Minor updates.

33 files changed, 1305 insertions, 601 deletions

diff --git a/src/notebooks/00-testing-stuff-out.ipynb b/src/notebooks/00-testing-stuff-out.ipynb
index 6f01dfb..dd02098 100644
--- a/src/notebooks/00-testing-stuff-out.ipynb
+++ b/src/notebooks/00-testing-stuff-out.ipynb
@@ -2,18 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -22,6 +13,7 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from PIL import Image\n",
+    "import torch.nn.functional as F\n",
     "import torch\n",
     "from torch import nn\n",
     "from importlib.util import find_spec\n",
@@ -32,74 +24,386 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from text_recognizer.networks.residual_network import IdentityBlock, ResidualBlock, BasicBlock, BottleNeckBlock, ResidualLayer, ResidualNetwork, ResidualNetworkEncoder"
+    "from text_recognizer.networks import CTCTransformer"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from text_recognizer.networks import WideResidualNetwork"
+    "model = CTCTransformer(\n",
+    "        num_encoder_layers=2,\n",
+    "        hidden_dim=256,\n",
+    "        vocab_size=56,\n",
+    "        num_heads=8,\n",
+    "        adaptive_pool_dim=[None, 1],\n",
+    "        expansion_dim=2048,\n",
+    "        dropout_rate=0.1,\n",
+    "        max_len=256,\n",
+    "        patch_size=(28, 32),\n",
+    "        stride=(1, 28),\n",
+    "        activation=\"gelu\",\n",
+    "        backbone=\"WideResidualNetwork\",\n",
+    "backbone_args={\n",
+    "            \"in_channels\": 1,\n",
+    "            \"in_planes\": 64,\n",
+    "            \"num_classes\": 80,\n",
+    "            \"depth\": 10,\n",
+    "            \"width_factor\": 1,\n",
+    "            \"dropout_rate\": 0.1,\n",
+    "            \"num_layers\": 4,\n",
+    "            \"num_stages\": [64, 128, 256, 256],\n",
+    "            \"activation\": \"elu\",\n",
+    "            \"use_decoder\": False,\n",
+    "},\n",
+    " )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pathlib import Path"
+    "backbone: WideResidualNetwork\n",
+    "          backbone_args:\n",
+    "            in_channels: 1\n",
+    "            in_planes: 64\n",
+    "            num_classes: 80\n",
+    "            depth: 10\n",
+    "            width_factor: 1\n",
+    "            dropout_rate: 0.1\n",
+    "            num_layers: 4 \n",
+    "            num_stages: [64, 128, 256, 256]\n",
+    "            activation: elu\n",
+    "            use_decoder: false\n",
+    "          n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = torch.randn(2, 1, 28, 952)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "True"
+       "torch.Size([119, 2, 56])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Path(\"/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/TransformerModel_EmnistLinesDataset_CNNTransformer/1112_081300/model/best.pt\").exists()"
+    "model(t).shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Failed to run torchsummary. See above stack traces for more details. Executed layers up to: [WideResidualNetwork: 1-1, Sequential: 2-1, Conv2d: 3-1, Sequential: 3-2, WideBlock: 4-1, Sequential: 3-3, WideBlock: 4-2, Sequential: 3-4, WideBlock: 4-3, Sequential: 3-5, WideBlock: 4-4, AdaptiveAvgPool2d: 1-2, Encoder: 1-3, EncoderLayer: 3-6, MultiHeadAttention: 4-5, _IntraLayerConnection: 4-6, _ConvolutionalLayer: 4-7, _IntraLayerConnection: 4-8, EncoderLayer: 3-7, MultiHeadAttention: 4-9, _IntraLayerConnection: 4-10, _ConvolutionalLayer: 4-11, _IntraLayerConnection: 4-12, LayerNorm: 2-2, Linear: 2-3, GLU: 2-4]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m----------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                   Traceback (most recent call last)",
+      "\u001b[0;32m~/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torchsummary/torchsummary.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(model, input_data, batch_dim, branching, col_names, col_width, depth, device, dtypes, verbose, *args, **kwargs)\u001b[0m\n\u001b[1;32m    123\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m                 \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n",
+      "\u001b[0;32m~/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/networks/ctc_transformer.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, trg)\u001b[0m\n\u001b[1;32m    109\u001b[0m         \u001b[0mcontext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext_representation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 110\u001b[0;31m         \u001b[0mlogits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m         \u001b[0mlogits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrearrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"b t y -> t b y\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n",
+      "\u001b[0;32m~/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n",
+      "\u001b[0;32m~/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(input, weight, bias)\u001b[0m\n\u001b[1;32m   1691\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1692\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1693\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (238x128 and 256x56)",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mRuntimeError\u001b[0m                   Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-85c5209ae40a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m952\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdepth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torchsummary/torchsummary.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(model, input_data, batch_dim, branching, col_names, col_width, depth, device, dtypes, verbose, *args, **kwargs)\u001b[0m\n\u001b[1;32m    125\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m             \u001b[0mexecuted_layers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlayer\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msummary_list\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuted\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m             raise RuntimeError(\n\u001b[0m\u001b[1;32m    128\u001b[0m                 \u001b[0;34m\"Failed to run torchsummary. See above stack traces for more details. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m                 \u001b[0;34m\"Executed layers up to: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexecuted_layers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Failed to run torchsummary. See above stack traces for more details. Executed layers up to: [WideResidualNetwork: 1-1, Sequential: 2-1, Conv2d: 3-1, Sequential: 3-2, WideBlock: 4-1, Sequential: 3-3, WideBlock: 4-2, Sequential: 3-4, WideBlock: 4-3, Sequential: 3-5, WideBlock: 4-4, AdaptiveAvgPool2d: 1-2, Encoder: 1-3, EncoderLayer: 3-6, MultiHeadAttention: 4-5, _IntraLayerConnection: 4-6, _ConvolutionalLayer: 4-7, _IntraLayerConnection: 4-8, EncoderLayer: 3-7, MultiHeadAttention: 4-9, _IntraLayerConnection: 4-10, _ConvolutionalLayer: 4-11, _IntraLayerConnection: 4-12, LayerNorm: 2-2, Linear: 2-3, GLU: 2-4]"
+     ]
+    }
+   ],
+   "source": [
+    "summary(model, (1, 28, 952), device=\"cpu\", depth=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GEGLU(nn.Module):\n",
+    "    def __init__(self, dim_in, dim_out):\n",
+    "        super().__init__()\n",
+    "        self.proj = nn.Linear(dim_in, dim_out * 2)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x, gate = self.proj(x).chunk(2, dim = -1)\n",
+    "        return x * F.gelu(gate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e = GEGLU(256, 2048)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 30, 2048])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "e(t).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emb = nn.Embedding(56, 256)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with torch.no_grad():\n",
+    "    e = emb(torch.Tensor([55]).long())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from einops import repeat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ee = repeat(e, \"() n -> b n\", b=16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleAttributeError",
+     "evalue": "'Embedding' object has no attribute 'device'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleAttributeError\u001b[0m                      Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-58-657f11e4a017>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0memb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-N1c_zsdp-py3.8/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    776\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 778\u001b[0;31m         raise ModuleAttributeError(\"'{}' object has no attribute '{}'\".format(\n\u001b[0m\u001b[1;32m    779\u001b[0m             type(self).__name__, name))\n\u001b[1;32m    780\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleAttributeError\u001b[0m: 'Embedding' object has no attribute 'device'"
+     ]
+    }
+   ],
+   "source": [
+    "emb.device"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-1.0624,  0.0674,  0.9387,  ..., -0.1852, -0.1303,  0.8005],\n",
+       "        [-1.0624,  0.0674,  0.9387,  ..., -0.1852, -0.1303,  0.8005],\n",
+       "        [-1.0624,  0.0674,  0.9387,  ..., -0.1852, -0.1303,  0.8005],\n",
+       "        ...,\n",
+       "        [-1.0624,  0.0674,  0.9387,  ..., -0.1852, -0.1303,  0.8005],\n",
+       "        [-1.0624,  0.0674,  0.9387,  ..., -0.1852, -0.1303,  0.8005],\n",
+       "        [-1.0624,  0.0674,  0.9387,  ..., -0.1852, -0.1303,  0.8005]])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ee"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([16, 256])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ee.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = torch.randn(16, 10, 256)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([16, 10, 256])"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = torch.cat((ee.unsqueeze(1), t, ee.unsqueeze(1)), dim=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([16, 12, 256])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "False"
+       "torch.Size([1, 256])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Path(\"/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/TransformerModel_EmnistLinesDataset_CNNTransformer/1112_201649/model/best.pt\").exists()"
+    "e.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.networks.residual_network import IdentityBlock, ResidualBlock, BasicBlock, BottleNeckBlock, ResidualLayer, ResidualNetwork, ResidualNetworkEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.networks import WideResidualNetwork"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,16 +413,17 @@
     "            in_planes=64,\n",
     "            depth=10,\n",
     "            num_layers=4,\n",
-    "            width_factor=1,\n",
-    "            dropout_rate= 0.2,\n",
+    "            width_factor=2,\n",
+    "            num_stages=[64, 128, 256, 256],\n",
+    "            dropout_rate= 0.1,\n",
     "            activation= \"SELU\",\n",
-    "            use_decoder= True,\n",
+    "            use_decoder= False,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,16 +432,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
-    "backbone = ResidualNetworkEncoder(1, [64, 128, 256], [2, 2, 3])"
+    "backbone = ResidualNetworkEncoder(1, [64, 65, 66, 67, 68], [2, 2, 2, 2, 2])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -146,27 +451,31 @@
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
-      "├─Sequential: 1-1                        [-1, 64, 28, 952]         --\n",
-      "|    └─Conv2d: 2-1                       [-1, 64, 28, 952]         576\n",
-      "|    └─BatchNorm2d: 2-2                  [-1, 64, 28, 952]         128\n",
-      "|    └─ReLU: 2-3                         [-1, 64, 28, 952]         --\n",
-      "├─Sequential: 1-2                        [-1, 256, 7, 238]         --\n",
-      "|    └─ResidualLayer: 2-4                [-1, 64, 28, 952]         --\n",
-      "|    |    └─Sequential: 3-1              [-1, 64, 28, 952]         147,968\n",
-      "|    └─ResidualLayer: 2-5                [-1, 128, 14, 476]        --\n",
-      "|    |    └─Sequential: 3-2              [-1, 128, 14, 476]        525,568\n",
-      "|    └─ResidualLayer: 2-6                [-1, 256, 7, 238]         --\n",
-      "|    |    └─Sequential: 3-3              [-1, 256, 7, 238]         3,280,384\n",
+      "├─Sequential: 1-1                        [-1, 64, 12, 474]         --\n",
+      "|    └─Conv2d: 2-1                       [-1, 64, 12, 474]         3,136\n",
+      "|    └─BatchNorm2d: 2-2                  [-1, 64, 12, 474]         128\n",
+      "|    └─ReLU: 2-3                         [-1, 64, 12, 474]         --\n",
+      "├─Sequential: 1-2                        [-1, 68, 1, 30]           --\n",
+      "|    └─ResidualLayer: 2-4                [-1, 64, 12, 474]         --\n",
+      "|    |    └─Sequential: 3-1              [-1, 64, 12, 474]         147,968\n",
+      "|    └─ResidualLayer: 2-5                [-1, 65, 6, 237]          --\n",
+      "|    |    └─Sequential: 3-2              [-1, 65, 6, 237]          156,325\n",
+      "|    └─ResidualLayer: 2-6                [-1, 66, 3, 119]          --\n",
+      "|    |    └─Sequential: 3-3              [-1, 66, 3, 119]          161,172\n",
+      "|    └─ResidualLayer: 2-7                [-1, 67, 2, 60]           --\n",
+      "|    |    └─Sequential: 3-4              [-1, 67, 2, 60]           166,093\n",
+      "|    └─ResidualLayer: 2-8                [-1, 68, 1, 30]           --\n",
+      "|    |    └─Sequential: 3-5              [-1, 68, 1, 30]           171,088\n",
       "==========================================================================================\n",
-      "Total params: 3,954,624\n",
-      "Trainable params: 3,954,624\n",
+      "Total params: 805,910\n",
+      "Trainable params: 805,910\n",
       "Non-trainable params: 0\n",
-      "Total mult-adds (M): 31.16\n",
+      "Total mult-adds (M): 21.05\n",
       "==========================================================================================\n",
       "Input size (MB): 0.10\n",
-      "Forward/backward pass size (MB): 26.03\n",
-      "Params size (MB): 15.09\n",
-      "Estimated Total Size (MB): 41.22\n",
+      "Forward/backward pass size (MB): 5.55\n",
+      "Params size (MB): 3.07\n",
+      "Estimated Total Size (MB): 8.73\n",
       "==========================================================================================\n"
      ]
     },
@@ -176,31 +485,35 @@
        "==========================================================================================\n",
        "Layer (type:depth-idx)                   Output Shape              Param #\n",
        "==========================================================================================\n",
-       "├─Sequential: 1-1                        [-1, 64, 28, 952]         --\n",
-       "|    └─Conv2d: 2-1                       [-1, 64, 28, 952]         576\n",
-       "|    └─BatchNorm2d: 2-2                  [-1, 64, 28, 952]         128\n",
-       "|    └─ReLU: 2-3                         [-1, 64, 28, 952]         --\n",
-       "├─Sequential: 1-2                        [-1, 256, 7, 238]         --\n",
-       "|    └─ResidualLayer: 2-4                [-1, 64, 28, 952]         --\n",
-       "|    |    └─Sequential: 3-1              [-1, 64, 28, 952]         147,968\n",
-       "|    └─ResidualLayer: 2-5                [-1, 128, 14, 476]        --\n",
-       "|    |    └─Sequential: 3-2              [-1, 128, 14, 476]        525,568\n",
-       "|    └─ResidualLayer: 2-6                [-1, 256, 7, 238]         --\n",
-       "|    |    └─Sequential: 3-3              [-1, 256, 7, 238]         3,280,384\n",
+       "├─Sequential: 1-1                        [-1, 64, 12, 474]         --\n",
+       "|    └─Conv2d: 2-1                       [-1, 64, 12, 474]         3,136\n",
+       "|    └─BatchNorm2d: 2-2                  [-1, 64, 12, 474]         128\n",
+       "|    └─ReLU: 2-3                         [-1, 64, 12, 474]         --\n",
+       "├─Sequential: 1-2                        [-1, 68, 1, 30]           --\n",
+       "|    └─ResidualLayer: 2-4                [-1, 64, 12, 474]         --\n",
+       "|    |    └─Sequential: 3-1              [-1, 64, 12, 474]         147,968\n",
+       "|    └─ResidualLayer: 2-5                [-1, 65, 6, 237]          --\n",
+       "|    |    └─Sequential: 3-2              [-1, 65, 6, 237]          156,325\n",
+       "|    └─ResidualLayer: 2-6                [-1, 66, 3, 119]          --\n",
+       "|    |    └─Sequential: 3-3              [-1, 66, 3, 119]          161,172\n",
+       "|    └─ResidualLayer: 2-7                [-1, 67, 2, 60]           --\n",
+       "|    |    └─Sequential: 3-4              [-1, 67, 2, 60]           166,093\n",
+       "|    └─ResidualLayer: 2-8                [-1, 68, 1, 30]           --\n",
+       "|    |    └─Sequential: 3-5              [-1, 68, 1, 30]           171,088\n",
        "==========================================================================================\n",
-       "Total params: 3,954,624\n",
-       "Trainable params: 3,954,624\n",
+       "Total params: 805,910\n",
+       "Trainable params: 805,910\n",
        "Non-trainable params: 0\n",
-       "Total mult-adds (M): 31.16\n",
+       "Total mult-adds (M): 21.05\n",
        "==========================================================================================\n",
        "Input size (MB): 0.10\n",
-       "Forward/backward pass size (MB): 26.03\n",
-       "Params size (MB): 15.09\n",
-       "Estimated Total Size (MB): 41.22\n",
+       "Forward/backward pass size (MB): 5.55\n",
+       "Params size (MB): 3.07\n",
+       "Estimated Total Size (MB): 8.73\n",
        "=========================================================================================="
       ]
      },
-     "execution_count": 20,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -211,7 +524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,7 +535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -231,29 +544,34 @@
        "Sequential(\n",
        "  (0): SELU(inplace=True)\n",
        "  (1): Sequential(\n",
-       "    (0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "    (1): Sequential(\n",
+       "    (0): Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    (2): SELU(inplace=True)\n",
+       "    (3): MaxPool2d(kernel_size=(2, 4), stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+       "  )\n",
+       "  (2): Sequential(\n",
+       "    (0): Sequential(\n",
        "      (0): WideBlock(\n",
        "        (activation): SELU(inplace=True)\n",
        "        (blocks): Sequential(\n",
        "          (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "          (1): SELU(inplace=True)\n",
        "          (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          (3): Dropout(p=0.2, inplace=False)\n",
+       "          (3): Dropout(p=0.1, inplace=False)\n",
        "          (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "          (5): SELU(inplace=True)\n",
        "          (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
        "        )\n",
        "      )\n",
        "    )\n",
-       "    (2): Sequential(\n",
+       "    (1): Sequential(\n",
        "      (0): WideBlock(\n",
        "        (activation): SELU(inplace=True)\n",
        "        (blocks): Sequential(\n",
        "          (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "          (1): SELU(inplace=True)\n",
        "          (2): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          (3): Dropout(p=0.2, inplace=False)\n",
+       "          (3): Dropout(p=0.1, inplace=False)\n",
        "          (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "          (5): SELU(inplace=True)\n",
        "          (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
@@ -263,14 +581,14 @@
        "        )\n",
        "      )\n",
        "    )\n",
-       "    (3): Sequential(\n",
+       "    (2): Sequential(\n",
        "      (0): WideBlock(\n",
        "        (activation): SELU(inplace=True)\n",
        "        (blocks): Sequential(\n",
        "          (0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "          (1): SELU(inplace=True)\n",
        "          (2): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          (3): Dropout(p=0.2, inplace=False)\n",
+       "          (3): Dropout(p=0.1, inplace=False)\n",
        "          (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "          (5): SELU(inplace=True)\n",
        "          (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
@@ -280,34 +598,28 @@
        "        )\n",
        "      )\n",
        "    )\n",
-       "    (4): Sequential(\n",
+       "    (3): Sequential(\n",
        "      (0): WideBlock(\n",
        "        (activation): SELU(inplace=True)\n",
        "        (blocks): Sequential(\n",
        "          (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "          (1): SELU(inplace=True)\n",
-       "          (2): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          (3): Dropout(p=0.2, inplace=False)\n",
-       "          (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "          (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "          (3): Dropout(p=0.1, inplace=False)\n",
+       "          (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "          (5): SELU(inplace=True)\n",
-       "          (6): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "          (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
        "        )\n",
        "        (shortcut): Sequential(\n",
-       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "          (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
        "        )\n",
        "      )\n",
        "    )\n",
        "  )\n",
-       "  (2): Sequential(\n",
-       "    (0): BatchNorm2d(512, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
-       "    (1): SELU(inplace=True)\n",
-       "    (2): Reduce('b c h w -> b c', 'mean')\n",
-       "    (3): Linear(in_features=512, out_features=80, bias=True)\n",
-       "  )\n",
        ")"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -318,7 +630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -328,36 +640,32 @@
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
-      "├─SELU: 1-1                              [-1, 1, 28, 952]          --\n",
-      "├─Sequential: 1                          []                        --\n",
-      "|    └─SELU: 2-1                         [-1, 1, 28, 952]          --\n",
-      "├─Sequential: 1-2                        [-1, 512, 4, 119]         --\n",
-      "|    └─Conv2d: 2-2                       [-1, 64, 28, 952]         576\n",
-      "|    └─Sequential: 2-3                   [-1, 64, 28, 952]         --\n",
-      "|    |    └─WideBlock: 3-1               [-1, 64, 28, 952]         73,984\n",
-      "|    └─Sequential: 2-4                   [-1, 128, 14, 476]        --\n",
-      "|    |    └─WideBlock: 3-2               [-1, 128, 14, 476]        229,760\n",
-      "|    └─Sequential: 2-5                   [-1, 256, 7, 238]         --\n",
-      "|    |    └─WideBlock: 3-3               [-1, 256, 7, 238]         918,272\n",
-      "|    └─Sequential: 2-6                   [-1, 512, 4, 119]         --\n",
-      "|    |    └─WideBlock: 3-4               [-1, 512, 4, 119]         3,671,552\n",
-      "├─Sequential: 1-3                        [-1, 80]                  --\n",
-      "|    └─BatchNorm2d: 2-7                  [-1, 512, 4, 119]         1,024\n",
-      "├─SELU: 1-4                              [-1, 512, 4, 119]         --\n",
+      "├─Sequential: 1-1                        [-1, 64, 7, 237]          --\n",
+      "|    └─Conv2d: 2-1                       [-1, 64, 12, 474]         3,136\n",
+      "|    └─BatchNorm2d: 2-2                  [-1, 64, 12, 474]         128\n",
+      "├─SELU: 1-2                              [-1, 64, 12, 474]         --\n",
       "├─Sequential: 1                          []                        --\n",
-      "|    └─SELU: 2-8                         [-1, 512, 4, 119]         --\n",
-      "|    └─Reduce: 2-9                       [-1, 512]                 --\n",
-      "|    └─Linear: 2-10                      [-1, 80]                  41,040\n",
+      "|    └─SELU: 2-3                         [-1, 64, 12, 474]         --\n",
+      "|    └─MaxPool2d: 2-4                    [-1, 64, 7, 237]          --\n",
+      "├─Sequential: 1-3                        [-1, 256, 1, 30]          --\n",
+      "|    └─Sequential: 2-5                   [-1, 64, 7, 237]          --\n",
+      "|    |    └─WideBlock: 3-1               [-1, 64, 7, 237]          73,984\n",
+      "|    └─Sequential: 2-6                   [-1, 128, 4, 119]         --\n",
+      "|    |    └─WideBlock: 3-2               [-1, 128, 4, 119]         229,760\n",
+      "|    └─Sequential: 2-7                   [-1, 256, 2, 60]          --\n",
+      "|    |    └─WideBlock: 3-3               [-1, 256, 2, 60]          918,272\n",
+      "|    └─Sequential: 2-8                   [-1, 256, 1, 30]          --\n",
+      "|    |    └─WideBlock: 3-4               [-1, 256, 1, 30]          1,246,208\n",
       "==========================================================================================\n",
-      "Total params: 4,936,208\n",
-      "Trainable params: 4,936,208\n",
+      "Total params: 2,471,488\n",
+      "Trainable params: 2,471,488\n",
       "Non-trainable params: 0\n",
-      "Total mult-adds (M): 35.01\n",
+      "Total mult-adds (M): 27.71\n",
