/0UKRHd/6QfQr55+yhRMFJlIy+OoDBUAGfiYP82VUeMB6tg0fp9aQcp/Kdww43lLkaK6F3Si1xvDpyJQ8/SHmhmUyBSbSwnNZjjLBzy3uZMaBVFEcMPDENIC1DPUTftqIpKavl6C+R2ufRw9cfGfLgaDwpGqqgYKyL4nNTgzHGmAzJNc7Z9nwz9PtJYbrEklneWGkEhWEDvFb3Ie/OQW+XoubUHqk4aflcr5GY5Oq/slBJa/5koDylsFolVCST+Odry/UJq+V4JrIKF8yypEin8h3DTDgWlYKSKAk8GIGSkJwzzYdTgbCIYhpF2R0az0ZNr6n9uTX4oj94relHwF6BwKtBzWw8d29UgzS4atP1ESmgNPh1E4r3mSGgtJUsYemC04d7Jdmr5XkHGrxih3lUNIScg8HRzNkKjYKRKMusziL4WBSCdRVMC2MSp2lVo+fpoNvoj9T23JjeQQ+/iXhTDRSjdSvTc1kNn8TXMcrilfO157/IiN9j4jat8j/rAx6U+SJuSyEllMMxY8UJ0eRTZ/myVVzTzKBipAlEU4XvYB4iA6RATtF/QVntJdctxmgMyowz5wTwiL9Xn3JrMhgx6l/SiFCtB1aNCz9IeyC3yai+L7ITzHCe0u2shZAUM3DQAPt24Ca3J95OI/SMGMIDrAhdYOb11VJp2rhaGAw2ACPgfa75GaHRFVUHszRgy66SyOOeZ1LVwKmRaGZyLpH/DwLP1N7cmvS6Nviv7wCd5qHdVo/vmbnBGah5VD1iORcfiDsitchSGCvDc5oFQrN/due3hc3x5HlLL11ugnUsPT9PFeaYTDMcg9X4Kyf/U/9HzRFAwMsP4HF9u/2s72QZ5Cz0BT5X5cHNdzq1JfZBC39V9EDICtLtr0X1jNzgdBSL1hDNy6LiyA4xCegPx3uGt6+PCrYmiCN9DPhSDRcht8vLASjI9lK3KsaPnx5qrERoFIzMs9LuQNGrbqYD1BGu1l9MQGJZB2w/H5tZkN9b+eWtidQL91/dDzEtD77qu64JMTdVU9UgzW4POq0crbN5NSCW/fGO8iYT/FEZ0eVQ6nrnQBZmKrtHpZjvVBplehvxAXvq3bxIUjMygvD+P8B+l+TPOc5w0aruCtF/UwniQERCB4QeHa7r/Q/ydOAZuHoBYFGHYz4DOqzrBKulaqGf6eXp0LOkAZEB8ZRyeW+v/yCb+bry8i9t6Tiv082hMxUzgdGM9pwK/DTR0l+nN0R1wBvkf90MsitDuqYVhgaHay2k4zrNH59asy2DkryPVXs42RVdE4bnNA/CA8WAj3Fe4KRmwQRj2NaBzSScYudSDZOCGAfC5+nwjyWzIwHunFxAByzEWtBxPzc1mkuUYi9RlOlZC6A/N0QiN7oIzJPVhColVCYCVJlxSKW/lKayK8hhz/xN+5P211Zl15JURDN49CIiA+Ugz3Je6wXL0K9hIDAsM6Lq2C6yKRWptCv1X99ddlU0hXMDAjQMQCyJ083V0v6oClmPL97LwH8MohAtVXtH0ozvhDBB5EcMPDQMAWo5tgaqDOmpOF8vRFmi/pIWYFzH0y6GaSSYM/SmE4ftHr4HjWuA6n7pXNirdnjp039QNVitNl+5d0ls3VV6bpkSXIiUoO5XouLwDjIyu02ow7GeAZjcNxIKIwG8bvxEaBSMzIPK3CPIDecj0MthPo/kz04lhGLgucIFRMkh/lIb39upWN4iiiMBzAfgfHaugav0BNblrdJpdNei5tQeciUOuL4eNV2ys+adbkRfhvd2LXH8OMqMMXdd00YiKKtq8EVpsRQzZ3tpPzJ8KCkamWSlZKke1jjMc4PRUujndlK1KdCzukM7u303Ac4sHQmHmAxJRFOF/wo/g06Pzh77rgHORkwKRJqHuUqPnth7IbVIfkt7Le2t6qKPvUR+S/06CUTDourqLOgDXAM0cDYwHS4n5vkd9NZ2YP1UUjEyzwNMB8Ckeqi4VLEdZqr2cpmHY2yCVWyoYJFcnMXDjwIyO597UnyH8B6l6qvX7reVR4aR5KNtGW/u7FCiGiui9orcmW32H/xrGyItS0rf7Ujc0u9KU6FrhXOQEI2eQ/jCN5OrGbYRGwcg0yg3kEHlFalHe+oNWyhGYYfov69F13WbJhNf3z0h1gyhI+Sojfx0BGKDtJ22wfoN6yjQrhU0hTV/uVqEUK6F3cS8yn2aqvayyxL8T8D0kTb52nOmA8UBjlVdENqewK8r3D99j9T2Da0coGJkmojg62l4ADAcYoNtDV+0lNSXdl3TouqELrIZF+qM0+q+Z3uoGoSTAe6dXGoLIAu2XtKPlKCqLbHZykxzdt3RD8wUNhLSAvqv6kPowVe1lIdufhfc2qYTXfIQZthNpAF4tsp1sg8woQ2GogOirjdkIjYKRaZL8VxKptSkwHFMu0SLVoZ2rHatuWJdB39V9KKUqX90gFAR4bvUg/lYcDMeg44oOmA8xV/znkPrE6Th03dgF3TwdhJyA/uv6kfhXomrrKUaKGLh+AEJOgHYPLdp+TCW8tUqmkcFxutSKP/BMoO7KxceDgpFpIBQF+B6Vtj2t37RC6VRWeUVEM0eDnlt6INPLkF2fRd+VfSglKheQCHkBAzcOIPmelADYeVUnjAfQdjfZkkwlQ+fVndAv0EMsihi4eQCxN2Mzvg4+x2PgpgEUw0UoXAp0LOmgnjc1zvI1C5TtSvAJHqHfN14jNLr6psHIiyMo+ArgzBxsJ9O2Z61Q96jRs7QHMqMMud4cepf0ohgrTvl1+QyPvmv7kFqbAqti0XVtF/TzqXU22TZWIU37NR1iAgTAe5cXI3+buY7Boihi8BeDyK7PQqaXoevaLhrQWAcYGQPn90Ybof05jEKwtkvFJ4qCkQorxooIPieVcjrPdNLwsxqj6lRJ/R8sHPKePHoX96I4MvmApJQsoe+qPmQ+yYDVstI2POUHkZ1gORbtl7TDcoxFmqf06+EZa/vtf8KPxLsJMJy0g6dspZ3beqHfWw/tHlqIRaltQCOhYKTCAk8FIGQFqOeoYTrUVO3lkG1QtUsBidw62v9hSS8KoYk/ZRRjRfT9vE96wjTI0HNLD7Rf0E7DikkjYhgGbee1wXaStHvq/40f/qf809pLIvJqpFxu7rrIBe0X6XqtJ+VGaIw0kDHzWe1UZU0VBSMVlO3Nlkc+U5fN2qZslfo/yB1yFHwF9C7undAsm0JY+p5cfw6chUPPrT1Q96inccWkETEMA+eZTjjOlJITQ8+HMHTf9IwxSK5NYuiBIQCA/TQ7zF+l5Op6pO4Ze9D1PdI4jdAoGKkQURydPyMCxoOM0M6lJ45ap7BL/R8UbQoUg0UpIBnaeUBSCEiBSGGoALlNLvWQcNO8ITJ59pPsaPtRG8AA0eVR9F3VV9F5NjlvDp5bx6ZF01iK+ub8rhOMgkFmXQaJd6tXkVVJFIxUSPydODKfZMAoqJS3niisCvQs7YHSrURpRGpIlfPktvv1+aE8Nl6xEcVAEYpWKZihM3dSCS3HtKDzmk6wGhaZTzLovbK3Ik36SvES+q/vh5AWoJmrQfuF7bRrW+fkLXLYvjV2vNcIjdAoGKkAoSDA/9joILSTbFBYaaZDPZFb5OhZ2gNV12iHzCW9yPZ9vmV3tj+LjVdsRGmkBKVbKe2q0PwOUkGGvQ2YdecscGYO+YE8hu8fntI2vFAQMHDzgBQ8OxXovLITrIJu+43AeqIVnJlDwVdA5KVItZczZXRVVkD4j2EUQ0XIrWPRKqkvnJFD9y3dUM9Wg0/w6Pt5HzIbxpLDMusz6FvSBz7OQ9UzmgBrkVdxxaRRqdwquC93AwwQez2G6N8n13FTFEUM3juIzDqp0qvz2k5wRirhbRQylQyOM6Rco+BzQZSSlW/kOJMoGJmi4kgRwd+NlvKe7QSrpH/SesXpOXTf1A31rmrwKR791/YjP5RH+pM0+q7sA5/iofmC1DyNM9BNnUwf3e46OBZJbzTDDw4jvS494dcIPhNEfGUckAGdP++Eqp3ymhqN+XAzVF0q8CkewWXBai9nSuidc4r8T/gh5kVo5mpgXEgdN+udTCtD941jOySf/eQzbFy8EUJWapnddWMXZFrqHUOmn+1EGwz7GcqdWifS5Cr6erTc78j1Exf1vmlQDDvWCC3yUgT54fFXBNYaCkamIPNpBrHXpVbOVMrbOGRqGbqu6wJkQOKfCWQ+zkiByLVdkKkoECEzg2EYtP+0HaoeFfg4L02dzuw8oTX9SRpDv5RKeG0n2WA5wjLdSyVVpP+yHrr5OoglEb7HfNVezqRRMDJJ5VJeSFtlmjmaKq+IVFLqwxSEnACGY8CoGIilxqjlJ/Vl0yybTR2DPbd7IPLbvxbzvjwGbh6AWBJhOMBQPuohja31+60ACyTfSyK5Nlnt5UwKBSOTFHsjhuxnWbAqln7hG0zktQi8d3jBylk4z3RC9yUdMv/NwHunF6JAQQmZWQqrAp1XdYJRMEitSZWHcG6tlJRKePkkD/UcNdyXumm3tkmo2lVoObYFwGgjtB0ErLWKgpFJ4HM8/L8ZLeU91Qa5maoqGsXISyMYuncIEAHLURZ039SNzqs7wXAMEu8mMPzQ1EotCZkMzRwN3D91A5AGcYZfDG/x50JJgGepp9yIr/PqTkqmbzL20+yQ6WXID+QRebX+Sn0ndbXef//96O7uhkqlwvz58/HWW29t92tfeOEFHHHEEbDZbDAYDNh///3x6quvTnrBtSD0uxBKkRIUTgWsx1urvRxSIaEXQhj+P+noreUbLWj7cRsYhoHuSzq0/7QdYKQksfAL4Z28EiGVZzzAWG4b73vYh/i7cQDSkfHQr4aQ/igNVs2i85pOekBqQpyeg+M70vUR+G0ApVR9lfpOOBhZtmwZLr74Ylx55ZV4//33sXDhQhx99NHweDzb/PqVK1fiiCOOwMsvv4w1a9bgkEMOwXHHHYf3339/youvhkKwgPAfpTcj5/ec1ECoAYiiiMDTAfgfH9vtaj1ny4Rk01dM0oAqSB0PY2/GqrJW0txsJ9pgOUqa9Ou904v0ujRCvw8h9o8YwAAdV3RA3UUzkpqV5SgLlG4l+CRfrqaqF4w4wT3nBQsWYK+99sIDDzxQ/tzcuXNxwgknYOnSpeN6jd122w2nnnoqrrnmmnF9fSKRgNFoRDweh8FgmMhyK27g1gEk3klAu4cW3Td105lsnRNFEf7H/eUA03GmA/aTtj+3Y/iRYYz8eQQMx6Dr+i4qmSQzTuSlUt/k6iT4HA8GDFgVi7YftaHlmJZqL49UWfL9JPqv6QdkwC6/3gVKV3XHVYz3/XtCj/WFQgFr1qzBkUceucXnjzzySKxatWpcryEIApLJJCyW7Zeb5fN5JBKJLT5qQerjFBLvJACGSnkbgSiKGH5guByItJ7busNABABaz2mF8StGiCXpDSHb