       "==========================================================================================\n",
       "Input size (MB): 0.10\n",
-      "Forward/backward pass size (MB): 14.88\n",
-      "Params size (MB): 18.83\n",
-      "Estimated Total Size (MB): 33.81\n",
+      "Forward/backward pass size (MB): 5.55\n",
+      "Params size (MB): 9.43\n",
+      "Estimated Total Size (MB): 15.08\n",
       "==========================================================================================\n"
      ]
     },
@@ -367,51 +675,47 @@
        "==========================================================================================\n",
        "Layer (type:depth-idx)                   Output Shape              Param #\n",
        "==========================================================================================\n",
-       "├─SELU: 1-1                              [-1, 1, 28, 952]          --\n",
+       "├─Sequential: 1-1                        [-1, 64, 7, 237]          --\n",
+       "|    └─Conv2d: 2-1                       [-1, 64, 12, 474]         3,136\n",
+       "|    └─BatchNorm2d: 2-2                  [-1, 64, 12, 474]         128\n",
+       "├─SELU: 1-2                              [-1, 64, 12, 474]         --\n",
        "├─Sequential: 1                          []                        --\n",
-       "|    └─SELU: 2-1                         [-1, 1, 28, 952]          --\n",
-       "├─Sequential: 1-2                        [-1, 512, 4, 119]         --\n",
-       "|    └─Conv2d: 2-2                       [-1, 64, 28, 952]         576\n",
-       "|    └─Sequential: 2-3                   [-1, 64, 28, 952]         --\n",
-       "|    |    └─WideBlock: 3-1               [-1, 64, 28, 952]         73,984\n",
-       "|    └─Sequential: 2-4                   [-1, 128, 14, 476]        --\n",
-       "|    |    └─WideBlock: 3-2               [-1, 128, 14, 476]        229,760\n",
-       "|    └─Sequential: 2-5                   [-1, 256, 7, 238]         --\n",
-       "|    |    └─WideBlock: 3-3               [-1, 256, 7, 238]         918,272\n",
-       "|    └─Sequential: 2-6                   [-1, 512, 4, 119]         --\n",
-       "|    |    └─WideBlock: 3-4               [-1, 512, 4, 119]         3,671,552\n",
-       "├─Sequential: 1-3                        [-1, 80]                  --\n",
-       "|    └─BatchNorm2d: 2-7                  [-1, 512, 4, 119]         1,024\n",
-       "├─SELU: 1-4                              [-1, 512, 4, 119]         --\n",
-       "├─Sequential: 1                          []                        --\n",
-       "|    └─SELU: 2-8                         [-1, 512, 4, 119]         --\n",
-       "|    └─Reduce: 2-9                       [-1, 512]                 --\n",
-       "|    └─Linear: 2-10                      [-1, 80]                  41,040\n",
+       "|    └─SELU: 2-3                         [-1, 64, 12, 474]         --\n",
+       "|    └─MaxPool2d: 2-4                    [-1, 64, 7, 237]          --\n",
+       "├─Sequential: 1-3                        [-1, 256, 1, 30]          --\n",
+       "|    └─Sequential: 2-5                   [-1, 64, 7, 237]          --\n",
+       "|    |    └─WideBlock: 3-1               [-1, 64, 7, 237]          73,984\n",
+       "|    └─Sequential: 2-6                   [-1, 128, 4, 119]         --\n",
+       "|    |    └─WideBlock: 3-2               [-1, 128, 4, 119]         229,760\n",
+       "|    └─Sequential: 2-7                   [-1, 256, 2, 60]          --\n",
+       "|    |    └─WideBlock: 3-3               [-1, 256, 2, 60]          918,272\n",
+       "|    └─Sequential: 2-8                   [-1, 256, 1, 30]          --\n",
+       "|    |    └─WideBlock: 3-4               [-1, 256, 1, 30]          1,246,208\n",
        "==========================================================================================\n",
-       "Total params: 4,936,208\n",
-       "Trainable params: 4,936,208\n",
+       "Total params: 2,471,488\n",
+       "Trainable params: 2,471,488\n",
        "Non-trainable params: 0\n",
-       "Total mult-adds (M): 35.01\n",
+       "Total mult-adds (M): 27.71\n",
        "==========================================================================================\n",
        "Input size (MB): 0.10\n",
-       "Forward/backward pass size (MB): 14.88\n",
-       "Params size (MB): 18.83\n",
-       "Estimated Total Size (MB): 33.81\n",
+       "Forward/backward pass size (MB): 5.55\n",
+       "Params size (MB): 9.43\n",
+       "Estimated Total Size (MB): 15.08\n",
        "=========================================================================================="
       ]
      },
-     "execution_count": 26,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "summary(backbone, (1, 28, 952), device=\"cpu\", depth=3)"
+    "summary(wr, (1, 28, 952), device=\"cpu\", depth=3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1131,16 +1435,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "pred = torch.Tensor([1,1,1,1,1, 81, 1, 79, 79, 79, 2,1,1,1,1, 81, 1, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79]).long()\n",
+    "pred = torch.Tensor([1,21,2,45,31, 81, 1, 79, 79, 79, 2,1,1,1,1, 81, 1, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79]).long()\n",
     "target = torch.Tensor([1,1,1,1,1, 81, 79, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79]).long()"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask = (target != 79)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ True,  True,  True,  True,  True,  True, False, False, False, False,\n",
+       "         True,  True,  True,  True,  True,  True, False, False, False, False,\n",
+       "         True,  True,  True,  True,  True,  True, False, False, False, False])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 1, 21,  2, 45, 31, 81,  0,  0,  0,  0,  2,  1,  1,  1,  1, 81,  0,  0,\n",
+       "         0,  0,  1,  1,  1,  1,  1, 81,  0,  0,  0,  0])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred * mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 1,  1,  1,  1,  1, 81,  0,  0,  0,  0,  1,  1,  1,  1,  1, 81,  0,  0,\n",
+       "         0,  0,  1,  1,  1,  1,  1, 81,  0,  0,  0,  0])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target * mask"
+   ]
+  },
+  {
+   "cell_type": "code",
    "execution_count": 32,
    "metadata": {},
    "outputs": [],
diff --git a/src/notebooks/02b-emnist-lines-dataset.ipynb b/src/notebooks/02b-emnist-lines-dataset.ipynb
index c37dfa4..f82342b 100644
--- a/src/notebooks/02b-emnist-lines-dataset.ipynb
+++ b/src/notebooks/02b-emnist-lines-dataset.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,28 +31,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transform = [{\"type\": \"ToTensor\", \"args\": None}, \n",
+    "             {\"type\": \"ApplyContrast\", \"args\": {\"low\": 0.0, \"high\": 0.15}},\n",
+    "             {\"type\": \"RandomAffine\", \"args\": {\"degrees\": [-0.25, 0.25], \"scale\": [0.9, 1.0]}}\n",
+    "            ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "emnist_lines = EmnistLinesDataset(train=True,\n",
-    "                                  max_length = 97,\n",
+    "                                  max_length = 60,\n",
     "                                  min_overlap = 0.0,\n",
-    "                                  max_overlap = 0.15,\n",
-    "                                  num_samples = 25_000,)"
+    "                                  max_overlap = 0.3,\n",
+    "                                  num_samples = 50_000,\n",
+    "                                 transform=transform,\n",
+    "                                 )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-12-07 22:39:57.990 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_generate_data:157 - Generating data...\n",
-      "2020-12-07 22:40:20.268 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_load_data:150 - EmnistLinesDataset loading data from HDF5...\n"
+      "2021-01-02 22:02:47.979 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_load_data:152 - EmnistLinesDataset loading data from HDF5...\n"
      ]
     }
    ],
@@ -62,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,29 +85,92 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/akternurra/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py:876: UserWarning: Argument fill/fillcolor is not supported for Tensor input. Fill value is zero\n",
+      "  warnings.warn(\"Argument fill/fillcolor is not supported for Tensor input. Fill value is zero\")\n"
+     ]
+    },
+    {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "412 We___________________________________________________________________________________________\n",
-      "new structure that for supporting the basic______________________________________________________\n",
-      "expect a negative association except for the possibility that____________________________________\n",
-      "Federal Share By law this is 70 for the Virgin Islands Guam and Puerto Rico______________________\n",
-      "left I was held up a bit trying to make a left turn By the time Id made it he was gone Traffic___\n",
-      "11 which compares 8 MonKhmer languages with the__________________________________________________\n",
-      "and teachers not to look at each other as white or Negro but as human beings Again_______________\n",
-      "you in a plain straightout shootdown He had lots of friends then as always Even as_______________\n",
-      "the Gallery leaders had__________________________________________________________________________\n"
+      "412 We______________________________________________________\n",
+      "new structure that for supporting the basic_________________\n",
+      "expect______________________________________________________\n",
+      "you come out when you saw them gang up on___________________\n",
+      "fashion Passing_____________________________________________\n",
+      "life________________________________________________________\n",
+      "in__________________________________________________________\n",
+      "that________________________________________________________\n",
+      "a dilution of the intermediate sera to______________________\n",
+      "and Wilson remaining ashore determined to catch_____________\n",
+      "are of two types participation______________________________\n",
+      "nonetheless_________________________________________________\n",
+      "will begin as soon as the shelter is occupied_______________\n",
+      "their orbits but allows the wind to bend a blade____________\n"
      ]
     },
     {
      "data": {
-      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -104,7 +180,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -114,7 +190,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -124,7 +200,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -134,7 +210,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -144,7 +220,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -154,7 +230,7 @@
     },
     {
      "data": {
-      "image/png": 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+IWSmpc3KiDngaqTbX5px2V8JIUutrtVqOddZWz0M8zukQW4PWf4+ZmUZ0R/627/tcdc/+ssGmK3xZll99x6AXxBCcokZBSFkrkUpdxJmK6BfEUJElnKOcnOPzwE8a3muIJjjLbnqfxoAj1ja41KY42bRFOUXAKywHBsBs0KN0gjze3Q1PvjCPD51EUJSATxEDxBCRlqeWQyz4qUPAxvDrgGQWepMDHP8noGmqnY3PgyEuwgh6Ralyh8BfGlpy4PtdwBs5rzfW+a8MbCa8/oxVyyEua2lw2ztNgzmtnAUwD2WMm+x3MfH8j6tlT++MCt1GgGICCHrYI4LxMPDw8PzPwKvtOHh4fEYhmEKAfwdZoGlHkAmgOP9uMQdMAdtbIFZaP7Yxb2uwizklBGziXo4gDcAbAewjxDSCXMmjVzLTz6G2cS9GkCh5Zg1vwFQAHNAxhYAL8OzMdDVPbkIBfAlzIvwIpiF3Q0e3McGhmEOwBxLZjPMO7YJAFZYnfJ7AB9Z6mYZxyUOArgCoI4QQk3q18AshJXBHH/jM5iVWe7KUgzgLpgD1TbBLHjMZxhGZ7FSmA9zYNkmmAO73mN5f9bXYGAOxloFYBuxC4JpcSl6BGYFUSvMbWW71XGu9mDPUwCuAzhFzC4fB2C2rgGA12F20WqC+R3usfvt3TALTVdhjoXxmLt6ccLtMFt51cAcqPZ5y7sEgE2Wf5sJIefdXciiBFoAsxVEI8wWAE/Aw7nbg/46EsBpQkgXzHX9KMMwZU4udxhm4Y8qbY7BrBw94uR8d2Xrb/+2/727/tHf8hhhVqIEWH13FsBqmF2FWmFuW/dajukALIZZKdcGc//4GmZlpjNegFmIvwTzWHTe8p0zTsMcWL0J5gxXSxiGoQGBn8P3sYb+AHNfpuXusZx/3FK3ozmu/RuY+1gnzMop62DmfpbvWmEeU5sB/M1FOTlhGKYd5hg378M8LnfD3P/7jbvxYYBsgDnOUh3MCrtHLPcaVL+z406YY2s1w/yuv8D3bcTTuWIlgA8ZhqmwWMfVMQxTB3O7vJOY08n/CmYL0zrLNT63us9emMe7azC/zz547jrIw8PDw/MTgNi6S/Pw8PDw8PDw8PQXQshpmAOJf/hjl4Xnpwkh5AsAVxmGed7tyYO7z8sAQhmGWen2ZB4eHh6enzy8pQ0PDw8PDw8PTz8hhEwihIRa3KNWAsiCowUXz/9hLK5mCYQQASFkFswWPFtvwX1SCSFZFje+UTBbgH3l7fvw8PDw8Pw4iH7sAvDw8PDw8PDw/A+SArO7jgJml8MllvhIPDyUUJjjzQTC7Br2EMMw+bfgPr4wu0SFw+wK+XcA27x5A0LIbpizx9mjgNn1jf/eO9+/yDDMixzf8/Dw/B+Gd4/i4eHh4eHh4eHh4eHh4eHh+QnCu0fx8PDw8PDw8PDw8PDw8PDw/ATpl3sUIYQ3y+Hh4eHh4eHh4eHh4eHh4eHxLk0MwwTbf+l1S5vg4GD4+/t7+7KQyWQIDQ31+nVvJRKJBAKB96pYKBR67Vo8///gzTY2UAght+zaEokEUqn0J/GcniAQCCAWi71+3f+1/n8r28StghCCmJiY/4m29r/WHniAsLAwyGQy9yfyeAVCCCQSyY92f4FA8JMZS5RKJcLDw3/sYvzkUavVCAgI+LGL0S8iIyMHPB+IxeKfTBvl4eFhucn1pVd7KiEE999/P5YtW+b0HJlMhoCAgH4v6JOTk/HLX/5ysEUEAERERPR78upveUeMGIHnn38emZmZnMf7O5mLRCI8//zzSEhI8Pg3t2IgFggECAkJgVwu99r1RSKR1xVcABAbG4vgYAdF5aBRq9VITEz0+nUHQkhICJ577rkftQzBwcEYPnw4NBrNLbn+yy+/jL/97W/IysriPJ6YmHhLFMXW9Kdtjh49Go8//rhTxQ0hBEql0uPrxcbGYsWKFfjzn//s8W+8TUxMTL/e76RJkzBr1ixERER4/JufwsJRKpXiX//6FxQKhcvzfHx84OfnB7lc/gOVzJHnnnsOycnJHs9NntRvQkJCv/vS/6Jyrj8IhUKIxWKPhCJ3dfyrX/0KycnJP4m27oqIiIhbNp7/kISGhuIvf/mLWyW6UChEYGBgv8ZldwiFQtxxxx24++67f/Q+QgjB2LFjsW7dOqfnCIVCBAcHe6Ws/v7+iI2NHdQ1+tNHPCkzXWe6O3fGjBlYtGiRx/cGzGX18/NDYGBgv37nDQQCAZ555hn4+vr2+7cjRozAc889hxEjRrg8jxDiUR0HBwfzmwk8PLcQr64c7rzzTgwZMsTlzkZ8fDzmz5+PuLg4j6+r0WiQk5ODqVOneqOYGDVqFEaOHNmv3ygUCo8VFYQQPPjgg5gzZ47ThU9AQAAiIiI8GuQJIcjIyMCcOXMQGBjo0eBJCIGfn5/Dd0KhcFALRj8/P2RnZ0Oj0XjNmiA1NRXjxo1DbGysV4WgyZMnIyMjw2vXo8TGxmLWrFlev+5AUKvV+NnPfvajliEhIQF33HGH1+tEIBAgMjISEyZMwNSpU51a2s2aNQvp6em3TICmCzJPUKvVSEtLw/jx4zFq1CjOc+RyObKzsz1eHGdmZmLFihWYNm2ax2X2NpMmTepXX7r99tuRnZ0NlUrl0fl0vLKuE4FAAKlU2u+yDpSAgAA88MADbgU3oVCI8PBwxMXFISQk5AcqnS1yuRwzZszwWLAWiUQeteFZs2YhPj6+X2Xx9fX1SBjyBkKhEEKh8AcVguPi4jB06FBERka6Pde+DVsjFAoxbdo0jB071ummkVgshkj04yf1HDVqlNMNJwohBDKZzGbc9VS4AwCVSgWVSnXLnjclJQU///nPMXHiRIwZM8blfVQqFdLS0rxqiZKQkICJEydi2LBhDnUilUptyuOqzryh4Js0aRJmzJjhco5UKpVYtmwZEhISBi14JyQkYPLkyQP+Pdd8wIVAIIBcLnerZBcKhcjIyMCECRNcju+RkZEYO3Yshg0b1q/yKpVKxMXFISkpqV+/GywikQhJSUmYM2dOvyz4CCGIiIjA6tWrMWfOHISFhTk9VyKRQCaTubVYEwgEWL58OdLS0uDj4+NxWXh4eDzHa0qbqKgorF27Fk1NTThz5ozNYEsnJ0II0tLScM8992Ds2LEeXzstLQ0TJ070mtXE2LFjkZub6/H5crkcQ4YMQXJyMtRqtctzBQIBoqOjMXnyZHR1dUGr1TqcQwhBeno6Ro8e7ZHFhlAoxMKFC9Hb28vew10ZVCoVcnNzIZfL2XchEokgl8sHNKASQiCVSjF69GjMmTMHcXFxgx6Yabny8vKwbNkyjBw50qs7FXl5eRgyZIjXrkdJTEzE3LlzvX7dgSAWi/ulAL0VxMXFYfny5bjzzju9el2pVIo5c+bgypUrqK6uRl9fH+d5c+fOxZgxY27JzrBYLEZoaChGjRpl05ecMWzYMERERKCpqQkLFy50OC4QCBAQEID58+d7rGSKj49HTk7OLXG58pS8vDynlk7Ozo+KinK70KPjikajQW5urs0zymSyW2Ip5wyNRoOnnnoKH330EQwGA+c5hBBERUVh3LhxGD169KB3kwdKREQEuru7wTCM2/lAKBRCqVQiNjbWbfudM2dOv6wIfXx8kJOTg9jYWLeCkzeg89cPpdgQCoUYM2YMZs+ejfT0dKfnUeu53NxcKJVKzneSkJAAg8GAtLQ0xMTEcF5HrVYPaMfc24wbN85tf5dKpYiPj0d6ejr7vP7+/h7P4YmJiUhMTLxlAt7QoUOxatUqFBUVYcmSJZxjESEEPj4+GDVqFGbOnOlVoXvKlClITEyESCRyUNDExsayLjhCodCp0Nxfq0xnzJ8/HxkZGdi+fbtN27TexPP398fq1asxYcKEQc81SUlJA95kpRY/ubm5bucPqVSK4OBgl6ETCCEQiUTIy8vD7bffjsDAQKdj5oQJExAcHIy2tjaPykoIgVwuR2ZmJvLy8jB69GiPfucNqGJr/vz5aGhogMlk8vi3YrEYc+fORV5eHnp7e52urQCzUl6j0bi1wBQKhbj//vsxefLkW275zMPzfxWvKW1+8YtfQKlU4sCBA8jPz7eZaAIDAyESiSAWixEQEIDU1FTk5eV5fO309HQMHz4cLS0tXilrUFAQgoKCPN7BSE5OxmOPPYZVq1Zh6NChLs9VKBR46KGH8N577+HNN9/E9evXHc7x8/PD4sWLceedd7o1SwTMg+GaNWvwxRdfoL293e1EplQqkZOTgz//+c+Ijo5md02oT3N/XBbo/X18fBAbG4uXXnoJ9957L5KTkwdl2SAUClkheMqUKZgyZQrGjx/vUggSiURQKBQeCwchISHw9/f3uim6UqlERESER9dVKBQQi8W3bGdYIBCAYVzHB6d15snumVAodHkuIQQBAQE2zy6TyRAWFub1BYuvry/+9re/Yc2aNXjllVdQUlLCeV5ERASGDh3abwsBTwgMDMScOXPwwgsvICYmxq2wuGLFCoSEhOCNN97AxIkTHY77+vpiyJAhWLBggU3fdEVXVxfq6+s9Xkh6Au1LntyfKq7605fq6uoQERHh1tJGJpMhKioKM2bMwAsvvAC1Ws32FX9/f7e7/d5CLBbDx8cHWq0W77//PqRSqUPdCAQCKJVK3HXXXVi7di0WLlz4oyhtBAIBxo4di88++wzNzc1urZEkEgkiIyMxffp0t8JYeHg4fH19PbbmTEtLw/PPP4+lS5d6rS6USiVEIhFnGUJCQhAREfGDKTYCAgIwceJEzJ492+UGgFQqxZAhQ/Diiy8iKSnJ4Z0IBAI8+uijeO+991BZWQmZTMbZ9xISEpwqdFwhkUigUCi8pgAJCwtzqzD19/fHsmXL8Mtf/pJdk4wfPx5Tpkxxe32BQICpU6di3LhxbjfCBoJMJoNUKkVVVRUeeeQRTJkyxaHtC4VC+Pr6IisrC3/605+watUqpKWlea0MK1asQFxcHPR6vc16WCaTYenSpaw1h6+vL5YvX845t4hEIqSmpg6qHHScv3r1KrZu3WpjcadSqSCXy9n5ID4+HtOnTx+0haNKpUJ0dPSAy0vnA/u1hj3+/v7IysrCiBEjOMdrGg9PLBZj2rRpmDJlCqKjo51apaxcuRKXL1/GO++8A8C9BZRSqURGRgbWrl2Lxx9/nHPOv1VQpelvfvMbrFy5Ei0tLR6vMxUKBV555RWsX78e//jHP1BUVOT03JiYGGRnZ3Mq8wkhEAgEEAqFkMlkiI+PR15eHoKCggb8XDw8PM7xijRLCMEjjzyCX//61zhy5AjS0tJsLBHWrl2LoKAgdsEVGhqKBQsWeLzA2LRpE55//nmcPXvWG8VlcSfsAuaJffr06Rg2bBhGjhzp1nJDoVDgsccew+uvv44zZ86go6PD4ZyHH34YP/vZzzB9+nQkJye7LQPdjaaxO1wtvAkh0Gg0mDlzJlJSUpCens4uqOLj47F8+XI89thjbu9JEYlEiI2NxbJly/Dyyy8jKysL5eXluHDhAurr6z2+jjVCoRBBQUEYP3485HI5pk2bhuTkZNx9992YN2+e03JkZGTggQcewKpVqzy6D53APHnP/YUQ4va6UqkUDz30ECZOnHhLJjGlUonAwEDcvMkZrwqAeWHxwAMP4IEHHkBUVJTLBZBQKERcXBzWrFmDtLQ0zgWAUqnEnj17kJSUxAawo7t13lSO0QVXaWkpenp6UFxcjPb2dqfnt7W1uTw+EEQiESIjI3HbbbchNTUVGRkZLvueTCZDS0sL6urqAIBzsT127Fj87W9/Q0xMDObOnetRkMyLFy/i2LFjHrlneIJIJEJWVhYeeOABjwSCsLAwVkHraV/65JNPIJFI3Cp2g4KCMHbsWCxatAipqalQq9UQiUQQCATIzMzEE0888YO4SI0aNQp33HEHbrvtNgBm9y5ryy06Dq5duxaPPvookpOTUVBQgL17997ysnGh0WiQmJiIwMBAt4qYoKAgLFq0CI899hiCgoJcKur6o1wWi8WYM2cO0tPTMXbsWK8obaRSKdasWYPc3FxOYX7GjBl47LHHkJOTM+h7ecKMGTNYK1tnGyyEEPj6+mLOnDlITk5Genq6w9qGukzv378ftbW1iI6OdtgAEgqFuPPOOzF//vx+B8+dMGECHnjgAZfxBPuLu7agUqkwb948TJw4kW1TU6dOxezZsz269tixY9HS0sJpjTxYli1bhrCwMKxevRparRbp6ek27d7HxwdpaWn49a9/jX//+9/IyspCQUEBrl275rUySCQSaDQahIaG2igJgoKCMHLkSHbzzM/PD6tXr+aM9ahQKPDAAw8Mam598skncebMGbz66qtQq9W4++672WN33XUXJk6ciLCwMMTFxUGpVGLx4sVQq9U/StwloVCIgIAAdj5QqVQux6vc3FzcfffdWLFihcN4ERgYiMzMTIwYMQK+vr6YMWMG4uLisGTJEqcWVVKpFG1tbWhoaGDL4qyc0dHRWLt2Lf7xj39g+vTpaG5uxqlTpwb87P0lIiICCxYsgFwuR1lZGYxGI3x9fd3KVdZrq7///e84efIkWltbnZ4/bdo0PPLII1iyZInDMbVajYiICAQFBSEpKQkKhQJz5sxBTEzMj2oZzMPz/yuDHpWpML127VocO3YMXV1dDn7Od9xxBwIDA1lNNzVJnT9/vkcdOycnB9HR0fjnP/852OJCIpFAKBSCYRi3AohAIMCsWbPwyCOPsGbAw4cPd7qYoZYsa9asgcFgQFJSEmccgUceeYSdyD0d2GQyGe677z4IBAJ0dnY6PS8pKQlLly7Fgw8+CB8fHyxfvpy1TBGJRAgODkZ2drZH9wTMwsGoUaOwePFijB49GgzDYM+ePaivr4fRaPT4OtYoFArExcVhwoQJyMvLYxeofn5+CAsL4zQVVqlUGDZsGJYtW4bFixe7vQcNbOzJe+4P1GLMk+uGhYVh0aJFmD9/fr8CSHtKQkICxo4di48++sjpOWKxGEuXLsWyZcsQFxfnUgBWKpVIT0/H0qVLMWfOHM5Fm1AoxPDhw3HbbbfBz8+PrQ9vWxINHz4c7777LvLy8ti+xGW1IZVKQQhBTU0NampqvFqG0aNH48EHH8TMmTOhUCiwdOlSl6bq7733Hs6fP4+///3vAByFnoCAACQlJbECRG5urlvLHV9fX4wYMQKjR49GQ0PD4B8K5r40dOhQLF261KM4RAsXLkRoaKhHbZ4QgsDAQCxduhQvvfQSTp486fRckUiEUaNGYd26dZgzZw4UCgVGjhzJWlHI5XKEh4ff8qyB4eHhmDBhAqZMmYLCwkJ2B95aWFAqlViwYAGWLl0KlUqFuro63Lx5E83Nzbe0bFwIhUKsWrUKDz30ECQSCefGgDVUePTz84OPj4/Tvkr7kifvmS7O165di8DAQOTm5iIhIWFQ44BAIEBYWBiWLl2K+fPnc1qEKhQKZGZm3rJYQjTbEB37hg8fjpCQEAgEAqd9NTw8HAsWLMAjjzwChUKBRYsWcVqpNDc3sy4My5cvxx/+8AeHc9RqNcLCwvqVvUYgECAnJwfLli37QWOt0VhftH5o1jxPXNcIIZg8eTLKy8vR1dXl9bKNGDECKSkprBLGvl2Gh4dj4cKFWLJkCRITE2EymXDy5EmvKm2kUim6urpQXV2N2tpa9nuxWIygoCB2XSYQCBAcHAw/Pz+HOVcqlWLq1KkD7lfp6elYuHAhZDIZqqurIZFIbOawWbNmYcyYMazyAzCPF7NmzfpBXVMpSUlJ+NWvfoW5c+dCoVBg1KhRLq011Wo1goKCWIsha3JycjBjxgxMmjTJRrF42223cVqNBAQE4O2338bx48cRFRWFe+65B6+88grnfX18fLBw4UIsXboUaWlpEIvFuHr1Kk6cODGIp+dGIBBwKrCjo6Mxc+ZM/OY3vwHDMJg/fz5+//vfY86cOS6vl56ejg0bNmDKlCno6+tDQkKCS2s3Hx8faDQaznhP6enpGD9+PFJSUvDwww+z4+eYMWOQkpLS30fl4eFxw6CVNn5+fvjoo4+wY8cOdHR04N5778WSJUtsJh8fHx/IZDJER0cjPDwcDMNAKBRi3LhxHpnnJycnIzk5GRUVFYMtLtRqNSQSSb+sbPz9/SESiSCVSuHj48MpzIpEIgwbNgwvv/wyNm/eDJPJhKysLAffThpf4OrVq2hpafF4N0MoFKK9vR06nc5l2ePj4zFy5EgoFAoQQhAdHc0uoqiSJSQkhHMRYP+dUChkF6Njx46FWCzGwYMH8Z///Ie1JhgIAoEACoUCMTExSE9Pt7FaUavVnGa1M2bMwO23346srCwMGTLE7SImICCAVc55E6VSCYVC4dZ/WCgU4te//jXS09Mxb948ZGVleV2xERMTg1GjRuHYsWOcx+VyOSZOnIjMzExkZWUhOzvbpbCTm5vLur7deeedTtumUCjEiBEj4OPjg9TU1EGbb3NdX6FQQK1Wo729HTNnzsSYMWM4/aSpf3p3dzd6enq8VgaxWIykpCSMGjWKVUpFR0c7VbJKpVIEBATAaDSyVmT2pKamIj09HVqtFrW1tUhKSnI7/lFFj1Qqdamc6w/Tpk3D8uXLMWTIEKxYscLt+ePGjYO/v79HfYkK3sOGDUNjY6NLBbOvry/CwsIQEhLCKm4jIyNZxSIdJ5zFWPFWf7rvvvuQm5uLd999F0KhEG+99ZaNC6ZMJkNiYiJ+9atfITo6Gl1dXXj33Xexf/9+6PV6r5Shv0RGRqKrq8vtfACYyx8eHg6xWOwyYDDtS57ERvDx8cHUqVNZVyo6xw/mnUilUjzxxBNISkrCokWLON+7VquFv78/lEqlw7280R7EYjGCg4NZISYiIgJKpRKtra2crs7UGm/8+PGsMBwVFcXpflFbWwuTyQQfHx8oFArOcyQSCYKCgjit6pw93/DhwzFixAhkZWUhJSXlBw3SbJ1Sm7ZDT+/v4+ODzs5Or/ehn/3sZ6ioqMC2bdtYVxtr/Pz8MGLECKxatQqRkZEQiUT473//i6+//hrl5eVeKwchBEeOHME333zj0EepNSFFr9dzKrtoDDRCCBYtWmTTLmQyGR588EHcd999ThUb//73v7Fv3z6cO3cOo0aNwjPPPGNzXCaTISgoCLGxsUhKSmL7/ogRIzwOIu8tRCIRoqOjMWHCBLZNRUREOHVlIoQgKCiItc60rz/6bHFxcewahWEYBAYGsutja3x9fVFUVITa2lokJydj9erVnHGspFIpEhISsGbNGkRHR0MqleLAgQPYsmULLly44IWa+B5q/fzBBx84HPPx8UFAQABOnjwJhmHYoNuulG10beXv789aJmdmZjqNQ0WDjSuVSk6rWYlEgsDAQERFRSEjI4NtP+np6YiKihrII/Pw8LhgUEqb0NBQLFq0CLt27UJTUxNMJhNycnIcsowIhUJW8aLT6XDixAkYjUYMHTrUI6VNYGAgNBoNG4i3vxBC2FR0MpkMer3eI5NciUSC7OxsiMViGI1GNvsSFzNnzsSyZctw4MABtLS0QKlUYsaMGTZWIzSoHMMw2Lhxo9MYHfZlp0LMzp070dTU5PRcoVCIyMhI1vSTmkvSxUFTUxNu3rwJuVzO7urQuk1NTcXYsWNtdvgkEgn73oxGI86ePYt33nkHN2/eHLRJs1wuR0JCAqZMmYLa2lpcuXIFra2tUKvVDoO9UChEZmYmhg8fDplMBh8fH873QAhh/bdlMhl0Oh10Ot2gykkJCgpi/aMJIW7bokQiwezZs6FQKBAWFmYTq8Nb0F1OZ7uDfn5+uPfee+Hj4wO5XI6YmBinbloCgQBJSUls8Gp3QUuHDh0KuVyOMWPGYMyYMQC854Y2Y8YMzJ49G3/961/BMAxWr16NRYsWsTvvVJincZH6+vrQ2dnpMphef6Fxi8LDw9mgtNZ9yRpfX1+88sor2LJlC/Lz85GamsppShwVFQU/Pz+cPHkS27dv5xQ87UlOToZGo0FNTQ0OHDgw6OcSCoUYMmQIhg8fzrYJLujuLx07TSaTR31JJBJh5MiR6Ovrg06nc6kAoO6yMpmMrWOlUgmhUAiTyQSj0QihUIhhw4ZBIpFALBbD398f4eHhSExM9EpmuPnz50On02Hbtm3Ys2cPhEIh5s2bZ6Ock8vliIqKQlRUFE6cOIFvvvkGR48eRUVFxYDbvEgkcrCmoEoVT5DJZNi1a5dbF1W66NZoNJxtTSQSsS5TMpkMfX19bi0o6ThLs+LQ9zRYdwq5XI5Zs2axSiauuDVFRUUwGo1QKpXw8fGBWCyGRqOBRqPB0KFDERERMahYawEBAViyZAkbYyQ0NBQKhQIlJSXYsWOHw/k+Pj6Iiopi3aZp2ewFSIZhcPz4ceh0OmRlZSE8PNzBQoq2+aCgIKSkpLAbPBqNBlFRUUhPT3fY0BCJRFiwYAGysrIgl8s9znDpjv7MVZ5sAFFo7BTAPMZ0dXU5Dfo9EJRKJWbNmoW2tjacPn0a/v7+uP/++23OUalUiIqKQmBgIE6cOIHKykp88cUXuH79utfmEGqtdfnyZVy6dMnhOFeWrbCwMId1DSGE3TS4/fbbbeZlmUyGlStXYtGiRQ4WoGKxGPfeey++/fZbfPnllygrK0NUVBQmTZpkcx4d5+Pj4xEYGIiDBw9Cr9cjIyNjUHGj+pNFjOLj44PQ0FDExsaybcLZWo/eIyAgwGlWPKFQCLVajZSUFIwbNw7V1dUoKChgj9mjUCjQ2NgIhmHg5+fHxjmzh84H0dHROHHiBMrLy7Fr1y6cPHnS61ZjKSkpWLp0Kfbt2+dwjFq3VVVVsW6yUqnU5RwyefJkLF68GC+++CIYhoFSqcTUqVOdxh+iSlmuGG/A93Gh4uPjERsbi2+++Qbd3d2IjY295RayPDz/FxlUCgZqYrp27Vp2oRcYGOhgakcIQU5ODuLi4lBdXY19+/axbjCeDOxSqRQymWzA7jgSiQR33XUXKioqIBaL4efnh7q6Ope7imKxGFFRUUhMTERtbS1EIpHTAI10Vz40NBSvvvoqALPAnJKSYrNbIRAIEBcXh7a2Nhw+fJjNzuAqPopSqcTs2bPR2tqKbdu2uTTHDw4ORkxMDNRqNSorKx1cV9ra2lBdXQ2hUIjY2FhUVFSw6fliYmIQEREBo9HI+uXSyPhisRgtLS24ePEivv32W68sbOiOZnx8PLZt24b6+nrMmTOHNR22xs/PD+Hh4ZDL5aivr4dKpYJUKnVY7FGTfb1ezwYiNBgMHu8eu2LFihVoamoCwzCIiopCb2+v0+tSX+iYmBiUlZXZuMJ5039fLBazcVTsoXEW8vLyUFVVhfDwcISHhzuN6u/r64vQ0FD4+fmhtLTUbXwKjUaDiIgI5OTkICUlBb29vbh8+bJXnikpKQmRkZF45plnQAjBxIkTERgYyPYlf39/TJ06lX3PAGAwGLxqVZWSksIqPwsLC5Gamsq5GJJIJFi0aBGmTZuGt956C+Xl5Zg0aRJycnIcyhMeHg6BQICvv/4aN27cwKJFi9yOfyNGjADDMNi/f7/L2EWeQvuSRCJBeXm5U8srGnA3Pz8fwcHBMBqNHvUlmpVi586dbt120tLSkJKSgubmZlRXVyMjI8MmAK1er2cFiMDAQDAMg9jYWAQHB0OlUkGhUAy6zS1YsACnTp3C7t27UV9fzy7IrQVf6vYhEAhw8uRJNDc34+bNm4MaB/38/JCXl4cvv/wSgLm/JiQkQCAQoLCwEIB5POvu7rb5nbXif8eOHWhsbHR5H4lEwip3qRLbul0GBQVhxYoVuHnzJmspQzconPUnOl8kJiayc+pgg8lSJVZsbCyuXbuGyMhITiHhxo0b0Gq1CAgIQFhYGDo7O1krxrS0NFy7dg0FBQWoqqoaUDkCAwOxePFiGI1GrF+/HiqVCmKxGDU1NcjPz3c4PzY2FsOHD0d4eDhbF1zlZhgGRUVFSEpKQlBQEIxGo4NlIMMw6O3thVKpZN1Bw8LCEB4ezipvysrKbCyO1Wo1xo4dC4VCwa4NpFLpoK0O+yN0O1Pa2LcfqVSKuLg4jBgxAkVFRew53tzMmD9/Pnp7e1FaWor6+nqkpKRgwoQJNucIhUKIRCLo9XqcPHkSJpMJ+fn5Xo2JNnv2bBBCUFVV5VHsP7FYjPj4eKcxUcLCwpCRkcEGgzcajZBIJBgyZAiampoc3J4lEgnuu+8+/PKXv0RJSQm0Wi0UCoWDIE0IgUqlYse7Tz/9FPHx8QgKChp0TJL+vtfo6Gikp6dDKpWioKCAjSHn7DoKhQLBwcHw8fGBXq/nVP7J5XKEhYVBIpFg+/btqK+vx0MPPcRaOlnLFBEREWhsbMTQoUMRFxeHQ4cOcbobUldJgUCAU6dOoa+vDxcuXEB1dXW/ntcddE6YM2cOnn32WURHR6Oqqspm/mUYBn19fVi5ciVOnjzpMp6NWCxGQkIC4uLi8NRTTwEwr/uSk5OdrgupIphhGKfzuVwuR1BQEJRKJT755BNoNBoolcqfRBY8Hp7/3xiw0kYgEMDX1xcREREOZsMMwzgs7Kl7w3fffYetW7di6NChmDRpkscDO9c1PcXHxwcvvfQSampqUFVVhYCAAFRUVECj0aC5uZnTPFcmk2HEiBHQaDTYuHEjm/WKq7xxcXHo6urCkSNHWPNaLjcqsViMyZMno7i4GNXV1ejt7WWjrztTSAUHB+O5557D6dOnceLECZfuBtQqhmEYHD16FDExMTZCWV9fH1paWtDT04OJEyeisLAQkydPZgV2f39/1NbWsgsHvV6PlpYWtLW1wWAwoKurC52dnV4TjqVSKfz8/PDOO++go6MDYWFhSElJcRAAkpOTERgYiLq6OhQUFGDGjBkIDg5Gb2+vTZtQq9V45plnoFKpUFFRgbCwMCiVSgQFBaG1tXVQZth/+MMf0NPTg9raWhBC0N3djfDwcNTX1ztcVyKRIDMzEwKBADt37sSCBQugVCqhUqm8FpcEMPcJZ+2GxgMJCgrChg0bsHTpUkRGRnLGSqDKxLCwMDQ1NWHnzp145JFH3N5/woQJSEtLg6+vL6qrq/Hf//530M8UExOD7u5unDlzhv1OoVDY9KWwsDD2Pd+8eRMqlQpBQUEICwtDXV2dV6yrJk2ahMzMTDQ0NGD79u1OLVLkcjnWrVuH/fv3s31TJBJBJpM5WGOFhYXBZDJh165d7C6gu/Fv9OjRKCkpwaeffuoVNwIqNFZUVODEiRO46667OM8LCAjAX/7yFxw5cgQxMTGsMOmuL0kkEsydOxeZmZlurQJp3ImLFy/iyJEjDtm/uru7UVtbi5SUFIwaNQoMw2D48OHQaDTw8fEBwzB49913BzweicViREZGore310Gwsh5XtFotGhsb0dHRAZ1OhytXrrgchz1Bo9HgwQcfxJdffgmxWAyVSoVZs2ZBq9WisLAQAoEAQ4YMwdmzZ236uFgsRkxMDFpaWnDmzBm3gqZSqURoaCgCAgLQ1dWFrq4um2eLjIxk58abN29CrVbDz88PgYGB7LhvT0BAAIYNG4bAwEB8+umniImJGXSWLx8fH9btddu2bVi5ciVn36DzUVRUFMaOHYvW1lZMmzYNgHnsoGPsQJQ2NHZEbm4ujEYjq2jX6/Xo6upyUKAB5rgZkydPhkwmw969exEfH28TwNoapVKJO++8k80exTV219XVISMjA2lpacjOzkZSUhKrMKZuIAcOHGDb/JAhQxAREYGKigrodDo2KOhgXcn7I3ALBAIbJQ8Vau3HYT8/P0yYMAHPPvssdu3aBUIIYmJioNPp0NbWhp6enkFvrjz99NN47LHH8N1337HPYZ9tsqurCy0tLejs7IROp0NRURF6enq8qvR/7rnn0NTUhJqaGs73bB+4XyQSITEx0anCb/LkyayiWCQSsckcrl27hpiYGJtrUevsuLg4lJWVOWwU2T+nr68vTCYTamtr8eWXX2Ly5MmYMGGCR1bw3iQ7Oxtjx45FZ2cnvvzyS8THx7tshyEhIdBoNBCLxejs7OTsn0KhkM0e9eabb6Kvrw8rVqxg3ams301oaCgSEhKwbNkyiMVivP7665g7d66DAlKr1aK5uZmdD65du4a2trZBt117qOVReno6Zs6cibq6Orz33ns2mwU0u9jTTz+NKVOmIDk52WmdRUdHQ6vV2sSZs19b2UNjLfX29jpVSkmlUgQGBqKjowNffvklcnJyMGnSpB+8/fDw/F9gwEqbgIAA9PX14cUXX7T5nmEYaLVaBzPB9PR05Ofno6CgAI2Njdi1axd+9rOfuV0cCIVCEEIGNSBSE9OYmBhER0ezpv6rVq3C5s2bcfXqVYffSKVS1p/18OHDmDJlCoxGI+cE/OSTT+LKlSt466232O9EIhFMJpNNuSUSCe6880688847aGlpgdFodPtcEokEcXFxuP322zknJetnjI2NhUajQUtLC6u0sd5Z7evrQ1VVFS5evIh169ahra0NISEh0Gq16OjoQFVVlc2OjV6vx/79+xEZGclmCfHWxCQWi6FUKlFbW4vi4mLo9XpcvnwZ0dHRDm4PK1asQFpaGi5duoRPPvkEM2bMQE5ODqqrq23KIxQKERgYiMjISMTExMBoNGLGjBno6urCzp07cePGjQEJvrT9UHcZk8mEpqYmPPLII1i/fj2Ki4tt2oVarcYvf/lLGAwGbNmyBaNHj0ZERAQSExO9prQRCoXQ6/VOo/7HxsZi8eLFMJlMePPNNzF37lwkJSVxBnlWq9VYvHgxcnNzUVVVhS1btuDRRx91OZmrVCqsXr0aKpWKrY+tW7cO+rmeeuopFBcX44033rD53mg0su2Yxn2IiIhg3/Mdd9wBlUqFLVu2oKioaFAKDhq/RqVS4caNGzhz5gwIIZzKIJqJ4emnn3bYibIfK6jbxs2bNxEYGOg0rbH1tQkh6OjocGtR4SnLly9Heno6CgoKsHPnTqxcudKp9YxEImGDYMrlckydOhWdnZ3YsWOHU5c8Ol5XV1e7dHuQSCTw9/dHX18fjh49yiqKrS2mysrK8OWXX+Kll17C66+/DsDcVru6utDU1DRgawpKfHw8/v3vf7Mm8xSTyYSOjg72/fX09OD69es4dOgQq2hxNRZ7AlVSiEQixMTEYP78+Vi6dCkuXLjAxoj5wx/+gCVLltjMpTKZDCNHjsT27ds9svTx9/dHbGws5HI5WltbHYRT2n5jY2PZvrRgwQIwDIN9+/ahrKzM4ZoqlYoN3nrgwAHcfffdNv1zIISGhuKBBx6AXq/Hxo0bsWjRIrb9W1+3tbUV58+fx6xZs5CXlwetVouwsDD09fWhtrYW169f9ygQLhd0E0EikcBkMiEsLAxisRiNjY2c/Y8QgtDQUERERKC7uxsHDx5EXFyc03Hid7/7HQBgyZIluHnzJmc2qn379iE2NhYLFy5EdnY2a1FWVVWFjo4OB1ebBx98ECEhIdiyZQs6Ojrw0EMPITs7e9BKG5PJ5PH7pG7j9Dd0bre3AKWWQ1FRUazL0pNPPon8/HwcPnwYZ86c4bQa9RTalhsbGx36p/VY3NraiitXrqCgoACrV6/GG2+84XWBOzExEatXr8bp06c5j9PNOlpvLS0tSElJcdp2n3/+eSxYsACdnZ3w9fWFUqnEkiVLcOedd+Ls2bM2c7VUKkV4eDh++9vfOrRFg8HgoOiNjIyEXq/H0aNH0dPTg+3bt2PWrFn9zmBmT3/GA0IIG9+spaXFZj5wRlxcHBuLpbu7m9NlXSKRQCaTobKyEsXFxRAIBKzVkVKptFFoHTlyBP/85z+hUCjw1VdfoaurizOuT29vL65fv45vv/0WDzzwAN59912vumZThg4dilGjRiEgIABr164FwzD47LPP2HsZDAYolUps2bIFx44dQ0NDA/u8XDz66KNoaGjAyy+/zH5n3W/toesgtVqN3t5ep3Es1Wo1EhISsG/fPvT19WHv3r3Izc11UJby8PAMngE7P99555144oknWPNuSnNzM44ePYpt27bZfC+Xy1FSUoKioiJotVqUlZV5pImNjY2FXq8f1CLEZDKxAzrdBcrKysLTTz+NTz/91GGQEwgErNl4d3c3kpOTkZ2dze6C2k/wNOCt9QQzc+ZMHDp0yEY7TQhhY1rQXU9PFv9GoxGXLl1y6R5GFSupqakQCoVIT09HdnY2vvrqK5t79PT0oLy8HP7+/oiMjGR3mg4cOIDNmzfjm2++sbnu4cOHsXPnTphMJsycOROZmZle0aDTGApXrlxhlVuVlZVobGx0iEkwZMgQ+Pr6oqmpiU3FGB0d7VCO3t5eHD58GMD3Lg1jxozB888/j48//pg1We4v1GydYRi2/YSEhODRRx/F5s2bERgYaHNdqVSKESNGoL29nRVC/f39ne7ADoTMzExIJBJs3ryZ83hISAgbDLe6uhpGoxE+Pj6cC7Hg4GBkZWVBo9Ggp6eHbcdcShv6HSEEERER7Hu5ePEiKisrB/1ctC9ZK11EIhF2797NXr+jowPHjx9nyyMWi5GdnY3HHnsMb7/9tkPgyf5C40LRoK1z586FTCbDli1bbATooKAgTJ8+HZMnT0ZHRwe78KGxGnbv3u1wbaPRiL6+PtZaxdXClmEYlJeXe2Re7ykZGRnw8/NDd3e3y/dM2zzdQReJRBgzZgyee+45vPfee06zkBkMBhQUFLh1ZZ08eTJbxyqVCrfddhvkcjnOnTvHWrF0dHSgpKQEAoEAkZGRCA0NRVdXF86ePYstW7bg66+/HpSiYOfOnSgpKbGxFGUYBpWVlfjwww/ZRarJZEJ9fT1efPFFREVF4cEHH3Qb88kdVMAEgPfffx/PP/88EhIS0NXVBZFIhIiICMTGxjq8G6PRiKamJqxZs8at+xlg3lyJioqCSCRCW1ubQ5szGo02c6NYLMa0adPwwgsv4PXXX3d4z0KhEAkJCawbSmpqKnJyclBRUYHKysoBvw+lUonhw4fbjJnOXHQqKyuh0+kQHByM4OBgdHR0oLS0FFu3bsXOnTs5FU2eIJVK2ThTAoEATz/9NDQaDfLz8zkDjIaGhiIxMZGN+ZSeno6RI0di9+7dnEqeoqIiLFy4ECdPnkR9fT1n0FvqTiOTyRAVFQVfX1/U19djz5492Lx5M86ePWtTxzk5OVAqlWhra0NrayvEYrHT+BS3EpVKxSocJBIJp2tEc3Mz6urq2PGEEILp06dj7dq1eOmll/DQQw8N+P4ikQjLly/H/PnzHRTKhBB8/fXX7JxiNBpRUFCAf/3rX4iNjcWjjz6KqKioQSsprGEYBiUlJS6VUDQgNWAeN9PS0pwqbTQaDTs2SCQShIaGYsWKFbhx44bDuampqdiyZQs++eQTmzVpd3c3CgsL8eGHH9qcHxAQAIZh2MxHpaWl/X7ewUKtXNRqNTo6OthU1mfPnnVqTRgbGws/Pz8YDAanAa1pfdKNWZPJBIPBwBmfsqKiAp2dnfjwww/x9ttvs0Gg7TGZTGhsbMQLL7yAyMhI/OIXv8CwYcO8qqSgMYx+/vOfo6mpCc899xzuvfdeG7fH/Px8/PrXv0Z3dzdWrlyJlpYWlym/udZWM2bMwMmTJznHIoFAwCpt+vr6nFrO+vj4IDAwEIcOHQLDMLhx4wb6+vp+lJTxPDz/vzOgLSlCCFpbWzmFtPLycrS0tDho+E0mE65fv46ysjLodDqUl5d7tOidP38+fHx8cO7cuYEUFYDZnPEf//gHHnroIXZRRs1mk5OTMXv2bOzYsYOd4Ki5enBwMBiGwYwZMxAUFIQzZ86gpKTEZtE0e/ZsbNu2zcZah+7Avfjiiw5xKMRiMRoaGmAwGHDp0iXo9XqXAo7BYHBqTWFNcnIyoqKioFQqIRAIMHPmTAgEAhw5csRmF6CzsxNnz56FUCjExx9/jDNnziA/Px8VFRXo6Ohw2K0wmUxoaGhAYWEhRo0ahTlz5qC8vNxrblLUpxwA68c8fPhw9rhMJkNgYCDKy8tt/M65FoVtbW34+9//jsWLF7MTF3Xjy8rKwrp167Bz584BxUb697//jXvvvRfh4eHsZC8Wi5GYmIiZM2di586daGlpYaPzBwYG4ssvv0RfXx8bzG0wATLtoYual156ifO4Wq1GcnIyqyCgGdu4JtK8vDwkJSWhoaEBZ8+eBeDePN76+JkzZ/D6668Puj3Mnj0b27dv57R8e+GFF1jBqbq6Gq+//jor6NPy+Pr6YtiwYfjtb3+LnTt3DrgcycnJUKvVkMlkiIiIQGRkJNra2nD06FGb/hEbG4s//vGPSE9Pd3j2qqoqPP300w7XpucZDAacPXvW5U6iTCbD/v37bdLFDgbal27evMnGgnH2njs6OvD222/j8ccfZ9s77UtpaWmYM2cOtm7d6mC1AQD/+c9/3Pax7OxshIeHIyQkBNOmTYNcLkd7ezuuXr3K1jE1ydbpdBCJRPjggw9w6NAhFBQUoLa2dsAxzmhZuQKPmkwmXLx40cEixWg0ora2Fr29vcjLy8ORI0fQ2Ng44DgYNK22QCBAZmYm2tvb0dfXB4PBAKFQCH9/f84xViaTITs72yOrNoFAgIiICKSmpkKr1eI///mPw7zc0NCAd999Fw8++CDblwQCAfz9/ZGRkYGZM2di+/btNvf39/dnYwwtWLAAMpkMV69eRVVV1YDGABpwV61W46uvvnIbmLagoAD19fWorKzEzp07cfbsWVy8eBEVFRXo7u72WjaiRYsWwcfHB5cvX2bjDFkTHx+P4OBgiEQiKJVKzJs3D4QQnDhxAm1tbTbnGo1G3H///SgpKUFfXx+am5s5N6KamprQ3NyM1tZWXL58GadOncKBAwdw+fJl9PT02Lw/uVzOKow6OzvR2dnJBmkfLPbuO64ghECtVrsdp9rb23Hjxg2UlJSw8cIEAgFr1ZybmwuRSDSgwMQymQyvvfYam+zCGr1ej7Vr19psXvX19aG+vh49PT3QaDTIzc1FS0uL1yxhnbkWUqj1orWA7ay+CSE4fvw4tFotMjIy0NbWBoZh0NPTwxm4WKfTcWY6a2lpQVFRkcOYJ5VK0d3djfz8fDAMg7KyMq9YHvVHqR0XFweNRgOZTIbg4GDMmjUL7e3tKCkpcer2NGzYMAQEBODixYtO13VCoRC9vb027tbNzc2cljEMw+D5559HS0sLO4Y4ewbr+SAgIABDhw7F1atXOdcuA+G5555DdnY2Nm3ahNdff53dtA0NDUV9fT36+vrQ0NCALVu24PLly+yc6ay8M2fOxP79+x2Sn2g0Grzyyiuciu6AgABkZWUhODgYRUVF7IaoPdRdj7pdVVRUDDhpDA8Pj2sGpAqdNGkSuru78fnnn9t87+vri8LCQs6Bq6GhAbW1tWhvb2ddqDxh+PDhNjvrA0Gr1eKDDz7A+vXr0drair6+PvT19cFkMrHWGNaTn1Qqhb+/P+RyOSQSCWJiYqDValFaWuqwC/Hoo4+it7fXQVM9ffp09Pb22jynSCSCv78/u9t58OBBHDp0yGm5aVDDb7/91u0z0l0KqVQKX19fhIeHo7m5GeXl5TaLBxqkj2EYHDlyBMePH0dxcTEaGhrQ1dXFOfEVFxfjvffew7lz53DPPfdg6tSpXkkHqdfrcerUKXah39PTg97eXpt3kZ2dDYVCgRMnTuDIkSMuFxN6vR4lJSVYv349ey2tVguGYSCRSBxiZvSHjz/+GB9++CHrytXd3Q2TycTG5qBKJH9/fza+A93RuhU7DhqNBiqVinO33TrjBI2J4arepk2bhtDQUBQUFGDfvn0ulUv2gSP1ej0aGhqcust4ikAgwJo1a9DX1+fQl0wmE1pbW9m+1NfXh+LiYqxfvx4Gg4EVZhiGgUwmQ1xc3KDKEh8fz2an0Wg0CA4OxpUrV3Djxg2bvtTX14fS0lKHumUYBjqdDjU1NTbPZx2DyGAw4C9/+YvLgKH33nsvWlpavLbzOWzYMCiVSpw8eRLHjx+HTCZzKmR3dXVh/fr1rGLZui/JZDLk5uY6LBDDw8Nx7733Yv/+/W4X/XFxcWzq5piYGISFheHKlStoaWlh65jG0aJ1tG/fPnz33XesgnmgmTrEYjFuv/12vPHGGzZCmkAggFKpxJ49exwEP6PRiObmZqxfvx4ajQYPPPAAJk+ePKi+TTO2CQQCbNq0CceOHUNzczNUKhXmz5+PzZs3OyggFAoFFi5c6JFARNtvaGgoDAYDp5VoY2MjPvzwQ6xfvx56vd6mLymVSowcOdLh/iqVCjKZjJ0b29raUFhYOGCLsJCQEKSlpUGv1+ODDz5gM+84o7KyEm1tbaivr8fhw4dx4sQJXL9+HW1tbR6lQPcEuqlDCEFPTw+n4BgdHQ1/f38IhUL4+PggOjraZZDqa9eusRabNHCq/Xvs6+tDV1cX2traUFxcjK+//hoXL15EU1OTTYY8gUCAnJwciEQiFBUVOXVdGMzzWyORSBAeHu7UEiQtLY1z3vDz82OvZTKZUFRUhH/84x+sdS39iEQiyOVyp64dnnDt2jWb/kKTThgMBgdXap1Oh6qqKmzYsAG1tbW4//77MXr0aKcBWfuDn58fDh065HZ8Cg0N5bSKUqlUDsqcwsJC9tmkUimMRiO+++47+Pv728SpSkpKQmZmJl577TWba8rlcjQ3N3Ouowkh0Gq1rCWFVqv1itLGug9LJBKEhIQ4tQKhfUkqlSI4OBjh4eG4cuUKmpqaOJVffn5+iIqKglwuR1NTk8tU7Z2dnTZKG5pencuy6saNGw7WUVxjLXVp++ijj1BeXo45c+Zg/vz5nO7n/UUgEGDs2LE4deoUXnnlFVy6dAk9PT3sZjltB1RZe+XKFbdj3sMPP8wq5KyZPn06DAYD53gVHh6OmJgYKBQK9PT0uIxRZzKZ2DFIp9Oxwex5eHi8y4BWnNOnT4dGo8GVK1dsvg8ICEB1dTVnrIG6ujq0trayiypX5s/W0Jgrg4nMbjKZUFpaio0bN6KtrQ0XLlzA3r17ceLECZSVldnsEFLXqIyMDNZcnGYZOHr0qI05KiEELS0tnJr72NhYG+WDQCCAj48PfHx8WMGtsrLS5XNR33AqhDvDx8cH2dnZ7AJSIBCgs7MTe/bsQUNDg40iRqfTsYNre3s7amtr0dnZ6XJXqKmpCUePHsXHH38MX19frFixgs2EMxDoOzcajSgvL2frng701pPpxIkT4evri4qKClRUVLBxDmprax0mKrr7tHHjRvT29uLUqVPYt28fTp8+jbKyskFl37lx4wZ27NiBsrIyVFZWYvfu3Th8+DBKS0tRVVXFCnlBQUEYPXo0GIZBfn4+oqOjIZfL0dnZOSh/fXvEYjHEYrHTWCQymQxarRaHDh1iXbq6u7s5laUJCQlQKpWoq6tDVVUV4uPj2T5qjUAgsFmYt7W1Ye/evThw4MCgspUQQrBkyRJotVq0tLQ49CWDwWDThhmGQWdnJzZu3AiDwYBDhw5h//79OHfuHG7evDmo90z7klqtZtNMt7a2YtOmTTZ9KSUlBRkZGXj//fcdrkEzLVn3KZlMxio/APOYdPLkSZf9btKkScjKymL99gfLpEmToFQqUVVVhZaWFja4MteCT6/X4/r16+zOLO1LJ0+eRFlZGee4pVarWastV+O6v78/oqOj2Wx8hBB0dXVh06ZN6OzsZNu0yWRCX18fKzCXlJSwu4yDEczFYjF+/vOfIz8/30YYpxmM8vPzOZXXWq0Wn3/+Ob777jskJycjKytrUFmTaDna2tpw8uRJ3Lx5EzqdDgqFAuPHj8eRI0cclDY0y0xycrJbN9XAwECEhYVBpVKxylV7qAKU9qXDhw9j//79OHv2LG7cuOGgeIyOjkZ8fLxNBp5vv/0Wp0+fHrDiIDIyEjk5OTAajcjPz0dcXBwkEgnnGASY5yNqUdPW1oaamhpWGeJNaNvkimUnl8uRlZWFkJAQNjZJX18f9uzZg9raWk5rH+txraWlBVVVVQ79hI4RNMh+YWEhWltbHbLjiUQi5OXlQSQS4ezZs6itrWVjU3lTgUNdgadPn45Vq1YhODiYs29nZmbazA207uLj423WCY2NjThx4gRMJhOuXbuGo0ePshtH1DWuv/j7+2Pu3Ll4++23beqdWoUZjUaH98EwDFpbW/HZZ59hw4YNiIyMxNy5cx3i6fUX62xMNMOQM8LDwzmzNEZFRTlk0Dx69Ci0Wi0bCyslJQXffPMNoqOjbWL1paWlIS8vzyGWjlKphMFg4NxQpa79tH2aTKZ+WVpxYf1bHx8f5OXlYeXKlUhPT3cYt+RyOTIzMxEaGsoG8eeaD6yJiIhASEgIRCIRurq6OC0eaRvU6XQOXgE0po09Wq3Wpg3SuuCCzgcbNmxgs1xOnjzZZb24QyAQYNmyZUhMTIRcLodCoUBYWBgEAgEmTJjgYJlP3ZgpXLHFqLKnubnZwQKGywWXkpaWxlqW9/T0uIzbYx2CglqQ8kobHh7vMyD3qOjoaM5o7WlpaaxAYA0hBKWlpWhubrYZgLmUNoQQVuA0mUyQSqX9ssxxhsFgwJUrV9jAl8ePH0dISAgSEhJw5MgRG9eo8PBwZGdnAwDro7l//36cOXPGZuGr0Wiwbds2TrNg+x0FkUgElUrF+pUCcJvlRqlUIiEhAWlpaRg3bhyOHTvGuagJDQ1FVlYWa+3R09ODq1evssHU7M38acwIlUrlMElxQXeltm7diqlTp2Ly5MlISEhARUXFgHa7JRIJFAoFGIZhTX2B7wMaWu/mjRs3DlKplN1NCg4OBmBOw8wlWDEMg4KCAhgMBuzduxfXrl1DeHg4m+FroAt7o9GIa9euoaWlBSUlJfjwww+h0WiQlZWFw4cPsxYvGo0GI0eOhE6nQ2trK/te6uvrvZKy2Rpnz6JWqxEYGIienh4cPXoUISEhEAqFqKmp4eybKpWKzYhFCMHQoUM5s7UJhUL4+fkB+F7p8PHHH3tkCeYKQggb4NVeGUDLZf+uaZwnk8mEbdu2oaWlBVFRUYiJiRlU0N7Q0FA2hhJgjgNQUFCAr7/+2iZjUFZWFsaPH481a9Y4XMM6TgjF19cXOp3ORnhw51oTFxcHhULB7iQORKCxZty4cfDx8UFnZydkMhmGDBniVDAGzGOmVqtFT08Pdu/ejevXryM0NBTh4eE4evSoQ/uQyWRIS0vDHXfcgaNHjzqN15WcnIywsDDI5XJ0d3ejpqYG1dXV2LFjh804Ty2TqIshdSEaLDTmV2dnp027kslkyMzMxMmTJ52OLadOncIXX3yBuLg4JCQkICkpyWmwUVfQDC933HEHzpw5g+vXryM9PR1KpRIymQyJiYm4ceOGQx3T3y1ZsgSvvfaay5hoYWFhiIyMhI+PD9ra2tDd3c35rnU6HWt+v23bNjQ3NyMyMhK+vr42O/MymQxJSUlISUlh6+P69ev4+uuvcfny5QG5itHYWEOHDmXHzOHDh0MulzsNkNnV1QWtVguxWAwfH59Bp7e2LgsVKPv6+lhLNJoq3ZqQkBBkZGSw85FWq8W1a9ewdetWjzLJNDc3Q6vVOqx/TCYTa4kbEBDgVNEvFAoxceJEMAyDs2fPorOzE2q1Gnq9HkVFRQOtAhuodc2kSZNw2223YeTIkTh79ixOnDjh8K7T09NZpQ1NM08FzatXr7L9iVp0MAyDvXv34uLFixCJRIiPj0d7e/uA1nmBgYG45557MHfuXJvv/f39kZyc7LSP0Ew6VVVVmDRpEnJzc5Gfn49jx471uwwUmokxNTUVubm5bAY8LsLCwhAdHc1muqLWsXFxcaipqbFxnztz5gwbQDcnJwe9vb145ZVXMGXKFFy9epWtt4CAAERHRztY4EZFRcHHx8dBaUMIgcFgYK0j6HeebKi6QygUQqVSYejQobjnnnuQnZ0NvV6PmpoaG2UwV18qLCzE119/zelmQwhBUlISAgMDYTKZ0N3dzTkGyGQySKVSVrlrjadWXe4sRk6dOoWqqiokJSVhxIgRyMvLc/BA8BQ6FtLg4jk5OWAYBsXFxdiyZQuWLVtmk/nJGfZjZnBwML7++msHxRUNeO9M+Z+dnY3Q0FBotVpWcWwPrWPr9mN9fftA8jw8PINjQEqb8+fPc8azWbRoET777DMb/2+qsT916hRqampYCxtn2l2pVIqMjAzWTNF+l30w+Pn5QSgUshMCVaQ0NjayA4tSqURUVBSSk5MBfB/c9vz582hoaLBRMEycOBF79uzhTP9qHz+ERvTvD+Hh4cjLy0NqaireeustTJgwwWHyIYSwQWQlEglrCnzw4EGnZpN0EgoNDXVpwWMNzRD02muvYfr06cjIyEBRURGn77Q71Go1wsPDYTQabRYXtM6syzxkyBB2UREWFobc3FzWisWZEEtNsvPz8/HNN9+wi0i9Xj8os1+lUgmxWIza2lrs3bsXAoEAe/bsQVNTE9tGVSoVkpKS0NbWBkIIRo0axVo3cAUNHAg0A4IzEhMTkZ6ejr6+Ply+fBmzZ8+GRCLBhQsXHBRHMpkMQqEQ3d3d6Ovrg1qtxqhRozjdDGi8HsC8sHrzzTdx/PjxQac/pvW0evVqhwWuRCJBXV0dp/BKFUinTp3ClStXIJfLoVQqB5zym/al0NBQSCQS9PT0oKysDAcOHHBQBIWGhnLuylLrA3tTYhp7oj9KzqCgIKSmpuLy5cs4dOjQoDNI0b6k1Wqh0WgwbNgw6HQ6p32CEILMzEy0tLTg9OnTOH78OCQSCZRKJaeptEQiQUREBObPn48//vGPThVNEyZMYNOdNzU14dSpUygsLERjYyNnWayFCG8sAg0GA/bv3+9QNrVajTvvvBM7duxweg+qJFy0aBHi4+MxYcKEASltgO+tGD7//HNUVVUhNDQUcrkcfn5+UCgUThVUQqEQTz31FN59912nAikVbOhOam1trdO2Z92XTp8+jYKCAshkMshkMhulgUqlQkJCAqKjo8EwDLq7u7F//35cvHjRJhB3fxCLxQgNDUVcXBw7Zo4ZM8apFSEtL2AWvOwtEgaDTCaDn58fjEYjrl+/joyMDDbIqb1QOGTIEISFhUEqlbJx5w4ePIjLly97pFzV6/UuxynqTuIMgUCArKwsdHZ2oqioiLUO7uvrw8WLFz1/aBdoNBpMnToVzz33HCIiIiAQCLBkyRLU19fj/PnzDufStURgYCAyMzNRUlKCu+66Cxs2bHBoy4QQfPzxx6zLHl2bDaQNKZVKzkxcCQkJmDt3rkvLI2rtu23bNvziF79gkwoMdIwRiUSYPXs2UlJSsG7dOmi1WodkHRRfX1/WzZz2QTqG2tdXfX092x8iIiJYK++YmBhUVlayba6yspJzPKJ9ylrop9YQVDFvbW3uDXx8fDBy5Eg8+uijmDp1KsRiMaZPn46qqips2rSJPY/2JbpZW11dzVqJc40BAoGAtYZtb29HU1MTp3InNDQUgYGB6O7udlij9Cc7mitMJhMqKiqwf/9+REREICoqasDtRyKRYNGiRcjNzYVEIkF6ejoSEhJQU1ODzs5OLFiwAE8//XS/N0onTpyIgwcPOsgOEomEzfZoj0wmQ05ODhvPhoZUsIYQgsDAQAQFBdlYRHFl/ePh4fEOAxqdP//8c4csQwAwduxYNngvewPLBFBWVsYOGgKBAH5+fg4psQGzouKjjz7CmjVr4Ovri8bGxkELhRSxWMzu3hqNRmi1WhtFDGDenUlISEBCQgK7k7h9+3Zcu3bNZoFMCMGyZcvYlHnWcA2Cvr6+GDVqVL8mxIiICOTk5ODQoUNISUmBj48Pp2XSmDFjEBAQAJFIhJaWFly4cAE7d+7kXKwIBAJ2h8HX17df5aE+sTqdDqGhoQOOa+Pv74+oqCiHWCthYWGsjzYlICAAzc3NkEqlmDZtGh5++GF2990ZUqkUJpOJfc+9vb2or68ftHsSzSZEy20wGFBfX2+jVKQZSKqrqxETE4NRo0ZBq9Wiubl5wMoEe6RSKYqLizmDYwLA6NGjMXHiRDY+wrp166BQKHD58mUH10WNRsMKz4DZJ3748OFuM8G0trayAam9QUdHBxtniiISiZCamori4mLOurN/z11dXairqxvweyaEYPTo0QgICIBAIEBFRQUOHz6MXbt2OSjJdDodp8AcHh4OmUzmoKDLzMxEQkKCU59+LiQSCb7++ms0NDTYBOceKLQvyWQypKamIiMjw23abF9fX9Z1gfYlayW3NdQ9480338SECROcBkSdPn061Go1DAYDSktL8dVXX2Hbtm1OUyVbB5v2Bn19fVi3bp3D/agZvzvFrlarxdWrV6HX6xEZGTmgMtBx12g0Yv/+/Whvb4dIJMLQoUPZNOzWVoj0N1RJUF9f7zJlvJ+fH6ZOnYrs7GzWjdFVQHtq0Ur7U3d3N5qbm23uHxISgqSkJDbrYElJCbZu3cpmcxoIKpUKISEhkMvlqK2tRUxMDHJzc9HR0eFUIUXj6YhEIs6A9AOFjtc9PT1Yu3YtdDodq7CxV8SMGjWKdc/o6OhAYWEhvvrqK04FMxeuAtNTCyJXAYWpwEStJ6dNm4bFixezLoXeIC8vD8888wyio6PR2tqKsrIyLFq0iLW0AsA+67fffsvOBWlpaVi1ahViYmIQHR0NmUzm0E4ZhmHdinU6HZqamgYcE8lgMHDGdgsNDUV2djaKi4vdXiM/Px9dXV1OhVhPEYlEmDNnDo4cOYLAwECX7pPXrl1jrUXFYjEbDy81NRVBQUEQi8Xspifth9QygsYXsx8DvvvuO7z77rsO90pPT0d6erqDpbtAILBxQQXArusGu1kaExODV199FbNnz0ZnZyfKy8uRnZ3t4EI0cuRI1jWqsbERx44dw9atW53OB/7+/rj99tuhUqnYIORcioywsDAEBwc7rDN9fX3R0tLiUXvzdH1cXFyMmpoaty5xzhCJRAgLC8Nrr70GmUyGqqoq/Otf/8ITTzyBr776CtnZ2QgKCvKobVqfQwjB0qVLHZTgQqEQaWlpnGUVCoUYM2YMhg0bBl9fXxw+fBi7d+/mdJfXaDTQaDQ2/c/X15dVwPKKGx4e79JvSxuJRMKpAY+NjWXjrziLkWCtiZXL5Q7nZWRkYNWqVYiPj8czzzyD2tpabNmyBZcuXepvMTmh1geuFpitra1oa2uDSCRCaWkpVq1ahYKCAgefaIFAgBkzZnCmRHWXDtbTRYGfnx9kMhlWrlyJc+fOITExEe3t7TYTLMMwuHDhAhiGQWNjIz766CO8//77nJpxexQKRb8nmMbGRtTW1rIxVQYC1cTbL4SDg4PZ9JMUGr8mJCQE4eHhCA4ORk9Pj8td2LS0NHR1dQ3ancQeGmvBXYYJo9GIyspKjBs3DjKZDJcvX2atzLzBE088gZKSEqfBualA09HRgcDAQERERECv16O8vNzBQoIuDBsaGqBUKpGZmQmTyYTvvvvOZXkDAgIglUq9sqNiMpnw3//+12E3W6VS4fPPP8emTZsc+ix1cWlra/OaJR7DMLh48SJMJhPOnTuH9evXY9OmTWhubnY49+rVq5xprx966CFkZWXZZNyhUEsgT6CL6Q0bNiApKQkrVqzAvn37+v9QVtC+FBwcjLCwMOh0OjZbmLPzVSoVdu3a5XaBS8e0lpYW/PGPf0R+fj6OHDnC6TJTUVGBnJwcHDlyBOvXr8eePXtc9lXa16lANZj2RmNmOUtxShWB7vjiiy+gUCgGFLiUWgwZDAbs3r2bHat0Oh3S0tKQnJxsE+uLEhoaiszMTLz00kvYuXMnu1jmEliUSiU0Gg2USiWam5tdjlm0L7mLKULjCRkMBly+fBkPP/wwLl68OKj3IRQKIRQKodPpUFtbi3HjxkEikeDKlStuLcto8FpvERcXh3HjxqGnpwfHjx9nXYm5YigVFBSwyrsdO3bg008/RVFRkcu6GD16NM6fPw+dTsdmp+QKMk6v4SzoL4UQgsrKSshkMoSEhMDPz48z1ttAEAgErFvtZ599hqeffhptbW04deqUjQvZ1atXkZycjH379kGpVMLPz48NYL1hwwY2ELZ1IHm65mhtbR10wFsab4PLmoXWn7PNDWtOnTrllZhzVBiePn061q9fz6l4pP2sqKgI+fn5bApvQgjeeecdqNVq9Pb24uDBg6winDJ58mQkJiayFsSzZ89GTEwMPvnkE7S3t6Onp4dTgSUSiSASiTjbhlartdmU6M/GgjMIIWymwdLSUsyZMwf19fV44403HNaNBQUFbGytL7/8Ev/85z+djlfUIom2zba2NqcbR9aKcWvkcjlEIhFn26NuzLQ+PF2rX758GTdu3GDjxPWX5ORk7N27F/X19Th06BBeeOEFlJSUwGg0sjHOaLB2a+gGuL0VjTXTp093GCcVCgU+//xzzmDMAoEAGRkZrLLVmfsZAHYT0/q4j4+Pg8U8Dw+Pd+i3SpjLB5IQgvfeew9yudxhUKGZINra2tgdIKFQCLVabZPSlBCC1NRUzJ49G48//jj27NmDJ598ks2A5A0SEhLcmiV3dnaisbER9fX1KC4uRmlpqdOFLNcOkkqlwn/+8x+HOurs7GTTltNAdZ7S3d2N1tZW1q3AGipo9vT0oLKyEtevX3epIKABwxiGwciRI7266PUUsVgMkUjkIAyrVCrW3NSalpYWTJo0CfPmzYNWq8WBAwecXpsQguTkZPT29npdaRMTEwOJROI2naHRaERLSwueeOIJ+Pj44KuvvrLJXjBYsrKyAMCpIE0nTJlMhscffxxSqRSHDh1CUVER54IOMC8khwwZghUrVqC7uxtvvfWWS2WIVCpl4xwEBAQMeoeSK4CeUCh0askgEAjYeAXeyHQBfN+Xent7kZ+f7zJOB1fMH8CszPL393d4lsLCQvT09LBuKO6IiIjA+fPn0dTUBIVCgeDgYE4lUX+h48i8efPQ0tKCt99+2+X5fn5+uH79uluTbOo6ePr0aTY1qTOh8/jx4+jo6MCVK1dw8eJFl/1Uq9Wy6ckDAgIGrCim0IDw9giFQojFYqf9w57Bmtfr9XrcuHEDL774IjsfFRYW4vLly2hsbMQ777zj8Jvw8HCMGTMGmzdvRmNjIyZMmICAgADO69PsKDTlrXUMCXtoX7LOQsNFW1sbGhoaUF1djWvXrqGsrMxrC3O9Xo+uri48+eSTEIlE+OSTTxwSHVB6e3uh0+nYVLveQq1WQ6PRoKGhwaXbIGAWNDs6OnD16lWcPn0aFRUVLutCIBBg3bp1bP9vbW11+k4aGxtx8+ZNBAYGug023djYiNtvvx0jR45EU1PToOKx2EPda1988UU2PTbN/ikSidjseYB5nFi1ahWGDh0Kk8nEBsxlGMbGgtLaNd4bynYauNb+XdFxwj5ulTNof/ZW5rHe3l62ruzHLJr4QSqVIjY2FhMmTGBd2yMjI9k2wrVOVSqVKC0txZ49e1jFiL+/P6tw5hLCJRIJp2UoXZvfvHmTzf5IiDl9O1ccuf5CXfV/8YtfoLKyknVTovGoKJcvX2b70rlz51xaBNKkHFS53tjY6FRhQd3I7a8nEomg1Wo5LdJ8fX1t5llP68DZesBTxGIxNBoNWltbsW7dOly/fp3d6A4MDMQbb7yBZ5991uYdymQyREREcMoE1shkMgd5gwaVd7Zms46/6ayuRCIRO2fm5+ez39NQBIONQ8rDw+NIvy1tent72TgY1uknMzMzcenSJZudfLqDZzKZoNVq2QFQLpcjLS3NRkOuUCjYRQp1CXj//fdhMpm85lbi7+/vVrDU6/Worq5mg/tRM1R7GIbhDGwrlUpZodo+xWRNTQ1MJhNCQkI8cpdpa2tDUVERjEYjNm/ejLlz52Lnzp0OwVorKyvR3NzMZuhyJQTRTAEMw7DBAvuDUChkLSwGSlhYGGJiYmzSD9JJRKlUOpgzDxkyBCqVis1O9sknn7i8Pp3EvK3p9/X1ZbOauEIqlWLkyJFISEhAXV0diouLXaZL7C80loKr9kPNrefNmwej0YgvvvjCIe2pNcnJyRCLxVCr1WzWIFfPSQjB7bffjlGjRqGurg7Nzc2orKzEhg0bPBZ8ra9FA05T6C4dFYjslcF0ccnlYjkYKioq0NTUhOrqapc7wREREWw/p1DLn4KCAgeFWn19PbRaLeei2h5CCGbNmoXNmzezGWaio6OxYsUKfPTRRwN/OJitGf38/CCXy3HhwgXObCLWUCsIT1J4x8bG4uOPPwbwfVBNLo4dO4b29nZ0dna6VQYZDAY2fpq7VNCeQJXWKpWKVcgRQjB8+HDcddddHgu9Go0Gfn5+Ax5jamtr8frrr+PKlSts3Z44cQJisRhRUVGcVlXBwcEYPnw4fve734EQgttuu43tH/ZQa56enh7U1NS4fM+0L7kTWrVaLUpLS3Hp0iXU19d7bV4GzPP/iBEjkJiYiOrqatYljwvq7iAWi73mHkWFf4Zh2DnXFVVVVWhsbERNTQ0aGho4s0VZQwhBZGQkq4QJDg6GRqPhjHNGN44SExPdzrM5OTmQSCTQaDS4dOkStm3b5uZJ3UNdcqgVUVlZmU0gYbFYDJlMBqPRyLrw3XvvvUhNTYVCoUBzczNriUktIayD3LpTRPUHg8EAkUiE0aNH23w/b948jBs3DidOnPAo86iPj49Ld0NPoRnQDAYDDhw4gNDQUIwfP94mWD/t72PGjIFGo2Hj6JSWliIxMRECgQA6nQ49PT3w9fV1cJGsqKhgLSQFAgEb+5HOL0qlkh1XBQIBHn74YdaVyJq0tDTIZDIUFxezse4IIRgyZAj6+voGrLShirmWlhZs3LgR586dY8cKnU7HZkWiyhbal2pra9HY2Oj2vtbXLykp4bTKIYSw8e2sXYBppjdnigj7zSehUOhRPUil0kFvqtB4ePZjq0QiQWRkpEO8rICAAOTk5LCbGsD3VuzW2CvjFQoFhgwZglOnTrHBzK2hijHArFxubW3lrKu4uDhER0ezVmOUlJQUdt3Aw8PjXfq9AtbpdJg2bRrGjRvH+qPff//92L59u0MwQqFQiMzMTOj1epuUlWKxGBERETYKEalUCplMBoPBgJqaGnYHh+4WeoPo6Gi3O7UMw6CjowPl5eWQSqVswEyu83bt2oXc3FyEh4cjIiICEydOxM9+9jNs2bLFwZyQBivs7e1Fbm6uzW6vQCBwEOZkMhna29vZTCa7d+9GYmIip3tFZ2cnOwEqFAqXvvC07DQGiCcCL11oCQQChIWFwdfXF52dnW4tTpzh7+/Ppta1vkdAQACb3caamJgYBAUFoaenB4WFhTbB9Ox9iAkhiImJ8erCkBIeHu6Re4tEIkFSUhJkMhl27dqFiooKmwlssNYCcrncpZsITVPp5+eH2NhYFBcX4/jx4y5NaCMiIhAaGor29naHTEnOFrJpaWmYNm0alixZgjvvvBN33XUXcnJy3Jr120MFmry8PLYvjR8/HosWLcJHH32EwsJCTrPgmJgYr8U5oXR1daGtrQ1yuRy+vr5O35VSqURsbCyrUBwyZAhuv/121NXVYdeuXQ7CWEdHB3Q6Hbuotn52ewghmDFjBs6fP8+mQJfL5Zg5c+agno1hGMTExCA4OBh1dXXYu3evjdKEyx/fUyVJbGwsUlNTWZe9trY2SKVSzrZQWVmJ3t5eqNVqBAQEuHyHRqORVYDZpz0eCFQx8fOf/xxBQUFQqVTIy8vDHXfcgbFjxzpdoBNCWMtKqpSlO6MDKUNLSwu2b99uU//Xr1/Hnj178NVXX3Fal8pkMgQGBqKrqwtdXV2skMwFVXB1dnaisrLSaQYbwPO+ZDKZUF9fj/r6eshksgHHNONCLpcjISEBUqkU27dvd0ibbd8P6UaQq+xZ/SEoKAgBAQHo7OzEoUOH3J5PxwmhUAh/f3+PlEfWfSE+Ph4jRozgbM80npAn7Z3G3+vt7cWlS5dYa156v4GMj0qlEiKRCNXV1Q6u4VQZSy1oSkpKwDAMJk2ahMTERLS0tODSpUsoLS1FW1sbzp49a2MNKRQKvWrdSxV4UVFRyMrKglqtxowZMzBv3jw2MDNXTBtqmUIVADTL42DGF+r2f/DgQRiNRhw/fpwVrK3fA40blZiYiLy8POTk5KC7uxtbt25FS0sLWlpa0NHRwWYRs7dYaG9vt4lXeOXKFWi1Wuj1eiQlJWHx4sVQqVRQq9VYuXIl5s2bh/DwcIe1XkpKCkQiESorK20Uv5GRkZxp7j2FZmbq6urC6dOnbdqPSCRyCDhN+xJNDOJK+UHdWwGgpqYGVVVVnJtEdLxubW21cY+jymyj0eiRJbardRadDwQCAZKSkhAeHj4oay2DwYAdO3Y4KDto/DD7DQ61Wo3MzEzOMluze/du5OTkICoqCmFhYRg3bhyWLVuG9evXO82oKpFIYDKZUFtbi9bWVk6ldFxcHMLDw6HX6236WHh4OEQikdct3Xl4eAZgacMwDBYvXgyDwcBGv3/xxReRlpaGjRs32pj/CwQCREZGOpha0oUv16QgEokQERGBIUOGQCgUQiaTQSwWe8WUNjU1lQ1G7AoawDU+Ph7JycloaWlxGEhNJhPeeOMN/Pa3v2UthPLy8jBs2DCMGzcOM2bMsJkkqZKkpaUFCxYswN69e9He3g65XA6VSgWj0WizYxAUFASj0YgTJ06AYRhWcPX19YVMJnPQfLe3t0Oj0SApKQkREREOQSzt0Wq1uHnzptsdQplMBl9fXygUCphMJkyePBl+fn6oqKjgjPXhCXRH095liMZXsS5Tb28vAgMDodfrcf36dezfv9/GasXX15fNfgB8H9PG28I8YF5o0wDazqABrn18fNDd3Y3169c77PT4+/u7jYvjCneKtu7ubnR1dbFxUbZv3466ujrOd20wGGAymVjz7NLSUgdrDme+8ADYLDMqlQoajQaLFy/GqVOn+jVhMwyDy5cvY9WqVWycpQkTJiApKQlPPfUUfHx8nMa0uRV+0x0dHUhKSkJaWhpu3rzJKfA2NTWhq6sLd999N06cOIEJEyZgypQpePnll3HgwAEHpWFvby/0ej38/f2RmpqKc+fOQSgUIiQkBE1NTTYLc0IIcnNz0dPTA51Oh4aGBtTX17MZ7QZKb28vlEolDAYDCgoK8N///tfmuH1fAr53tXNHWFgYkpKSWCVGQUEBIiMjUVVV5TBOGAwGGAwGJCYmIiMjA8XFxU7HIaPRyGaV6urq8so8IJFI8NRTT6G2thZ9fX1YuXIlEhMTUVZWxrkIBsxjU2pqKpqamhAUFITZs2eDYRi3lkrOoFlSrOnu7kZJSQmuX7/O2cdp+mn6f5p1Ry6XOyjQlUolhEIhmpubcfPmTZcKdoFA4HFfolYTSUlJSEpKcgjk31/oeKnX69kx87333rNpMyKRCGq12ibGjcFgQHt7u0dWFJ4QExODyMhItLW14eDBg2zZXD1bV1cXIiMjMWLECDQ2NjoEbrbHWgEaFxfnNLh4X18fO744G+sZhkFvby98fHyg1+tx4cIFHD9+3EaJ6O/vj7a2NrdzvD1RUVHsfE835urq6tgsjDTYsUAgQH5+PoxGI+umV1paigsXLiAqKgqZmZnYtm2bzVpFIpG4DM47EKjyaOXKlTh27BgefvhhVqkUGxvLuv5Y4+vri5iYGLS0tIAQgoULF0KtVnNmTfQUqVSKyMhI7N27F0ajEWVlZZDL5QgMDISfnx9rOUatQ2liCYPBgLKyMnz00UeYNGkSu3FJFQDUnR0wt0m6CUr/PnHiBNu/hw8fjgcffJBVZLz44os4f/48AgMDERUVZVPeyMhI1oXJeo5ztjb3FGo9JBQK4efnh7S0NFy4cAFCoZCNe2m/qdnV1YXw8HAMHToURUVFnNlpge9j5ZhMJty4cQPNzc2c7ZtuNDY0NKCgoMDhGBfUwsy6nzpbx1jPB4QQTJ06FSkpKaxLXH/p6+vDpUuX8Oqrr9qsdWhdWWfbovj6+iI5OdlmrWFvecwwDN566y088cQTCA4OhlarxcSJE5GZmYknnngCf/jDHzgTqSiVSuj1elRWVjqVJTQaDdRqNdra2mw2qahSjY9pw8PjfQaU8rugoADLly/H8uXLodfrceXKFbS0tEAulztYjFD/XvuBwd69qK+vj919feihhzBv3jxW2+stVCoVuru73fpa9vT0oKmpCYmJiZg+fTqKiorQ3NzsUJazZ88iIyMDEydOZE0uz5w5g5aWFpt4PRS9Xo/CwkJMmjQJoaGhaG5uRnp6OkaMGIHTp0/bCPJJSUlQKBRs/Ja+vj6Ul5dj6NChuHHjhoOwUFVVheTkZIwYMQJlZWUoKytzGjyMYRi0t7ejqqrK7YIuJiYGo0ePxtChQ6HVarFixQpWyHaVStMdWq2WUxjW6/U2Asa1a9cwZMgQNDY2Yu/evdiwYYND+RobG22eVa1Wo6mpyeuafrpYcCUAdXR0sGbOBQUFuHbtmk3ZRCIRkpOTB6W0cZe6vKCgAGfOnEFkZCRKS0vxxhtvOOxI0wCHNHihTqdDfX09Ll++bOO2Bph3zmi2LAq15qFQk9olS5bgd7/7Xb+ssEwmEx5++GEcOHAAL7/8MoRCIUpKSvD888/Dz8+P002JBsl1lhZ0MFRWVmLs2LEYN24cysvL0dDQ4LCI/eabb8AwDD7//HM8+uijMBqNeOqpp3DlyhU2hbo1VOBOSEjA6tWrUVRUBH9/f6xcuRKffvqpjWUFIQShoaHs3xcuXMDBgwdx9913cypsPeXatWuQyWRoampCYWGhQ/r36OhoNDc3O4wb7tyjBAIBfH19ERgYyJ738ccfY9myZejq6sLhw4dtzjeZTOju7kZycjJGjhyJQ4cOoampiVNQoO5Rer1+UCb79tf89ttv8d5770EgEKCqqgqfffYZNm/ejF27dnH+xt/fH7/97W9RXFyM1NRUZGVlYdOmTTZuD97A2U4tjc9gPW7cvHkTQ4YMQWlpqY2JPAA2UGddXR1KS0tdjvMCgYDtS+7ql8amS01NxbRp03DhwgV0d3cPeIHe09OD6upqNohnQUEBSkpKbAQXqVSK5ORkG6VNX18f6xLsDYKCgtiMZnRTwNqVmIuamhqkp6djypQp6OrqQlFRkUvLH2uXQV9fX6fxiDo7O1FbW8u5hqCYTCYUFRUhLS0NtbW12LZtG/bs2WNzTkJCAoqKipy6mTlj5MiRUKvV7CZFdHQ03n33XcTGxrLxUaiFCxWaqWKfppy+evUqysvLcejQIQdLHW9aaAFml9Ynn3wSZ8+exS9/+UvodDo88cQT6OjowMMPP8wZqDYlJQWPPfYYSktLIRAIcPvtt6Ojo8Ot4s0Vfn5+GD9+PD744APWUkUqlSIlJQXjx4/Hzp072XMNBgOMRiMbG+jcuXOoqKjA8ePH0dzcjJKSEs57dHV1sem5af2fP3+enROqq6vR1taGzz//HCaTCZcvX8aTTz6J1atXs1lDKdbpvu3nlO7u7gGvn1JTUxEXF4egoCA8++yzKCgowKpVq1jlFZcisba2FhkZGRg3bhyKiopQW1vLeX+hUIjw8HA2CLi7RARdXV0OAc2djbGEEPj7+0On07FhAJy5+KhUKnY+EAgEmDNnDvz8/FBYWDig9nPjxg0sXbrUwXKTYRg0NDTghRdecPiNj48PYmNjERQUhOLiYtZl0b7ezp49y1pFCwQClJeX49lnn4VKpeJM2EEtn9vb21FcXOw2QLfJZHJYM9A1JQ8Pj3cZkNLmtddeQ3l5OebPn4/S0lK89NJLMBgM8PPzszFjZhiGdQmyX/SbTCYcOXKEHXC7u7tRUVGB+vp6PPnkkyCEYM+ePSgvL/dKCkuRSITg4GCcPHnSrcDc2NiIc+fOoa2tDQsXLkR5eTm2b9/Omc3i0UcfxcMPP4yGhgZs2rTJpWl1T08Pnn76aZw6dQq/+MUvUF1djfT0dAQFBeH999+3OVcmk7ETOuWZZ57BG2+8wU5Y1mzbtg2RkZFISkrCokWL0Nraiq1bt3KWw2g04saNG6iurnY7MYeGhmLUqFGYPXs2jEYjIiIicPXqVY8D/HHR19eHlpYWzngWNTU1uHDhAvv3rl27IBQKcfnyZRw/ftxhAWod4BowTzjBwcHYvXu3V7JBUIRCIQICAlBVVcW5c0epra3F3r17IRAIsGnTJoe2azKZBl2u8vJyl65OV69exfHjx5Geno7t27dzLkTp352dnbh27Rp0Oh0uX77sIGADYM20ramsrERYWJiNkpa6uA3EyunmzZtYs2YNHn74YTQ1NeHzzz/HwYMHERcXx7Y1+6xiGo0GW7dudaqcHCjbt29HbGwsRo4cCcD8Tu37W0dHB86dO4dXX30VixcvxiuvvILNmzdDKpVCoVBwKhiKioowbtw4LFiwAPX19UhPT0dKSgr27t3r4A5jLQjTdMJJSUmYNWsWtm3bNqCF4e7duyESiVBYWIjTp087HG9vb7dRtlF3Di5LQ3vsla1btmzBH/7wBxw4cMDBxJxhGBw/fhwqlQojR47Evffei507dzooHgCzcHPjxg02JpA3du96e3tx77334v3330drays2btyI06dPIykpyalAKZPJWOW1SqViYzIMxD1qoFRWVmLHjh3s37/97W/x6quvwmQy4cqVKzZ1Exoaip6eHly9etVtymMaBHPbtm1u3Y3Ky8vZ2AqLFi1CcXExtmzZMuCgk9R95cCBA5g0aRI2bdrkINAZDAYHa62mpiaUl5cPOE20PTKZjE2nTsfW5uZmVjDkYt++fYiJicGYMWOwaNEiVFdXY+PGjU7vER0dDYlEApVKxVpCcdHe3o7y8nKXLm1GoxE7duyARCLB4cOHUVBQ4DCfOnNrcMfVq1fxxRdfYN68eZgwYQKys7Mxe/ZsjBkzBgcPHmStIBiGQXd3N4qLi5Geno7a2lqcO3eOVWRyZYgMDAzEiBEjvLqhQt1HXnrpJSxZsgTr1q3DkSNHMHv2bKjVagiFQof7BQQEYNy4cazrEo0JNBglIA1ybj0fFRYWYsyYMfjzn/9so7Shlso00Pu6devQ09ODP/3pT9BqtazFD8MwUCgUbF02NzezVjS9vb3461//ilOnTrFj74EDB1BRUYE//vGP7L+dnZ2crqqtra2scG0/V506dcrlGsMVFRUV2L17NwwGA1asWIG4uDhs3LgRWVlZuHr1Kmciif379yM6OhqjRo3Cfffdx44z9giFQkRERKCurg7nz593Wka6SWJvOQqAVahxySTPPvsstm3bhvz8fKxcuRJXr1516j5E5wNCCMLCwnDt2jWXa0NX6HS6fidc6ezsRHNzM/7zn/8gPT0dDz30EMRiMaec8pvf/AYPPPAAenp68Nlnn2Hfvn2Ij4/nvC4hBOHh4cjPz8d3333ndIzt6Ohgs+vZ968rV654LYEMDw/P9wxIaaPX67F161bs2rULBoPBRjClgbDooPnll19CIBA4LG71ej3OnDljM1kcPHgQs2fPxvTp0/H+++9j7dq1Drv+g0EkEqGgoMCtW49er2fTjdMYK852mg8cOIATJ06wJsMGgwGEEERFRTksyoxGI4qKitDY2IjFixeDEIKamhrs3r3bYdFbVlbmMMmeOnUKXV1dnIu94uJi7NmzB8XFxejq6nK5iO7u7sbjjz+Ompoat0Fj6+vr0d7ejvDwcADmBeVvfvMb1pd9IHz77bcoLy93aBMmkwmdnZ02SrV33nkH27ZtQ1tbG6e1E1fWDpFIhAsXLjhNBTlQhEIhbt686bJN0sXjhg0bcP36dQeLE4PB4FaIcoVMJsPp06ddugXU1dVh06ZNOHbsGG7evMmpXKN1ZjAY8Otf/xpyuRytra2cCk36DFKplFXgTJs2DQsXLsTMmTORkpICpVIJtVo9YPNgwLEvUT7++GM88sgjNgtqQghEIhHOnTvndaXNtWvXcPDgQYSFhXEGeaVUVVXhT3/6E1555RV0d3dDr9dDo9EgLy8Pw4cPx29+8xub848fP46EhASsXbsWa9euhUAgwKOPPupg8WIymfDyyy+z7ZdhGJSXl+M///kPRo4cie3btw+ojt955x1s376d7Uv2OMuA487ShmEYnD592uaaNG4DwB0X4Pjx42x8n/r6eqfPQ/3l16xZg5aWFq+ZXGu1Wjz00EMwGo02Qgs1q7fvM1qtFhcvXsT06dMBAEePHsX58+f7HXR7oDAMg4qKCnz99dfsd9999x06Ozs5U6EXFxfj7bffRnl5udMsTNZ42peoK8pXX30FqVQ66A0Vo9GIc+fO4fr163j//fc5szVqtVoHy4OvvvoKBw4ccJsW3FNu3ryJ6upq+Pr6su/+d7/7Hbq6upxaPdy4cQOHDh1ihV93iiu9Xo9XX30VMTEx6Ovr4ww2DZiF6aNHj7oce3Q6HV5//XV89dVXqKmp4Rx3r1+/PqCNlXPnzqGwsJB1/4qLi8OkSZMgFovxwQcf2Lib6HQ6vPTSSxg7dix27tzJuks5gwZ8due+3V90Oh1efvllvPnmm+xYDJhjta1fvx633367zfltbW24du0aJkyYAMA8Z37wwQc4evTogMtAY7hYc+DAAURFReG2226z+f7jjz9GcXEx5HI5zp8/zwq49uOJVqvF2bNn2br69NNPWeWcTqfD3r17bTZVTCYTSkpK8POf/xxGo9Fm/UFdf+hYvmPHDmi1WgeFr8lkQn5+/oDHNhrY+Nq1axg2bBhraSQSibBv3z5Oa8YbN27gyJEjaG5udrmpqtfrcfHiRfzud7/D/v37nZbRZDJhy5YtMJlMDufYb/RRGIZBeHg4lixZghkzZiAnJwdz5szhvL5Op8PFixcxbdo0EELQ0tKCnTt34tNPP3VVNV6FurJv3LgRTz31FB555BE89dRTNop9ysGDB3Hy5EnWbZASFRXl4B1hMBjwxRdf4ODBg7hx44bTZAF0051m8qIwDIPi4mKvua7y8PB8z4CUNoB50OLafaWBvuguQWNjIzZu3GhjIdHb24sLFy7gypUrNhM8VZZQM9+B7hRxQYMolpSUuN1BoKkE33vvPYhEIrS3tzs1MTYYDJwTh7MsMTqdDq+++iruueceBAQE4OTJk5zZkOwFOfpbLq02YLYG2Lp1K+vK4mrCpRO7J2mxb968if379yMwMBCAWSlx7ty5QQWApLuI1pMBwzD47rvvoNPpbMre0dHBPjPXYpDL7a6+vt6tqXp/YRgGTU1NKCsrc7kDqtfrWSsiZwLNYFw8/Pz8EBYW5jLYNMMwaGtrQ3d3t0cmquXl5R4F56PPRuOMfP755zh58iTi4uKQnp6O+fPn4+jRowPus/Z9iQa/TkxM5AxEWldXh6tXr3o9tWRtbS02bdoEiUSCvr4+l7t5fX19Du+ZuhTYU1ZWhmPHjmHMmDGIjo5GWVkZDhw4wKm8XL9+vY0A3dvbi5KSkkEF4+3s7GQFOU/6EnWJc9deGYZh3VqsaWxsdBqX4/z586isrIRIJEJra6vThSGt4/Pnz3ttLqA4uyeXpVh7eztef/111NbWgmEY7Ny5E2fOnPnB/PapO7F1W6MZZriUamVlZaiurmYDmrrCaDSirq4ORUVFbvsSVTq/++67EIlEaGtrG7QZvFarRXNzM9rb253e374NNjY2oqWlxWt9v6SkBAcPHrSxgty/fz+n6T+loaEBu3fvxtGjR1lXU2eYTCb89a9/xX333Qd/f398+OGHbKY1e4xGIzo7O91uWHV0dLDxoLj62EDnGRoz5ZtvvoFCocDjjz+O4OBg1gLIut/Q886fP4+Ghga3c25XVxeuX7/ucg4dKFxjsVgsRmJiosO5169fx5tvvsnG4jh27BhOnTo1qI2e7u5unD9/3ua7pqYm1NbWOrTT8+fPs65Z7e3tTt9VW1sb1qxZwx4vLCx0yEpqD42fyIX12NbU1ITdu3ez7laAeZyhWQUHqrQxGo2sld+f/vQnrF69GmPHjkVnZyebKcoe2peOHDkCnU7n1BrZYDCgpKSEVSa42kw4cuQIO25SGIbB9u3bcf36dc7fVFRUYNSoUSCEoLi42CYeoTWdnZ149dVXUVNTA0IILl68yLq2/VB0dnaisLAQ169fx0MPPYT29nZUVlZyjkMGg8GmbVPXdq6MjEajkd1YtQ4ibk9bWxuOHj3qEPfu8uXLaGhoQE1NjTcek4eHx4oBK224qKysdMggZTAYUFdXZ/OdVqtFcXExG2DSGqPRiKamJnz77bdeFcYYhsHmzZs5rR+40Ol0qKioACEEJpOp34tzVzuQX3/9Nfr6+qBUKlFUVMQZzNLZ78+cOcNpdqjT6VBXV8dOyq7KSycyTxZ1VFik2vuqqiq0t7cPKo4I1+KKmvjbKw64sie4gmEYbNq0CRUVFV5tPyaTCfv27cOlS5c8Uvp5w6WPi56eHrS2trp9NppdxRM8FbqoJdT+/fuh1WrR0dGBtrY2lJaWorCwEJWVlbh69apXFa2tra04cuSIQ5/V6/XYtGkTKisrvR67SKfTsQuO/maD6OnpQWlpKWc2GZrh5d1330VoaCiqqqpQV1fHWX57M329Xo+mpqZBBcq0DmTrCbTNe9KOuru7HQS2EydOOBXO2tvb0d3dzZr9uxuvmpqavB67yB6dTocbN25w3oda2tDMN/n5+V6z8vAUk8nk8P7Onz/PmTa6ra2N0z2FC61Wi02bNnkU4wwwv+ve3t4Bz41c9DdbDW2T3moTnZ2dOHPmjE0AVHdurHq9Hg0NDWw7cGeN9t///hdZWVmor6/Hrl27nAZbBczrJncKkP7Ojf2BxqfZs2cP/Pz8MGLECKfxvVpaWtzGFqG0t7fj7NmziIiIuCXltoa6/h45csThGHVvpRQUFAx6jKHzozU0uPiJEycc7t/Z2cluljjDYDDYZD8aqFVpU1MTRCKRzfPR+E32rqtXr171KI29K2jcxG+//ZZdb9bX1zsNOk/7Et3wdfYeaBwfd/UGgN0MsbcicpUGfv369SCEwMfHB++//77T8VOv1+PSpUus61pJSQlqamq8EnPNU2jA6r/85S/44x//iLfeegvFxcUetWH6fvr6+jg3a6hSzNXzGI1GtLa2OmxylJWVob293anikIeHZxBQocSTDwDG1eexxx5jhgwZ4vIcAIxAIGAUCgUjFAqdHl+5ciUjlUrdXqs/n5ycHMbX15chhHj1uvYfQgjz+eefMxqNxunx4OBgJiwsjFGpVP269uTJk5nk5ORbWn77j1wuZ8LCwpjQ0FDGz8/vltWfn58fo1QqB133I0aMYORyudfLmZGRwYSEhDACgeAHrX/7z9SpU5nY2Ngf5d5isZhZvny5Q98Vi8VMcHDwoN8f1/3Gjx/PyGQym++FQiEzYsQIRiKR/Kjvwv4jFAqZiIgIZtiwYU6fR61WD6gvEUIYkUj0gz0LIYRZunSpQ917+pk4cSITHh7+o78TTz8ajYZZt26d0+NisZgJCwtjwsLCvD43DfQzfvx4JjU1lfPdedq2fqp96Yf+iESiAbd1Tz/Lly9nsrOzGbVa/aM/rycfqVTKJCYmMsuXL2dGjx7NyOXyQV2PEMIoFApmxIgRt7zsw4YNY5566ilm3LhxnOWQSqVsf5bJZLdsXRMTE8NMnz79R32PCxYsYBYtWuTRuUql0qvzjJ+fH7NkyRJm4sSJTtfE/W1Dg3lX/v7+Tvu5QCBgli1bxtx3331u13nW84EreeZWf0QiEfPmm28yAQEB/fqdWCxmNmzYwERFRXHWw0DL4+Pjw4jF4lsuZ/Ef/vP/+ecslx6G9GeXzNIJnSIUCr2280azFHgTrtgKtwqayveH1LzzmLlV7/mHbD8/ZbhifvwY8O/j1vNTedc/FP/XnpfC96UfBhoX7FZbjXkbb67tgB+mvRFCIBAI/k/2Z3uoC8yP1e6EQqFLC5qfEmq1GgKBwKuJLG41NDhzf9u6RqNBS0uL162VeXh4Bs05hmFG2H/pVaUNDw8PDw8PDw8PDw8PDw8PD0+/4VTa9DemTRMAxwi5PDw8PDw8PDw8PDw8PDw8PDwDJYbry35Z2vDw8PDw8PDw8PDw8PDw8PDw/DAI3J/Cw8PDw8PDw8PDw8PDw8PDw/NDwytteHh4eHh4eHh4eHh4eHh4eH6C8EobHh4eHh4eHh4eHh4eHh4enp8gvNKGh4eHh4eHh4eHh4eHh4eH5ycIr7Th4eHh4eHh4eHh4eHh4eHh+QnCK214eHh4eHh4eHh4eHh4eHh4foLwShseHh4eHh4eHh4eHh4eHh6enyC80oaHh4eHh4eHh4eHh4eHh4fnJwivtOHh4eHh4eHh4eHh4eHh4eH5CfL/AIPoAKvnu4g8AAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -164,7 +240,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -174,7 +250,7 @@