//m28YRMJ0bGwH25G1wLh8zHGWQ+zcB8hJkCEQJAKvXV76MHeGw32bkWTSgYCYfD4HkeDseW1SMOhwN+v39cr3HXXXchnU7jlFNO2e7XLF26FEajsfzhdrsnssxpIQoifA9L/7GWoyy0FVrnRF7E4D2DiLwSARjAdaEL1uN2nv/DMAzaL2mH5osaCBkBA9cPoBAefzMqQiqBT/AQMgIYOQNWzSKzPoNCiK5DImk9pxWQAcnVSSTfr49S30klPGy9IyCK4rh2CZ599llcd911WLZsGez27T+BLlmyBPF4vPzh9Xons8yKii6PItebA6tly0lCpD4JJQHeO72IrYgBLOD+mXtCjaFYBYvOqzqhbFeiGC5i4PoB8OmpT1clZDz4tNQATcgKMB9hhm4vHfL9eWy4eANSH6SqvTxSA5QuJVq+Plrq+3B9lPpOKBixWq2QyWSf2wUJBoOf2y3Z2rJly3DOOefg+eefx+GHH77Dr1UqlTAYDFt8VBOf5uF/Svo7O05z0LCpOiYUBHhu8SD+dhwMx6Dz550wHWSa8Otweg5d13eBM3PI9ecwcMtAQ4zxJrVN5EV4bvMg78mDs3Doua0Hc+6dA9UslTTg8eo+hF4IUfk5gf3bo6W+3jwif6v9Ut8JBSMKhQLz58/H8uXLt/j88uXLccABB2z3+5599lmcddZZeOaZZ3DsscdObqVVFFwWBB/noXApYDmWWivXKz4nPVEmVyfBKBh0Xt0Jw4LJB7oKuwJd13WBVbFIf5jG0L1D9CZApo0oihh+cBip91NglAy6rumCwqqAwq7ArNtnwXSYCRAB/+N+eG/3gs/Sbl0z43Rceapv4OlAzU/1nfAxzaWXXopHHnkEjz32GNatW4dLLrkEHo8H5513HgDpiGXRokXlr3/22WexaNEi3HXXXdhvv/3g9/vh9/sRj8cr97eYRvnhPEZeHAEgtdxlOSrlrUd8mkf/Nf1If5gGq2LRdX0X9Hvpp/y66h41On7eAcikrryBJwNTXywh2zDyl5FyjlPHZR1QzxrLW2MVLNovakfbj9rAcAzib8ex8acbkR+q38FpZOosX7NA2Tla6vtsbZf6Tvid9dRTT8U999yDG264AfPmzcPKlSvx8ssvo7OzEwDg8/m26Dny4IMPolQq4Sc/+QlaW1vLHxdddFHl/hbTyPeoD2JJhG6+Doa9q3tcRCanlCyh76o+ZNZlwGpZdN/UDd3ulSvJ1X9Zj/YL2gEAod+HMPLySMVemxAASLyXKFdGOL/n3OaOHsMwaDmmBd23dEuzbLx5bLh0AxLv1UY1Ipl5jIyR5tZA6i6d8+aqvKLtm3CfkWqoVp+Rzeu15/xqDlTtqhn72aQyitEi+q7uQ34gD5lRhu4bu6Hunp5KqMBzAQSfDgIM0Hnl1I6ACNkkuzGLjVdshJgXYTnKUu4MvCPFaBGeWz3I/DcDQGrk5zjdAYaldgTNqP+mfiTfS0I3X4fu67pn9GdPS5+RZiLyY6W8Lce2UCBShwrhAnoX9yI/MJrst7Rn2gIRALCfaof5a2ZABDy3e5D+X3rafhZpDoVwAf039EPMi9B9WYfWH46vv5HcLEf3zd1oOU6qqAgtC6H/hv6azxsg06P1e61gOGnQYnJNbZb6UjCyHSOvjCDvzUOml8F+2o4bYZHak/fl0XtFLwrDBcjtcvTc2gOVe3oDSoZh4PqRC/q99RALIgZuGKAzezJpfI7HwI0DKEVKULqV6LiiY0I5ayzHou3cNrRf2l6e+Lvx0o3I9lGjvmajbFOWA1PfI76arPyjYGQbSsmStN0OwHGGA5yOSnnrSc6bQ+/iXhSDRSjaFOi5rQfK1plpiczIGLivcEM9Rw0+yaP/un6U4vQ0SiZGFER47/Ai15uDzChD13VdkGllk3ot8yFmzLpjFuR2OQr+Ajb+bCOib0QrvGJS6+yn2iEzypAfzEuJ0DWGgpFtCD4TBJ/ioexUwvI1KuWtJ9neLHqX9EpPk51K9NzaA4VVMaNrkKlk6LymEwqnAgV/Af3X94PPUZklGT/fYz4k/yWVoHdd3QWFfWrXsLpHjdn3zIZuLx3EgojBuwYx/PBwTT4hk+kh08rKpb7BZ4I1d2RHwchWcp5cuRqi7QdtYGSU8FUvMp9m0PvzXvBxHurZavQs7anaKHW5SS49zeplyK7Pwnu7ty66IJLqC78YxsifpXtQ+yXt0OyqqcjrcnoOXdd2wXaKDYBUKtx3VR+K0WJFXp/UPsuRFqi6VGAUDArDtTU+gIKRzYjiaNKqABj2M0C3J01krRepj1Pou6oPQlqAZq4G3Td1g9NX93hN6VKi85pOMAoGydVJDP/fMDVFIzsUXxUvJ847Fjlg+srEuwPvCMMycH7XiY4rO6SZNp9ksOHiDZRs3SQYlkHHkg7s8uAuFQtyK4WCkc0kVyeRWpsCwzFwfs9Z7eWQcUr+J4n+a/sh5ARo99Si64bJn69XmvYLWrgvcwMMEPlbBKHfhaq9JFKj0p+k4b3TC4iA5RgLbCfZpu1nGfczYvbds6F0K1GKlNC3pA8jr4xQsNwElG1KyFS1cX/cHAUjo4SSAN8j0hOJ9QTrjCU8kqmJ/zOOgRsHIBZE6PfRo+uarpr7RTPuZ0TbD9sAAIGnAoj+g5IHyZZy3hwGbhqAWBRh2M+Ath/uvJfIVCldSsy6axYMBxgglkQM3z+MwbsHqY08qQoKRkaNvDiCgq8AzsSVz1RJbYutjMGz1AOxJML4FSM6ft4BVlGbl3TLsS2wnmgFAAzeN4jk2tqs9Sczrxgpov/afvApHpovaOC+zD1jzclkahk6FnfAeZYTYIDY6zFsuGgDMp9lZuTnE7JJbd65Z5goikj+S3pzcCxyQKaurSdr8nmR5RFpS1sATIea4P6Zu+bnBjnPdMJ4sBHgAc8tHmR7qd9Ds+PTPPqv7UcxVITCpUDnNZ0zHlAzDAPbiTYp4dsmR8FXwMbLNyL4fBCiQMc2ZGZQO/hRoiAivioO44HGad8eJZMniiLCL4Th/40fAGA52iINB6uT/zOhKKD/OmlgH2fhMOvOWVDYZrb0mNQGoTR6LXyQBmcavRYc1b0W+DSPoV8NIf62NMhUu7sW7T9tn/HyeNI4qB38BDEsA9NXTHXzptaMCuEC+q/pLwci1m9a6yoQAQBWzqLz551QdkqJg/3X9qOUqq16fzL9RFHE0L1DSH8wOkX6uq6qByKA1IvCfbkb7Re3g1WxSH+cxoYLNiD+Tn1MWSf1i4IRUhdiK2NYf/56qdpJwaDtR21wnu2sq0BkE5lW6qjJtUiTVT03eyAUqPlUMwk8GUDsjRggAzqWdEA9a/pmJk0UwzAwH2bG7HtnS52EUzw8t3ow+MtBat5Hpg0FI6SmlVIleO7wwHuHF0JagHqOGnN+OQctx7TUZSCyicKqQNd1XWA10tPn4N2DVFbZJMJ/DSP0e6nEu/2Cduj30ld5RdumbFOi5/YeqcSYAaLLo9hw8QZkNlByK6k8CkZIzUquTWL9+esRXxkHWMB+uh09t/dA6WqMsmt1lxqdV3aC4RjE347D/7i/2ksi0yy+Kg7fQ6NNzb7rgPkwc5VXtGMsx8J5plNqItjCoTBUQO9lvQi9EKLgmVQUBSOk5ggFAcMPDaP/6n6URkpQuBSYdccsOE5z1HzFzETp9tCh/eJ2AED4j2GE/xKu8orIdEn/d7OmZkdZYDu5floI6PbQYc59c8o9SfyP+9F3dR+KEWolTyqjse7spO5lNmSw4aINGHlRms1hOdaCOffOgWaX2mpdXEmmg01wnCkNsPI94kN8FSULNpqcNyc15yuK0C/Qo+28+kq8BqTZNh2LO+C6wAVGySD9QRrrz1+PxHuJai+NNAAKRkhNEHkRwWVBbPzZRuQH8+AsHLqu64LrPBdYZeNfprYTbbAcawFEwHunF+n/0qyQRlGMFNF/ndTUTL2rGh2XddTtAE6GYWA5UnpAUPWowCd5DNw0gKH7hyDkKQmbTF7j3+VJzcv78th4xUYEfhsAeMBwoAFzfjUH+vm1mdg3HRiGQdu5bdAv0EMsihi4cQC5wVy1l0WmiM/w6L++H8VgEYo2Bbqu6WqI4FrpUmLWnbNg/abUVTjySgQbLt6A6OtRqgwjk0JNz0jViKKIyKsR+B7xQcyLYDUs2s5rg+mrzdvvRcgL6L2yF9lPs5Db5Zh15yzIzfJqL4tMglASMHD9AFJrU5AZZZh15ywonY2RfL255NokBu8eRCki9cuRGWQwHmCEfoEeuj10NTuigcyM8b5/UzBCqqIYLWLol0NI/ltqw6/dQ4v2i9upGymAUqKEjZdtRGG4ANUsFXqW9tCIgjojiiIG7x5E7PUYWBWL7lu6oZnTuHlPpWQJkVciiLwSQTE8ltTKqljo9tLBsJ8B+r314PRcFVdJqoGCEVKz4qviGPrVEPgkD0bOwHmmEy3H13ffkErL+/LYeNlG8HEeuvk6dF7V2XCVRI3M/4Rf6iXCAl3XdDXNkaPIi0h9kELivQQS7yVQGtmsuzALaL+olQKTBfqG3CUin0fBCKk5fIbH8EPDiP0jBgBQdavg/qkbqk5VlVdWmzLrM+hd0gsxL8J8uBmuC10UsNWBkZdHMPzAMADAdZELlsMtVV5RdYiiiOyGLBLvJZB8L4lc/5Y5UMpOJQwLDDAsMEA9R03XdoOiYITUlPxQHv039qMwVAAYqXrE/h07Pe3vRGJ1AgM3DgAiYDrMBNf5Lvo3q2Hxd+PwLPUAImD/jh2ObzuqvaSaUQgUyjsm6Y/TwGZ5rpyFg2FfAwz7GaDdQwtWTtd4o6BghNSMxOoEvHd6IWQEyK1yuC9zQ/tFbbWXVTeir0cxeM8gIAC6+Tp0XNFBOSQ1KL0ujb6r+iAWRJi/ZobrJ7STtT2lZAnJfyeReC+B1JoUhNxYZLJ5nolhfwNkKrrW6xkFI6TqRFHqHRJ8OggA0OymQcfiDshNVB0yUYnVCXhu9UAsiFA4FWi/tB3auRTQ1Yr80GiOT5KHfh+91Oa/TnuJzDShKCD9YXoszyQylmfCKBkYDzDCdKgJuj10YFj6N603FIyQquLTPLx3e5F8T6qWsRxrQev3W+mIYQoyn2bguc2DYqgoHXWdbIP9NL0QYk4AABsvSURBVDrqqrZipIiNl21EMViEehc1um/upqf5SSrnmfwzgfjbcRSGC+U/41o4mA8xw3SoCSo35ZnVCwpGSNXkvDkM3DyAwlABjJyB6yeumh8IVi/4NI/hB4cRe300CbhHBffP3HRzrhI+y6N3cS9yvTkoWqUZSpyRylcrQRRFZD/LIroiivjKOPgUX/4z9Ww1TIeaYDrIRP/eNY6CEVIV8XfjGPzFIISclB/S8fOOhu6vUC2xt2MYvn9YKo9WMHCe5UTL16k8eiYJJQEDNwwg9f5oU7M7ZkHZSuWq00EoCkiuTiK6Iir1JtoUl8gA/Xw9zIeaod9HTw3WahAFI2RGibyIwLMBhJaFAADa3bXoWNxBTy3TqBgpYvDeQaT+kwIA6Obp4LrIBYWVGsdNN1EUMXjPIGIrYmCUDHqW9lDQPUNK8RJib8UQWxFDdn22/HlWy8K00ATToSZovqChwLxGUDBCZkR+KI/oa1FEV0TLiWct32iB8ywn5TLMAFEUEXklAt+jPogFEayWhevHLpgOMlV7aQ3N/5QfoeelpmadV3XCsA/dl6oh580htiKG2BuxLTq/KloVMB1qgvkQMxQOCs6riYIRMm34DI/423FEX4sisy5T/rxML0PrD1phPoTyQ2ZafigP713e8pOi8SAj2n7UBk5HO1OVFn4xDN9DPgCA60IXLEc0Z1OzWiIKItIfpRFdEUViVWKLUmHt7lqYDjXBeIARMi0lFs80CkZIRYmiiPTHaURfiyL+ThxifvSyYUbPbA83Q7+vnpoVVZFQEhB6PoTgsiAgSNUH7Re3Qz+vOVqRz4TQH0PwP+YHANhPs8NxOjU1qzV8jkfi3QRiK2JIfZACNt2qFAwM+xlgPtQM3TwdlV7PEApGSEUUggVEV0QRfS2KYmCzbVCXAubDzTAfaobcQn1Daknm0wy8v/CWyyJbjm+B80wnJfdNgSiKCD4bRPBZqWeO7RQbHGc4KC+hxhXCBcTekPJL8t58+fOciYPpq1J+ibpbXcUVNj4KRsikCQUBiXcTiCyPIP1huvxkwapZGBcaYT7cTAliNY7P8fA/7kfk5QgAaQ5Ix+IOqNqpBHiiREHE8P8NI/KK9G/pWOSA/WR7lVdFJkIURWQ3ZqX8kjdj4BNjZcKqLpVUJvxVE+RmerCqNApGyISUa/pfiyL2VgxCerMz1z20MB9uptbMdSi5JonBewZRipXAqli0/aQN5q9STs94CQUB3ju9SLybABig7bw2tBzTUu1lkSkQSgJSa1JSmfC/khBLY0fOur10MB9qhmE/A+0kVggFI2RcitEiYq/HEH0tusU2ptwuh/kwM8yHUTZ6vStGi/De6ZV2uQCYv2ZG27ltdLPdCT7No//GfmQ+yYCRM3D/zA3jAcZqL4tUUClZQvztOGIrYsj8bywZn9WwMB4otaHX7qalXeApoGCEbJdQGm0g9NpoA6HRTRBGIc2BMB9uhnYP+gVsJKIgIvhcEMHngoAobU13LO6A0kVNuralOFJE/3X9yPXnwGpYdF7dCd3uumovi0yj/HBeejBbEUUxOJYfJ7fLYT7UDNMhJijb6PdloigYIZ+T7R89hnl9yzNT9a5qmA83w7TQRKVvDS65NgnvnV7wcR6sioXrAupJsrXcYA791/SjGCqCs3Dour4L6i5KcmwWoigi/UkasRUxxN+OQ8iOHVlrvqCRyoQXGqlsfpwoGCEApG3I2JvSMUxuY678ec7MwXSICebDzTTXpMkUI6PHNh9JxzaWoyxo/UErHdtAqkTqv74ffJKHwqVA9w3dUNjpmLJZCXkBifcSiK6ISp2ON6WXcAz0C6Q29Lq9dNTgcQcoGGliIi8itTaF6GtRJP6ZKCdoMRwD/b6jPUH20lOdfRMrt+9/PiQd23SPHts08TZ0YnUCnls9EAsi1Luq0XVNFzgDPf0SSTFSROxNqUw41z/2YCczyGA6eLRMeJaajre3QsFIkxFFEZn/ZRB7U9pa5ONblq6ZjzDDdDBNuCRbSr4/emyT4MGqR49tFjbfsU3ktQiG7hsCBEC/tx7uK9xUOUa2K9uXLbehL8VK5c8r3cpymTDNiJJQMNIERFFErj+H+Mo4YitjWyRdyfRStG4+3AxVj4qidbJdxZHRY5uPR49tjrGg9ZzmOLYRRRGh34UQeCoAADAdZoLrfBdtu5NxKe9CrxjdhS5sVia8pw6mQ01N3xKBgpEGlvflpR2QlfEtynFZFQvD/gYYDzJCN4/OMcn4ibyIwDOjxzYAVD2jxzatjXtsIxQE+B71lRvD2U6ywbGIuqqSyeHTPOKrpDLhTYE9MHpfPsAA82FmaL/UfFWKFIw0mGKkiPhbccTe3HJsNsMx0O+jh/EgIwz7GMAqKQAhk5dck4T3Li/4JA9Ww6L9wnYYD2y83hrpT9IY/OWg1DKfAVp/0ArrcdZqL4s0iEKggOjrUcRWxFDwFcqfl1vlMB1igukQU9MUDlAw0gBKyRLi78QRXxmXIu1N/1OjW4DGg40w7k+TKEllFcIFeG/3licyt3y9Bc7vORtiCCKf5eF/wo/IS9JuCGfh4DrfBcM+zXNfITOnnMu3Iva5ztbqOWopv+QgU0MnSlMwUqf4HI/ke0nE3owh+Z8kMJaHCs1cDYwHGWH8ihFyE81QINNHKAkIPh1E6PfSsY16thruK9xQOuv32Cb5nySGfjWEYkjKrTIfaUbr91opmCczQiiMNptcEUVyzWb3dpmUNG0+1Az9Po03+ZyCkToiFAWk/pNC7M0YEu9tlgQFqRLGeLARpoNM1O+AzLjE6gQG7x4cO7a5uB3G/evr2KaULMH3qA+xf8QASB01XRe4oJ+nr/LKSLMqxUuIrZTKhLMbxo7dZToZjAcZYT7UDPUujVEmTMFIjRMFEemP0lIi6qr4Ftt3ilYFjAcZYTq4ec4VSe0qhAvw3uYtz+6wftMK55nOuuhTE383juEHhlGKlgAGaDmuBY7vOpq6uoHUlpwnV25