     },
     {
      "data": {
-      "image/png": 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yiIuLo66uDr/f36NvjdvtZtKkSTgcDhoaGkhLSyM7O5u5c+cya9YsMyTLysrCbrd3Gz8qKorrr7+e8vJyAoHAsOYa6ffi8XhYsGABmZmZBINBqqqqgI7v5tzxoqOjSU1NHdQ8vF4vEydO5Pnnn+81iLDZbGRkZDBu3DiamppobW3t9fo5OTkkJSWhtcbv95OZmcns2bO54oorzF5EFRUV7Nq1C6vVSnNzM3l5eTz22GNs3bp1yCGIYRjExMT02UenN+Em1YNtJu12u5k6deqAVUlCCCGEEEIIIT5fww5tBuJyuUhKSoqo0ia8HffXv/51xowZwxtvvMHJkyfx+XzDWi5isVhwu928+OKLlJWVkZ2dzYQJE3o8YFutVqZPn87tt9/OnXfeOeTrDUdKSgpLly7F6/Wya9cu3n77bQzDoK6urtt8bTYbVquVDRs28Pvf/56NGzfywQcfUFZWhsPhMMOI+fPn9whtwve5vb39M6m0sFqtfOMb32DOnDlUVVWRl5dHWVkZAC0tLeZxFouFcePGcfPNN3PfffcN6hpjxozh2muvJS8vr8d7breb2bNns3z5ckpKSnjttdeoqKjA7/f3qI5xu90UFBTw4osv8vTTT/Pee++xe/du3G43drsdwzA4efIk7733HoFAgKVLl2IYBocPHx5Wbx6lFDabLeLdqzweD3PmzGHZsmWD3gnN4XDg9XqHHdgJIYQQQgghhPh0jcjyqL6EH0TDS3yg48HcZrNhsVh69LgxDIPExESWLFlCdHQ0xcXFjB8/nlAoRGtr65ACBqvVSlxcHD6fj82bN9PU1ERqaiqBQKDbeIZhMGnSJG644QauvvpqlFL8+te/jqjXykgGH9OmTePqq6/GMAwOHTpEVVUVFouFY8eO9XjIbmpq4tVXX+Xdd9+loqICu91OKBSioqICrTVKKS699FJeffXVHvP1+/0jNuf+hCtnlixZQkZGBjt27KC6uprY2FgCgQBnzpxBa41hGGRlZXH11VezZMkSMjMz+eEPfxhxrxuv18uMGTN6rR7xer1cddVVLF68mIMHD1JaWkpbWxvBYNAMj8K01nz88cesW7eOsrIyTp48CUBbWxt+v9/sm7N792527NjBTTfdxPbt29m9e/ew7qlSCovFQmxsrLncLBzMAWZlEHT8VmfMmMGXv/xlZs+ezdGjR1m3bt2grnduhZEQQgghhBBCiPPPiIU2Sini4+NpbW2lra3NDDKUUqSmppoPn9HR0Xi9XmJjYzl06JB5nGEYuN1uMjIymDx5MoZhcMEFF5CRkUFbWxvl5eVDCkdiY2OZOnUqBw8epKSkBL/fj8vl6lFlkpCQwNKlS/nqV79KdnY2hw8fJjo6OqLmyT6fL+JdmMLLyfpawjN//nyysrLMcCk5OZm2tja2bt3a7Rrt7e1UVVWxZcsWKioq+Oijj7BYLOZ8wgHClClTemyrrZSisbFxxMKmUCjU532yWCykpaUxZcoUoqOjcbvd5vKnqqoqjhw5gtYaj8fDl770JW655RZmz55NdXV1xPcfOpYKud3uHiGPw+EwdyFLSUmhpKSE3NxcXC4XBQUFHDhwoNvxdXV17Nu3z2ycHQ5LtNbU1dWZ311paSlPP/00Tz75JLfffjsVFRUcO3ZsWBVhSiliY2NRSqGUIi4uDo/Hg9aaEydOmN9XXFwc119/PUuXLsXj8ZCRkTGo6wSDQerr64c8TyGEEEIIIYQQn41hhzbhpRyxsbHcfPPN7Nq1i8OHD5sNboPBIBaLBcMwiIqKIi0tjdmzZ5OUlERxcbH5UB4fH8+MGTO4+eabzS3Eb7vtNgKBAK+88grr168fUsiQnp7OPffcw91332025LVYLGbAEbZo0SJuv/120tPTOXv2LAUFBXi93ohCg8OHD1NaWhrRfILBoPnw35VSirS0NL7+9a+TkpKCYRjk5OTg8/nIy8vj+eef71ZtUVtby549eyguLgbo9frhECC8W1W4osVms1FaWjoioY3Wmra2Nnbv3t3r+9HR0dxxxx14vV4Mw2DWrFloramoqOC1117jk08+QWvN/PnzueWWW5g1a5YZnCQlJdHS0jLgPG02G3a7vdfqkczMTBYuXMjll1+O1Wrl8ssvR2tNcXExmzdvZuPGjd2Oz8vLo7Cw0Oy5U1JS0us1m5qa2LRpE08//TTf+c532L59O3V1dcNaIhWujjIMA4fDwcSJE5k8eTJ+v5/Tp0+bS8kuu+wyZs6cid1up7i4mLFjx0Z8DZvNNuytyYUQQgghhBBCfDaGHNpERUXhcrnIysrCZrOxc+dOvF4v9957r9lXJBgMkp+fb26LPW/ePL761a9y2223YbVacTqdPProo7S1tZGSksLcuXOZP39+t2qF0tJS9u/fz9GjR4c0T5fLxdy5cyksLOxW/XNu35Af/OAHjB49mrfffpuDBw8SCASIj4/v86G9qw0bNkS05TVATU0NNTU1PSoyDMNg+vTpZmAVruhobGzkjTfe6BbYADQ2NppbVfcnFAqxaNEiNmzYQHl5ObGxsaSnp/OHP/xhxLaVbmlp4cMPP+z1vZiYGL70pS91qzBqbW2luLiYDRs2mOHVt7/9baZMmcLevXt59913sdvtJCQkcPLkyQFDG6fTSVxcXK+fZ+LEicyaNQvDMMzlZeElUAcPHuyxs9axY8cG3DI9rK2tjd/+9rfk5ORw77334nA4WL16NQ0NDRGd31UgEDDPGzt2LNOmTeNb3/oWc+bMoaamhuTkZB5//HHa29u55557GDNmDOvWrWPjxo0888wzEV8nNjYWq9Ua0W9HCCGEEEIIIcTna0ihjVKKK664gltuuYWLLrrIXMrx4osvcvjwYfPhORQKUVZWhtPpZPLkyWRnZzN79mzcbjdaa6677joef/xx2traSEtLY9KkSSQnJ5tLoYLBIFu2bGH//v3U1tYO/sNZrRiGwenTp7s9nO/YsaNHf5ixY8eybds2nn32WZRS3HvvvVRUVLBv374Br3P27NmIHvS11jQ3N/caQlitVi644AIsFguVlZX4fD5CoRClpaVs2LChxzla64gqZd555x1+/OMf853vfIempiasVitKKZYsWTIioU04jOltLMMwiI6OJjMzk/LycqKioswdwXbs2NHt3qampnLo0CFWrVrFnj17eOyxxzh58iR5eXkD9rVJS0sjISHBrDrqKiUlhaysLHw+H1VVVWZfmq1bt1JQUNDj+Eh76IT5fD5++tOf8vzzz7N06VLa29t59tlnBzUGdASc4UqauXPnctlll5GTk2Mul7rmmmv405/+RE1NDSkpKaxdu5bnnnvO7A8V6Rbpo0ePJjY2ls2bNw96jkIIIYQQQgghPltDDm0mT57Mddddh8vlQmvNf/zHf/DGG29QVFRkHqe1NoOCefPmkZWVRUpKCo2NjbS3tzNu3Diz182UKVO46KKLaGxs5OGHHyYYDNLa2srx48fNXjSDlZaWxtixY3n33Xe7vb5x48YegYdSildeeYWPP/6YuXPnkpqaisfjGfAaMTExtLW1DdjTxmKxEB0d3euOPYZhEB8fz+LFi9m3bx/r16+nrKzM3N3o8OHDPY43DGPA0EZrzZtvvsmtt95KTk4OwWAQpRQNDQ3m8p/hCjfQ7W0ubreb6dOnExUVxSOPPEJ0dDR1dXVUVVVRXl7ebQ5KKd555x3efvttnE4n6enpxMfHR7STUk5ODqmpqezcubPb6zExMYwePRqtNS+88ALbt283Q7M9e/Zw9uxZ81ir1WoGWoNVVlbGxo0bueaaa7jooov4z//8z0EvPdNam7+NK664ggsvvJCYmBjq6+sJBAJkZmYSExNDbW0tSilKSko4ffo0ubm5QORbr+fk5JCZmdnjXgkhhBBCCCGEOP8MeXmUxWLB7/eTn5/PtGnTeO211ygsLDR72YQFAgEMw2DevHnExcVRXV3Npk2baG9vZ8WKFebDZlJSEqNGjaK8vJx3332XUChEW1sbjY2N3RobD8aUKVOYO3dut92T4uLiaGpqIhQKERsbS1tbG4FAgGAwyLFjx6itrcXhcBAXF0d0dPSA15g9ezaFhYVUVFT0e5zb7ebiiy/u9T2LxYLT6SQnJ4fVq1fz3nvvUVZWZs6ra1+d8I5c5zYX7svBgwc5e/YsdrsdrbUZTpy7LGiobDYbTqez1/diYmLIzMykvb2dzZs3ExUVRWNjI83Nzd2aJUNHpcnJkycpLy9nypQpxMXFERMTE9EcsrOzSUpK4vXXX+/2enR0NE6nk+bmZjZs2MCuXbvMZWYNDQ1m0KaUwuFwYLfbhxTa+P1+Nm7cSFpaGg6Hg/T09IiXy3UV/o1fcsklJCYmsmfPHk6cOIHb7WbhwoXmdx4KhWhpaSEqKoqMjAwMw8BisUS0hXdGRgYTJkygsbFx0PMTQgghhBBCCPHZGlJoE27k+tZbb3Hs2DEyMjK6NR/uKhQKoZRiypQptLS0sHPnTlatWoXNZmPlypXmcQ6Hw2ySeubMGUKhUJ/LbiKVmZnJ1KlTeeCBB8zXLr74Yux2O+3t7bS1tXHmzBkOHTpkBkMej4ekpCSzSiMckoR7opz7YDx//nyqq6spLCzsdy5xcXFceeWV5nbOXRmGQWxsLC6Xi127dnHq1Clqamp6Xapjs9nMgCESlZWVbNq0ieTkZLTWJCYm9hkeDZZSyuxt1JuoqCiSkpJoamoyG/S2t7cTDAZ7hHB+v59QKITT6SQlJQWHw8Ho0aPNxsmGYRAKhXqtuAp/X+d+B+H71NDQwN69ezlz5ky3vjZhhmGQnJyM0+kcUmgDsH//fj766COmTJnCtGnThhXajB8/nsbGRj788EM++OADcnJyuPbaa83fTnt7OxaLhbFjxzJz5kyCwSBOpxO/3282/e7rXnm9XlJTU0esn5EQQgghhBBCiE/PkEOb9957j48//hiAlStXDvgQqJSirq6OEydOUFBQQGpqarf3LBYLoVCIpqamHlUg4YfVwfYbCZ8Tfni12Wzcf//9JCcn09TUZC49uv/++2lubmbs2LGkpKQwY8YM7HY7M2fOJC4uDq/Xi9VqpbKykurq6m7jjx8/ntjY2AHnERsby8SJE3sNBSwWC/Hx8URFRZGfn28uiQmz2WzmZ4iJiTG3zY5EIBDgoYceMpcwzZ07l9WrV0d07kDC31t4idu5HA4HiYmJNDQ04PP5uv1GwmFY+HO1tLSQnJzMxRdfzLx588ydpuLj47Hb7Wb4cvr0aXOMcCWNy+VCKdUjUHM6ndjtdlpaWigtLe32fnj3sPDuZtOnT8ftdvfYVSxSfr+f3bt3ExUVxezZs3nrrbeGNA503JvKykoKCws5fvw4SUlJ3d5vaWkhKSmJK6+8khtuuIGzZ88yefJkDh06hNvtxm6309TURFlZmXmO0+kkOjoal8s15M8ohBBCCCGEEOKzNeTlUXV1ddTV1ZGYmNgxkNXab9+Z9vZ2jh8/zuHDh6mvr2fUqFE9dnMK97HpKvzAHg50BsPn83VbBjJ37lwmTJhAVlYWwWCQUCjEvHnzuP/++2ltbeUnP/kJ0dHRhEIhiouLyczM5Lvf/S5TpkyhuLiYdevWsW3btm7XGMwDcF9VHOGt0KEjZOlaBWKz2UhLSzOb7DocDqKiogZVERIMBs1+NnV1dbz//vsRnzuQ/ubhcDhITU01G+yGhRsUJyQkmBUpra2t3H333djtdnN3o4yMDP7lX/6FzMxMqqqq2Lx5M+vXrzfHWb58ObfeeivTpk1j+/btPUKbpKQknE6nuWtVV4mJifj9fmpra7FYLKSnp/daBTUYx44dY+LEidxxxx387Gc/G/I4WmsOHDhASUkJTU1NPX4TPp+PFStW4HK5CAQC/P73v+eBBx5g//79ZGZmUldXx5YtW1izZo15zve//30WLlzIpEmTevyGhRBCCCGEEEKcn4b3lDoI27dv59///d9Zu3Yt0LEVd21trVl9oZTC7/d324XJ4XCwYsUK7rrrLmbOnDmo6yml2L59O3/4wx/M1+666y7i4+MBePXVV1m2bJl5/R07duDxeEhLS6OwsJCf//zn1NbW8uCDD/KVr3yFBQsWkJ6e3uM6cXFx2Gy2AXvuWK1WPB5Pr9VC4dAmfA/CYzmdTmbPns1TTz1lhkPhHbGGIjo6GqvVysGDB4d0fm/j9dd3JrzEqaamptvrM2bM4Hvf+x4/+tGPzNf27NmDYRiMGTOGhoYGHnjgASorK7n//vtZunQpV111FRMmTOg2zgMPPMCll15KQkJCj98O/Peyqfb29m7fz5gxY3jwwQe55ZZbMAwDpRRutzviICxcYXSuQCBAKBSKuAqqL1VVVTz22GPs27cPq9WKw+GgtraW+vp6tNbs3bsXrTVer5ezZ8/ywgsvcO211/KDH/yAW2+91Qxnwux2O3fddZfZK2e44ZQQQgghhBBCiM/Gp/70Fn64PX78OKWlpTQ1NWGz2UhNTaWystLsb6K1JioqioSEBLPyZuzYscyfP5+UlJQeFTiRCPfHCTty5AhFRUU0NTVRX1/PiRMnaGhoAOCPf/wj+/bt48iRI+Tl5bFz507WrVuH1WqlqamJt956i/z8/B7XcDqdZn+cge5DdHS0+cDdld/vN+cZHx9vBjNer5fc3Fzi4+N7DYUG25z5yiuvZNmyZbzxxhuDOq8vDocDwzC67cLUlc/no7KyEq/XawYFNpuNCRMmcPHFF3frgbRq1Sq2bdvG0aNHyc/PZ/fu3WbA19zczEcffdStQiQqKgqn02kuqwsEAj2+g5qaGnw+n1mtBR2hV25uLunp6URHR3e7h0qpXqtyzpWdnc1NN93UbYlfWG1tLfn5+UPqjRM+p6qqiqqqKtrb24mNjSUhIYGqqiqz0uyll17iww8/ZNu2bbz88su0tLRQUVGBYRi0tLSwbds2PvjgA6CjqmnlypVms+hwryghhBBCCCGEEOe/IS+PioRhGIwfPx6lFLt376a8vNxsmnrxxReTl5fXI7SJj483G9vOmTOHcePGUVJS0mcw0BetNWPHjmXq1KlmZcmrr77Kvn37+P73v08wGMTn85mhzZEjR3jqqaeIi4vj2LFjnDlzhpdffpmZM2eye/du3nzzTU6ePNnrtcK7IQ2kvb2d9evX9+j/4/f7qaioQGtNdnY2x48fx+FwkJOTQ25uLmVlZT2ChGAwOOA24+caNWoUmZmZlJSUDOq8voSXW+3du7fX99vb26msrGTatGm4XC6amprwer1kZ2eTmJjI3/72N/PYoqIi/vKXv/Dee+9RUVFBXV0dq1ev5sILL6SwsJA333yTgoIC87qpqan4fD7y8/Pxer29Xr+6utrs/5KSkkJpaSler5e5c+cCHUv8zr2v4UbJ/cnJyeGb3/wmwWCw285k0LHMq7y8HMMwBtXs12azmYHlzp07zR3O0tLSmDBhAgcPHjRDqaKiIl544QU8Hg8FBQX4/X6effZZLrvsMgoKCtiwYYMZMBqGwTXXXMOOHTtwu92MHTs24jkJIYQQQgghhPh8faqhTbjBq1KK4uJic/mKw+Fg0qRJFBYWmg/NgUAApRQpKSksWrQIgBtuuAGAsrKyHg2AI5Gens6FF15o/v3w4cMUFRWxbNkyoKPqILyddlNTE++//z4WiwWfz0draysHDhzgv/7rv9i3bx+FhYW9bpOstcbv90f0gB4IBDh48GCPSodwpY3P52PRokW0tLQQExPD7NmzGT9+PH/96197hAs+n4/y8vKI70VUVBQej8fc8nykNDc397lTUktLC6dOnSI+Pp6FCxdSVVVFTk4O06dPp6GhgV27dnUbZ+fOnezduxe/308gECA/P5/Vq1dz9OhRCgsLqa2tBTqCiBtvvJFjx46xefNm5s+f3+u217W1tTQ3N5OSksKSJUvYs2cPubm5XH755XzyySccPXq023y11pSXl/fowXOuuLg4cnJyui1BCrPb7X2GSP2x2+2kpKRgGAZ5eXlmCJiYmMjYsWM5dOiQ+btpaWlh165dGIZhhjtr166lpKSEo0ePcuTIEXNJmlKKCy+8kJ///OeEQiFuuummIe+QJYQQQgghhBDiszXs0Kav/h7QEdrk5uaitaahocGsDDEMg4SEBGw2m3lsS0sLbW1tjB492twKfNasWbzzzjsUFBT0GpgMxOl0dtt5x2q1MnnyZDweD83NzQQCATMIALr9GTqCkZdeeonm5uY+l5SEq4QiEQqFel3mFQgEqK6upq6uji9/+cu4XC6zKqaioqJbRUpYY2Mjx48fj+i6AMnJyeZW2oNdVtWf3ppHhzU3N3Ps2DGcTid33303p0+f5oILLiAqKor333+fAwcOdDs+XPUU5vf7WbNmDa2trd1CMavVyp133smuXbtYtWoV8fHxjBs3Drvd3i20aWhooKWlhTFjxvDP//zPfPDBByxYsAC3280zzzzTa2+foqKiHvM4V11dHaWlpYwfP574+Hjq6+sJhULYbDaSk5OZNm3agPftXA6HgzFjxqCUoqioyGzq7XA4zL5JXZ377yEvL48TJ07g8/m63YNwVdL7779PcXExWVlZTJ8+fdDzE0IIIYQQQgjx2Rt2T5twE9feWCwWxo0bRyAQoLm5udvuUn6/n1GjRpm9Turr66msrAQ6GtXOmDGD5uZmduzYYe6cNFh+v5/W1lZzt6Lk5GSeeOIJLrnkEnw+H21tbRw5cqTfMRobG/vtAdK1cXB/wluP99b7JrwzVllZGVFRUcyZM8es4igqKjLvS1eBQGDAipCuLrvsMnJzcwe1ZCcSfr+/z5CjtbWVU6dO0drayoUXXsgVV1zB2LFjOX36NDt37oxoeVdTU1Ovc46NjSUjI4PY2FgMwzAriboK7x4WCARITU3lyiuvZNSoUZw6dYry8nKzyurcOQ90jw4ePMhf//pXFi9ezOLFi3G73cTExDBmzBgmTJiA1+sd9H222+2kp6ejtebkyZPmv5XwOElJSf02ENZamztNnUspRU5ODi6XS5oQCyGEEEIIIcTfkRGptOlvByGtNUePHu0WflRVVfHHP/6RSy+91Hxtw4YNnD17ltzcXG644QZCoRC//OUv2bhxY6+hRSROnTpFYWEhc+bMYdGiRVxwwQVMnz6d/Px89u/fT01NDatWrRrS2GFVVVUDNiGG/14Cdfbs2R4hT7gS6Re/+AXLli1j8uTJ1NbW8v7777N69epu4wcCAbPp7kAVIV1dfvnljBs3jk8++STyDxeBqqqqHrtDhTU3N5OXl8dvf/tbbrvtNpRSbNiwgTVr1vDWW28N67oNDQ0sXLiQBx98kAkTJlBdXd2jGsXv97N582bi4uJYuHAhsbGxbNq0id/97nccOHDADDjC9z8QCFBfXz9gmFRWVsZHH32Ex+Ph8ccfJzc3l9bWVqZPn052djbHjh0b0mfSWpvVSeHQJi8vj5deeomJEycOaczwuGvWrGHdunXm9ulCCCGEEEIIIc5/ww5tQqFQn02Cg8EgeXl5KKW6NeptaWkxm62GH5xLSkpoaGjgyJEj+Hw+QqEQW7du7bVZbKTKy8vRWvPmm2/icDhQSlFRUcFTTz3F2rVrzd4gw1FaWhrRzlZtbW2cOnWqW7VRV6FQiI8//hi73c6kSZOorq4mLy+vR/Pj1tZW6uvriY2N7bVSpC9JSUl8/PHH/OpXv4r4nEi0tbX1GVqFw5A1a9ZgsVgwDINPPvmEvXv3DrqJcleBQICnnnqKJ554gptuugmLxcLWrVvNHZK6KiwsZP369dTU1BATE0N+fj4FBQXd7l0wGKSoqIiqqiqzMqc/WmvOnDnDo48+ykMPPcR9992H1pr29na2bNnCI488MujP5PP5OH78OIWFhd22KD9z5gzbt2+nrKxsSLs+aa0pKioiMzOTJUuWYLFY2LRpE1ardcDPKYQQQgghhBDi8zXs0CYQCHDo0KFeg5W2tjaefPJJgG6NhP1+PyUlJVRVVZnLP8I7MFVXV3P69GkATp8+3WfIEYnKykqOHz9ObGwsZ86c4fjx4zz33HNs2bLFXFo03OVCe/fuNRss96exsZHdu3f32xunurqa9957j507d+L3+2lubu4RiDQ1NbFhwwbcbveggg+lFDU1NUNeatab8A5W/YVWgUCAY8eO8ac//QnoqJAZTNjU13X/9re/cdNNN3H55ZfjdruZNm0aDz74IP/0T//UbdlYXV0d+/fvp7i4GIvFQnNzM3V1dd2+d7/fz7Zt2/jNb35DRUVFRL+J2tpannvuOdLS0rjqqqsIBoNs27aN9evXc/jw4UF/pvr6erZs2WJu9R3W1NTE0aNH+2z2PJBAIMA999zDM888Q1paGlarlXHjxrFy5UqeeuqpIY0phBBCCCGEEOKzMezQpr29nXfeeafX/9c+GAyaPWO6hg9a6x4VGqFQyOz7En7oHm7D3ObmZvbt28evf/1rSkpKqKysZPfu3UNebtWbTZs2RbQdeU1NDW+++Wa/1RLhhsTh5Ua9ff5gMMjBgwex2WyDuj+nT5+OaGekwfD5fASDwX6Xh2mt8fl8ZsXQSDVBrqqq4tlnnyU+Pp6kpCQqKyupq6vrcX+DwSCNjY3mjlm9XT8cmL3++usRLzkLBAKcOnWK5557joKCAoLBIIcOHSI/Pz+i7d/P5ff7OXv2LC0tLd0+Q7jRcyTVXL3RWrNjxw5eeeUVFi1aRGxsLMeOHRvRHcSEEEIIIYQQQnw61GAeopVSPQ62Wq1MmjSJ/Pz8Xh+Iw9sLj+SORYNhs9lISEigqqqKUCg04vOwWq0Eg8EBx1VKYbVah1U5NBy33XYbtbW1bNy4ccTGVEqhlBrSsp2R4HK5+Na3voXH46GqqooTJ06wcePGIc9HKTXo34fFYsHr9ZqNgIcbihmG8ancz5kzZ7Jw4UJiYmIoLi5m+/btFBYWjvh1hBBCCCGEEEIMyW6t9axzXxx2aNP5+ucWyojIfN7h2aclvBvSYLZe/0cl90oIIYQQQgghzlu9hjbDXh4FX7wg4Ivoi/odfV5VPn+P5F4JIYQQQgghxN+XwYY2VUDJpzERIYQQQgghhBBCiH9QGb29OKjlUUIIIYQQQgghhBDis2F83hMQQgghhBBCCCGEED1JaCOEEEIIIYQQQghxHpLQRgghhBBCCCGEEOI8JKGNEEIIIYQQQgghxHlIQhshhBBCCCGEEEKI85CENkIIIYQQQgghhBDnIQlthBBCCCGEEEIIIc5DEtoIIYQQQgghhBBCnIcktBFCCCGEEEIIIYQ4D/1/Yn6X5jBawXUAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -184,9 +260,7 @@
     }
    ],
    "source": [
-    "\n",
-    "\n",
-    "num_samples_to_plot = 9\n",
+    "num_samples_to_plot = 14\n",
     "\n",
     "for i in range(num_samples_to_plot):\n",
     "    plt.figure(figsize=(20, 20))\n",
@@ -201,9 +275,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EMNIST Lines Dataset\n",
+      "Max length: 50\n",
+      "Min overlap: 0.0\n",
+      "Max overlap: 0.2\n",
+      "Num classes: 80\n",
+      "Input shape: (28, 1400)\n",
+      "Data: (35000, 28, 952)\n",
+      "Tagets: (35000, 50)\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "print(emnist_lines)"
    ]
diff --git a/src/notebooks/02c-image-patches.ipynb b/src/notebooks/02c-image-patches.ipynb
index 9e8d4b2..ee9a800 100644
--- a/src/notebooks/02c-image-patches.ipynb
+++ b/src/notebooks/02c-image-patches.ipynb
@@ -34,22 +34,32 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "emnist_lines = EmnistLinesDataset(train=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-08-30 21:31:37.785 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_load_data:164 - EmnistLinesDataset loading data from HDF5...\n"
+      "2021-01-04 19:10:11.431 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_generate_data:159 - Generating data...\n",
+      "2021-01-04 19:10:17.812 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_load_data:152 - EmnistLinesDataset loading data from HDF5...\n"
      ]
     }
    ],
    "source": [
-    "emnist_lines = EmnistLinesDataset(train=False)"
+    "emnist_lines.load_or_generate_data()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -79,7 +89,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -91,7 +101,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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pVoL7Ukqt1zuw4Bo/t0tE7H+/4UeSqoP9VKp+9lPpzmBflaqf/VQr3Q2nekmSJEmSJOnOZPAjSZIkSZK0Qi1H8PO1ZfhOSTfHfipVP/updGewr0rVz36qFW3Ja/xIkiRJkiRpaTjVS5IkSZIkaYVasuAnIp6OiOMRcSoi/mipvlfSu0XEvRHxYkQci4juiPhSZX9LRPxtRJys/G6u7I+I+G+VvnskIh5b3ieQ7i4RkYmI1yPiB5Xt+yPilUqf/D8Rka/sX1XZPlU5vnlZGy7dJSKiKSK+HRG/iIieiPh7vlOl6hMR/6byt+/RiPjfEbHad6ruFksS/EREBvjvwD8EuoAvRETXUny3pF8xA/xhSqkL+ATwLyv98Y+Av0spPQD8XWUb5vrtA5Wf54C/XPomS3e1LwE912z/F+ArKaVO4CLwe5X9vwdcrOz/SuU8SYvvz4H/l1LaBjzKXH/1nSpVkYhoB/4V8HhK6SEgA3we36m6SyzViJ+dwKmUUm9KqQh8C/jMEn23pGuklN5OKR2sfL7E3B+o7cz1yb+unPbXwGcrnz8D/M80Zx/QFBFtS9tq6e4UEZuAfwR8o7IdwK8D366c8t6+erUPfxvYXTlf0iKJiEZgF/BNgJRSMaU0iu9UqRplgdqIyAIF4G18p+ousVTBTztw7prt/so+ScuoMmx1O/AKsCGl9Hbl0ACwofLZ/istn/8K/DugXNleC4ymlGYq29f2x/m+Wjk+Vjlf0uK5H3gH+B+VKZnfiIg6fKdKVSWldB74U+Asc4HPGHAA36m6S1jcWbpLRUQ98B3gX6eUxq89luaW+3PJP2kZRcRvAkMppQPL3RZJ7ysLPAb8ZUppO3CZX07rAnynStWgUmfrM8yFtRuBOuDpZW2UtISWKvg5D9x7zfamyj5JyyAicsyFPv8rpfTdyu7Bq8PNK7+HKvvtv9LyeBJ4JiLeZG6K9K8zV0ukqTJMHd7dH+f7auV4IzC8lA2W7kL9QH9K6ZXK9reZC4J8p0rV5e8DZ1JK76SUSsB3mXvP+k7VXWGpgp/XgAcqVdPzzBXS+t4Sfbeka1TmJ38T6Ekp/dk1h74HPFv5/Czw/DX7f6eyEskngLFrhq9LWiQppX+fUtqUUtrM3Hvzxymlfwq8CPx25bT39tWrffi3K+c7ykBaRCmlAeBcRGyt7NoNHMN3qlRtzgKfiIhC5W/hq33Vd6ruCrFU//1GxG8wV6sgA/xVSulPluSLJb1LRHwSeBl4g1/WDflj5ur8/F+gA+gD/nFKaaTycvwL5obDTgK/m1Lav+QNl+5iEfEU8G9TSr8ZER9hbgRQC/A68M9SStMRsRr4G+bqdo0An08p9S5Tk6W7RkT8GnMF2PNAL/C7zP3PVd+pUhWJiP8IfI65FW5fB36fuVo+vlO14i1Z8CNJkiRJkqSlZXFnSZIkSZKkFcrgR5IkSZIkaYUy+JEkSZIkSVqhDH4kSZIkSZJWKIMfSZIkSZKkFcrgR5IkSZIkaYUy+JEkSZIkSVqhDH4kSZIkSZJWqP8Pp1EZ2J+goy0AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -103,7 +113,7 @@
     },
     {
      "data": {
-      "image/png": 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t7e20t7ff9TaGhobyxBNP8NBDD2G1WmUkzPT0tIz6SkpKYv369Vy6dGlZCz/zx2ibzYbb7cblcskxOjc3l9DQUCYnJ2W073IcoxUKhUKhUCiuxR0JP3eKpmnYbDa++MUv8qMf/Yje3t77mjJiMBgWJXYYDAaSkpJ44YUX2LFjB0FBQXR2djIwMLBsUz1MJpOcIO/cuVOG+GuaxtDQEIODgzL9orm5mdHRUfr6+hgfH1+WApzRaMRsNhMXF0deXh5FRUWsW7eOzMxMnE4nFotF+i6ZTCa2b9+O2Wymv7//lia1eiSPpmlX9UWj0UhMTAzJyclER0cTGhoKQHNzM0ePHr1h5FdERASZmZmkpaWxcuVKcnNziY+Pl20PDQ3FZDLh9/sZHBzkgw8+4ODBg1y8eJGuri68Xu+SHxchBFFRUaSmpuJ2uwkEAmRnZ9PU1HRbwsGtoveBhIQE8vPzWb9+PQUFBWRmZsqJMSBTMB588EEsFgu9vb23fZ5mZmYSGxsrBd6pqSl+9KMf0djY+KGL9DObzaxZswabzSbFurKyMt566y06Oq6yZ7shVquVqKgosrKyWLVqFZs2bSIpKYng4GAp/oSFhQEwMTHBmTNnOHv2LBcuXKCiooLu7u5FR2LppsFms5nY2FhWr15NZmYmBoOB1tZWLl68SF9fHwMDA7e8TwwGA9nZ2YSHh+N2u4mNjSUpKYmWlpZ7PpG/0ptq/nije2np7UlISMDtdtPf309VVRWVlZX35Zy7Efp4+PTTT2O32xkYGKCkpITXXnuNsrIyYmNj+cY3vsHGjRuJjo7GYrEsaXtvRnh4OOnp6aSlpZGdnU1+fj6JiYlyjNbTSQOBAMPDwxw6dIhDhw5x8eJFKWou9TFRKBQKhUKhuB5LJvxomkZvby8ffPABX/jCFwgPD2dgYOCqybbFYiEhIYHMzExSUlIYGRnh3LlzNDU13ZHo4na7Wbt2LaOjozK0/1o3+kIIgoODef755/n617/OxMQEb731Fu+//z5VVVUMDg7edhvuJTabjZiYGNatW8ezzz67IDVlYGCAgYEB+aSytbWV/v5+iouLaWxsZGRkZMlFBkBGK6WlpZGenk5WVhZbt24lLS2NoKAgAoEAZ8+eZXBwkA0bNhAXF4fD4SAlJYWnn34agDNnziz6ZtzpdLJ+/XpWrFhBZ2cnTU1NMmIoMTGR1NRUnnvuOTIzM4mIiMBms+Hz+aipqaGjo4OGhgampqYW9CMhBGazmbS0NAoLC1m3bh0rV64kMTERh8OB2WyWE1tdcLJYLGzatEk+8b906RLNzc14PJ4btl9fhx5NNDo6yvj4uEwNudOJrC78pKWlERISQiAQICcnh0OHDjE+Pn5PJsoGg0Ee0/T0dFasWMH27dtJTk7G6XQyMzPD6dOnGRoaYvPmzcTExGC328nMzOTpp5/GaDRy8uTJ2xorwsLC+NrXvsbGjRuBWa+a1tZWXn/99bueQnivMZvNJCUl8Zd/+ZdSxOrv7+fs2bMcOnToltZlMpkICwtjxYoVbN68mQcffJAVK1YQHByM2WyWaUl6OpbNZiM/Px+73Y7b7cZms3H+/PlFiSsWi4WkpCQKCgrIycnhkUceISEhgZCQEIQQ9PT0UFpaysGDB/nhD394y+OW0WgkJyeH8PBwQkJCSEhIICUlhRMnTtxTUV9/mJCTk0NcXBydnZ1UVVXR29tLVFQUCQkJrFy5kl27dpGVlUVkZCQmk4ne3l7efvttfvjDH9LW1rak4mNISAjp6enExMQA8Oabb/L973+fmpoaxsfH8Xg8DA8PYzKZ2LRpEyEhIbS2ti67yBh9jE5JSWH9+vWsXbuW3NxcOcbcbIy2Wq1cunSJpqam2xIfFQqFQqFQKO4HSxrx09/fz/79+/n85z/Prl27aG9vl34hRqORiIgIXnzxRbZv3y6fuk1PTzMwMEBVVRV/8zd/Q0NDw23doDudTgoKChgYGJBeMD6fb8Fn9BvCoKAgsrOzpflnWVkZVVVVN03vWSqEECQlJbFlyxYeeughsrOz8fl8jIyMSAFgbGyMwcFBzGYzTz31FEajkbi4OA4fPkx5efk9SQu4FYxGI3a7nZiYGB5++GFWrlxJWloaycnJWCwWRkZGaG5u5t1332VgYEDeoMfExGA0GnE4HAQHB2MymRZUYrkeBoMBp9PJ2rVr2bp1Kx0dHVy8eJGqqioAtm7dyooVKygsLCQsLEymOE1NTREXF0dSUtJVvjxCCCwWCxEREWzevJn169eTnZ1NYmIiwcHBCzxirvxObGwsa9asYXp6GpPJhM/nY2xs7KqJrclkIjQ0lKSkJFatWoXT6SQrK4vo6GhaWlro6upieHiY+vp6zp07d1Ufv1Xm+xvpk/t7VSJaTxWJi4tj586d5OTkkJGRQXJyMiaTCY/HQ2NjI++99x4DAwPyiXx0dDRCCBwOB0FBQXL/3eq5GhQURFxcnEwjnJqaorKyko6Ojg9dhSKTySSFDfh9+k99fT39/f23tJ7g4GDS09PZsmULa9euJTMzk/DwcJk+BrP9OBAIyD4SGhpKZmYmZrMZo9GI3++nu7v7KqF0PkFBQWzevJmHH36YrVu3EhMTQ0REBGazWXqvAFKo1cXOWz3OujfR/HLk9xqTyURCQgIbN24kJyeH9vZ2bDYbVVVV5Ofnk5ubS3Z2NuvWrSM8PHyBB1NCQgJxcXH09/ffsvCjRxndaf8VQpCbm8vTTz+NxWLB4/Hwi1/8gpqaGsbGxrDb7SQkJFBUVISmafT19S3bCDmz2UxkZCSbN29m3bp1ZGdnk5ycTEhIyA3H6JiYGNasWYPf78dsNkvvHxX1o1AoFAqFYjmypMKP1+ulqqqKzs5Odu7cyeuvv87IyAiapmG321m9ejVPPPEERqOR8vJyGhsbcTgc5OXl8fDDD2O1Wvne974nq7As9oZfF0by8/PxeDwylebKCZDdbicqKorc3FxycnKoqanh4MGDlJWV0dfXx8TExL3YLXeMEIKwsDDS0tJISUkBoLu7m6qqKqanp+nq6qKlpYX+/n7cbjd79uwhNDSU7Oxs2tvb6erqoqOjY8lELX1SlJOTQ35+Po8//jhhYWEYDAYaGxtpbm6mtbWVuro6Lly4gM/nY/Xq1SQkJBAaGirNn3NycggJCbmmqDcfg8FAREQERUVF7Nq1i5ycHLKzs0lPT6elpQWAtWvXEhERQVRUFFarVU4OTSYTFosFq9W6wPBZr9QVHR3Nhg0bePLJJ2WkgtFoZGBggJ6eHnp7e/H5fNKwODw8nMjISIKDg6XIFRMTg8ViYWZmho6ODsbHx3E4HHIynZeXx+rVq8nKypLVexwOBx6Ph7GxMcbHx6mrq8Pr9VJeXn5HkQxXijz3SvQxmUyygl5BQQGPPfYYoaGhGAwGampqaGlpoaWlhbq6OkpLS/H5fKxfv574+HhCQkKw2+2EhYWRk5OD2+3G5/Pd0oRMCEFiYiIul0se65GREX79619fMzJxueNwOEhMTJQCQiAQ4NSpU5w9e5aRkZFFrUOfIOtRV4899hgxMTGEhIRIn6vBwUFGR0eZmZmR6WCxsbEEBwcTERGB1WrF7XYTExNDe3s7DQ0N8vPw+7SukJAQsrOzeeyxx2SEl9lsZnBwkNbWVjIzM2XUjxACv99/2z4r96oPXw+j0UhWVhYPPPAAO3bsIDExkZycHBITE2loaCAnJ4fk5GTCwsIIDQ3FZrPJin/6eKNHVt0KeoROaGgoH3zwwR1FdQohiI6OJicnB5g1j66trcXr9aJpGlarldDQUBkZeDcq7N2oLaGhoaxduxaAY8eOLUrg1qN1oqOj2bRpE3v37iUpKYnQ0FDZ17q7u6VoZTKZCAoKIjw8nOjoaIKDg0lKSsJqtRIbG4vL5WJycpK2trZ7FgGpUCgUCoVCcbssqfAzPT1NX18fFRUVrFmzhpCQEHp6eggEAkRHR7N7926Cg4N5+eWXOX36NPX19bhcLjZt2sTu3bvZtWsXExMTBAIBysvL8Xg8i7rZEkKQnJxMWloaQ0NDJCQkUFtbu0D40VOM9NBvt9vNa6+9xu9+9zsaGxuX7Y2dnhoTHx8vTY8bGxs5c+YMxcXFTE5O4vF4GBgYYHx8nJiYGHw+n4z4SUlJoaamhrKysvu+ffqkz+FwkJmZybZt2ygqKmLNmjX4fD5aW1s5e/Ys58+fp7W1lfb2dvr6+jCZTPT390uPBYPBgMvlWjBxv1HUjx4ptGHDBgoKCggPD0fTNGJiYlixYgUA8fHxGAwGfD4fMzMzC7yAZmZmpAePpmkyRS00NJT09HQ2btxIQUEBDocDmE21q62tpby8XE6WbDabNK3Oy8sjNDSU4OBgKWD09PTQ2NjI8PAwZrOZoqIiMjMzWb16NStXriQjI0N6qujH2O/343K5iI2NJTExkeLiYiorK5etLxX8vg84nU6ys7PZvn07hYWFFBQU4PP5aG5u5tSpU5SWltLa2kpnZyf9/f0yDUb3qDIajQv6wODg4FXVn27UJ0JDQ9m2bZsUF/QS8adOnVqWaV66eHGt7bHb7SQmJrJlyxYpFujRS42NjYuaJBuNRoKCgkhMTGT16tWsX7+e3NxcWSWqpqaGy5cv09jYSG9vL1NTU7hcLhm1kp2dLf1/goODCQsLY9++fbLqmt/vl2b/LpeLnJwcNm7cSGFhIcnJyRgMBoaHhyktLaW8vJzg4GCcTieapjE9Pb0sPLAWg+7dk5ubS1FREStXriQ4OBhN04iMjCQ7O5uYmBiCgoKA2Wuk3p9hVrDz+XwyTXexWK1WVqxYwXPPPUdYWBi1tbV0dHTc0Vhgs9kIDg7G7/fT2NjI6OgoQgiCgoKkaKtHd/X29t6zSBibzUZqaiovvPACAGVlZfT3999QnNVFH32M1sf+4OBgADweD/X19Vy6dIna2lrGxsakCXh6ejoFBQVXjdEej4eqqiqGhoaYmJhYlvcHCoVCoVAoPr4sqfADsxOQ0tJSHnroIWJjY2lpaUEIQVZWFk8++SSnT5/m29/+9gJRp6ysjPLycl588UWeffZZvF4vRqOR0tJShoaGbvqbekRMUFAQfr8ft9stKwLpy2NiYnjggQd48sknSUlJoaWlhddee43a2tplG+kDs54YUVFR0kjYZDLxwQcfsG/fPo4dOyafumqaJiuY6JOuyMhIEhISZAWj+43T6SQ4OFiG0OvihqZpDAwMUFlZyblz5ygtLWV4eFiKb1dWKYPZiapedetG22KxWHC73aSkpLB27VppsgyzqSZ6euH4+Dher5eBgQHcbjfR0dFYrVb8fj8TExP09vbKfatXNdKjlvRIHIPBwMzMDH19fVy8eJFTp05RVVUlI3gSExPxer2EhISwYsUKWUY4ODiYyMhIIiMjZRn7L33pS6xZs4aoqKgFaWeDg4M0NTXJqJiQkBDWr1/PypUriYqKWlSUwPx0rvudtuB0OmVEyLp16ygsLCQtLW1BHzh79iyVlZUMDw/LCZY+yZtv1j6/D+jo6Zt2ux2bzcbw8DA+n2/BJNFgMJCVlcXTTz9NeHg4MGtQ3NPTQ19f333cGzdHL51us9lkCuf09PSCcyE8PJx169axZ88eKXb19/fT19e36CpLDoeD2NhYVqxYQXZ2NvHx8dL7ZGpqisuXL3Py5Elqa2vp7u5menoal8uFx+MhJCSEpKQkgoKCFky4Y2JicDqdMh3TarWSlJREUlISO3bsIC8vj+joaEZHR+nt7aWzs5P333+f7u5utm3bJs97/fz8MERhWa1WIiIiWL16Nenp6TIVEZDRPbp5sNfrZWRkhJCQEBITE9E0DZ/Px/DwMAMDA7ckQLpcLtauXctXv/pVAH784x/T3d19R8KPz+eTHmLd3d3ExcUhhCAuLo4NGzawe/duDAaDHDdvZyzRr1G6wH6t9kZHR7NlyxaeeeYZAL7zne8wNDR0w/5gNptxu93ExcXJMVofR2dmZhgcHOTixYucOHGC6urqBeL88PAwISEh5OTkyDHa5XIRERFBZGSkjP78MPRHhUKhUCgUHx+WXPiB2aeYVquVrKwsampqCA4OZsOGDWiaxr//9//+qkiekZERDhw4QF1dHfv37+cLX/gCKSkp/PSnP+WNN9646ZO2QCAgjRj1lAJ9suz3+7Hb7WzZsoVHHnmEgoICenp6eOmll6itrV2WT/vn43Q6SU1NJTMzk6ioKIaGhjh9+jRVVVUyOkrnyugH3bdlKUQfo9FIYWEhhYWF5ObmsmbNGjIzMxFC0NzczIkTJzhw4AAlJSV0dXXd8BhfGQVwvc8KIYiNjSUnJ4ft27ezYcMGWRlK/8709LT0+6mrq6Orq4sNGzawc+dO4uLi8Pv9TE1NLSiJHRoaSkZGBvn5+dIbSJ/cTU5O0trayqlTp2TFrunpaWw2GzMzMwQHB5Odnb2gnbrJs9vtJicnh89+9rN88pOfxGazIYRgZmaGoaEhamtr2bdvH++9956sMpOdnc3ExARCCKqqqhY10dMFOIPBQFtb280P3l3CZDJRWFjIhg0byMvLY/369aSkpMhUkRMnTvDee+9x4cIF+vv7F90H5gshTqeThIQE1qxZQ3Z2Nvv27aOxsVF+LhAIYDab2bp1K6mpqVitVmZmZujs7OTs2bNSZL7yt5ZqkhcVFUV+fj6ZmZnMzMxw8uRJmpubZRSNpmkkJyezfv16EhMTgdkJ+9mzZ2lvb18QTaJzLRFV95zavHkz+fn5xMXFycmt1+vlzJkznD9/nvb2dkZHR/H7/TgcDpxOJ21tbQvEcr1stp7CpItxeuXEhx56iNWrV2MwGOjo6ODgwYO8++671NTU0NzczCOPPILdbsdoNDI5Ocno6OhtefvcbwwGA8nJyRQVFbFt2zZSU1NlpSu97RMTE5SXl1NTU0N7ezsjIyNs3ryZZ599FkCWS7/V6ot6ipjRaGRkZIS+vr47En0CgQCtra2UlJSwYsUK9u7dK9PT9JRbPT2tu7ub4uLiRacUwu99dKKiooiPj5cl1K8UXi0WC9u2beNrX/uaNHvXKzzeaN1ut5u0tDQ5RmdnZ8tj4fP56Orq4tSpU1y4cEF6t1mtVqampnA6nXR1dS1Ypy4y66LPcu+LCoVCoVAoPn4sufCjP6UGZLpKXl4ejz/+OB6P57pVQDRNo7m5mYMHD/LMM8+wceNGenp6OHXq1E2NiTVNo6qqipaWFpKTk9mxYwcOh4NLly7R1tbG5s2beeaZZ8jLy6Orq4vDhw+zb9++ZR3poxMaGsrKlSuJi4tjbGyMM2fOcPDgwWv6kuhCz3LAaDSSl5fHjh07yMjIICoqCqPRyOjoKGfPnuXw4cNcunRpUel8gUCAsbExWlpapCHyld/RfSH+8A//kB07dpCVlUVYWBiapiGEoL29nbq6Oo4fP86xY8eoqqrCYrHwrW99iw0bNhAeHs7U1BQ9PT0y+gRmfTQ+9alP8dhjj5GdnS0jg3RDVf2JvZ6eFB8fDyCrlmVnZ5OVlSUnLj6fj76+Purr6xkeHmb9+vXs2rULu90u19nS0sL+/fv5wQ9+cFVEWk1NDS+99BKvvPIKDQ0Ni3rqbrVaMZlM8rycv8/uZZ8xmUysXr2ahx56iIyMDCIjIxFCMDo6ypkzZzhy5AhlZWWLmuT7/X7ZB7xer0x7e+GFF3jxxReJi4vDaDTy53/+5/T391NfX09DQwMdHR0IIXjwwQdxOp0YDAbGx8cZHx/Hbrfz4osvLvgdTdNobGzknXfekX3gfhEeHs6vf/1rVq5cid1uR9M0Jicn6e3t5eLFi9TU1ODz+UhOTmbNmjVyQqxHrz3++OPs2rVrwToDgQCvvvoqra2t0nsqMjKSF198kU2bNhEbG4vD4ZBCpqZpeL1eent78fv9hISE4Ha7sVgssiR2fn6+TKHR29jZ2Ul5ebkclzIzM3n88cd54YUXZJWoiooKXn75ZQ4cOEBzczMzMzOEh4fzxBNPkJSUhNlspquri6NHj3L8+PHbSvW6X2OgXgntG9/4BkVFRaSkpMhUNb/fT1tbG+Xl5bz55puUlJQwPDxMeno6X/nKV9iwYQMmk4mhoSHpcTY2NrZosdFoNJKbm0teXh6NjY389//+36moqLhjcaKnp4fLly+jaRqhoaFs375djltXCi+3IowajUYSExN56qmn2LlzJy6Xi+PHj/PGG29cJfzExMSQkZFBUlISMBtBfCMjd130efzxx9m7dy+5ublER0dLEX3+GN3X14fFYiEuLg6AlJQUMjMzyc7Olt5GMDtGDw4O0tDQQE1NjfQpVCgUCoVCoVhOLLnw4/P5OHDgAH/5l38pJ+MpKSmEhITwxhtv3HCi6vf7+da3voXf75cmoB6Ph29+85s3rSAyNjZGZ2cnk5OTZGRkYLfbefTRR3n77bfZs2cPK1euxOv1cunSJerq6j40N3IulwuXy4WmaQwODi54An8lulmlnmqxVOgRLXFxccTFxREREYHFYsHr9dLW1sbhw4cpKSmho6NjUZVhdOGnra2NsbGx6257cHAwGRkZpKSkEBoaKpf5/X5qamooLi6WvjiTk5NERESwatUqYmNjsVqtDA0N0dHRIT0gdFPypKQkUlJSpJnt/MmQ1WolNTWV3bt3Mzw8LCsf6RXLYmNjZVUqfVLd3NxMdXU1paWljI2NMTQ0JCfS+rbo6VG1tbULtnNsbAyv1yt9ahbD4ODgNVMlbDYb4eHhMhVQ07QF5Y7v5BzRU4Di4uKIjY0lLCxMik8tLS0cOXKEkpISOjs7F+VJ4/f7GR0dpa2tTQo/usjc29srJ3N2u524uDiio6PZuHGj3Ee6gS7MRgnl5uayYsUKmV4UCATo7+/H4/Fw7Ngx9u3bd9vbfrtomkZnZyfp6ek4HA7pjZScnExcXBy7d+8GZifS88/xkJAQnn32WblP9DSa7u5uBgcHeeedd6TIp3v7rFixgpiYmAWiD8z2aZfLxfbt21m1apU8NrqnTEZGBrGxsQs8a4aHh6murqa6uprh4WFcLhdbt27l2WefJT4+npmZGfbv38+Pf/xjzp8/T19fHz6fT/rH6BUWJyYmaGlp4eLFi3R2dt7y/tMrv8XHx8v9YzabsdlsMgX2bqCvV08PiouLW1Cla3JykoqKCg4ePMiJEyfo7+/H6XQSGRlJTk4OUVFRwGwVzMbGRtrb22/JYy4yMpLc3FxSU1Pp7e2lpKTkrlzPWltbOXDgAJs3b2bXrl0LtkknEAhQVVWFx+NZlDBnNpuJj4/nT/7kT/jyl7/M9PQ03//+93n77bdlhUWddevWSZHdZDIxMjLC9773PVpaWq5736B7LMXHx5OamkpkZKQUffTlFouFxMREdu3ahcfjkWN0cnIyycnJxMfHEx0dLSN7xsfHaWlpobq6mqamJrxer0rzUigUCoVCsexYcuHH7/fLEG6LxYLT6cRmszE1NUVZWdlNvz84OMjPf/5zZmZm2LFjB4888ghnzpzhjTfeuOH3JiYmaG1tpb+/n4yMDFwuF2lpaWzZsoXCwkJGRkY4duwYv/3tb+XkcbmiTyzCw8N56qmn2LRpE2azmfr6eurr66UZsclkkuaqoaGhJCYm8sgjjxAREYHRaJSeDfez7K5eVWXlypVs27aN+Ph4rFYrLS0tXLp0ibNnz3Lo0CFZungxkzG/38/w8LCsmHXlJMdqtUpz2qysLFnFJRAIMDIyQn9/P/v375eG4l6vl+joaNatWyef1E9OTlJaWspvf/tbTp8+LfeZPlnWy1bPF9Tm+5js2LFDlrLWBc+QkBCCgoKw2+1yO3SBsqOjg56eHgwGA6WlpbK6mMFgICoqiq1btxIcHExsbCwHDx6UZqrzPW8Wy/UmLTMzM0xMTMiIIv3p+Z16WujVz1atWiX7gMViob6+nrKyMhnxpRsBL+Z3ZmZmZB/Q98PExASXLl3ijTfeYHJyksLCQjnh171EgKsiQPx+P5OTk9I/p6WlhaamJi5cuEBnZyetra1XRUfdD0ZGRnjjjTfw+XwUFhYSHx8v+9z8yk9XirrT09OMjo7i8/nweDx0dnbS3NzMyZMnZdWs+RNnfXzRy57Px2Aw4HQ62bJli6zoBLNiU2RkJGFhYdLHZ75HTXNzM319fczMzJCSkiIFDo/HQ3V1Nd///vc5f/48Q0NDC9oyPj5OdXU1TqeTwcFBzpw5w+XLl6+KtrLZbNjtdsxmM5OTk9dNM5qenl4QPWOz2XC73ZjN5rsyDhoMBiIjI1m5ciW5ubkkJCTISDI9Ta2jo4N33nmH48eP09nZKdN18/PzSU5Oxm63Mzw8zL59+yguLqa8vHxR4qfOxo0b2b59Ozabjddff/2mEbGLZXp6mvr6ev76r/+ac+fOsWXLFjweDzExMaSnp5OQkICmadTV1TE8PHzDsdtoNJKamsq2bdt45plnyMnJYXJykh/+8Ie89dZb1NTULEizjo6O5tlnn2X37t2kpaXh9Xo5efIkr7766lXHTRf59Sidnp6em47R8fHxbN++/bpjtC5y6WO0XglzcHBQlXNXKBQKhUKxLFly4Ud/Yub1eklPT5epBBMTE9TX19/0+4FAgIqKCt577z3cbjd79+5l7969vPfeezcUa3w+H+3t7XR2duL1ejGZTKSkpMgnjjU1NVRUVHD58mVGRkaWfSUkh8NBcnIyGzduJCsri5aWFgYGBhgdHZUTDafTKT0YkpKSSEtL44EHHsDhcGAwGKToMTw8fN8inJxOJ2lpaWzYsIG0tDRcLpecXJ88eZIzZ87Q2dl5w6fFenTC/PSo3t5eaV46f1v0/bB582bWrFlDXFycTJuamJigoaFBlolvbGzE4/FgNpsJDQ0lOTlZRkgNDAzQ2NjIhQsXaG5uJhAIyDSatrY2WltbcTgchIWFyYgY+H3Kh91uXyBgzJ+o674YeorXhQsXpN+Hpmm8/fbb0oA4Pj5eRi04nU7pq3L69GlZBexuPX3W/YzmT8Dme43okSW6cKVHW92sL7lcLjIyMmQfcDqd+Hw+GhsbOXnyJOfOnVtUH5j/v94Henp65Lnr9/tpbm7m3XffpbW1lYKCAiwWC+Hh4VIACgkJYdeuXRgMBvx+P/X19dLfaWhoSKbe9fT00NbWxsjICBMTE0sy2ZuZmaG4uJjBwUFOnz5NXFycNA/W98PWrVtlOuHU1BRVVVUcPnyYnp4epqamGB0dZWhoiL6+PhoaGqQYofcZPXKqpqZGmtfq3jw6FouFtLS0BcdaF5903xSPx4PH46G3t1cKel6vF7PZTHp6uvRT6u/vp6KigrKyMlmJTUePgDt37pw0SdcrOM0XQoxGIxERESQkJBAUFERfXx+XL1++qv/Mr8g339NL90LTTc718VH317mVa4HFYiE2NpYtW7ZQUFBASEgIJpNJRozp3nIXL16kvb1dRhZGRkYSFxcnx+6hoSFKS0uprKykt7f3lsbnzMxM4uPj6evro7i4+K6KlF6vl7KyMsbGxmSaU2FhobyO6qbsN0q/gt+bTz/++OM88MADTExM8Jvf/Ia3336bmpqaBddys9nMhg0b2LZtG2lpadjtdtra2jhy5Ai1tbULxruQkBDS0tJYu3YtmzdvxuPx8PLLL9PZ2UlLS4uMrJo/RhuNRtxuN5mZmQvWZTabpVikaRo9PT309vbK64Besl5F+ygUCoVCoViOLLnwA7MTmJ6eHjIyMli9ejURERGMj49fZaB4PUZHRzl//jzx8fFs3bqVBx54gMzMTMrLy697k+73+2ltbeXy5cukpaWRkJBAVlYWCQkJ0juiq6uL0dHRZf8ET0+TKigoIDMzk/DwcDweD5GRkWRkZMgUpNDQUCIjI0lMTCQpKYno6GhSU1NlWkN3dzetra309fXdF+FHTxPJysqioKAAt9uN0WiU6T1lZWXS2+N6GAwG7Hb7gqew+iSkubl5wfHXn+YmJiayefNmcnJypAnpxMQEzc3NHDp0iKqqKhoaGmQUQXh4OCtWrCA9PV0+qW9qaqK6uprW1lYZTaCnEtXV1XH58mWZFhMUFCRNo/VS7/prnUAgwMzMDD6fj5GRES5fvkxNTQ2VlZXU1NTIFKehoSH279/P2NgYDz74IIWFhTI1MiIiggceeACXy0VUVBQHDx6ksrKSoaGhu3I89Yny/EiikZERzGazFGyDgoKk0NDQ0EBfXx8ej+e60RN61FBmZib5+fm43e4F+7i8vJyWlpZF9QE9+khv17X6gL5vdQHEbDYTFxeHxWIhKCiIgoICdu7cKQXpgwcP8uabb3Lp0qUFflHLJfWztbWVtrY2jh8/LqMlExISMBgMxMfHs2LFCuLi4ggEAgwNDfH666/z8ssvS1FFN3K+3vbokVMXLlwgNjYWo9FISEgIdrtdimX6RPnKdfj9fimk1NfX09jYSFNTE7W1tVRUVODz+QgKCiI9PZ2kpCQCgQDNzc2UlZUxNDR0zbF7cnKSU6dO0dXVxcjICI2NjbKano6eZlZUVERkZCRtbW0yLfPKfqR7uuj7QBc27Xa7TG1LTEzEYrEwPDxMV1cXfX19izqndCExMzOTzZs3y+p+MJu2pUc01tbW0tLSwvj4uIwIzMrKklWyxsfHuXz5MrW1tbJ64GKxWq0ySrGiooL6+vq7/hDD5/NRW1tLW1sbPp8Pp9MpizMEAgF6enpuKoYkJiZSVFREQUEBPp+PQ4cO8corr1BRUbHAs8xqtZKXl8fevXtZsWIFDoeDoaEhLl++zNGjRxkfH0cIgd1uJyoqitzcXB544AFp4FxWVsZPfvITGhsbKS8vl5Ubg4ODF4zRVqtVipY6gUAAv9+Pz+djbGxMmnBXV1dTU1NDa2vrbflMKRQKhUKhUNwPloXw4/f7OXv2LKtWrWLXrl1omkZbWxvj4+OLXsfAwAAXL17kyJEjfOELX+CRRx6hpqbmuobMmqZRW1vLkSNHsFgsfOITn2Dz5s1MT09TWVnJpUuXaG5uXvaijy6erF27lt27d8tJbHp6OpGRkRQVFdHf3094eLgUIPRJm16JBGZv3nUxo6Oj474JP0FBQWRkZJCTk4PFYpHl0Ts6Oqivr6e/v/+G67Db7cTHx5OVlSXNgLu7uzl8+PAC4U9P5wkNDWXz5s1s2bKFkJAQjEYj09PTtLe3s3//fv7lX/6Fnp4eWQlLL738qU99ijVr1kiB7MCBAxw9epTu7u4FN/t+v5/29nYqKytxuVwYDAYSEhKkb8+10FOJxsfHGR4epq2tTQpQTU1N0ktGN4Lt7u7m7bffprm5mZqaGjZv3iyrNtlsNoqKikhISMDtdvP222/LalR343iZTKYFnj5Op5OVK1eSkZFBYmIisbGx5OXlAXDy5EmOHTvG6dOn6ejouOa5NF/4yc7Oxmw24/f7ZeRUQ0MDAwMDN2yT3gd00ROgs7OTI0eOUF5efs1Jp9/vp6enB4COjg5pgLtz506ZztTR0cHrr7/OuXPnlrWxu6ZpsrIVQEtLC2azmT/+4z9eEM1WX1/PD37wg1sqSa+nZ1VXV5OUlIQQgsTERCIjI3G73df9jn4ej4yM0N3dzcmTJykvL6e1tZX29nb6+voIBAI4nU5SUlKIjo5mfHycc+fOcerUKSYmJq45Bk1PT3P+/HkuXrwoxZorP+d2u9m0aROf+cxniI+Pl5GPv/rVr66KgNP9xfSIQbPZTFhYGOvWrWPTpk1ERUWxdu1a7HY7XV1dXLx4keLiYikyXA/dSyYjI4OtW7dSVFRESEgIQggmJycpKSnhtdde49ChQwwODjI5OYkQgqSkJHbu3MlDDz1EWloaPp+Pzs5OXn31VWpra285gi88PJycnBycTifDw8P3LCVRr+4Gv0+zg9njVVZWdsPUNKvVygMPPMCGDRtwuVyUlZXx4osvysIOesl0/Tx/8cUXefTRR7FYLIyOjnL69Gl+/vOfc+7cOYxGI2FhYWRkZLB792527txJQUGBTMkqKytjZGSEmZkZqqqq5MOGxMREoqKirmv0rY/Rep/u6OiQY3Rzc/OCMVqhUCgUCoViObIshB+YjdIwGo1kZmbi9/sZHBzE4XDc0jr6+/ulL1BhYSEWi+WGE7aZmRlGRkYYGBhgbGwMIQRer5dXX32Vd999l8bGxmV/I2c0GnG5XOTk5MiIFL/fj8ViITQ0FKfTKauWwOwT8+HhYaampjAajYSGhuJ2u2WEVV9f330vWX9lZR19MndlWekr0f0z1qxZw8qVK4mMjJSTYN2XSf++7vOQlJTEtm3bZMrW9PQ0Ho9HVvDq7u6W0Skmk4nExETWrVtHfn4+kZGReL1e6urqZAW4a0UQtLe3c+7cOYaHh+np6aGoqIjw8HDMZvNV26N7jOg+Pm1tbVRXV3PgwAF6e3ulF8uVT+lnZma4ePEilZWVFBcX8+CDD/LEE0/w4IMPYrFYSEhI4DOf+QzBwcFMT09z/PjxOzpGMJuWFx8fT3x8vPSO2bZtGzt37iQmJgaXyyV9YAwGAzk5OeTl5REREcHBgwepra297vHUv3PlvlxMH9D9l1auXElERIQ0EG5vb1/URFfTNIxGI8HBwURGRgKzKSxvvPEG9fX1y1r0uRaapmE2m2VFOT118q233rol0UdHF8ONRiO9vb3k5uayevXqBQbjOoFAQKaQtbW1yVSmo0ePUl9fz8jICF6v95pCQCAQkJPrm23f9aJW9Kg+TdOYnp7GaDSSkpLCZz/7WU6ePLnAw8xgMOB2u1mxYgUWi0Ua+H7605/mD/7gD0hOTpYCucFgIDc3l8LCQvLy8vD5fBw7duy6Dwb0lDu9qpkuAk9NTdHb28u5c+coLy9fkKZmsVjIy8tj7dq1ZGZm4nQ6GR0dpaqqiosXL95SJS99+9atW0dMTAwej4eOjo57noakC6irVq2S6YA3izDasWMHX/ziF8nPz6empoZf/OIXtLS0yOVRUVEUFRXx6KOP8sgjj5CcnAxAe3s7P//5z/nVr35FaWkpDoeDzMxM/vEf/5Hc3FxcLhc+n4/m5mZ+85vfcPz4cQ4ePMjU1BRhYWGcP3+e0dFRent72bhxI6GhobLvzEcfo7u7u+ns7KStrY2amhreffddOUZPT08v63RwhUKhUCgUimUj/MwP6daf8M2vHrMYurq6OHLkCO3t7axateqqUO0r0U0cV65cSWpqKgaDAaPRKFNulvuNnMlkIiMjg0cffZRnn32WiIgIDh06RHNzM8PDwzIlR0c3oWxtbWVoaIjw8HC++MUv8qlPfYqRkRHq6+uXzKgWri4BfDMcDgdPPfUUn/70p1m5ciVms5mBgQGGhoYWmMKazWbWrl3L1q1b2bJlCzt37sRqtTIzM0N1dTUlJSUcPHiQgwcPStHLarWSkJDAnj17+NznPicnG1VVVZw6dYrq6mqGhoau2a7+/n6Ghoaoq6uTXiVr1qwhODh4gbjh9/vp6uri4MGDvPrqqzQ0NOD1evF6vdeNeDAajYSHh7Nq1Sqmp6cJCgoiJiaGiIgIBgcH8fl8st9HRESQmZlJZmbmXRF+XC4XSUlJJCcnywiJ/Px8YLZv9fT0MDQ0xNjYmIyS2LBhA/D7dE6Px3PD37iVPqBHjO3du5dnn32WFStWIISQlc+GhoYWlXohhOBzn/scL7zwAps2bZKVhP7hH/7hhtFGyxWTycTf/d3f8dRTTxEaGkpJSQk/+9nPePnll29rfZqm0dLSQnd3N5cvX2b9+vUEAgHpr6ITCAQYHh6mvr6eI0eO8NOf/pTBwUHGxsaYmJi45rEYGRmhqamJnp4eYmNjyc/Pp7W1ldbW1qtSuG6EHrmTnp7Oc889x86dO0lLS8NmszExMUFxcbE0+tbRU7FycnJk9b2oqCgpIOueRIODgyQnJxMdHS3Llnu9Xqqrq6WP2HwsFguPPvoo27Zt48EHH5TRjD6fjwsXLnD48GEpKuqijx4d9NWvfpXCwkLcbjdjY2M0NDRw/Phxmpubb8nQWd8+/bf1dKT7Fck5f5zTKx5e77NPPfUUiYmJTE9P09TUxNGjR4mIiMDtdhMUFMRnPvMZdu3aRVZWFg6HQ44RR44c4cCBA3R3d7N+/XoeffRRPvnJT7J69WoMBgMDAwN88MEH/PjHP+bYsWML/JkGBwdlX9VTC3NzcwkLC1vgX6WP0UeOHOG1116jpqZGVkq8lcpqCoVCoVAoFEvNshF+qqqqGB8flya5t0MgEGB0dJTq6mqys7MxmUzXrTbkcDh48MEH2bt3L5/4xCeIjY2Vhs9XVpJZjui+JsnJyRQWFhIaGorH4+H999+XosSVEwXdm0A33RVC4HQ6ZdqOHmWz1OgTB/1p+5UTK91XxGazycmY0WhkamqKgYEBurq6ZF/S/Y8ee+wx9uzZQ3JyMg6Hg8nJSVpbW3nppZc4e/YsLS0tCyINwsLCWL16NatXryYmJoapqSn6+vr4yU9+wsmTJ+nq6rrhvtKjF4aGhmhsbKSnp0caH+sTi/n7XBd89GiIa00odC+dr371q3ziE59A0zSsVisOhwOXy4XdbsfpdMrP6wLm3YzgutJIW49i+OCDDzh+/LisvvPkk0/y3HPP4Xa7SUlJISMjQ3pPLRbdLPp6fUDfnzExMYSFhWEwGJiYmKC/v5/u7m7ZB26GwWAgLS2NjIwMLBYLY2NjnD59eoHJ8YcJo9FIYWEhwcHBBAIBWlparvJKuR2mp6cZGRmhvb2d1tZWBgYGiIyMXOBXpfdp3RR6bGzshtX4dOPotrY20tPTeeCBB4iIiODSpUvU1dXJqkr6unV0kV6vZBcfH09BQQFbt24lNzeXiIgIrFYro6OjfPDBB/z617++bpqUXtlJ/6e3/ZVXXqG4uJj+/n4KCwt5+OGH2bhxIw6Hg7y8PGJjYxkYGFiQTqp76nzxi1+U7TCZTNK37H/+z/9JdXW19MPRfz8yMpJNmzaRmZmJ1WpleHiYqqoqfvazn3HixInbqjCmj6PDw8NUV1dTW1t7y+u4VVwul/Rb06srjo6O3jDSLyMjA5vNhsFgoKCggH/+53/G4XDIVFyTycTQ0BDHjx/HbDazbds2AB588EGysrJkSq7b7cZms9HY2Mgrr7zC2bNnqauro7Oz85pCum5W7/F4aGhooKenR46nVz500sdzXfS5mVm1QqFQKBQKxXJj2Qg/eqpLbGysnLzeqgCkm3M2NjayefNmwsPDZUWRKwkNDWXdunXk5uYSFBQk031ef/11Tp8+fdcMce8VYWFhpKSkUFRUxKpVqxgYGOD06dMcPnyY7u7ua1YX0c0pdW8WfVI+fyIP3PftvtKnw2g04nQ6CQ0NlWKOnvKjP9l3OBzExMSQmJiIy+XC7/dLPxHdHyoQCEhfiJSUFBISEmR6ysDAAHV1dVRUVNDQ0CArZumYTCZsNhsWi0WWeW9oaLimSHS9bZqZmWF0dJSGhgZqamowm81ERkbicDjkZNNutxMXF0dqaiqTk5N4PB68Xu81o1X0ie7atWtlaWKj0Sj/6U/ZdSPf8vJyfve731FVVXVXjtO1mJmZobS0lAMHDkjT3ZmZGUwmE7t37yYoKIiQkBCysrLIzc29rlHzlX3AYDDgcrkICwvD5/MxODgo+4DuTeVwOIiNjb2qD+illfXqTDdCj4rIyMggLCyMsbExfve733Hy5MlbjrBYDthsNvLz80lMTMRsNnPu3Dk++OADLl++fMcRjIFAgImJCbq7u6mvr6e+vl5GoOk+OWazmeDgYOLj40lLS6O9vR2v1ysNvq88Hn6/n97eXlnaPSQkhNTUVLKzs+nv75fRh3oKmd/vx2g0EhQUhMvlIjExkdWrV5ORkcHatWtZsWIFISEhUgTU/YVuZhA+fxs9Hg9nz57l7bffprq6Gq/Xy+joKKGhoTKFKCYmhsLCQing6A8sdDPotLQ0IiIisNvtUjSura3l8uXLdHd3XzV+GI1G6b2mp4PV1NRw/vx52tvb72hMnp6eliLcvcbhcBAcHIzNZpMPYm4k/GiaxunTp+ns7JRpiVNTU5jNZhm52N3dTW1tLQMDA2RlZclIS5/Px8zMDG63W1ZKq6mpYf/+/Rw9elSmet7ouOveP3pKotVqJSoqCqfTKc3LbTYbMTExpKSkyNTw8fHxq6rOKRQKhUKhUCxnlo3wMzY2RlVVFRkZGTidToKCgli9ejUNDQ2LXoc+8dCrzuhPEa8kKCiIoqIi1q9fT1BQEI2NjTQ3N3P06FH2799PT0/Psp306dEuCQkJFBQUyPD02tpazp8/L9OFbjRRsFqtOJ1OmdZwPUPLe40u1PX19dHd3U1aWpoUQ1atWsWePXvo6emhtrZWThotFgsul4vQ0FBZ/cZsNtPa2kp9fT2XLl2ivLxcPiF3OBzExcURFRWFw+GQKRzFxcWcPXuW5uZmRkZGrjre4+PjdHZ2UlFRgc1mk2WL9TS6xU4iJycnaW9v58CBA/T395OVlUVKSgqxsbEy4iorK4tHHnmElStXMjg4yMDAgCxjPz4+Lifs+vra2toYGxvDZrNd1Q6fz0dHRwe1tbUcP35clnW/2+j9a3BwkPfff59Tp04t8MOpqqqis7OTuLg4bDYbKSkprFmzhqNHjy6I+tE0jYmJCVl6PTU1Ve6X/Px8OQmeH/2hV+AKDw8nOTmZzMxMTCYTjY2N1NXVSe+jxURJBAcH89BDD8mKS11dXRw7doz29va7vs/uNSaTiaioKPbs2SOjfcrKymTJ87uBz+djYGCA8vJy3n//fXp7e1m1ahUZGRlysq4boj/11FN0dXXh8XgoLS2ltbUVj8ezQJTWNI2uri4qKirIyckhOzub4OBgdu/ejcPhkFFXenWxyclJ2Z/Cw8NlNUY96sdkMtHT08PAwAA9PT00NjZy4sSJG6Yb6eiV3Jqamti3bx+lpaUMDw/j9/tpaWmhsbGRvr4+oqOjCQoKYvPmzRQXF0tRy2Aw4HQ6SUpKIiQkBIvFwtTUFK2trZw6dYpz587R29t7lSCp/25zczPnz5/HZrPR2dkpReY7FWz0CKz7kbrscDiw2WxSwOrp6bnheahpGu+++y7BwcELUqzmMzAwIL3XWltbOX/+/ILlLpeLiIgIXC4XtbW1lJSULPoaPn9M/d3vfsfg4CCZmZmkpaURHx8vx6LMzEx27txJRkYGg4ODDA4OcurUKTlGKwFIoVAoFArFcmfZCD+BQIBLly6xYcMGYmJiiIyM5Omnn+add95ZdNqV1WolNjaWoqIi6ZNy5RNmIQTx8fHs3buXvLw8ent7OXHiBKdOneLo0aPLOtJH9zQJCQmhqKiIoqIikpOT8Xq9VFZWUl1dvagQdIvFgtvtliW49YnB/U5r0Y2Ya2pqSEpKIj8/H6fTidPpZOPGjeTk5ODxeLhw4YKM4LFarQQHBxMREUF0dDRRUVGMjY1x8uRJzp07R1lZGY2NjfJGXDdZdblcmEwmJicnaWho4LXXXqO0tJSurq5rTkxGRkaorKwEoKamRkaS9fX13dIEanp6mv7+fn7729/S0NDAunXreOCBBwgKCsJut2O320lPTyc6OpqJiQnGx8dllakTJ07Q3t4uJ8rT09P09PTw1ltv4XQ6pZny/P05NjbG2bNnKS8vp6Kigp6enrs6KbkyMqyuro733nuP+vp6OUEVQshS23l5efKJuT6pv1L40VNREhISWL16tUxde/DBB8nPz2doaIjS0tIFfcDtdss+EBkZycjICCdOnODMmTNUVFQs6AM3Ij4+nt27d5OamkogEGBgYIALFy7ctf11P7Hb7aSlpfHkk09iNpulMXBnZ+dd+w09DaqqqoqRkRFqamp4+OGHCQ8Px+12y6pYISEhcmwaHh7mrbfeori4mJqamgVigF7Bsbi4GIvFwszMDLGxsezatYv09HQ5eZ+enqavr4/x8XEcDgdZWVmEhobicrlwOBwy2qulpYX6+npqa2tpbm6mra2N0tLS615Drowq9Xg8lJWVsX///gX+TuPj4/T09NDR0cGqVaswmUzk5eURGRlJS0sLU1NTC8qI6xEv/f39XLhwgddff53Lly9fM/pFjzIqKSnB4XBgtVoZGBigsbFRRjveCUaj8Zrlye82RqOR2NhYgoKCAJiYmODy5cs3bL+maZw8eXLRv9HV1XXN9/W0ttsRt2ZmZhgYGOCdd96hvr6eNWvWsGXLFoKDg3E4HPK8mj9G9/b2YjAYOH78OB0dHYtOK1UoFAqFQqFYKpaN8ANQV1dHe3s7aWlp8olqdHT0osqLGwwG4uLi2LZtGykpKZw/f57u7u6rnvoZjUays7PJy8vDarXS2dlJZWUlTU1NDA8PL1vRB35vUlxUVMQzzzxDYmKiLGf7+uuvU1FRsSiRzGKxEB4eLst/T0xMyKfp99vQWhd+nE4n27dvJzY2FrvdjsPhwOFwEB4eTkhIiExRm5mZWfCvvr6e5uZmDh8+TGVlJZ2dnQwPD8v1+/1+vF4vIyMjsuTumTNnKC0tpbe397pPhfVqX+Xl5TQ1NUlR5Xb3T39/P1VVVUxPTy+IQgoKCsJqteJyuXA6nfj9fpmu09TUJFMV9TSn0dFRDh48SHl5uUxF0NH3T19f3zXTau6U+RW7YHYfnTp1iqampgWlrfV2NjY2SiHSZDJd0zsDkH4meh+IiYmRfkV6HwgLC5N9YHp6mpmZGfx+Pz6fT/YBvbxyd3f3gj5wPYQQ5OXlkZCQgN1ulwLUlREFHxZCQkLIzc0lJiYGgObmZkpLS+9J9NLk5CSdnZ2ygmBeXh6JiYkEBwdjt9sxm83SnDc0NJRVq1bR1dUlzdfni63j4+NcuHCBtrY2KisrycnJ4bHHHiMhIQG3272g0pLexwcHB2VEyejoqKykd/LkSTkG6ILpjYyF9ZRLmB0rWltbKSkpuWqfTU5O0tvbS1tbm1yf0+mUKW46uo+ax+ORUawlJSVUVFTQ19d33bboAnFxcTFGo1Gu507PYU3TsNvtJCQkEB8fv6Ba1t0mNDSUT3/602RnZ2MwGBgZGeH999+/L9fUG1V6Wyz9/f1UV1czPT2N2WwmOTmZmJgYOUbr41FISAgul4vVq1fT2Ngo+7MSfhQKhUKhUCxnbir8CCESgZ8A0YAGfE/TtL8XQvw34MuAXh/4P2matu9OGnPixAlgVgz43Oc+R0JCAn/zN3/D17/+9RuKMlarlTVr1vDpT3+aF154gZaWFv7Lf/kvjIyMyM8YDAasVishISFs27aNhIQELl68yNtvv827774rQ/qXM/oTVd3QcmBggObmZk6fPi2fwC/mJlv3yxgaGqK5uRmv10tFRYUUQu6n+DU+Pk5tbS2tra14vV5WrVpFVlaWjILRfW306kFnz57l9OnTlJaW0tDQwODgoPQQmZmZuartPT09HDp0iN7eXiIiIujt7eXixYv09/ffdDunp6dlWP+dMjk5KdMPdE+eFStWyO10u90yekc3Lb5S2NHx+Xy0trbecZtuBSEESUlJxMfHS4GqtbWVEydOXNMEWRdorkxpuRZjY2PU1tbS1tbG6OgoeXl5sg/o5rh6hBTMjhOnT5+mvLychoYG6Yvk8XgWHR0ohCA8PJxvfvObZGVlAbORXfv27fvQlW+H2RSboqIi/vzP/5zw8HAmJyf59a9/TVNT0z1JQ/H7/TI6ze/3k5KSQk5ODsnJycTGxhIeHi79tOb7ZRkMhmv26ZmZGbq6unjnnXc4cOAAr776qvReCg0NXfCdsbExSkpKpMA5PT3N+Pi4HAMWi8ViIScnR3oC9fX1UVlZSVlZ2VVRgLoJuy4+Alf1eX0bfvvb38qy9fX19fKhwo3QNE2mad5t3G43mzZtor6+ntOnT98zgcLtdstILD2F8176i91t9LRjXWh3OByyD+oivS706UK22Wy+yiNPoVAoFAqFYjmymIifGeDPNE27IIQIAs4LIQ7OLfvfmqb97d1qjM/nkyH6ugjzxBNPkJKSwl/91V9x8eLFBQKN2Wxmx44d/NEf/RFr164lIiKCgYEBvvnNb3L06FE5CdDL9G7atImHHnqIPXv20NzczCuvvMIHH3ywrNO75jM5OcmhQ4e4cOECNpsNIQQTExMMDg4uSsjQGRkZ4fLly7S1tfHee+9J88++vr5bKqF8t9A0jcnJSc6dO0draysVFRWkpKQQHR2NxWIhMzOTxMRENE3jxIkTHD9+XJp96k/1ryfa6U/SL1y4gMVikQbKS3G8fT4fvb29FBcXc+HCBYKDg4mJiSE1NZX09HQcDgcwK4bp6Vq3Opm9VxgMBpKTk2V0zNjYGPv27ePYsWPXFEoCgQAVFRW0tLRgMpno6OigrKzsuhNg3ThY9zVJSUkhNTWViIgIWeo6ISEBTdMoLi7m5MmTNDU10d/fL5+234pwq5fyTkxMlO0rKSmhuLj4tvfRUmK32wkPD5fRPnV1dRw4cIC+vr6bfPP20f1RWltb+e53vyuje6Kjo0lKSiIjI0NWVSwvL6eqqorW1tYbetboXjQdHR14vV7q6uquiqqZmpqiv79/QUTP7Rx/i8VCdnY2brebQCBASUkJBw8epLS09KrxQdM0+vv7qayspL29ncjISM6fP7/AS0YXb7q7uzly5AjT09OyCtRSoJ+Dw8PDJCYmsmbNGuLj42lra7tnv6mnXAkhbhhttVzx+Xz09/czPDxMRUUFbrdbGjunpqbKNLapqSlOnTpFWVmZMnlWKBQKhULxoeCmwo+maV1A19zfo0KIKiD+XjVocHCQxsZGzp07R1dXF+Xl5XzhC1/gO9/5Dn19fQvSbcxmMxkZGcTExMgb91/+8pccPHhwwY2Y2WwmOjqa9evXk52dTU9PDy+99BLHjx+nt7f3Q3NzqmkaQ0NDjI2NyYmQ7v1yK9ugP633+XxSBJmZmbnl9dxNNE2jr68Pj8dDS0sL586dw2KxYDQaZalegNbWVvr6+vB6vVdFlFxvvXpVKH0yspSRXbqh9fT0tIxS0avWmM1mYDZyQE9XWi5RaLo4NzAwICs1tbS0MD4+fs0+EwgEOH36NP/yL/9Camoq3d3dnDp16oaRD4FAYEEfOHv2LGazGZPJREREhIwg0QUf3VT1TiIY9Enq6Ogog4ODC1LWPmzoaXi6SDEyMnJfJqR69SY9ZbS3t5eWlpYF0R79/f0yOm8xx2t6epqhoSGGh4evMp/Xx6s7ERZ0sWi+p5Y+tlzL80s3of7d736HwWAgKSmJd955h6ampgXpovr53dXVJX9jqVKAAoEAx48f5xe/+AV79uxhzZo1/O3f/i3FxcUcP3580anBt8qVFfo+bOjXjJmZGRlJpo/Ruk9SIBCgs7PzupGmCoVCoVAoFMuNW/L4EUKkAGuAM8AW4GtCiM8DJcxGBQ3daYOmpqakx0Z/fz9NTU2EhYXx0EMPsXr16gXpL0II/H4/dXV11NXVcerUKd5//31Z7lVHL29dUlJCZ2cnMzMzHDlyhM7OzmVbvet66P4m87mdm05dMNJv/JfDjavu2zMxMSGPscFgoLu7W4oik5OTt5yOpk8UlxOBQEBOLnQBTp/g6mkS9zvt7kZomkZ1dTVvvfUWZ8+exefzce7cuRtOHAcGBjh8+DDBwcGytPbNjsP8PqCbQBsMBrq6umQfmJiYuGORUq+kVFFRQUJCApcvX6ampuZDK/zo5seVlZVERkZy8uRJhoaG7ptwqJ9jfr+fqakpvF7vghTJqakp2d8Xy70+Z6empjhz5gww28dqampoamq67j7To5v27duH2+2moaHhmj48eprjcsDj8fD+++8zMTHB6tWrCQsLY9u2bdTW1ko/m7vF+Pg45eXlREVFER0dTU9Pz4cybVJn/hg9NTV11Rh9O9cihUKhUCgUiqVCLPamRQjhAo4C39Y07U0hRDTQz6zvz18BsZqmffEa3/sK8JW5l+sW81u6sevU1BQTExOsXbuWT37yk2RkZCwo+6ppGh0dHVy6dIm6ujrpp3DlNgkhpIGuyWTC7/ffkh+IYmmZ76HwUTTQvJZHxHJ8aq6bblssFgKBAF6v96alpq80n75d7kUfcLlcfOlLXyIxMZHS0lLOnj1LfX39somyuhUsFgtZWVns2bOH2NhYfvWrX1FSUsLExMSS9KMr+/Ry7M96qe75Vaj0SMibfU+PHvwwoJvJp6amyn9vvPEGZWVld/Ua6HQ62bx5M+vXryc6Opr29nb+9V//ldHR0bv2G0vFh2WMVigUCoVC8bHnvKZp66+1YFHCjxDCDLwDHNA07e+usTwFeEfTtNybrOe275L0KjF2u31BCeuenp4l8aVRKBQffnRPkg+jH8m1uJOy1oqPB3p/v5frF0LIVDeFQqFQKBQKxX3jusLPYqp6CeAloGq+6COEiJ3z/wF4Crh8N1p6PXSTXoVCobhb3I0y0MuJj9r2KO4+91qMUWKPQqFQKBQKxfLjphE/QoitQDFQDuh3dP8J+CxQwGyqVzPwb+YJQddbVx/gZTZFTKFQLF8iUOepQrHcUeepQvHhQJ2rCsXyR52nio8CyZqmRV5rwaI9fu4WQoiS64UfKRSK5YE6TxWK5Y86TxWKDwfqXFUolj/qPFV81DHc/CMKhUKhUCgUCoVCoVAoFIoPI0r4USgUCoVCoVAoFAqFQqH4iLIUws/3luA3FQrFraHOU4Vi+aPOU4Xiw4E6VxWK5Y86TxUfae67x49CoVAoFAqFQqFQKBQKheL+oFK9FAqFQqFQKBQKhUKhUCg+otw34UcI8YgQokYIUS+EePF+/a5CoViIECJRCHFECFEphKgQQvzp3PthQoiDQoi6uf9D594XQoh/mDt3y4QQa5d2CxSKjxdCCKMQ4qIQ4p2516lCiDNz5+QvhRCWufetc6/r55anLGnDFYqPCUKIECHEG0KIaiFElRBik7qmKhTLDyHE/zt373tZCPGKEMKmrqmKjwv3RfgRQhiB/ws8CqwEPiuEWHk/fluhUFzFDPBnmqatBDYC/27ufHwROKRpWiZwaO41zJ63mXP/vgJ89/43WaH4WPOnQNW81/8D+N+apmUAQ8Afz73/x8DQ3Pv/e+5zCoXi3vP3wH5N07KBfGbPV3VNVSiWEUKIeOD/AdZrmpYLGIHnUNdUxceE+xXxUwTUa5rWqGmaD3gV2HuffluhUMxD07QuTdMuzP09yuwNajyz5+TLcx97GXhy7u+9wE+0WU4DIUKI2PvbaoXi44kQIgH4JPCDudcCeAh4Y+4jV56r+jn8BvCJuc8rFIp7hBDCDTwIvASgaZpP0zQP6pqqUCxHTIBdCGECHEAX6pqq+Jhwv4SfeKBt3uv2ufcUCsUSMhe2ugY4A0RrmtY1t6gbiJ77W52/CsXS8X+AbwCBudfhgEfTtJm51/PPR3muzi0fnvu8QqG4d6QCfcCP5lIyfyCEcKKuqQrFskLTtA7gb4FWZgWfYeA86pqq+JigzJ0Vio8pQggX8Cvg32uaNjJ/mTZb7k+V/FMolhAhxB6gV9O080vdFoVCcV1MwFrgu5qmrQG8/D6tC1DXVIViOTDns7WXWbE2DnACjyxpoxSK+8j9En46gMR5rxPm3lMoFEuAEMLMrOjzc03T3px7u0cPN5/7v3fufXX+KhRLwxbgCSFEM7Mp0g8x6yUSMhemDgvPR3muzi13AwP3s8EKxceQdqBd07Qzc6/fYFYIUtdUhWJ58TDQpGlan6Zp08CbzF5n1TVV8bHgfgk/54DMOdd0C7NGWr+5T7+tUCjmMZef/BJQpWna381b9BvgD+f+/kPg7Xnvf36uEslGYHhe+LpCobhHaJr2HzVNS9A0LYXZ6+ZhTdOeB44An5r72JXnqn4Of2ru8yrKQKG4h2ia1g20CSFWzL31CaASdU1VKJYbrcBGIYRj7l5YP1fVNVXxsUDcr/4rhHiMWa8CI/BDTdO+fV9+WKFQLEAIsRUoBsr5vW/If2LW5+c1IAloAZ7VNG1w7uL4T8yGw44Df6RpWsl9b7hC8TFGCLEd+A+apu0RQqQxGwEUBlwEXtA0bUoIYQN+yqxv1yDwnKZpjUvUZIXiY4MQooBZA3YL0Aj8EbMPV9U1VaFYRggh/gL4DLMVbi8CX2LWy0ddUxUfee6b8KNQKBQKhUKhUCgUCoVCobi/KHNnhUKhUCgUCoVCoVAoFIqPKEr4USgUCoVCoVAoFAqFQqH4iKKEH4VCoVAoFAqFQqFQKBSKjyhK+FEoFAqFQqFQKBQKhUKh+IiihB+FQqFQKBQKhUKhUCgUio8oSvhRKBQKhUKhUCgUCoVCofiIooQfhUKhUCgUCoVCoVAoFIqPKEr4USgUCoVCoVAoFAqFQqH4iPL/A0FPNoUoknrXAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -115,7 +125,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -127,7 +137,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -139,7 +149,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -151,7 +161,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -163,7 +173,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -175,7 +185,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -204,7 +214,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from text_recognizer.networks.misc import sliding_window"
+    "data, target = emnist_lines[8]"
    ]
   },
   {
@@ -213,60 +223,108 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data, target = emnist_lines[8]"
+    "from einops.layers.torch import Rearrange\n",
+    "slide = nn.Sequential(nn.Unfold(kernel_size=(28, 64), stride=(1, 54)), Rearrange(\"b (c h w) t -> b t c h w\", h=28, w=64, c=1))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "patches = sliding_window(images=data.unsqueeze(0), patch_size=(28, 28), stride=(1, 14))"
+    "from einops import rearrange"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from einops.layers.torch import Rearrange\n",
-    "slide = nn.Sequential(nn.Unfold(kernel_size=(28, 28), stride=(1, 14)), Rearrange(\"b (c h w) t -> b t c h w\", h=28, w=28, c=1))"
+    "data = data.unsqueeze(0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p=28\n",
+    "x = rearrange(data, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = p, p2 = p)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "patches = slide(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([1, 67, 1, 28, 28])"
+       "torch.Size([1, 34, 784])"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "slide(data.unsqueeze(0)).shape"
+    "x.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
     "# remove batch size\n",
+    "patches = rearrange(x, 'b t (h w) -> b t h w', h = p, w = p)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "patches = patches.squeeze(0)"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([17, 1, 28, 64])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "patches.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
    "execution_count": 17,
    "metadata": {
     "scrolled": false
@@ -274,7 +332,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 5 Axes>"
       ]
@@ -438,7 +496,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,
diff --git a/src/notebooks/03a-line-prediction.ipynb b/src/notebooks/03a-line-prediction.ipynb
index 539fb6e..13f4ff1 100644
--- a/src/notebooks/03a-line-prediction.ipynb
+++ b/src/notebooks/03a-line-prediction.ipynb
@@ -32,34 +32,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from text_recognizer.models import LineCTCModel\n",
-    "from text_recognizer.networks import LineRecurrentNetwork"
+    "from text_recognizer.models import CRNNModel\n",
+    "from text_recognizer.networks import ConvolutionalRecurrentNetwork"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-09-09 20:38:27.854 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_load_data:164 - EmnistLinesDataset loading data from HDF5...\n"
+      "2021-01-04 21:35:35.605 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_load_data:152 - EmnistLinesDataset loading data from HDF5...\n"
      ]
     }
    ],
    "source": [
-    "emnist_lines = EmnistLinesDataset(train=False)"
+    "emnist_lines = EmnistLinesDataset(train=False)\n",
+    "emnist_lines.load_or_generate_data()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -87,7 +88,7 @@
        "torch.Size([34])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -98,14 +99,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-09-01 23:37:30.291 | DEBUG    | text_recognizer.models.base:load_weights:405 - Loading network with pretrained weights.\n"
+      "2021-01-04 21:37:05.918 | DEBUG    | text_recognizer.models.base:load_weights:432 - Loading network with pretrained weights.\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "'NoneType' object is not subscriptable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m----------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                      Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-df17e62a822a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m           \u001b[0;34m\"patch_size\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m           \"stride\": [1, 14],}\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mline_ctc_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCRNNModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ConvolutionalRecurrentNetwork\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"IamLinesDataset\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#, network_args)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/models/crnn_model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, network_fn, dataset, network_args, dataset_args, metrics, criterion, criterion_args, optimizer, optimizer_args, lr_scheduler, lr_scheduler_args, swa_args, device)\u001b[0m\n\u001b[1;32m     49\u001b[0m         )\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad_token\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"args\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pad_token\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mapper\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mapper\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEmnistMapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad_token\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
      ]
     }
    ],
@@ -124,7 +137,7 @@
     "          \"num_classes\": 80,\n",
     "          \"patch_size\": [28, 28],\n",
     "          \"stride\": [1, 14],}\n",
-    "line_ctc_model = LineCTCModel(LineRecurrentNetwork, EmnistLinesDataset) #, network_args)"
+    "line_ctc_model = CRNNModel(\"ConvolutionalRecurrentNetwork\", \"IamLinesDataset\") #, network_args)"
    ]
   },
   {
@@ -398,7 +411,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,
diff --git a/src/notebooks/04a-look-at-iam-lines.ipynb b/src/notebooks/04a-look-at-iam-lines.ipynb
index 592e16a..036604d 100644
--- a/src/notebooks/04a-look-at-iam-lines.ipynb
+++ b/src/notebooks/04a-look-at-iam-lines.ipynb
@@ -33,19 +33,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "transform = [{\"type\": \"ToTensor\", \"args\": None}, \n",
-    "             {\"type\": \"ApplyContrast\", \"args\": {\"low\": 0.0, \"high\": 0.2}},\n",
-    "             {\"type\": \"RandomAffine\", \"args\": {\"degrees\": [-1.2, 1.2], \"scale\": [0.9, 1.0]}}\n",
+    "             {\"type\": \"ApplyContrast\", \"args\": {\"low\": 0.0, \"high\": 0.15}},\n",
+    "             #{\"type\": \"RandomAffine\", \"args\": {\"degrees\": [-0.25, 0.25], \"scale\": [0.98, 1.0]}}\n",
     "            ]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -53,8 +53,8 @@
      "output_type": "stream",
      "text": [
       "IAM Lines Dataset\n",
-      "Number classes: 80\n",
-      "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z', 36: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: '_'}\n",
+      "Number classes: 54\n",
+      "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'a', 11: 'b', 12: 'c', 13: 'd', 14: 'e', 15: 'f', 16: 'g', 17: 'h', 18: 'i', 19: 'j', 20: 'k', 21: 'l', 22: 'm', 23: 'n', 24: 'o', 25: 'p', 26: 'q', 27: 'r', 28: 's', 29: 't', 30: 'u', 31: 'v', 32: 'w', 33: 'x', 34: 'y', 35: 'z', 36: ' ', 37: '!', 38: '\"', 39: '#', 40: '&', 41: \"'\", 42: '(', 43: ')', 44: '*', 45: '+', 46: ',', 47: '-', 48: '.', 49: '/', 50: ':', 51: ';', 52: '?', 53: '_'}\n",
       "Data: (1861, 28, 952)\n",
       "Targets: (1861, 97)\n",
       "\n"
@@ -62,15 +62,17 @@
     }
    ],
    "source": [
-    "dataset = IamLinesDataset(train=False, pad_token=\"_\", transform=transform)\n",
+    "dataset = IamLinesDataset(train=False, pad_token=\"_\", transform=transform, lower=True)\n",
     "dataset.load_or_generate_data()\n",
     "print(dataset)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -78,7 +80,7 @@
        "(28, 952)"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -89,16 +91,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(97, 80)"
+       "(97, 54)"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -109,51 +111,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'He rose from his breakfast-nook bench____________________________________________________________'"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def convert_y_label_to_string(y, dataset=dataset):\n",
     "    return ''.join([dataset.mapper(int(i)) for i in y])\n",
     "\n",
-    "convert_y_label_to_string(dataset.targets[0])"
+    "# convert_y_label_to_string(dataset.targets[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "drawing-in great breaths that____________________________________________________________________\n",