DX4qMlQkrXAqpDf1X6/tBlIKRGrRpMu6mXiCl6NiFx1k4GBdKAYh6dmNExKRxCCUBgScCCP8pDADQ7q6F+zI35JbaPC4sxorwPehD/O04AOnG3n5RO7RztVVeGSHbJgoiUh+mpDb0q+IQ82NvzdovaaU29AcYIdPUVyBNwUiNKEaLSH+YRuqDFFJrU+XzakDakjMcaIDp4NHJkCwFIKS2xd+JY/DeQQhZAZyJg/syN3R71M4AOVEUEXsjBt/DPvBJHmAB24k22L9tb4q+KaQx8FkeiVVSG/r0R2PFC4yCgWF/A8yHmKHdU1sX7RsoGKkSPs0j/fFo8PFBCnlPfos/Z1Us9Av0MB1sgu7L1AuE1J/8UB4DSweQH5Cubd08HWwn26reQ6EQLmD418PSJGpIvVLaL2yHehYNuSP1qxAqIPZGDNF/RFEYGisTZrUs9PP1MOxrgG6+rmYH91EwMkOEgoDM/zLl4CO7PgsIW36NqkcF3Z466PbUQbObhs6rSd3jczz8j/oR+XukfL2rd1HDdpINhv0MMxqUiKKIyKsR+B/zQ8gKYDgG9tPssH7LSsE+aRiiKCK7IVsuE9585AdYQPtFLfT7SsGJ0lU7VW8UjEwTURCR3ZhF6oMU0h+kkf5veovqF0BKQNXtqYN2Ty10e+gauoacNLdCsIDQCyFEl0fLvwcKlwLWE6wwH2qe9qORvC+PofuGyhOI1buq0X5ROyV+k4YmCiIyn2WQ/FcSiX8lyruUmyhcChj2NUC/rx7audqqJptTMDIOHo8HgUAA++yzz+f+bPXq1XA4HHC73cgP5svBR+qj1BaVLwDAmbmx4GNPHRS2+s18JmQySvESwn8JY+SlkfLvh8woQ8vXW2A+zAy5VV7R3RJREBH+cxiB3wYgFkQwSgbO7zrRclwL5V6RplMIFJD4VwLJfyWR/jhdntQOSLmJuvk6GPY1IGqOIpwL7/A9r6Ojo6Jrm9Zg5P7778cdd9wBn8+H3XbbDffccw8WLly43a9/8803cemll+KTTz5BW1sbLr/8cpx33nnj/nnTEYx4PB7stttuyOVyePvtt7FgwYLyn/3z1X/iwmMuxC7cLrj4uIuhzG+55cVqWWh3lwIP3TwdlO1Kqn4hBFLiXfTv/9/e3cZEda17AP8PzMAAQXxBGUaEElNjr3hJQYtaql6TUmmtGj1WrDli2pLSihVfEjWNUZsmmjbxQ4NvJwWvjU30i3pMNG0hBRXxhQNUEblK6xRoHSAQhanAMMM89wMyx3EQZjjAno3/XzLpdM3azHry7OV62Huz90M0/7PZ5WJtvyA/BE4J7HlFPXlNDkSAIcDrmzw9rn4M8z/MzvszhPx3CCZvnKzqG7IRDZXu9m5Yyi2w3LDA8i9Lz4XcADo6OlBUVIQ2RxsWr1+M2V/ORqCxZ85cv34dycnJ0Ov1qKqqGtKCxNP12+vzB6dOnUJ2djYOHTqE119/HUePHkVqairu3LnTZwAmkwlvv/02MjIycOLECVy5cgWffvopJk6ciJUrV3r79UOmsbERnZ2dsNvtSE5OxqV/XEJ0ZzTu/3Qfxf8sxnJZDo1Ng47GDugn6hH8X8HO6z6Cpgap4h4LRCPNP8gf4cvCMWHJBLReaUXzuWZ01HTA0eFAx70OdNzrcN1AAwREBEA7Tgu/ID9odJqevxyQnqMfEACOnvPlEMBhdaDjbs/P8Av2Q+QHkRiXMo6/DBA94R/sj7HJYzE2eWzP6Zz/a0fbjTb8dv43dDu6ESzBuPy/l6F9X4s5xjnOQsRut6OzsxONjY1DfnTEE14fGUlKSkJCQgIOHz7sbHvllVewfPly7Nu3z63/9u3bce7cOVRXVzvbMjMzcfPmTVy9etWj7xyu0zRPJyHdLx1/m/E3VN6uhEMcMGvMWLNrDeJXxCP4lWD+WSDRIDnsDnSZu2Cts6KzvhPWeiu6HnTB+ocVjk7HwD/gWRpg3JvjEPH3CD4WgcgL1y5fQ9r/pGFC9wTc0t5CTk4OsrKyYLfbodVq3c4SDIVhOTLS1dWFsrIy7Nixw6U9JSUFJSUlfW5z9epVpKSkuLS99dZbyM3Nhc1mg07n/o+J1WqF1frvC3La2tq8GabHkpKSUFxcjOTkZFTYK9BS2YLf8Bvq/etRcKVgyJNC9CLy0/pBP0UP/RQ9wvDvW8mLCOwP7bA+sKK7rRuODgccNkfPNR8aOP/rfO/X814fo+cFqkSDMOeNOTh15ZTzl/DeyyWGqxDxhlfFSHNzM7q7uxEREeHSHhERgYaGhj63aWho6LO/3W5Hc3MzIiMj3bbZt28f9u7d683QBi0pKQk5OTnIzMzELdwCABw5eISFCNEw02g00I3X+exdXIlGo6fXvF45OTmKr3mDOvfw7PlZEen3nG1f/ftq77Vz5060trY6X/X19YMZpkeuX7+OrKwsl7asrCxcv3592L6TiIhICb665nlVjISHh8Pf39/tKEhTU5Pb0Y9eBoOhz/5arRYTJkzoc5vAwECMGTPG5TUcnr5mRKvV4siRI9Bqtc6LWpVODhER0VDx5TXPq2IkICAAiYmJyM/Pd2nPz8/HvHnz+txm7ty5bv1/+uknzJo1q8/rRUZKaWmpS1KKi4vx8ccfo7i42CU5paWlio2RiIhoKPj6muf1aZotW7bg22+/RV5eHqqrq7F582bU1dU5zz/t3LkT69atc/bPzMxEbW0ttmzZgurqauTl5SE3Nxfbtm0buigGISIiAnq93u3Cnd6LWrVaLfR6/XOP+BAREamFr695Xt9nZPXq1WhpacEXX3wBs9mMuLg4XLhwATExMQAAs9mMuro6Z//Y2FhcuHABmzdvxsGDB2E0GvHNN98oeo8RAIiOjkZVVVWfd2BNSkpCSUnJsNyNjoiIaKT5+pr3Qt8OnoiIiIaPp+s37+RFREREimIxQkRERIpiMUJERESKYjFCREREimIxQkRERIpiMUJERESKYjFCREREimIxQkRERIpiMUJERESKYjFCREREimIxQkRERIpiMUJERESK8vqpvUrofZZfW1ubwiMhIiIiT/Wu2wM9k1cVxYjFYgEATJkyReGREBERkbcsFgvCwsKe+7lGBipXfIDD4cCDBw8QGhoKjUYzLN/R1taGKVOmoL6+vt/HHKsV41Ov0RwbMLrjG82xAYxPzUYqNhGBxWKB0WiEn9/zrwxRxZERPz8/REVFjch3jRkzZtTtdE9jfOo1mmMDRnd8ozk2gPGp2UjE1t8RkV68gJWIiIgUxWKEiIiIFOW/Z8+ePUoPwlf4+/tj4cKF0GpVcfbKa4xPvUZzbMDojm80xwYwPjXzpdhUcQErERERjV48TUNERESKYjFCREREimIxQkRERIpiMUJERESKYjHyxKFDhxAbGwu9Xo/ExERcvnxZ6SF5bd++fZg9ezZCQ0MxadIkLF++HHfv3nXps379emg0GpfXnDlzFBqxd/bs2eM2doPB4PxcRLBnzx4YjUYEBQVh4cKFqKqqUnDEnnvppZfcYtNoNNiwYQMA9eXt0qVLePfdd2E0GqHRaHD27FmXzz3JldVqxcaNGxEeHo6QkBAsXboUf/zxx0iG8Vz9xWez2bB9+3bMnDkTISEhMBqNWLduHR48eODyMxYuXOiW07S0tJEOxc1AufNkX1Rr7gD0OQ81Gg2+/vprZx9fzZ0na4Cvzj0WIwBOnTqF7OxsfP7556ioqMAbb7yB1NRU1NXVKT00r1y8eBEbNmzAtWvXkJ+fD7vdjpSUFDx+/Nil3+LFi2E2m52vCxcuKDRi782YMcNl7JWVlc7PvvrqKxw4cAA5OTkoLS2FwWDAm2++6Xy2kS8rLS11iSs/Px8AsGrVKmcfNeXt8ePHiI+PR05OTp+fe5Kr7OxsnDlzBidPnkRxcTH++usvLFmyBN3d3SMVxnP1F197ezvKy8uxa9culJeX4/Tp07h37x6WLl3q1jcjI8Mlp0ePHh2J4fdroNwBA++Las0dAJe4zGYz8vLyoNFosHLlSpd+vpg7T9YAn517QvLaa69JZmamS9v06dNlx44dCo1oaDQ1NQkAuXjxorMtPT1dli1bpuCoBm/37t0SHx/f52cOh0MMBoPs37/f2dbZ2SlhYWFy5MiRkRrikNm0aZNMnTpVHA6HiKg7bwDkzJkzzv/3JFePHj0SnU4nJ0+edPb5888/xc/PT3744YeRG7wHno2vLzdu3BAAUltb62xbsGCBbNq0abiH9x/pK7aB9sXRlrtly5bJokWLXNrUkDsR9zXAl+feC39kpKurC2VlZUhJSXFpT0lJQUlJiUKjGhqtra0AgPHjx7u0FxUVYdKkSZg2bRoyMjLQ1NSkxPAGpaamBkajEbGxsUhLS8P9+/cBACaTCQ0NDS55DAwMxIIFC1SXx66uLpw4cQIffPCBy4Mh1Zy3p3mSq7KyMthsNpc+RqMRcXFxqssn0DMXNRoNxo4d69L+/fffIzw8HDNmzMC2bdtUcRQP6H9fHE25a2xsxPnz5/Hhhx+6faaG3D27Bvjy3FP+tmsKa25uRnd3NyIiIlzaIyIi0NDQoNCo/nMigi1btiA5ORlxcXHO9tTUVKxatQoxMTEwmUzYtWsXFi1ahLKyMgQGBio44oElJSXhu+++w7Rp09DY2Igvv/wS8+bNQ1VVlTNXfeWxtrZWieEO2tmzZ/Ho0SOsX7/e2abmvD3Lk1w1NDQgICAA48aNc+ujtnnZ2dmJHTt24P3333d5INnatWsRGxsLg8GA27dvY+fOnbh586bzFJ2vGmhfHE25O378OEJDQ7FixQqXdjXkrq81wJfn3gtfjPR6+jdQoCeRz7apSVZWFm7duoXi4mKX9tWrVzvfx8XFYdasWYiJicH58+fdJpyvSU1Ndb6fOXMm5s6di6lTp+L48ePOC+hGQx5zc3ORmpoKo9HobFNz3p5nMLlSWz5tNhvS0tLgcDhw6NAhl88yMjKc7+Pi4vDyyy9j1qxZKC8vR0JCwkgP1WOD3RfVljsAyMvLw9q1a6HX613a1ZC7560BgG/OvRf+NE14eDj8/f3dKr6mpia36lEtNm7ciHPnzqGwsBBRUVH99o2MjERMTAxqampGaHRDJyQkBDNnzkRNTY3zr2rUnsfa2loUFBTgo48+6refmvPmSa4MBgO6urrw8OHD5/bxdTabDe+99x5MJhPy8/MHfEx7QkICdDqd6nL67L44GnIHAJcvX8bdu3cHnIuA7+XueWuAL8+9F74YCQgIQGJiotvhtfz8fMybN0+hUQ2OiCArKwunT5/Gzz//jNjY2AG3aWlpQX19PSIjI0dghEPLarWiuroakZGRzkOmT+exq6sLFy9eVFUejx07hkmTJuGdd97pt5+a8+ZJrhITE6HT6Vz6mM1m3L59WxX57C1EampqUFBQgAkTJgy4TVVVFWw2m+