-      "became great sighs of ecstacy.___________________________________________________________________\n",
-      "\"They have come!\" he said reverently, gripping his_______________________________________________\n",
-      "hands together between his knees and leaning_____________________________________________________\n",
-      "forward. \"Isn't it a glorious thing! Long awaited________________________________________________\n",
-      "transcendent event, the exalted desire of all____________________________________________________\n",
-      "mankind through all ages! The Kingdom of the_____________________________________________________\n",
-      "Mind is at hand!\" He turned beaming eyes_________________________________________________________\n",
-      "upward and shook his head slowly from____________________________________________________________\n",
-      "A superfluous precaution for there was no other car______________________________________________\n"
+      "but since starting salaries would depend on grade a______________________________________________\n",
+      "or b in the finals next may, and since mating____________________________________________________\n",
+      "prospects would depend upon salaries, scholarship for____________________________________________\n",
+      "these fine young people was closely geared to____________________________________________________\n",
+      "economic and biological ends which, essentially,_________________________________________________\n",
+      "were really means. so, seeing them revolve in____________________________________________________\n",
+      "circles, harry had the feeling that moke (or what________________________________________________\n",
+      "moke consciously or unconsciously symbolised, any-_______________________________________________\n",
+      "way in harry's mind) had these splendid young____________________________________________________\n",
+      "people by the short hairs, and was diverting them ...____________________________________________\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -163,7 +154,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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hcGCz2dDr9TgcDjo7O6d8Dn5+fmzcuJG0tDQMhk9eMpvJZPpEnpcQQgghhBBCzDTXVdAG4NVXX6WkpITU1FS2bt1KSUkJbW1t7Nq1i9bWVmJiYkhISJjuadLR0UFTUxM9PT3TNocFCxYQHR1Nc3Mzubm547bPnz+fG264gZaWFsrKyqZhhuPpdDo2bdrEgQMHaGxsHLe9oKCAtrY28vPzKSoqor29fUrHt1qtpKSksGjRIg4fPszAwMCUHn+6eXt7853vfIelS5fi6+s73dMRQgghhBBCiE+06y5o09bWho+PD4GBgQwODnL69GnAtfyzwWCgq6tryh/kL9fw8PC0PvRv2bKF3t5eTpw4MW6b3W5n2bJlREVF8dprr03D7MYb6Vnk6+vLoUOHJuxN1NPTQ3V1NdXV1XR0dEz5HJKTk7nvvvsm7P/zcefl5UVcXBxZWVm0trYyODg43VMaR6/XY7fbCQoKktI0IYQQQgghxMfedRe0AYiMjMRkMlFcXOwpn8nKymJ4eJiSkpJxvVumS319/bSt2JSYmEh8fDwNDQ0UFRWN256RkYGfnx8lJSUcOnRoGmY4nq+vLzfffDOnTp2ir69vzDalFFarlcLCQvr7+6/K8u42m42UlBTS0tLYt2/flB9/ugUEBLBhwwZOnz5NZWXljMsiMhqNREVF8fd///d8/etfx8vLa7qnJK5TwcHBxMfHT/c0hBBCCCHEJ8B1GbSZNWsWPj4+FBQUMDQ0RFpaGuvXr+eDDz6guLh43AP/dBkYGKC7u3taxs7MzKSrq4uqqqpxGSsWi4Vly5ahlOL06dPTNsfzGY1GkpOTOXPmzLgsEE3T6Ovr48SJE1el+TC47quQkBDKy8spLS29KmNMF7PZTGxsLIsWLWL//v309vZelcDX5fL29iYpKYmNGzeyYMECCgsLp72/krg++fv7M3/+fG666SYpIRRCCCGEEFfsugvamEwmZs2ahcFgoLy8nOjoaNasWYOmaRw8eJDy8vLpnuK0M5lMLF26lHPnzlFVVTUuoyIoKIjZs2fT3d3N2bNnp2mWY40sPW61WikoKJiwdKevr4+6urqr8jBvNBpJSkoiICCA7Oxsurq6pnyM6RQWFkZaWhoDAwN88MEHMyogMhKwycrKYvny5dTV1XH48OEZNcdryWKxYDQar8lYISEhBAcHS3BiFLvdTkxMDFFRUVKiJ4QQQgghrtiUBm2MRiMWiwW73U5AQAB2ux1vb++pHOKKhYSEEBoaiqZpdHR0sGrVKjIzM3nllVc4duwYLS0t0z3FaaWUIjQ0lIyMDM6ePUt9ff24fWJjY9Hr9VRXV8+YUjKbzUZqaiodHR3TsopVcHAwKSkpmEymcaVRer0ene7jGx/19vYmOTmZtLQ0Dhw4QENDwzUZV6/XY7VaL7jPSJ+ddevWsXbtWqxWKy+88AK1tbUzKhPoWtDr9QQHB3tWfLsWkpOTiYmJwc/P75qM93EQGBiI0Wikra3N00jey8uLwMBAzGYzer1+mmcohBBCCCE+TqZk3V6lFHq9nri4ONLT0z09YwYGBnjnnXc4derUhA/ROp2O4eHhqZjCRUtOTsZsNlNfX8/w8DBbtmyhoKCA119/HafTeU3nMhPp9XrS0tIYGhqirq5uXOmTUopVq1Zx5swZTp8+Pa2rW41mt9uZM2cOBQUF0/KwvmTJEqxWK6WlpVRWVgKua6mUIigoiL6+Pjo6Oq75/T4VUlNTyczMxM/PjzfeeOOajOnl5UVwcDDLli1j586dE36mSimSkpL4xje+wbx582hsbGTnzp3T1gdqOul0OsLDw/nmN79JUFAQf/rTnyZcPW0qKaUwmUyUlpZOGNy9HimlWLhwIX5+frz33nue1+Pj49myZQt5eXmcPHlSrpcQQgghhLhoVxy00ev1JCQk8K1vfYvMzEwGBgZoaWmhq6sLm83Gl770Jb797W9z7NgxHA4H4MrICQ8PZ86cObz99tuEhobS1dVFZ2fnFZ/QR4mNjaW7u5uqqira2trIy8vjxhtvJCwsjOrq6hm5Is61pNfr2bBhA++99x5VVVVjAllKKRISEli2bBmPP/74jMmyAVemTWJiItu2bbvmY9vtdlatWkV5eTkHDhxAr9cTHh7Opz/9aTIzM4mMjKSkpIQ33niDl19++ZrP70qtXr0aq9XKzp07r0kmmslkYv78+Xzve9/DZDKxa9eucd9LvV7P0qVL+drXvkZwcDC5ubkcOHCA7Ozsqz6/mcZgMBAaGsqPfvQjFi9ezB/+8Adqamqu+rjz5s3D19dXSoBGWbRoEfPmzaOkpGTMqnsbNmwgLy+Pc+fOyS8HhBBCCCHEJbmioI3FYmH+/Pn84Ac/wNfXl9/85jfk5+fT2NjI4OAgs2bN4v777+eee+7h7NmznqCNr68vCxcuRCnFf/3XfzFnzhyys7N59dVXOXPmzJSc2GTmzp1Le3s7RUVFVFZW8tJLL7FlyxY2btzIjh07aG5uvqrjf5StW7fS19fHmTNnrvmy0V5eXiQmJpKZmcn27duJi4vDz88Ph8NBe3s7PT093HzzzbS3t1NcXDwjSsnCw8Mxm80kJiZis9mmJZC0dOlSvLy8KC0tpaGhgaVLl/IP//AP1NbWsmvXLnp6eli0aBHz58/nwIEDVxScDAoKwmg00tHR4fk+XU0rVqwgPj6empoajh8/ftXHs9vtZGVlcfvttxMeHs4vf/nLcVl6/v7+3HjjjXzxi1+krKyMgIAAiouLycvL+8T1Evoo3t7exMXFcffdd5OSkkJ9fT3vvffeVc+yAVcA/MyZM7S1tV31sT4O7HY7d999Nw0NDZw4cYLOzk50Oh1xcXFUVFR4yk17e3une6pCCCGEEOJj5LKDNjabjUWLFvHlL3+Z/v5+nnjiCXJzc2lvb6e/vx+bzcbs2bPx9fWlpaXF8+Blt9tJT09n+fLlDAwMMHv2bN5++20OHDhw1R+4/f39iYyMpLq6msbGRpxOJ2VlZbzyyits3bqVyspKjhw5Mm61pGtBKcWsWbP40pe+xPPPP+8pozEYDERGRhIVFcXRo0evSiZQfHw8qamppKWlkZmZSVBQEGazmVmzZgHgcDhwOBzodDpuuOEGzp49S2dnJ/7+/hiNRpqamiY8rt1uZ/78+SQmJuLr68u+ffsoKyubsmBDaGgod955J4mJiSQlJZGamsqmTZuw2Wz09vYSEBBAWFgYAwMDHDx48JJLEmw2G6tXryYlJYXS0lLeeustOjo6xuxjsVhYvXo1H374IeXl5cTExHD33XfT2dnJtm3bKC0txd/fH5vNhtFovKJzt1gsfPrTn6azs5OjR49ek6bZ8+bNo7KykhMnTlz1IF1gYCB33HEHWVlZzJo1i7q6Ok6ePOkpjTKZTMyePZvly5dzww03cPDgQTIyMqiqqqKwsJC6urrrqo+Nr68v8+bNIysri8TEREwmE9u3b6eysvKaZHMcPnyYrq6uGbPa3nSbO3cukZGRvP/++5SUlKBpGjqdDm9vb2pqamhqappxq64JIYQQQoiZ77KCNl5eXsydO5dVq1ZhNpvZsWMHhw4d8vTsMBgMBAQEMG/ePFJTU3nyySdxOp14e3uTmprKli1bPBkv2dnZ7Nmzh9LS0qu+dHRwcDA+Pj50dHTQ1taGpml0d3ezZ88eFi9eTGZm5oRLXF8Ler3ek31UU1NDR0cHer2eoKAgbrnlFoaHh6murqa2tpb+/n6MRiO+vr7jggiXo6enh46ODgwGA/Pnz2fPnj0cOnSI1tZW2tvbaW9vB1xZLfHx8Z7rFR0dTU9PD6+++uq45rQBAQHMmTOHpUuXEhoaSlJSEoWFhdTU1ExZ0MbpdFJYWIjNZiMqKoqKigpaW1ux2+34+Pjg7e1NdHQ0RqORY8eOed4XHByM2WwmICCA0NBQgoKC6O7u5uWXX/YEyyIiIliyZAnr16+npqaGG2+8kfz8fJxOp+chVSlFbGwswcHB7N27l4aGBhYuXMicOXPYtm2bp+eP0+kkNzcXvV5/2UE3g8HAqlWrPKVWH/VdMZlMhIWFsWDBAiwWCzk5OZ57x2AwYLfbWbx4MdnZ2TidzgkfJFNSUkhKSiInJ4f8/Pxxq4hNJYvFwvr160lPT8dqtdLU1MTRo0c999VIVl9mZiZxcXGeTK/Q0FB27NhBWVkZdrudtLQ0goODeeeddz7RQRyTycTChQvJyMggICCA9vZ2ampqOHDgAB0dHZd93oGBgYSHhxMYGIi3tzdnz56lurp6wp5kkwVrJzLS/+aTlGXi5eWFl5cXQ0NDOJ1OlixZQn19PWVlZZ7so+HhYZqamhgcHKSvr+8Tez8KIYQQQoir57KCNuHh4SxatIjExEQOHTrEK6+8Mqbkw2AwYLFYCAkJISgoiMLCQvr6+rBaraSnp7Nu3Tp6enooLCxk586dVFVV0d/fP2UnBa4giJeXF0opT5AgLCyM3t5eT88dcP2jOi8vj1OnTjF79myio6MnXTL6alFKYTabWblyJcePH/c8lPv7+5OcnMyyZcvYs2cPNpsNcD0sBAQE4O/vz+nTp6mtrb2i8RsaGvD29qa5uZne3l6ee+45jhw5MuZBzWw2M3fuXEwmEzqdjqysLBYtWkRlZSXZ2dljgjZGo5GUlBTWrFlDeno6AwMDDA0N4XA4PA9vSikGBgau6Dp3dHSwd+9eDAYDPj4+9Pf3k5OTg8ViQdM0LBYLBoMBX19fzz0wUn4TGBiIzWbDZrPh5+dHSEgI+/fvp7Ozk8DAQG644QbWrVuHt7c3paWlZGRkkJqaSmtrK3V1dYCr+Wt6ejq1tbUUFxfT0dGB0WhEp9PR0tLiCQB1dXVdUdmOUgqr1codd9xBUVERnZ2dREREEBQURENDw7jyFB8fHyIjI8nMzOT2228nIiKCuro6Wltb6e/vR6fTYTabycrKorKykrNnzzI4OIher8dgMHhWt9mwYQMGg4Gqqqqr2jjV29ubxYsXs3LlSnp6ejh37hwOh4MjR47gdDqxWq2kpqaycuVKwsPDqa6uJjs7m40bN9Ld3c3x48fp6+sjIyODzZs3ExkZSX19PQ0NDRMGG0JDQ2lsbLwmD9AGg2HKf5aMfL8WL16MzWajtbWVoaEhcnNzPZ/lpfL29iYmJob09HTi4+Mxm81YrVYiIyN5+eWXaW9vv6wm2nq9HpvNRlhYGEFBQZSXl3t+zpx/PKPRiNlsxsfHh8HBQZxOJ729vTO2z5jVasXf3x+Hw+Hpw7Rv3z5PcBRA0zQcDgdms1kCNkIIIYQQ4rJcctBGp9OxZMkST/+EZ599dswD6Ug6uMlkor+/n5MnT3qCMn5+foSGhmKxWMjNzeV3v/sd5eXlV2VFHYvFQlhYGD4+PhQVFdHX10dUVBSVlZXU1taOKR/o7OwkNzeXjIwMwsLCsFgsnuySa8HLy4vY2Fhuuukm/vmf/9lzPcPDw1m2bBkDAwO8++67WK1Wli1bxrx584iPj8fb25tdu3bx1FNPXdH4SimioqJIS0vjzJkzFBQUjHvY9fX1ZdmyZTQ3N3PrrbeiaRo9PT00NDSM6Z8xEjBYu3Ytd955J/7+/rS0tPDrX/+a0tJSfH19CQgIYHh4mObm5ilpPm0ymejr6+PEiRN8+OGHntfXrVtHQEAADoeDlpYW9Ho9y5cv5+tf/zpGo5GSkhKOHDlCXl4ejzzyCElJSZw+fZrly5dz++23ExYWxuOPP05MTAxOp5MbbriB6upqz2/OjUYjGRkZvPbaa7S0tOB0OqmsrCQ/P58777yT4uJiSkpKrnj5caPRSEREBDfffDO5ubls2rSJpKQk2traeO2119izZ49n35HG4CtXrmT58uVYLBa8vb0xGo2ehrH9/f20tLTQ1NTErbfeSkNDA319fZjNZsxmMwaDAaPRyF133cVzzz1He3s7RqORoaEhNE2bsofPkQBeXFwcDzzwANXV1eTk5BAcHIzNZqOsrAxvb28WLlzIrbfeitVq5fDhw+zdu5eoqCjWrFnDk08+SXV1NQsWLGDlypUEBwfT0dFBQkICx44dG1eOYjKZuOWWW3jxxRfHZXyNLMt+pT+PdDodXl5emEwmgoKCaG1tpbu7m4GBgcu6diMBaIPBQH9/v6fJtcPhoLS0FL1ez9y5c/n9739/2fdaVFQUX/3qV1m4cCHd3d0cOXIELy8v7r33Xk6dOkVhYSFOpxOl1Jjro9Pp0Ol06PV69Ho9Q0NDY8ql7HY7y5YtY8OGDQQFBXHw4EEOHDjAhx9+OOZnsNFoJDIyksTERCIiInA4HFRXV3t+Xl+uqVihcGR1RKUUg4ODns/QbrcTFhZGc3MzkZGRhISEkJeXNyaIajQaSUxMpL29/SM/+5FxDAYDOp0Op9P5sVxtTgghhBBCTK1LDtoEBQWxcuVKAPbt20dvby92u91TUhQZGcny5ctZv349SUlJfO9736OpqYnh4WG6u7s5dOgQNTU17Nmz56o22jUajWRmZrJixQoOHDjAvn370Ov1nDp1asK0/v7+fnp7e3E6ndf8N7s2m42NGzdSX19PQUGBp4QgICCA5ORk6uvrue2227jpppuoqKjg8OHDvPvuu6xevXpKHqD9/f2ZO3cuycnJPPLIIxOWhxmNRhISEkhMTORXv/oVoaGhlJWVcfr06TGBl/vuu49PfepTJCUl4efnh06n4/XXXyc9PZ1Vq1Z5slJOnTpFWVnZFc8dXA/ZHR0dlJaWjnk9MTGR/v5+CgoK0Ov1zJo1iwcffJCSkhIOHDjAkSNHKCsrw2q1Ul9fz+rVqykrK2POnDnExMSg1+u57777POc3e/Zsli1bRldXF8XFxcyaNQuz2cyJEyc8AYDCwkKeeOIJfvrTn3Lvvffys5/97IrL/nQ6HUajke7ubu6++26OHTtGeXk5wcHBbNmyZUzQJjExka9+9ausXbvWU/b2gx/8gMOHD48pbxoeHqanp4fvfve7NDU1ecqSysrKyM3NJT09nZiYGGJiYli0aBEBAQE0NDTQ0dExZQFNf39/Fi1axHe/+11KS0v53//9X6KiokhISPBkXCxdupRHHnmEI0eO8PTTT5Obm4vdbmfz5s0UFxfzwgsvEBERwaZNm9DpdPziF78gKCgIf39/0tPTOXPmjGdZeh8fH1asWMGmTZs4deoUxcXFnu+aXq8nMDAQTdPGZEldjoiICFauXMntt9/OTTfdxNmzZ/nJT35Cbm7uZWVcJSUlsXLlSubOncu+fftYv349Z8+e5eDBg0RHR7NmzRoqKirGlABeqvvvvx+LxcIzzzzDO++8Q1lZGTqdjt///veYTCYMBgP+/v6YTKYxWVdhYWEEBwcTHx/v6Re2a9cuz/ZVq1axevVqAH74wx9itVqxWCyebC5wBdLS0tL49re/TUVFBe3t7fj5+bFy5Ura2tp4+OGHL/u8QkJCaG5uvqKf6X5+fsTFxWGxWCgsLKS1tdWTyRcVFUVoaCjr1q2joKCAc+fOee4po9FISEgIs2fP5sUXX/zIe8rPz4+EhATS0tKw2Wxs3779mmWECSGEEEKImeuSgzZWq5WwsDA6Ojro6elB0zTa2toICwtjxYoVbN26lbCwMIqKinjwwQc5ceKE5x+djY2N7N+/f9xva6+G5uZm3nrrLZqbm7n77rvZtGkTv//978f1rNHpdCxdupS77rqLsrIyiouLr3pvndG8vb2Jiopiw4YN5OTkjGna3NjYyJkzZ1i3bh0AP/vZzygtLcXhcDBv3jyGh4enZEWfpUuXEhERQXZ29qRLJre3t/PYY48RGhrKkSNHePjhh6mtrR0XKKmrq+M//uM/aGpqQtM0IiMjCQsL8/Sfqa6uxuFwTOmDiNFoRNO0cSt/bd++nYGBAXp6ehgeHqayspL77ruPhoYGBgYGPPegw+Hgxz/+MUFBQfT29vKXv/yFkydPYrPZyM/Pp6ioiOeeew6TyURrayuNjY1YrVbWrVtHV1fXmHMZCRbExsbyzDPPTEkfGKfTSV5enidI197ejs1m46677uL+++/nK1/5Crm5uZw+fZrPfvazZGRkUFJSwl//+ldycnI4d+7cuOvtdDrZs2cPDz30EA899BAvvvgizz//PCdPnsRisfD5z3+eX/7ylwDExcURGxtLe3s7p0+f5vDhw1d8TrGxsaxfv5477riDgwcP8qc//Yn6+nrWrFnjCZh961vf4nOf+xxPPPEEjz76KM3NzQQHB7N06VJWrVrFv/zLvzA4OEhcXBxKKerq6mhra2POnDl84QtfYGhoiG9+85ucPXt2zNgLFizAbDZ7+hnFx8d7mnH/6le/uuxSIHBlrKxdu5asrCx2797N7373O7Zs2cKWLVvo7+/n0KFDgCurIikpidLS0gtmx/zd3/2dpy+Uj48Pjz76KL/+9a/Ztm0bCQkJLFiwAF9fXx5//PHLmu+IOXPmsGvXLrKzsz3BdE3TeOGFFwBXQ+qkpCTCw8MpLS1lx44d/Pa3vyU4ONgTkAfIyspi165d2Gw2Ojs7sVgsWK1WOjo6aG1tpbi42HPsEQEBAXzjG9+gqqqKl156ibi4ODZu3Mjs2bM5ffr0ZZ2P0WhkxYoVnmyxvLw8BgcHCQsLIyoqiurqasrKyj7y59DmzZvZvHkzQUFBvP/++yxdupTHHnvMEwhMSUnhnnvuITY2lm3btrF27VreffddGhoaCAwMZO3atRw/fhxN07DZbDgcjglLgcPCwti4cSOxsbH09vaSmZnJu+++S3Nz8xVn6gkhhBBCiI+3Sw7adHR0UFJSwqJFi/jOd75DUVERZrOZmJgYvL29OXnyJDt37iQvL4/a2tpx/yieyvKKj9LS0sLRo0dpbW3lzjvv5Pvf/z7l5eXU1NTQ3d3tWS530aJFHDt2jFdffdXzUHGt9Pf309zczOnTp3n77bfH/Ea4oqKCp556ihdeeIG+vj46Ozs9JRZFRUX84he/uOLyovDwcGbNmkVNTQ379++f9LPp7e0lNzcXg8FAb28vBQUFngbAH3zwgWe//fv3MzQ05CmlqaqqwmAwoGkafX19Y8oLpspIacb5RkoSRsbTNI3a2tpxv3UfGhqitLSU8vJy+vr6PKuL6XQ6+vv7GRwc5Oc//zmDg4PU1tbS2tqKn58fg4ODDA0N8dBDD+FwOPDz82PWrFkEBQXx7LPPsnv37inr1TTS0HTkz83NzeTm5nrGKywsZGhoiEOHDhEfH09iYiLLli0jPDx8TNCqoaGBuro6mpqaSE5O5vjx46SkpFBRUUFdXR1Go5GYmBhsNhsvv/yyp2H3yHWaipWCVqxYwW233UZ4eDjPPvssr7/+Oh0dHQwNDdHe3o63tzdpaWlERETwox/9iJ07d3qyG2bNmkVSUhLHjh0jNzeX4eFhent78fX1ZcWKFYSEhBAYGEhhYaGnKXRraytNTU04nU7ee+89cnNzefDBBykqKuLcuXO0t7fT1NREfHw8RUVFlx1oCwwMZNOmTURGRvL666+zZ88eYmNjPRlDbW1ttLW1UVJSQnx8PN/85jd58sknGRwcJCAgwNOwekRiYiI333wz3d3dHD58mKysLF555RWefvppOjo6SEpKAiAnJ2dc8PRS+fj4EBsbS0BAwJigTWVlJcnJySxfvpyWlhZycnJobGxkyZIlLFy4kP379/Puu+9iMplYu3Yta9euZevWrRw6dAilFEePHiU8PJzNmzfzk5/8hDfffJPjx49TX1/PwMAACQkJ3HHHHdx66620t7ezfPlyAEpLS3n66afJycm5rPMxGAysWLGCvXv34nA4WLFiBRkZGSxYsIDg4GCeeeYZKioqiI+PJyUlhfDwcJxOJ6+88oqnvOnGG29k7dq1+Pv7k52djaZpJCcne37WVFZWkpuby+LFizl+/Dj5+fnMmjWLtWvXUl9fz/DwML6+vhQXFzN79mzuuece3n//fYqKihgcHGR4eNhzrT/zmc9gs9morKyksbGR6Ohoz3daCCGEEEJc3y45aNPZ2ckbb7xBW1sbqampxMbG0tXVRX5+PsXFxZw+fdqT4n41V5u5GMPDw3R2dlJQUMC2bdtYsGABYWFhBAQEEBgYiE6no6+vj+3bt3uWUL6ShrGXQ9M0Ghsb+eMf/0hlZeWYf6QPDAx4HvTO19fXNyUP0B0dHWRnZzM8PExNTc0F5zm6B8Vrr72GyWQat3rVyG+gR1yLUrP+/n76+/s9PVtGTJQtMdl8BgYGPPfr4ODguP1GHqBGmir39PSwf/9+zGYzSUlJBAcHo5Ti7NmzHDhwgJycnCm/l0afz/DwMEVFRTz77LOegBjAyZMn0el0pKSk4OPj4+kNMhKcGFm+HVzBru3bt7N+/Xr6+/s9vW/8/PxobGyktrZ2SpcotlqtrF27lltuuQWn08nevXvJzs4ek/l26NAh2tvbCQsLo66ujjNnzngyqHx8fIiKiiIkJITdu3d77seSkhJycnKIj4+nra2N/fv3U1dXR21tLTU1NZ5ro2kaXV1d/OY3v8HPz4+Ojg4GBwdJSEjwrA52Jd+pkeXtNU1DKcWWLVuYPXs2DoeDrq4uAgMDiYiIoLa2ljVr1pCamspnP/tZfH19CQ0Npby8nPr6eqqrqwFITU3Fbrfj5+eHw+EgPz+fV155hdbWVkwmEzabjcbGRo4dO3bJP2tHVp8zm82e4MpIOdmpU6c8+9XV1fHlL3/Z0w+srKyM9vZ2Ojs7eeyxx8jLy6O6upqAgAAsFgt9fX188MEHdHV1eQKFb731Fu3t7URERJCQkICPjw/FxcWUlZXR0dFBfn4+x44dY82aNQwMDJCXl0d5eTmdnZ2EhoYSFxcH4GnmW1VVRWlp6aRL0I80G8/KyqK2tpa0tDRMJhNOp5PS0lLS0tIICAjgM5/5DPPnz/c0XQbIz8/3BM6sVis2m43g4GASExMJDw8nLy/P8zPabDbj6+tLbW0tL7zwAhUVFZ5+ULGxsYSEhHDy5En6+/tZtWoVcXFxDA8Pk5KSQnBwMAMDA/z2t78FXJlOdXV1nlUD29raPJ+vlEcJIYQQQlzfLjlo09/fz6lTp2hvb+fDDz8kJCSE1tZWqqqqOHfuHK2trdMerBltJHX/gw8+oLGxkbi4OOx2O97e3p5tZ86cob6+ftr+cexwOMZkq1zrsUc3771Yl/Oeq6WpqYmhoaGrupzw+cGo/v5+T3ZDc3MzISEhWCwWnE4ntbW1V7yi18UYvRz7iKamJg4fPuz5bg4PD1NfX09lZeWYoI/BYKC8vJy2tja8vLzo6+vDZDKhaRqDg4PU1dVNyRLFRqMRq9WK3W5n0aJF3HzzzSilOH78ONnZ2eMChZWVlTQ3N+Pl5TXu3Ox2u2e1ntGBhZaWFg4ePEhJSQl9fX2UlpbS1dXF0NAQNTU1Y+4LTdPG9H6Jj48nNDSU6Oho/vznP1/R+ZpMJsxms2clJl9fX9rb2ykqKiI6Ohq9Xo+Pjw9KKSwWC15eXp7GyTabjYSEBIKCgjxBG4PBQF9fH8PDw7S1tZGbmzvmvMvLy3E4HFRWVl7yXM1mM8nJycyfP5+mpiZaWlqIjIwkKioKHx8fzzVzOBz09vaSn5/P2bNnaW1txel04nA4eOONNzylZAMDA7z//vsUFxdTXl7uGcfhcFBQUODJZLJYLAwMDHjOq7u7mxMnTvDMM894VoxqaGjwNGe32+2eleEGBgZwOBwf2VzY39+flStXEhcXR2JiInq9ntraWioqKrBYLNxxxx0sXLiQrq4uBgYGqKmpISQkhHnz5rF48WJP0KahocHz343Ozk4yMjLQNA2z2YzT6SQ0NJSAgADee+89jh075sl6HOlJo9frPaV50dHR+Pv7ExAQgNlsJjo6mtjYWHbt2kV9fT3t7e34+vqSlJSEl5cXCQkJhIeHS3mUEEIIIYS4vCW/u7q6OHPmDGfOnJnq+VxV1+phWlxbZWVlVFdXj8v6uVZm2n3V2dlJZ2fnBQNrg4OD5OfnA3D48GFCQ0NxOp0MDAzQ2NhIUVHRlDW5Tk9PZ/bs2axcuZK+vj527NjB8ePHx6w6NtrobKDRRoJixcXFY5aYB1cAY3SwABgT4JiIwWBg9uzZpKamUlVVdcH+UAaDgYCAgDE9p843UmI3a9YsDAYDp06dIicnh9bWVlatWsXAwAC9vb309fVx8uRJYmNjOXHiBLm5uSxfvpyIiIgxGV4jjb67u7s5evTomMBub28vR44cYWhoaEwG3MXy9vb2NEweWaJ6JMvI19d3TKBr9+7dtLW10djYOGas0RlSF1rW3ul0UlVVNWnj+bq6Onbu3MmHH35IYmKiJ1hjNpvp7Oykvr6e7u5uqqqqaGtro6Wl5SP7jul0Ok+/oJycHAoLC+np6SElJYXy8nKioqI4ePAge/fu9TQft1qtREREeI5RVFTkyWoa6T02Eqjp6OjAbDbT39/Pvn37xpSpjjRh//DDDz1ZklVVVQQEBFBRUUFjYyNNTU1ERERgtVopLS0lOzubzMxMkpKSCAgIID093ZMFJYQQQgghrm/qUh7MlFKSpy2EuGjz58/ni1/8ImFhYezZs4c9e/Z4+plcKrvdTmBgIDA1mV5hYWE88MADpKen88c//pHdu3dPuN/IymNbt27lqaeeuqzg4Oc//3lPQ+mioqIrnfqUGOkdtGXLFiIjIykuLuatt96atqy/qTKy0pVOpxu3UqCXlxcREREYDAZKS0s9gUmr1UpoaCgw8b3l4+PDjTfeyMaNG3niiScoLy/HYrFgMpnGZYtt3LiR4eFhTp48OSYw6efnR29vL15eXkRFRZGRkeEpHRuZw4IFC7j99tuZN28en/vc5zwN3YUQQgghxHXhuKZpi89/UYI2Qojr0oYNG7j99tvp6uriBz/4wYT7eHl5ER0dzUMPPcSOHTt45513rmoZnrh+JSYmcuedd3Lbbbfx6KOP8vzzz0/3lIQQQgghxLU1YdDmssqjhBDi4y4pKYmWlpYLZpbExsbywAMPcOLECfbv3z9lq4GJ64+fnx9Op3PCeyg2NpYvf/nLJCcn88ILL/Diiy9OwwyFEEIIIcRMJEEbIcR1qb+/37MS0URSUlJYsmQJ9fX1vPjiixKwEVckKysLs9lMbW0tJSUldHd3YzAYSE5O5q677sJgMLB79272798/o5r5CyGEEEKI6SVBGyHEdamsrIz+/v4JGyLb7XaioqIwGo3s3bt3XONjIS5VaWkpCQkJhIWFERgYiNFoJCoqirCwMPr6+jh8+DAnT570rB4mhBBCCCEESNBGCHGdKioqYmhoaNzS4gChoaF4eXlRUVHxsW/MK2aGgoICHA4H0dHRhISEEBYWRkxMDIODg+zfv/+CK6oJIYQQQojrlzQiFkKI8yxdupTBwUHKysrGLG0txFTw9vYmJiYGHx8fWlpaaGhokJIoIYQQQgghq0cJIcTF8Pf3Z3BwkJ6enumeihBCCCGEEOL6MCVBmyagYipnJYQQQgghhBBCCHGdi9E0Lfj8Fy8paCOEEEIIIYQQQgghrg3ddE9ACCGEEEIIIYQQQownQRshhBBCCCGEEEKIGUiCNkIIIYQQQgghhBAzkARthBBCCCGEEEIIIWYgCdoIIYQQQgghhBBCzEAStBFCCCGEEEIIIYSYgSRoI4QQQgghhBBCCDEDSdBGCCGEEEIIIYQQYgaSoI0QQgghhBBCCCHEDPT/AW9gxA1WAOUiAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -173,7 +164,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -183,7 +174,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -193,7 +184,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -203,7 +194,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -213,7 +204,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -223,7 +214,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -233,7 +224,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -243,7 +234,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -253,11 +244,12 @@
     }
    ],
    "source": [
-    "for i in range(10, 20):\n",
+    "for i in range(190, 200):\n",
     "    plt.figure(figsize=(20, 20))\n",
     "    plt.xticks([])\n",
     "    plt.yticks([])\n",
     "    data, target = dataset[i]\n",
+    "    target = [x - 26 if x > 35 else x for x in target]\n",
     "    sentence = convert_y_label_to_string(target, dataset) \n",
     "    print(sentence)\n",
     "    plt.title(sentence)\n",
@@ -266,132 +258,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data, target = dataset[12]\n",
-    "sentence = convert_y_label_to_string(target) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([97])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "target.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "h, w, s = 28, 5, 5"
+    "from text_recognizer.networks.util import sliding_window"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from einops.layers.torch import Rearrange\n",
-    "slide = nn.Sequential(nn.Unfold(kernel_size=(h, w), stride=(1, s)), Rearrange(\"b (c h w) t -> b t c h w\", h=h, w=w, c=1))"
+    "data1 = torch.stack((data, data))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "140"
+       "torch.Size([2, 1, 28, 952])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "28 * 5"
+    "data1.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
-    "patches = slide(data.unsqueeze(0))"
+    "patches = sliding_window(data.unsqueeze(0), (28, 32), (1, 28))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\"They have come!\" he said reverently, gripping his_______________________________________________\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2880x2880 with 50 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "# remove batch size\n",
-    "n = 50\n",
-    "patches = patches.squeeze(0)\n",
-    "fig = plt.figure(figsize=(40, 40))\n",
-    "print(sentence)\n",
-    "for i in range(n):\n",
-    "    ax = fig.add_subplot(1, n, i + 1)\n",
-    "    ax.imshow(patches[i].squeeze(0), cmap='gray')\n",
-    "    ax.set_xticks([])\n",
-    "    ax.set_yticks([])"
+    "patches = sliding_window(data1, (28, 32), (1, 28))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([190, 1, 28, 5])"
+       "torch.Size([2, 33, 1, 28, 32])"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -402,285 +334,180 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fabf098e9d0>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1440 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plt.figure(figsize=(20, 20))\n",
-    "plt.imshow(data.squeeze(0).numpy(), cmap='gray')"
+    "patches = patches[1]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from text_recognizer.datasets.transforms import Compose, AddTokens"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "target_transform = Compose([torch.tensor, AddTokens(init_token=\"<sos>\", pad_token=\"_\", eos_token=\"<eos>\")])"
+    "from einops import rearrange"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 54,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "IAM Lines Dataset\n",
-      "Number classes: 82\n",
-      "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z', 36: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: '_', 80: '<sos>', 81: '<eos>'}\n",
-      "Data: (7101, 28, 952)\n",
-      "Targets: (7101, 97)\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "dataset = IamLinesDataset(train=True, init_token=\"<sos>\", pad_token=\"_\", eos_token=\"<eos>\")\n",
-    "dataset.load_or_generate_data()\n",
-    "print(dataset)"
+    "p = rearrange(patches, \"b t c h w -> (b t) c h w\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 342,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data, target = dataset[181]\n",
-    "sentence = convert_y_label_to_string(target, dataset) "
+    "patches = rearrange(p, \"(b t) c h w -> b c h (t w)\", b=2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 343,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "([], [])"
+       "torch.Size([2, 1, 28, 1056])"
       ]
      },
-     "execution_count": 343,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 5760x1440 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.figure(figsize=(80, 20))\n",
-    "plt.title(sentence)\n",
-    "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n",
-    "plt.xticks([])\n",
-    "plt.yticks([])"
+    "patches.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 344,
-   "metadata": {},
+   "execution_count": 58,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor(0.0631)"
+       "torch.Size([66, 1, 28, 32])"
       ]
      },
-     "execution_count": 344,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data.mean()"
+    "p.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 345,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor(0.1638)"
+       "torch.Size([1, 28, 952])"
       ]
      },
-     "execution_count": 345,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data.std()"
+    "data.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 346,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from torchvision import transforms"
+    "p=7\n",
+    "x = rearrange(data.unsqueeze(0), 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = 28, p2 = p)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 347,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n = transforms.Normalize((0.5,), (1.,))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 390,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ra = transforms.RandomAffine((-1.1, 1.1), scale=(0.5, 1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 399,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d = ra(data)\n",
-    "d = (d > 0.15) * d\n",
-    "d = (d - d.mean()) / d.std()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 400,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "([], [])"
+       "torch.Size([1, 136, 196])"
       ]
      },
-     "execution_count": 400,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 4320x1440 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.figure(figsize=(60, 20))\n",
-    "plt.title(sentence)\n",
-    "plt.imshow(d.squeeze(0).numpy(), cmap='gray')\n",
-    "plt.xticks([])\n",
-    "plt.yticks([])"
+    "x.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 351,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import torchvision"
+    "patches = rearrange(x, 'b t (h w) -> b t h w', h = 28, w = p)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
-    "d  = torchvision.transforms.functional.gaussian_blur(d, 7, (0.75, 0.75))"
+    "patches = patches.squeeze(0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "([], [])"
+       "<Figure size 1440x1440 with 12 Axes>"
       ]
      },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 5760x1440 with 1 Axes>"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.figure(figsize=(80, 20))\n",
-    "plt.title(sentence)\n",
-    "plt.imshow(d.squeeze(0).numpy(), cmap='gray')\n",
-    "plt.xticks([])\n",
-    "plt.yticks([])"
+    "fig = plt.figure(figsize=(20, 20))\n",
+    "for i in range(12):\n",
+    "    ax = fig.add_subplot(1, 12, i + 1)\n",
+    "    ax.imshow(patches[i].squeeze(0), cmap='gray')"
    ]
   },
   {
diff --git a/src/notebooks/Untitled.ipynb b/src/notebooks/Untitled.ipynb
index 7e812cd..d39e111 100644
--- a/src/notebooks/Untitled.ipynb
+++ b/src/notebooks/Untitled.ipynb
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,18 +67,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "config_path = \"../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1116_082932/config.yml\"\n",
+    "config_path = \"../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1213_175148/config.yml\"\n",
     "with open(config_path, \"r\") as f:\n",
     "    experiment_config = yaml.safe_load(f)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -87,7 +87,7 @@
        "'CNNTransformer'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -98,14 +98,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-11-22 20:36:09.684 | DEBUG    | text_recognizer.models.base:load_weights:432 - Loading network with pretrained weights.\n"
+      "2020-12-30 01:24:06.949 | DEBUG    | text_recognizer.models.base:load_weights:432 - Loading network with pretrained weights.\n"
      ]
     }
    ],
@@ -115,41 +115,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-11-22 22:45:47.919 | DEBUG    | text_recognizer.models.base:load_from_checkpoint:379 - Loading checkpoint...\n",
-      "2020-11-22 22:45:47.920 | DEBUG    | text_recognizer.models.base:load_from_checkpoint:381 - File does not exist {str(checkpoint_path)}\n"
-     ]
-    },
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: '../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1110_073929/model/best.pt'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-121-f064cb8bef04>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mckpt_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1110_073929/model/best.pt\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_from_checkpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mckpt_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/models/base.py\u001b[0m in \u001b[0;36mload_from_checkpoint\u001b[0;34m(self, checkpoint_path)\u001b[0m\n\u001b[1;32m    381\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"File does not exist {str(checkpoint_path)}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 383\u001b[0;31m         \u001b[0mcheckpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheckpoint_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    384\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_network\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"model_state\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[1;32m    579\u001b[0m         \u001b[0mpickle_load_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'encoding'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 581\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    582\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_is_zipfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m             \u001b[0;31m# The zipfile reader is going to advance the current file position.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_open_file_like\u001b[0;34m(name_or_buffer, mode)\u001b[0m\n\u001b[1;32m    228\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_is_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_open_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m'w'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode)\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0m_open_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_opener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_open_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1110_073929/model/best.pt'"
+      "2020-12-30 01:25:47.777 | DEBUG    | text_recognizer.models.base:load_from_checkpoint:379 - Loading checkpoint...\n"
      ]
     }
    ],
    "source": [
-    "ckpt_path = \"../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1110_073929/model/best.pt\"\n",
+    "ckpt_path = \"../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1213_175148/model/best.pt\"\n",
     "model.load_from_checkpoint(ckpt_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -158,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -177,7 +161,7 @@
        "torch.Size([98])"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -188,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -206,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,21 +199,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/akternurra/.cache/pypoetry/virtualenvs/text-recognizer-N1c_zsdp-py3.8/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py:876: UserWarning: Argument fill/fillcolor is not supported for Tensor input. Fill value is zero\n",
+      "  warnings.warn(\"Argument fill/fillcolor is not supported for Tensor input. Fill value is zero\")\n"
+     ]
+    }
+   ],
    "source": [
     "data = ra(data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 4320x1440 with 1 Axes>"
       ]
@@ -249,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 26,
    "metadata": {
     "scrolled": true
    },
@@ -257,10 +250,10 @@
     {
      "data": {
       "text/plain": [
-       "('because droch fis at Caully<eos>', 0.2531574070453644)"
+       "('becane gool big alls at boasty<eos>', 0.31098294258117676)"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -271,17 +264,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data, target = dataset[110]\n",
+    "data, target = dataset[0]\n",
     "sentence = convert_y_label_to_string(target, dataset) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -290,13 +283,13 @@
        "([], [])"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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dIAiCIAiCIMyRfsuyZnjAzVe04dprryUQCNDb20tnZyeQ+mPZ4/Gwdu1a6urqeOaZZ4jH4/Mq31ne5WS+wszVYFTm5eVxww03MD09zYsvvsjk5OSMY5wGjRZFzHbp/Xrf9PS0/d0sw1mO27XweDwXLbgAXHvttWzcuJH29naee+652U+4jMx2jcw+e71eiouL+eAHP0hRUREPPfQQHR0d9tjNtR7nmM61LfNp9+tJunsFuKxtvBIC7MXgvGeuFOnuTXO/3mfeg3rO6m1u5znvVbf71+2a6jLdjtP7zXHJNC+cYqVlWUxPT6f023we6c8ejwe/308sFrPPcatD7xcEQRAEQRCENygX3DbOOxFxZWUl69evp6amBpgpsPj9fsrLy6mrq8Pj8czbeHIKDvNhth/ssxnr8zl+vuVfDDk5OdTW1rJ8+XLbe8mtDjfDyNxnjovbd/O/W7lu2y/WOPJ4PBQVFVFaWsro6OhFlXEpzMXbRhMIBKiurqa9vZ1wOExtbS1FRUWz1uEc33R1znW+KKXweC5/zvDZrqHH4yEnJwefL1XbTXevXO75f7HlXS3i1sXgdm9qnIKf83O6+1Jvd97r+pq5za9Mc8Msz+354dzn9kyanp627w3zL11f1q9fT3l5+Yy5mKkeQRAEQRAEQXgzMG9LUClFIBDA5/PNMNSUUni9XnJzc8nPz7e3mcfNZtxdbAiKLvtKcyWM03QsWLCA1atXMzw8TFtbm+ub/kyeQ5mMuHTnuOHmuXMp+P1+vF4vQ0NDl1TOlSYrK4tFixZx+vRpXnvtNRYuXMiCBQtmvf7O+e7cdrWQSUzyer1ce+21VFVVEQwGX+eWXV1cikh5Oeo2P5veKk7xJZ0A6zy2pKSEqqoqCgsLZ52fbn13evikO36ugo7zXnHi9XrZuHEjJSUleL1e1/G52u4tQRAEQRAEQbhczFu0GRsbY2xsjFgs5uqGb/6lc/NPRyahIRPpXPJnK/P1MsTSecDMdk5JSQlLly6lqamJc+fOzQg1c453pjoyiQZzHfPLNV5erxev18v4+PhlKc/kcrVRKWWLNp2dnRw/fpzS0lIKCgquqIGYyVNiPvh8PvLz82cYuXMlJyeHm2++mcLCwjl7zF2Osc/KysLv919yOZebK/GscBNMZruf0+13iqrpvGpKS0tZt24dW7ZsYdWqVQQCAYAUQXg2Lxu3Op0Cjlv7p6enXYVnt/rM8fD5fFxzzTVkZ2fbc3E2oUcQBEEQBEEQ3izMe8nvSCRCVlYWgUAg7VvYqakpOwlxuvwDVxI3Y+Zi63WGt7iFJThzNcylzHRlm98DgQB5eXmMjY0RiUTSluMW2uDxeJiampq1LXPB7Odc+5gOj8dDXl4eBQUFrn26WEyD9nIRDAZZtGgRExMTRKNRsrOzCQaDl60Ot7G8XGUvWLCA5cuXc+zYMdcwtEzX0ev1smjRIoqKimhtbSUUCs3p/rkcba+srCQSidDT0zPnczL1Jd39+5sy9s08LZkEEBNnm3U402w5dtzCoLZv3866desoKCigt7eXvr4+zp8/7/qsdmvLbH0zxRvzuZhOrEkn2Ohypqen8fl8rFmzhmg0aj/TLqfnnyAIgiAIgiBczczb08bn8xEMBgkEAq5vUn0+n/223CkmzMWodsuvMBecBsBcPGsuxnAzPXdM42IuOSDMet3GwmmIaNFmYGAgRaBxGm+mUaT3OQWb+XprZAqVulh0mbFYjPHx8csq2ujxvFy5XwKBALm5uQBEo1Gmp6cJBoMEg8HL5glyJYWD0tJStm3bxsKFC9OGzaTD7/dz3XXX8dprrxGJRC7JKJ7PuR6PhxtvvJG1a9fOq45M45jOG/A3gQ47y8vLY/Xq1SmCks7xoj/rv3TjNzU1ZYuzWsDRn01hSHtaWZaF1+tl+fLlHDhwgK997Wv86le/YufOnTPOMzGTA+s+mGQ6z9l3t3vT3K7bodsLCQGxsLCQcDjM4ODgjGTE5n8RbwRBEARBEIQ3I/O2cCcnJ/H5fPj9fvsHt/mj2xRs9H+3H+PpSPdWdjYyhQuY+zPlYHBrS6YQsHR1p2uXW9iC87Nbe2OxmGuYix4rbbA5c0Xoc7TxZhpNs4kbmbww0olOmdBzQOezmZqaIhaLzfn8uaL7PZeQnkx9yM/Pp6Kigmg0itfrZXp6mry8PHw+3xVfRehicF7PSCTC4OAg5eXl9r5M94fepq9RfX09p0+fdl2tLB2Xw2jWHnrOhLNXkktt91yFQsuyyMnJYenSpWzevNnersddXxPnve6cp6aQop9RWrgx7039fNDfly5dSktLC62trbS1tXHmzBmqqqrw+XwZ72stiDiflaawlEmINkOanAKRLjOdEJqTk8NNN93E3r17GRsbS+mPKWRfTNJ7QRAEQRAEQXgjMG/LSAs2pnu/+T8YDGJZFiMjIzO8PS41VOlyMJ9wFGd75yICzHX/bAa0x+OxjalMS9mab+ednjrT09O24JDuvPliGmnzQRtnWVlZtqeWZrYlxGerzymMmfXNBaeYp5SisLCQ6upqQqEQU1NTtlhztb7Nd7YrFosxOjrKxMREyna/309ZWRlr1qyhurqa06dPc/ToUaLRqF2ODmEbHR1NK1Dpa6YNZh0WeanebBc7vy6FS61rPvMsPz+fJUuWpAitzjL0uKarQ4uSzrmuxR7T+0+pXy+lvWbNGgYGBgiFQrbI4fV6ycnJIRQKpdRnCkfONpqefpDq5eL0qDE9AHW/3ISXqampGV6LOqn9tddeyyuvvGKLvOnqEgRBEARBEIQ3Ixcl2pjeMNow0D+28/PzycrKIhKJ2D/OTUFB/1i/3N4Kutzi4mKGh4ddw4Mu1jhzGin681z6MN+QJLMOj8djixuzhbg4jT6nEOHmYXQxzDfUxnme3+8nOzubrKysOQsszvG/XP3LNCeCwSD5+fm2YObz+WyD80rMXbc2mx4Mei5YlsXk5OSMMXOeOzk5STgcprCwEEgYwJWVldTW1lJRUUEwGGRsbMzOTxWLxZiamrLFwpKSEqLRqKsIEwwGWb58OdXV1eTn5+P3+5mammJ4eJhDhw4xNDR0UfNLKUVOTo69zLj2urmaSefBl+6a5uXlUVZWRktLywyh0OPxkJ2dTW1tLYFAgP7+fgYGBlLCCE2R1rlNf9btMdvm9XqpqqqycxRNTU3Z1zs7O5twOGy3I9Pc0l57boK927nmWJgeX6bwkuk54PP5KC0tpbm5+Yp45gmCIAiCIAjC1c68RRu/3088Hk9Zycj0dMjPzycQCNgGn/nG1+m6f7kNMr/fz44dO9i1a1fa5KlmqJZpgPv9fnw+H7FYbMYqTZrs7GyKi4spLy8nGAzS2tpKb2/vFTMunWETMPeVXZxluO1/vQ1iM/zGDIdwGq/p2mZ6H5keJJfSj0wimPYMUCqxkpQZcnKp6HtCz6n8/Hwsy6Kjo4Px8XG7XcFg0J5z+fn5eDweJiYmGBwcZHBwkJGRkZT2mN4PU1NTLFiwAJ/Px/Lly6mrqyM7O5toNMqFCxcYHBy0jzPRwpo5r/UYlJeXU1tbS3l5OdnZ2bZHhtfrpbS0lK1bt3Lw4EFGR0ddPW8y4fP5yM7OtvNluYkh80ELTEop4vF4Ro+1+ZTpts28x5weKebzLxgMUlRURHd3tz2/gsEgBQUFFBQU2KJOOBwmHA6neJLM9tx0u4/0Z6UU5eXldi4pfd3TJSt38y7Mzs6mrKyMuro6pqen6evr48KFCyl5j2bztEr3jDL7Z27XK8319fVlbKuERgmCIAiCIAhvVuYt2gSDQXt1KI3pzp+Xl4ff72doaCjFbV67umtRZ3R01E6wa/7ovhSjyufz8b73vY+DBw8yNjaWstKI1+u1jSId6jI5Ocng4CDRaJScnBxqamoYGBigv79/hoEQCASoqKigvr6edevWkZeXx9GjR9m7dy/9/f22QTgX48E04sw+m2/ITTKJBG7jpo3InJwcCgoK7DL0cu2QWFrZ5/OljNPrgdnXuRpaWkDQnhilpaWcP3/+inpiRKNRQqGQLZTk5eWlrIp2Kej7JSsri+rqapYvX87ChQuZmpri8OHDvPbaa0xPT5Odnc3ChQtZuXIltbW19r0Tj8dpb2/n7NmzNDc309fXN6MOpRR+v5/c3FwWLVrEjTfeiN/v5+jRoxw9ejRFGHJ6Pvh8vhSDXo97dXU1GzduZPny5Zw+fZoDBw7Q399PJBLB6/VSU1PDe97zHiKRCI2NjbZw46wjHT6fz/bkSyeczgUtjhQXF1NVVYXX6yUcDtPT08Pg4KCrp5Y+L13Yp95vhvn4fD4CgYAdMmpZFtFolGg0aucCco6t9uAKhUL4fD6KioooLS2lpqaG8vJyAC5cuEBTUxOTk5MzcgppDz83kcgt5Mp81ujyzWsSj8cJh8MpnpNu+P1+qqqq2Lx5M9dddx0TExN0dHTw05/+lL6+PtsLRj9nQ6HQDG+gTKF2fr/f9vjSnmR+v59gMEgkEkmZr26IaCMIgiAIgiC8WZm3aOPxeFK8UbQgoikuLsbr9TI0NGQnnAXIzc3l1ltv5aabbqKmpobGxka+/OUvzyvR6WzE43E7cazzTXNBQQHvec97qKurs708hoeHefLJJ2lsbMTn8/HBD36QxsZGdu/eTW9vb8r5S5YsYe3atQQCAX7yk58wPDzMfffdRzAYZPfu3XR1dV2W3BhmGfpteLpy3YxMU6C6+eabuemmm4hEIoTDYRoaGti7dy9TU1OsXbuW2tpannvuOUZHR6+I+OHsj2UlkipHIhFisZhrv9zasW7dOnp7e+np6SErK4sbbriBeDxOa2urPb8u99v28fFxhoaGWLt2LdPT0xQUFDAxMTEjREN74sw13415jerr61mzZg3j4+M0NjaSlZXFRz/6UT772c8SiUTYuHEjW7duxe/388gjj9Df34/H46GgoICtW7dy/fXXs3nzZh566CHb88gUCLRw9+lPf5qXX36ZF154gf7+/hltdXqEeDweQqGQ7UHh9/vZsGEDt912GwBf//rXU3JWaVGgubmZRx55hAceeIBIJMLx48cJh8MopWyxKdM4abFIh2pdbEJyn8/Hxo0b+fCHP0x5eTlFRUUMDQ3x/PPP861vfYtIJDLj+aBFtOzsbFuAjcfjTE5Ozsiz4/V6ycrKorS0lLVr11JeXk5eXh6WZdHV1UVTU5MdzmMKRPq87OxsAKqqqrjrrrvIzc2lpaWFhoYG2tvbmZiYcJ1Tzs9OUUYLI2biX1Ngmp6etq+B1+slOzub/Px82/PGvP7OeiorK7n55pupqanh4YcfpqOjg49//OPU19cTi8UYGBgAEgLre97zHh599FEmJibsUCotdjo9eLQgunTpUoqKihgdHeX06dNMTEyQl5dHRUUF4XDYfl6kC7+6mvNNCYIgCIIgCMKlMC/RRhubJSUlKX/XXXcd5eXlnDp1inXr1hEOh+nr67PDj/Ly8vjABz6Az+dj9+7dDA0N8dGPftQ1V8mlMDU1xcTEhJ1/BLDzc3z4wx/m+eef59FHH2V8fJy8vDzWrVvHJz/5ST7zmc8wPDzMrl27uPXWWyktLeWb3/ymLUxVVlZy9913c+rUKZ599lnb6HvyySf50Ic+xJEjR+jt7Z2zx0q6/rqFQZnhXG44ywgGg6xYsYKdO3dy9OhRPve5zxGPx9myZQtLlizhjjvu4KWXXuLd7343u3fvnncIy3xwiiher5fJyUk7se9s6Bwyg4ODQMLbaWBggJdeeon777+fL33pS7ZX1GyCjTZinSttmdt1nUopIpEIY2NjlJSUYFkWS5YsYWJiIiUUxOv1cvvtt5Obm8sLL7xgG65zGZdt27aRl5fH888/T09PD5ZlUVhYyDvf+U6ys7NZtGgRt912GwMDA3z3u9+1w6CmpqYYGBjgueeeY2hoiDvuuIN3vetdPProoymGt/aMeOc738mDDz7IU089xcTEhOu1NsURv99PXl6eLYh5PB7e9ra3sWXLFkZHR/nqV786Q2g1yzx//jzf+973WLt2LZOTkzQ2NpKdnc3tt9+OUopnnnmGaDQ6QzSZnp6msLAwJV/KXDGvZ0FBAW9961t5//vfz9e+9jUaGxuJRqPcdNNNfPjDH6avr48f/vCH9rm5ubksW7aM7du3s2HDBoaHh+nt7bWfIX19fezZs4fm5mZ7PLZt28Ytt9zCkiVLOHr0KHv27GFoaIj8/Hxuu+023vKWt9Da2sojjzxiJ/jVolBubi7FxcXccsstLF++nD179nD69GlGRkaIx+MpIqTpTaPzgpkirR47t/lrJgCGxL0TDAZTPCIrKytn3D/aG6ikpASfz0dnZyc+n4/bb7+d8fFxHn/8cTo7O5menubIkSPU1NRw4cIFhoeHycrKYsmSJZSVldltNUNQzaXJ9Tytr6/nrrvuorq6mi9+8Yt2+JbH4yE3N5fKykq6urrwer3289hcxtwpVIlwIwiCIAiCILzZuChPmy1btnDPPfcwNjbG+Pg4zc3NHDhwgJaWFq655ho8Hg/xeNzOR3DjjTfaLv/hcJi6ujr7jT/MTHRpkimHg0YbLvqHvWmwVFRU8Hu/93scPHiQEydO2Ll2RkdHuXDhAp2dnWzfvp2f//znHDp0iNraWqqrq9m8eTOHDh0C4O6776azs5OzZ8+meDSYxsNsIUxmrhGzn+n6pxPPZmdnzyjbNHi1IaQ/b9y4kVWrVvHcc89x5swZO9SgoaGBvLw8Nm3aRGtrK1lZWfT09NhhR87rcCmkK296etr20vJ6veTn59urFrmhzz9//nzK946ODl566SXuvPNO/uu//ove3t4Z4+gmzJhhJOlypuh9WrQpLCwkOzub+vp6zp07x8DAQIpXw69+9Sve9773kZubayfANg3pYDAIYBvkgUCA+vp6br/9dh566CFb3NReGIWFheTk5PD2t7+d1tZWDh8+7JqfaXp6mnPnzrFv3z7e/va38+ijj9r9VkpRWlpKfX09+/fv52c/+1lawcZ53bKysliwYAHnzp3DsizKy8t5y1vewtjYGD/4wQ9cw5bM621ZFk1NTdx+++20trbi9XqJRqMcOHCAL33pS5w+fZqWlhZb/NKeMcFgkNLSUqampuw5Yc7t2doNUFJSwtatW/nIRz7C5z//eZqammyvlYaGBuLxOJ/4xCf46U9/SjQaJRAIsGPHDq699lpOnjzJ008/zcjIiC1K5efns3jxYnbu3Mndd9/Ngw8+SH19PUuWLKGxsZHvf//7jIyMEIlEiMfjDA0N8fjjj1NbW8uGDRv47Gc/yxe+8AVbpCorK2PZsmUsW7aMI0eO8Nhjj9HV1WULmabAYQoveizM+Wlu0wKPMxxVf9bjG41G7fOrq6vZsGEDDQ0NMwSekpISbrnlFhYuXMg3vvENqqurKS8v58yZM3R3dxOPx/F4PCxatIiJiQmmpqbs+3nZsmX2inemN6b5jNbzffPmzWzatAmPx8O//Mu/EAqFiMVi9v5AIEBhYSHd3d14vV474bUpaJuCjSAIgiAIgiC8GZn3eqlZWVmMjIzwy1/+ki9/+ct86Utf4uGHH+all16iu7vbFhvMsKjVq1fT1NREZ2cneXl59htqp8s8pIoYcxFs9HHwa68awDaYS0pKKCgo4NixYzOSI4+OjnLo0CGqq6tRSjE+Ps7+/ftpaWnhhhtusJc2X7x4MV1dXfT19aUIAVu2bKG7u9sWcjLlacnUTyf6LXW6cAB9TEFBAe9617vIzc21jaiKigoikYhtGGuDLBqNcv78eVpbW7nhhhuoqqpKCaNwa7dSihUrVrBx40aqqqpmvQ76nHRYVmJZ6HA4zOjoqL2yUbpjdb91fhW9LRaL8corr1BcXMzSpUspLi7OeL55zdPVpduux3xycpLx8XHi8TgFBQWsWLGCzs5OhoeHU84dGxvD4/FQXFxsh72Yda1atYr6+noWLFgAJDyhtm/fzquvvsrQ0JBt2GZnZ7N06VI714nOr9Te3u4a+mFZFqFQiLa2Nvr6+lKuY0lJCatXr2bVqlW8+uqrcxJsNMFgkEWLFjE+Po7H42H9+vWMjY3R1NRkewRlQilFKBTi0KFDdHZ22td8cHCQvXv38ra3vY3y8nJ7jufl5bFz504+85nP8Ed/9EesW7fOziOU6f5weqUFAgFWr17N1q1beeSRR2hubmZiYsIWQkZHR2lpaSEcDlNaWmonAA4GgwwPD7N//346OjoYHBwkFAoRCoXo7e3l5MmTvPDCC5SXl3P77bezbt06urq6OHHiBP39/XbojvaCGhsb4/z58xw5coSJiQl27txp58Pavn0711xzDS0tLezZs4fOzk6i0egModmZfNjtOaBFQ7ck1Ga4kA5NMpPHZ2VlUVJSQmlpKSdPnkypUye6PnfuHNPT01RXV7Ny5UogETI4PT2Nz+cjNzeXzZs309XVlfL8Ky0ttQUjZ9tNcbugoICNGzdSVFTE008/zcDAgB0Wp/uhn999fX226OlWrgg2giAIgiAIwpuZeYs2Y2NjDA0NcebMGfbu3cuhQ4c4ffo0vb299o96vdywz+dj9erVhMNhhoaGgEQeh+rqao4ePZrWGJ3NyE7bmWQokfYeKSgooKSkhP7+foaHh1Pq08b/wMAA5eXl9lvb9vZ2uru7KSoqspOYFhYW2slFtdDh8/nYtGkTTU1NjI6OzmjLbPla3PI76M/6jbTz7bnTWPX7/axcuZLKykr8fj/XXnstwWCQ5uZmwuHwjHHs7++ns7OTpUuXMjo6SmVlpR2m5BaqtWDBArZv385b3/pWWwybK+mu3fT0NMPDwwwNDVFTU5P2/HTjpw2//v5+GhsbWblyJUuWLLGXRk9Xf7p55laHKdyEQiGWLVtmJ1d1rpQzNTXF0NBQSliIWZ/Oy5GXl2d7sqxcuZLjx4/bZfl8PsrLy9m0aROHDh0iEAiQlZXF+Pg4Y2NjMwxTM7dKIBCwl2vW9erkxjk5ObZwMhe091hRUZEtTi1btoyRkRHa2trSrr7kHMt4PM6xY8fsMBp9r+3du5fy8nIqKyvJycmxx6+3t5fjx4/boUk6x5LbnNT3uNO7YunSpdTW1jI4OMjLL79sJ9g2hYuxsTE6OzspKCiwBSO90t34+HhK/hp9zujoKOfOnePQoUPccssthMNh2tvbU5KP6+ujRZTx8XE6Ozs5dOgQN910E0VFRWzZssVedamtrY3Ozk7Gx8dTcvdkEn3NOeW8r908ytzGTwsz2nNGr0Bm1u3xeIhEInR1ddmhrVqIDwQCtti1adMmAFu01t4+OqGwz+dLEZHM0C4tsi1YsACPx0Nra2tKviPzPjfzG5l9NO8BQRAEQRAEQXgzMy/RxrIs+vr67LwD2hNBGyv6bbM2Nr1eL2vWrKGrq4t4PM7ChQtZuHAh8XjcDnkxy3aKGvPF5/OleJcUFRVRUlJCW1uba4iRXtFKh7AARCIR+vr66Ovro76+PuWNvN/vtwWbsrIyioqKaG5utsNXnH2YzxtgnSy0pKSE8vJySkpKUjw33DxilFLk5eXZx65YsQKApqamGSu3QOJN+ejoKHl5eZw5c4a6ujqysrJmtNvj8VBSUsLmzZvZsWMHGzZssFehMstzYzaxTc+h3t5elixZknGMMu2bnp7m0KFD5ObmsmTJEntlnEvB2e7JyUn6+/u54YYbGB0dJRwOp4ST6XOGhoZYuHAhJSUlroZ3MBi0RSWfz0dFRYW93LYe62XLllFaWsq+ffts4VEbrG4opSgqKqKurm6Gx1p5eTk1NTW2QDaXvuoyA4EAeXl5tnBSWFjI+Ph4SojWbMKCZSUS8prLkVtWImyqu7ubyspK2/MoEolw5MgRnnjiCY4ePWoLwM5+FxQUsHTpUtatW8fatWtZtWoVtbW19spNq1evprCwkEOHDtHd3T1DAICEmNTZ2Ulubi5er5fKykri8Ti9vb12cnJn3yzLYmJigpdeeom6ujq6uroYHBx0TVCsn4GWZTE2NsapU6eoq6tjxYoVrFq1CstKLOmuV1tKJ9hk8iBxCjTprqUTM+Ro2bJlFBcXc+LECdv7xhSA4vE44+PjtqDb09NDbm4upaWllJaWUldXx/XXX8/Ro0cZGBiwn7nxeJxQKERpaSnZ2dm2SGR6Dpp9MMUZ879uy+TkJKOjo/bz2SnWzXcMBEEQBEEQBOGNyLw9bXSyVf3WVb/ZNX9M67wU09PTLFiwAMuyKCoq4oYbbqCiooJ9+/bNSEY6FzL9MNdeDJFIxP6ek5NDfn6+65LIkAgTWLRokZ1fR/elq6uLw4cPs2nTJvsNellZGQUFBbbQs23bNhobGxkcHHTN8zGbV41pbOi219TU2ElON2zYYIckmXkszHJ0W+LxODk5Ofj9fiYmJlw9f8z2RKNRjh49yqpVq2wDVhtAehni66+/nne+851UVlYSiUTIyclJGetLoa+vj7a2NhYvXpzyRt7JbPWEQiGOHTtmJ5XOlLD5YohGo7S1tfGOd7yDw4cPp4gQZht1klndF7PdgUBghtCmDWCfz0dOTg7r1q1jzZo17Nu3j9bWVkZHR+28Km590klaly9fzubNm2d4wOhcSIFAwE6E68Q5tkr9OgFtaWmp3e6RkREAO1QwUzidiXPOaw+UV155hbKyMqqrq23PC53/JDc3l7GxsRTPIU1dXR333nsvf/AHf8B9993H+9//fnbu3ElOTg5FRUWUlZUxMTHB0aNH7fqd7YnFYvT39+Pz+cjKymL58uV0dnayZ88exsfH7ba6iQs6d41ektwUlUwPEi0+eDweioqKmJiYYMeOHYyMjHD+/Hk6Ojro7++f8fwzRQtn+93EYLOds10Ty7JsL8iKigpqa2uxLIvDhw+n5MoxRSct8GhPI4/Hw4oVK9ixYwd33HEHxcXF/OQnP7ETKOvre+7cOaqrq6msrLSXqNfjo0OfPB4P4+Pjdr6h7Oxs177o5cjLysrIzc0lKysLIEXsmmvyd0EQBEEQBEF4ozLvRMT6Dazf77cNDUj8eM7JySErKysl2eTY2Bj19fXs2LGDWCzGoUOHUpJfOsn05jSTce/1eqmoqGBsbCwlmWw4HCY/P3/GOXrp5GuuuYZdu3alCC/hcJju7m6WL18OwL59+9iwYQPj4+NEo1GWLFnCe9/7Xv7qr/4qxdhzC+dw9kUfZ+7z+/0sXbqURx55hJMnT3L27FkCgYAd9uM0IvVn7aXR39+P1+ult7fX9qxwvqm3LIvi4mKqqqpoa2tj//79/Mmf/AmLFi1ieHiYsbExO5fKAw88wIYNG/jCF75AeXk5tbW1LFq0KOO1mM+b7lAoxMjICDU1NSxcuHCG15Um3RwxxYCDBw+yaNEiVqxYwb59+1I8S9J5K8yGPk/nBlqzZg0nT560Vw1z9rW4uJjh4WHC4fCMkL+amhpGR0ftfEqRSITXXnuNP/7jP+Yb3/iGnS+opaWF5557DkgIo4FAgMrKSkpKSujt7U3pR3l5OXfccQcrV64kHA4zPj6eEhbT1tZGd3c3119//Zz77/f7uf7663n/+9/PHXfcQVZWFseOHaOpqYlbb70Vn89Ha2sroVAoJe+I6fWRLmG2uf306dPccsstVFRUkJ2dzfj4uD1eFRUVtLS02Pl0TIO8p6eHH/3oR0QiEdsLSHv2veUtb2FoaIgLFy7MWNnKbKNepru7u5vFixfj9/uZnJy0k+m6tV8/W3QoYV5enu2Vo9vtTC4eCASoqamxE0Tfe++9nDp1Ckh4+5w+fTplfMyyzPKcXjhO4df5PHDifP6Mj49z7733cvDgQfbt25cSQmbWo73XCgsLGRwcxOv1curUKd773vfy7ne/m//4j//gn//5nwmFQrb3i2VZRKNRzpw5w49//GM+8YlPcPjwYRoaGmhtbbXz3ujn7PT0NKFQiKqqKm699VaefPLJlFBNpRRDQ0M0NTXx6U9/mpycHA4ePMj58+cJhUIpeYAEQRAEQRAE4c3MvEWbqakpOxxDizN6KVyv18vw8LC9ig7AoUOH+PnPf86uXbt4+OGH2b9/P5AwbPRqT3r1kWAwSE5ODh6Px056OlcxwOv1snTpUjo7O20Dob+/n6amJjvprmlclZaWsnr1agoKCti7dy+QCKeanJy03/yPjo5SUVHBs88+y4oVK/jIRz7CH/7hH2JZFo899pgdhmEmTTUNOWeSUNOw1f+9Xi/V1dXcd999fPWrX+Wpp55ibGyM0tJStm/fzk033eRqGOvxCgQCDA4OkpOTY393Eyv8fj/XXHMNW7Zs4cc//jFjY2M8+OCDPPDAA3R1dREKhVi4cCFLly7liSee4O/+7u/svDNve9vbuPPOO/nP//xP29ugoKCA4uJicnJyiMVinD9/3tXDx4nuQ0dHB0899RQPPPAAf/mXf5mSK0aj+1FSUsLExERKwlO9X+ff8Pv91NTUMDw8PCN8ZL6YYoNe6UrPUT3ntTDo8/m48cYb+dGPfkR3d/eMsgKBAG1tbfT399seG1/5yld4+OGH+da3vkVraytPPfUUu3fvtoWKWCzGd7/7XX7nd36H6upqdu3axcjICFVVVezcuROfz8f+/fs5ceKE7S1lcurUKX7xi1+watUqvvjFL/LFL36RM2fOpKzU5QyHW758OX6/n/3799PX18c//dM/MTw8zL59+8jLy+Otb30rd955J7t27eKFF16gq6srxbNE/+n7zFy62qzLXDZde62Y+ZtGRkYIh8MzPCh6enpcRVCdgPvChQu0tLTMWHHJPFaHnPX29jIxMWE/b7QIo4VmXYf21qurq+OTn/wkP/rRj1iwYAErV660lwPX80I/AwsLC9m0aRPbtm2jr6+Pffv2sWPHDtavX08oFKK9vX2GWOM2T00Pq7k+B51LamuUUnYC8MLCQvr6+mYkuNZtCAaDbNiwgQ984AMcOHCALVu2UFNTg2VZfPvb36awsJDrr7+ez3/+8/ziF7/g6NGjDA4OEo1GmZqaIhQK8fjjj/Piiy9SX1/P9ddfz6233sr4+Ditra2cPXuWzs5OYrEYu3btwu/386lPfYrt27fz4osv0t7eTiwWs0M/Fy1aRHt7OxMTE/zZn/0ZWVlZNDc3c/DgQQ4ePEhvb6/dDzePR0EQBEEQBEF4ozNv0QYgJyeHnJwcfD6fbQjq3A8vvPACk5OT+Hw+qquruf/++2lsbKS2tpZ7772X+vp6+vr6mJiYoLCwkIqKCgoLC4nFYnR3d3Py5EkaGhpcDRXtWu806LThU1xcTE9PD1NTU9TU1BAMBhkdHbVX+NFhLMuXL2f79u2sXbuWH/zgB7bBoT0bQqEQw8PD9PT0kJ2dTV9fHz/84Q/52Mc+xl133UUsFuNjH/sYO3bsoK2tzV4FSOdg0G+FzeSt6d6Ea8Oxp6eHp59+2vZk0Ibk0NBQSkJV02ugqKjIzq+hl5PWRqj5Jtrr9bJ27VoqKyvp7u62jdsXX3yRiYkJ6uvrycrK4siRIxw/fpz29nZGR0eZnp6ms7OThoYGKioq+NznPkdJSQkej4doNGqvfBOLxWhububBBx90vWZOLwzLSqzcdfDgQW6//XbuueceXnjhBQYGBmaspBMMBvn93/99Ghoa7HmhjVJ9XDgcZmJiwnUVqfmi2+vxeCgvL+euu+7iscce47bbbrO9QPQ1CAQC3HjjjQQCAfr6+lJCkbSgU1JSYs8N3fdIJEJ7ezsVFRUsXLiQ973vfWzbto0jR45w7Ngx+vv7aWlp4dlnn2Xp0qXceuutdg6V3bt309HRwcjICBUVFUxMTNhJvjXxeJyGhgb+9m//lptvvpm/+Iu/oKenh4GBAcbHx+nr6+PYsWOcOHHCnls9PT22YFJfX297kEWjUfbs2UNTUxMrVqxg6dKl3H///YyMjNjedvF4nGg0SiQSsYWDn//855w7dy7FC0gnqvX5fExNTdnL0WsBTAs6Om+QOW+c4ULm58nJSYaGhhgdHU3xSHEeNz09zcDAAD09PUxMTNDd3c2WLVtYvXo1zz//vJ04WV+39evXs3nzZqqrq9m/fz979uwhLy+Pu+++my1bttDQ0EBHRwfxeJzS0lLWr19PRUUFoVCIl19+mVOnTjE5OcnPfvYzPv3pTzM2NmYL2n6/37Vveu5lEmn0/HPeB2a/zfP9fj9r166lrKyMiooK6urqaGpq4sKFC/bxOtfSjh07WLp0Kc8++yy7d+9OEYG0V+CePXvsufu7v/u7HDt2jJMnT9Lb2wskhLn29nYGBgbw+/3k5eWxYMECqqurueaaa+wkx729vTz55JO89tprbNq0iY0bN7J582b72dvX10dzczMvv/wyo6OjvPTSS5SVlbFo0SKWL1/O1q1b+fGPf5yyEqEgCIIgCIIgvNmYt2hjWZYdkuH8oRyLxTh37pz9lnT16tU0NDTw2GOPUVxcTGFhIfn5+axatco2zoeHh2lubmZoaIjBwUH7LTi4hxeZuWc02oApKCigsbHRzoVQVVXFtm3bbDFH57DxeDwMDw/zzDPP2MdDYiUUHSK0devWlGWaq6qq6Orq4gc/+AHnzp0DsMUmn89ne2WEw2HbgDfFBxPzezweZ2JigoGBAUZHR+0x1WEY+k2yOQ46hKG8vJz9+/fbXg6nTp3Csiw7BEqPy7Jly9i4cSOxWIyDBw/auSRCoRCvvvoqbW1t+Hw+RkZG6OvrSwkxmZyc5MyZM0QiEcrKysjPz7e9kczlis08JG7hG+b80XNlaGiIJ554go0bN/Lud7+bSCRCLBazr/PZs2d57bXXqKmp4cyZM8Cvw9q2bt3KsWPHGBkZITs7m9zc3LTJZOeDKdisXr0av9/P97//fXbu3MnmzZsJBAL09/eTk5PDxo0bueeee9i1a5edbNvsb3l5OWNjY4yNjRGLxfB4POTk5LB+/XoOHDhAR0eHLUxpD6l169YRi8W4cOEC/f39DA4OEgwGbe+Vnp4eO6wlFArR3d09437Q9+ipU6cYGRnh+PHj9rLpOlmsNpz18XplLD03zPxUOrxrYGCApqYmysrK7HmvhRW97LVeMW7lypWMjY3R0tKSck1WrFhhe7GZQpb2tnGKdm5zx0lhYaEtIJnhh6YXi5mvSa8E19jYCMCaNWv42Mc+Zof7+P1+YrEYY2NjtLa2cvDgQc6ePWuHwL3yyitUV1ezcOFC1qxZY68yNjIyQnt7O319fXR1dTE0NIRSiqamJvbs2cPmzZvZunWrfc/rhMtmn93udfN/ppAoZ+ik1+uluLiYTZs2sWHDBnbv3k1xcTELFy7kAx/4AOfOnaOrq4uxsTGWLFlCbW0tsViMY8eOcejQIYaHh1M8yyBx/4VCIQYGBujq6iIvL4/BwUFbGNdt1c9CSKw4GAqF6Onpwefz2QKkXr1vfHycgYEBCgsL8Xq99jzU944OOxwaGqK/v5+Ojg7OnTtHTU2N/Yy+FM86QRAEQRAEQbiambdoE41GOXDgAAMDA3ZuC9NYCIfD9kpIAwMDnD9/nrNnz5KdnU1VVRVVVVUUFRXZOUO6u7tpb29nZGTE9ngxDRa3N+zOxK4670RRUREXLlwgFosxOjrK4OAg4XCYvLw8CgsL7ZCsnp4eTpw4QWNjox1OAzA8PGwbbQsWLODcuXMMDw9TUlKCz+fj2LFjdHd309rait/vp6KigkAggN/vZ2pqirGxMdtbQYd4uIU/mP2Kx+MMDQ3ZOS/09tzcXAKBAENDQ6791slUjxw5YocHnDhxgqKiIrKysmxPkMWLF3PdddcBcO7cOVpaWlLGVHsowMwcMrqdw8PDhEIh20tCew+ZHkCZvAMyCTcNDQ1kZ2ezcOHClOWCIWGMQyLMbWxszD53enqaoqIi7rzzTsLhMIsXLwagoaHBtf754PP57HFbtWoVjY2NHD58mJKSEioqKti6dSvj4+O2kNfa2srevXvtJeVNAoEAx44ds/fp8ZyYmGDfvn2cO3eOqakpgsGgHW6ml0GORCKMjIykCHnOcQyHw5w/f95eAcxEJ4Y9e/YsLS0t9rUDbM8f8xxzOXtnQlztHRSNRunr6+PMmTMp3h66Pu3NFolE7ATkzntYh7t0d3fbHnO6HHNFJl23E7c8MIODg1hWYhlpZ04bfYx5X+t6u7q6gMQ8XL9+vb06XDQaZXR01A7n0R5z+u/48eP09fVRW1tLZWUlU1NT9opzfX199ipj+poPDw/zy1/+kvHxcerq6qiuruaGG25gYGCAsbEx21ttdHSUnp4e29suE+Yzwfk8tCyLYDBIWVkZdXV11NXV0dvby4EDB/D7/WzZsoVVq1axbNkyampqGBgYID8/n0gkwpkzZzhx4gTd3d0p3n3mNYnFYoyMjDAyMuKaa8fMrwTY4u7IyIidO8gUBcPhMM3NzSlhic6cSeaxY2NjDAwM0NHRQVlZmeS2EQRBEARBEN7UXFQiYp0w1W1ZadPQ379/f4qhOjw8zMmTJ+3jTfd+pzjjlvDVWZdGL8utw4z02+umpiba2tooLCy038hOTEzQ399vv70122xZFoODg4RCIc6fP09hYSEDAwNUVFTQ1NTE0NCQbVDppYLN880lgN3Gxg2dx+PIkSMpx+nws56eHnub2U69HPWFCxfsetva2giFQnY4QlFREZs2baKmpobnnnuOkydPMjk5OcMLwem15CayxONx2yPJNBT1sXPtr7NcHX7j9/vJyckhOzubYDBoCwyRSIRDhw6l5FAJhUIcP36cj3/842RnZxOPxzl27BgdHR1zqjcTeXl5FBcXU1tbS05ODk888QTj4+Ps2rWL2267jeXLl1NUVEQ8Hqe5uZnvfOc7jIyMuPY5FArR0NCQ4lEyMTGREv6nBRHtpQGpRqs5Vk7MFXgyoQ1lM6eNxrzeWoDRiXnTeUw5l6o2y1JKcfLkSXJzc+0QK12Gx+MhFovZooC5z7ISS6drMclNpDLrMcfk8OHDxONxO0eO23gppfD7/WRnZ6csLd3R0UFnZycvv/yyvax9NBq181qZzybd50gkQltbG+3t7bZQZebwcQpPsViMpqYmurq6WLRoERs2bGDlypXE43EGBweBhGjW2dlJJBKxV3rS5zv7m+67+TkYDFJaWkpVVRU9PT0899xz9sp6fX19vPbaa2zYsIHq6mri8ThHjhzhxIkTDA0N2TllnNfBrMdcCcr5/DDbbop6Pp/PFiCdq6KZ803Pf12+Hl9zrmovHZ3rSLxsBEEQBEEQhDcraq4GNoDH47F0GMKcCk/+kNY/7E2RxvyBnqauOb9BDQaD1NbW8o53vIOvfOUrKUsg6x/0pqFg7jMNhHSGaFZWlm2omh4yuj9Ow1f3ca5j5BR5PB4Pd955JzU1NTQ0NHD48GHX89zq0kuHX3fdddTV1dHY2MjLL79sh2s5PRXmc/3d2qDLuRyYxtds4+fxeMjOzmbBggXEYjFCoZDrUtFzRV/HtWvXsnHjRqampjh69ChnzpxJGTefz2fPI3MlHKfwOFcyiZMX0/75Hm/mK9HLiN9222189atfTevtYZ6Tbr+uw008cbYDEnmXtm3bxpkzZ+ykzekw7xlt1Ovv6dqclZVFZWUl99xzD1//+tdneHPoNujQHmcf3Tx89DlmCJEz+bgeZ6fAoZ8pOiwMUj2gnJ5O6cbXbUz1Pqe4qoVQs51ObylzXujxdatLt90p7Hm93pRnvXm+UorJyUk755auR6lEomQtujlXzTLH0qzLPFaHVckS4IIgCIIgCMIbmAbLsrY4N15UThtTCHEzrvR+/aNch9GkW1HGjdmWe9bGgfbWaG5uThFsnIaMM4GxaQyYxolbfXq5Zmd5ptGoj9XGoGn0uQkzZvluY6GXKdfJQtO1zVlmNBrl/Pnzdk4b0ytCt1OPg9Mgmw9z7d9cSechkY7p6Wk758XlQM+H06dP09zcbOfc0G0yjVV9vO6zXnFovmiDdT4i32ztn01QcR5v1qsFCL2kdqbyMgl2mfqi7xvzGaLFln379s2hp6n3jFMoyUQ8HufEiRMAdjJkc/6azye3Z4VeIcqc9+aqWfpcfY7zPtMinw4FsyxrRkiYOU66HlPY0H0222Xe325jYbbfTQxy1m3OR9MDxixP990pzJh91G3T11kLPZD6/Nb3kHk99PGmqGTOHZ3/SAtFFyuaCoIgCIIgCMLVzkWtHjXbG3bzODM5qymUpDPyZxMA3IxN02U+XdtmMyRNA8lNzHC+aTfLMw15wH6TbBr78zHKtVEXDodTViQy68hkmJsGjHm807jTxpL55toNNw8Y5/inO3euHiCXGt5wOYQjLew539ZrozCdyHWxxqJbUm0n8/VmytT/2a6FNpz1Kkpmeena4QxNyVS/NtjdQm/MNl4O49spYExOTtLd3W0v0+30Qkl3r+vv+rngtkqRvrdMAUP31xRGzHvIeT+a5ehjNG7hcqbgZwpYznaZbTFX5TL367nt9DA0hRnTK8Yp1prPTS28mHXpY3Xiai0kp3vOmuNi3h9Ogc4Usy71+SEIgiAIgiAIVysXJdqkw/njezYjOlMYRbry0/04zyQaZNpv1p/O60EbgE4hxGlU6P9uhp2b0evcpusJBAJAQoSai7t/ulAOZ7vcxuByjH0mLrcxlU48cOvDfAUPpxGcbhzdPl+s0PB6egfM5VrolY0y4ZwL8xWUMl3DyzUemYSV2epx9s8tQbfzu9v9oesxw6ecoonz+WE+N2fzaMz0bHW2xSkiK6VsEc05v511uj2n3YQf/d8UgvWx+llpel86PS/1fh12ZXrbaNHJ2V4tGEkyYkEQBEEQBOHNyrxFm3SeAW4GC6S6tOvtbgbfXAzs+QgA6QxsZ3mmwZDu+Ezf3Qx6MxFnuna7rbakv3u9Xjs/y1zEidk8XTJtm6uoNdsxrxfON/zOfZdadqbt6eq7UuMyX9HpUpmammJyctJVtMkkWM2VuRw7m+fRpdQ127zXAoDb/ZTuHjPno9vxmUTadNvSCb5u7Z6r4Krb6fRumW2cMl2PdN5K5vPUKdxAqieg2T+niGXW4SYciVAjCIIgCIIg/DZwUZ42s4kn5o9up9u9c7+bsHEpRttsRoabh4Rbf5xvnWdrvz7WbXsm3Mqampqit7d3zp4HlyJezPfYy+05czFlzvWc+c6jTOElvw3E43Gi0SiRSGRW8eRSxuY3Oa5Og3+2e9Y51zJ5vjjPM+tyChmZws2com8m5ioEuWGG52V6BprClFPodhO5zDxPZlt0SJTbmJr1OMPXnG1xa6eERwmCIAiCIAhvVi7K00Yz1zfubvvc8jRcCs433m51Otuuj8m0Uko648JZt8bNiHHzDsn0ljgajdLW1jaHXmfmchszcxHr5lvfxbbv9TT8L5f3x3y4HKLIfMc2Ho8zPj5Odnb2nPp8uefXb0LMcRMknOGQGuc9bD4f3EKfNHpfJqHIzVvHWY557sXMSS2cuInXZohROq9IZzvcPArN55t5rs514/f7U8bbeb5Zjr4GZpJis35nO0W4EQRBEARBEN6MXNTqUfPxIkmXkyWT90267ZneCps/9NPhXEo23Vtb0wgwz9FvjzOtbJVOjJrPuE1NTTE0NJTxmLkYzJdqxKTzIsrUjosVDObSnyv1Rn22ctN5W10JwcI0XDMdOxcDf77EYjGGh4cZHx9/04eeuIX16Ps93UpykPoMcRNnM3n56WPSMdszIl04kls5mTAFKdO7T9fhHBPzv3NeOFfIMsM+M3nFmM9ZXa4WjpwJk93EIeezWVaPEgRBEARBEN6sqPn80FVK9QEXrlxzBEEQBEEQBEEQBEEQfutYbFlWmXPjvEQbQRAEQRAEQRAEQRAE4fUhfRyAIAiCIAiCIAiCIAiC8BtDRBtBEARBEARBEARBEISrEBFtBEEQBEEQBEEQBEEQrkJEtBEEQRAEQRAEQRAEQbgKEdFGEARBEARBEARBEAThKkREG0EQBEEQBEEQBEEQhKsQEW0EQRAEQRAEQRAEQRCuQkS0EQRBEARBEARBEARBuAoR0UYQBEEQBEEQBEEQBOEq5P8HWVjnahOypUIAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -315,16 +308,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "('Boyis cheed iitrincy- tarisaing one', 0.3990435302257538)"
+       "('he rove fron his breakfait-nook bench<eos>', 0.6715805530548096)"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/src/tasks/create_emnist_lines_datasets.sh b/src/tasks/create_emnist_lines_datasets.sh
index 0940549..6416277 100755
--- a/src/tasks/create_emnist_lines_datasets.sh
+++ b/src/tasks/create_emnist_lines_datasets.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/fish
 command="python text_recognizer/datasets/emnist_lines_dataset.py --max_length 34 --min_overlap 0.0 --max_overlap 0.33 --num_train 100000 --num_test 10000"
 echo $command
 eval $command
diff --git a/src/tasks/create_iam_paragraphs.sh b/src/tasks/create_iam_paragraphs.sh
index 9063d44..fa2bfb0 100755
--- a/src/tasks/create_iam_paragraphs.sh
+++ b/src/tasks/create_iam_paragraphs.sh
@@ -1,2 +1,2 @@
-#!/bin/bash
+#!/usr/bin/fish
 poetry run create-iam-paragraphs
diff --git a/src/tasks/download_emnist.sh b/src/tasks/download_emnist.sh
index d142324..18c8e29 100755
--- a/src/tasks/download_emnist.sh
+++ b/src/tasks/download_emnist.sh
@@ -1,3 +1,3 @@
-#!/bin/bash
+#!/usr/bin/fish
 poetry run download-emnist
 poetry run create-emnist-support-files
diff --git a/src/tasks/download_iam.sh b/src/tasks/download_iam.sh
index 4bf011c..e3cf76b 100755
--- a/src/tasks/download_iam.sh
+++ b/src/tasks/download_iam.sh
@@ -1,2 +1,2 @@
-#!/bin/bash
+#!/usr/bin/fish
 poetry run download-iam
diff --git a/src/tasks/prepare_experiments.sh b/src/tasks/prepare_experiments.sh
index f2787b3..95a538f 100755
--- a/src/tasks/prepare_experiments.sh
+++ b/src/tasks/prepare_experiments.sh
@@ -1,3 +1,3 @@
-#!/bin/bash
+#!/usr/bin/fish
 experiments_filename=${1:-training/experiments/sample_experiment.yml}
 poetry run prepare-experiments --experiments_filename $experiments_filename
diff --git a/src/tasks/test_functionality.sh b/src/tasks/test_functionality.sh
index cd7eb15..5ccf0cd 100755
--- a/src/tasks/test_functionality.sh
+++ b/src/tasks/test_functionality.sh
@@ -1,2 +1,2 @@
-#!/bin/bash
+#!/usr/bin/fish
 pytest -s -q text_recognizer
diff --git a/src/tasks/train.sh b/src/tasks/train.sh
index 71a68be..60cbd23 100755
--- a/src/tasks/train.sh
+++ b/src/tasks/train.sh
@@ -1,5 +1,6 @@
 #!/bin/bash
 