py+uy+qPbc9crNzUViYiLi4+MH7OsruRtoDfDpuTdsl8aqyMmTJ0Wn00lubq7cuXNHsrOzJSQkRH7//Xelh+aVTz75RMLCwqSoqEjMZrPz1d7eLiIiFotFtm7dKiUlJWIymaSwsFDmzp0rkydPlra2NoVHP7CtW7dKUVGR3L9/X65duyZLliyR0NBQZ572798vYWFhcvr0aamsrJQ1a9ZIZGSkKmITEenu7pbo6GjZvn27S7sa82axWKSiokIqKioEgBw4cEAqKiqcf03iSa4yMzMlKipKCgoKpLy8XBYtWiTx8fFit9uVCsupv/hsNpssXbpUoqKi5JdffnGZi1arVUREfv31V9m7d6+UlpaKyWSS8+fPy/Tp0+XVV19VPL7+YvN0X1Rr7nq1trZKcHCwHD582G17X87dQGuAiO/OPRYjTxw8eFBiYmIkICBAEhISXP4cVi0A9Pk6duyYiIi0t7dLSkqKTJw4UXQ6nURHR0t6errU1dUpO3APrV69WiIjI0Wn04nRaJQVK1ZIVVWV83OHwyG7d+8Wg8EggYGBMn/+fKmsrFRwxN758ccfBYDcvXvXpV2NeSssLOxzX0xPTxcRz3LV0dEhWVlZMn78eAkKCpIlS5b4TMz9xWcymZ47FwsLC0VEpK6uTubPny/jx4+XgIAAmTp1qnz22WfS0tKibGDSf2ye7otqzV2vo0ePSlBQkDx69Mhte1/O3UBrgIjvzj3NkwCIiIiIFPHCXzNCREREymIxQkRERIpiMUJERESKYjFCREREimIxQkRERIpiMUJERESKYjFCREREimIxQkRERIpiMUJERESKYjFCREREimIxQkRERIpiMUJERESK+n+A9UJPu1KsWgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### TESTING\n",
    "\n",
    "def generate_test_batch(n=4):\n",
    "    obs = torch.zeros(n, 2, 2)\n",
    "    obs[0] = torch.tensor([[0, 0], [1, 0]])\n",
    "    obs[1] = torch.tensor([[0, 0], [1, 1]])\n",
    "    obs[2] = torch.tensor([[0, 1], [1, 0]])\n",
    "    obs[3] = torch.tensor([[0, 1], [1, 1]])\n",
    "    tar = vx[:4, torch.arange(t_steps)]\n",
    "\n",
    "    return obs, tar\n",
    "\n",
    "\n",
    "model = WTA_CNP(1, 1, 10, 10, [128, 128, 128], num_decoders=4, decoder_hidden_dims=[128, 128], batch_size=4)\n",
    "model.load_state_dict(torch.load(f'saved_models/wtacnp_synth_1688469358.pt'))\n",
    "model.eval()\n",
    "\n",
    "o, t = generate_test_batch()\n",
    "\n",
    "# print(o)\n",
    "\n",
    "with torch.no_grad():\n",
    "    p, g = model(o, t)\n",
    "\n",
    "p, g = p.cpu().numpy(), g.cpu()\n",
    "\n",
    "for i in range(4):\n",
    "\n",
    "    # print(o[i, 0, 0], o[i, 0, 1], o[i, 1, 0], o[i, 1, 1])\n",
    "\n",
    "    dec_id = torch.argmax(g[i, :, :]).item()\n",
    "    plt.plot(range(t_steps), p[dec_id, i, :, 0], colors[dec_id], alpha=0.7)\n",
    "\n",
    "    plt.scatter(o[i, 0, 0]*200, o[i, 0, 1], color='k', marker='x')\n",
    "    plt.scatter(o[i, 1, 0]*200, o[i, 1, 1], color='k', marker='x')\n",
    "\n",
    "for i in range(32):\n",
    "    plt.plot(range(t_steps), tr[i, :, 0].cpu().numpy(), 'k', alpha=0.1, linestyle='dashed')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "166662\n",
      "166662\n"
     ]
    }
   ],
   "source": [
    "# pytorch_total_params = sum(p.numel() for p in model.parameters())\n",
    "# print(pytorch_total_params)\n",
    "# pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "# print(pytorch_total_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}