+
 # Add checkpoint and resume experiment
 usage() {
     cat << EOF
@@ -45,7 +46,7 @@ then
 fi
 
 experiments_filename="training/experiments/${experiment_config}"
-train_command=$(./tasks/prepare_experiments.sh $experiments_filename)
+train_command=$(bash tasks/prepare_experiments.sh $experiments_filename)
 
 if [ ${checkpoint} ];
 then
diff --git a/src/text_recognizer/datasets/dataset.py b/src/text_recognizer/datasets/dataset.py
index 95063bc..e794605 100644
--- a/src/text_recognizer/datasets/dataset.py
+++ b/src/text_recognizer/datasets/dataset.py
@@ -22,6 +22,7 @@ class Dataset(data.Dataset):
         init_token: Optional[str] = None,
         pad_token: Optional[str] = None,
         eos_token: Optional[str] = None,
+        lower: bool = False,
     ) -> None:
         """Initialization of Dataset class.
 
@@ -33,6 +34,7 @@ class Dataset(data.Dataset):
             init_token (Optional[str]): String representing the start of sequence token. Defaults to None.
             pad_token (Optional[str]): String representing the pad token. Defaults to None.
             eos_token (Optional[str]): String representing the end of sequence token. Defaults to None.
+            lower (bool): Only use lower case letters. Defaults to False.
 
         Raises:
             ValueError: If subsample_fraction is not None and outside the range (0, 1).
@@ -47,7 +49,7 @@ class Dataset(data.Dataset):
         self.subsample_fraction = subsample_fraction
 
         self._mapper = EmnistMapper(
-            init_token=init_token, eos_token=eos_token, pad_token=pad_token
+            init_token=init_token, eos_token=eos_token, pad_token=pad_token, lower=lower
         )
         self._input_shape = self._mapper.input_shape
         self._output_shape = self._mapper._num_classes
diff --git a/src/text_recognizer/datasets/emnist_lines_dataset.py b/src/text_recognizer/datasets/emnist_lines_dataset.py
index eddf341..1992446 100644
--- a/src/text_recognizer/datasets/emnist_lines_dataset.py
+++ b/src/text_recognizer/datasets/emnist_lines_dataset.py
@@ -44,6 +44,7 @@ class EmnistLinesDataset(Dataset):
         init_token: Optional[str] = None,
         pad_token: Optional[str] = None,
         eos_token: Optional[str] = None,
+        lower: bool = False,
     ) -> None:
         """Set attributes and loads the dataset.
 
@@ -60,6 +61,7 @@ class EmnistLinesDataset(Dataset):
             init_token (Optional[str]): String representing the start of sequence token. Defaults to None.
             pad_token (Optional[str]): String representing the pad token. Defaults to None.
             eos_token (Optional[str]): String representing the end of sequence token. Defaults to None.
+            lower (bool): If True, convert uppercase letters to lowercase. Otherwise, use both upper and lowercase.
 
         """
         self.pad_token = "_" if pad_token is None else pad_token
@@ -72,6 +74,7 @@ class EmnistLinesDataset(Dataset):
             init_token=init_token,
             pad_token=self.pad_token,
             eos_token=eos_token,
+            lower=lower,
         )
 
         # Extract dataset information.
diff --git a/src/text_recognizer/datasets/iam_lines_dataset.py b/src/text_recognizer/datasets/iam_lines_dataset.py
index 5ae142c..1cb84bd 100644
--- a/src/text_recognizer/datasets/iam_lines_dataset.py
+++ b/src/text_recognizer/datasets/iam_lines_dataset.py
@@ -35,6 +35,7 @@ class IamLinesDataset(Dataset):
         init_token: Optional[str] = None,
         pad_token: Optional[str] = None,
         eos_token: Optional[str] = None,
+        lower: bool = False,
     ) -> None:
         self.pad_token = "_" if pad_token is None else pad_token
 
@@ -46,6 +47,7 @@ class IamLinesDataset(Dataset):
             init_token=init_token,
             pad_token=pad_token,
             eos_token=eos_token,
+            lower=lower,
         )
 
     @property
diff --git a/src/text_recognizer/datasets/transforms.py b/src/text_recognizer/datasets/transforms.py
index 016ec80..8956b01 100644
--- a/src/text_recognizer/datasets/transforms.py
+++ b/src/text_recognizer/datasets/transforms.py
@@ -93,3 +93,12 @@ class Squeeze:
     def __call__(self, x: Tensor) -> Tensor:
         """Removes first dim."""
         return x.squeeze(0)
+
+
+class ToLower:
+    """Converts target to lower case."""
+
+    def __call__(self, target: Tensor) -> Tensor:
+        """Corrects index value in target tensor."""
+        device = target.device
+        return torch.stack([x - 26 if x > 35 else x for x in target]).to(device)
diff --git a/src/text_recognizer/datasets/util.py b/src/text_recognizer/datasets/util.py
index bf5e772..da87756 100644
--- a/src/text_recognizer/datasets/util.py
+++ b/src/text_recognizer/datasets/util.py
@@ -1,17 +1,14 @@
 """Util functions for datasets."""
 import hashlib
-import importlib
 import json
 import os
 from pathlib import Path
 import string
-from typing import Callable, Dict, List, Optional, Type, Union
-from urllib.request import urlopen, urlretrieve
+from typing import Dict, List, Optional, Union
+from urllib.request import urlretrieve
 
-import cv2
 from loguru import logger
 import numpy as np
-from PIL import Image
 import torch
 from torch import Tensor
 from torchvision.datasets import EMNIST
@@ -50,11 +47,13 @@ class EmnistMapper:
         pad_token: str,
         init_token: Optional[str] = None,
         eos_token: Optional[str] = None,
+        lower: bool = False,
     ) -> None:
         """Loads the emnist essentials file with the mapping and input shape."""
         self.init_token = init_token
         self.pad_token = pad_token
         self.eos_token = eos_token
+        self.lower = lower
 
         self.essentials = self._load_emnist_essentials()
         # Load dataset information.
@@ -120,6 +119,12 @@ class EmnistMapper:
     def _augment_emnist_mapping(self) -> None:
         """Augment the mapping with extra symbols."""
         # Extra symbols in IAM dataset
+        if self.lower:
+            self._mapping = {
+                k: str(v)
+                for k, v in enumerate(list(range(10)) + list(string.ascii_lowercase))
+            }
+
         extra_symbols = [
             " ",
             "!",
diff --git a/src/text_recognizer/models/__init__.py b/src/text_recognizer/models/__init__.py
index a645cec..eb5dbce 100644
--- a/src/text_recognizer/models/__init__.py
+++ b/src/text_recognizer/models/__init__.py
@@ -2,12 +2,14 @@
 from .base import Model
 from .character_model import CharacterModel
 from .crnn_model import CRNNModel
+from .ctc_transformer_model import CTCTransformerModel
 from .segmentation_model import SegmentationModel
 from .transformer_model import TransformerModel
 
 __all__ = [
     "CharacterModel",
     "CRNNModel",
+    "CTCTransformerModel",
     "Model",
     "SegmentationModel",
     "TransformerModel",
diff --git a/src/text_recognizer/models/ctc_transformer_model.py b/src/text_recognizer/models/ctc_transformer_model.py
new file mode 100644
index 0000000..25925f2
--- /dev/null
+++ b/src/text_recognizer/models/ctc_transformer_model.py
@@ -0,0 +1,120 @@
+"""Defines the CTC Transformer Model class."""
+from typing import Callable, Dict, Optional, Tuple, Type, Union
+
+import numpy as np
+import torch
+from torch import nn
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchvision.transforms import ToTensor
+
+from text_recognizer.datasets import EmnistMapper
+from text_recognizer.models.base import Model
+from text_recognizer.networks import greedy_decoder
+
+
+class CTCTransformerModel(Model):
+    """Model for predicting a sequence of characters from an image of a text line."""
+
+    def __init__(
+        self,
+        network_fn: Type[nn.Module],
+        dataset: Type[Dataset],
+        network_args: Optional[Dict] = None,
+        dataset_args: Optional[Dict] = None,
+        metrics: Optional[Dict] = None,
+        criterion: Optional[Callable] = None,
+        criterion_args: Optional[Dict] = None,
+        optimizer: Optional[Callable] = None,
+        optimizer_args: Optional[Dict] = None,
+        lr_scheduler: Optional[Callable] = None,
+        lr_scheduler_args: Optional[Dict] = None,
+        swa_args: Optional[Dict] = None,
+        device: Optional[str] = None,
+    ) -> None:
+        super().__init__(
+            network_fn,
+            dataset,
+            network_args,
+            dataset_args,
+            metrics,
+            criterion,
+            criterion_args,
+            optimizer,
+            optimizer_args,
+            lr_scheduler,
+            lr_scheduler_args,
+            swa_args,
+            device,
+        )
+        self.pad_token = dataset_args["args"]["pad_token"]
+        self.lower = dataset_args["args"]["lower"]
+
+        if self._mapper is None:
+            self._mapper = EmnistMapper(pad_token=self.pad_token, lower=self.lower,)
+
+        self.tensor_transform = ToTensor()
+
+    def criterion(self, output: Tensor, targets: Tensor) -> Tensor:
+        """Computes the CTC loss.
+
+        Args:
+            output (Tensor): Model predictions.
+            targets (Tensor): Correct output sequence.
+
+        Returns:
+            Tensor: The CTC loss.
+
+        """
+        # Input lengths on the form [T, B]
+        input_lengths = torch.full(
+            size=(output.shape[1],), fill_value=output.shape[0], dtype=torch.long,
+        )
+
+        # Configure target tensors for ctc loss.
+        targets_ = Tensor([]).to(self.device)
+        target_lengths = []
+        for t in targets:
+            # Remove padding symbol as it acts as the blank symbol.
+            t = t[t < 53]
+            targets_ = torch.cat([targets_, t])
+            target_lengths.append(len(t))
+
+        targets = targets_.type(dtype=torch.long)
+        target_lengths = (
+            torch.Tensor(target_lengths).type(dtype=torch.long).to(self.device)
+        )
+
+        return self._criterion(output, targets, input_lengths, target_lengths)
+
+    @torch.no_grad()
+    def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]:
+        """Predict on a single input."""
+        self.eval()
+
+        if image.dtype == np.uint8:
+            # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1].
+            image = self.tensor_transform(image)
+
+        # Rescale image between 0 and 1.
+        if image.dtype == torch.uint8:
+            # If the image is an unscaled tensor.
+            image = image.type("torch.FloatTensor") / 255
+
+        # Put the image tensor on the device the model weights are on.
+        image = image.to(self.device)
+        log_probs = self.forward(image)
+
+        raw_pred, _ = greedy_decoder(
+            predictions=log_probs,
+            character_mapper=self.mapper,
+            blank_label=53,
+            collapse_repeated=True,
+        )
+
+        log_probs, _ = log_probs.max(dim=2)
+
+        predicted_characters = "".join(raw_pred[0])
+        confidence_of_prediction = log_probs.cumprod(dim=0)[-1].item()
+
+        return predicted_characters, confidence_of_prediction
diff --git a/src/text_recognizer/models/transformer_model.py b/src/text_recognizer/models/transformer_model.py
index a912122..12e497f 100644
--- a/src/text_recognizer/models/transformer_model.py
+++ b/src/text_recognizer/models/transformer_model.py
@@ -50,13 +50,15 @@ class TransformerModel(Model):
         self.init_token = dataset_args["args"]["init_token"]
         self.pad_token = dataset_args["args"]["pad_token"]
         self.eos_token = dataset_args["args"]["eos_token"]
-        self.max_len = 120
+        self.lower = dataset_args["args"]["lower"]
+        self.max_len = 100
 
         if self._mapper is None:
             self._mapper = EmnistMapper(
                 init_token=self.init_token,
                 pad_token=self.pad_token,
                 eos_token=self.eos_token,
+                lower=self.lower,
             )
         self.tensor_transform = ToTensor()
 
diff --git a/src/text_recognizer/networks/__init__.py b/src/text_recognizer/networks/__init__.py
index f958672..2b624bb 100644
--- a/src/text_recognizer/networks/__init__.py
+++ b/src/text_recognizer/networks/__init__.py
@@ -3,19 +3,18 @@ from .cnn_transformer import CNNTransformer
 from .crnn import ConvolutionalRecurrentNetwork
 from .ctc import greedy_decoder
 from .densenet import DenseNet
-from .fcn import FCN
 from .lenet import LeNet
-from .metrics import accuracy, accuracy_ignore_pad, cer, wer
+from .metrics import accuracy, cer, wer
 from .mlp import MLP
 from .residual_network import ResidualNetwork, ResidualNetworkEncoder
 from .transformer import Transformer
 from .unet import UNet
 from .util import sliding_window
+from .vit import ViT
 from .wide_resnet import WideResidualNetwork
 
 __all__ = [
     "accuracy",
-    "accuracy_ignore_pad",
     "cer",
     "CNNTransformer",
     "ConvolutionalRecurrentNetwork",
@@ -29,6 +28,7 @@ __all__ = [
     "sliding_window",
     "UNet",
     "Transformer",
+    "ViT",
     "wer",
     "WideResidualNetwork",
 ]
diff --git a/src/text_recognizer/networks/cnn_transformer.py b/src/text_recognizer/networks/cnn_transformer.py
index b2b74b3..caa73e3 100644
--- a/src/text_recognizer/networks/cnn_transformer.py
+++ b/src/text_recognizer/networks/cnn_transformer.py
@@ -1,12 +1,13 @@
 """A CNN-Transformer for image to text recognition."""
 from typing import Dict, Optional, Tuple
 
-from einops import rearrange
+from einops import rearrange, repeat
 import torch
 from torch import nn
 from torch import Tensor
 
 from text_recognizer.networks.transformer import PositionalEncoding, Transformer
+from text_recognizer.networks.util import activation_function
 from text_recognizer.networks.util import configure_backbone
 
 
@@ -24,15 +25,21 @@ class CNNTransformer(nn.Module):
         expansion_dim: int,
         dropout_rate: float,
         trg_pad_index: int,
+        max_len: int,
         backbone: str,
         backbone_args: Optional[Dict] = None,
         activation: str = "gelu",
     ) -> None:
         super().__init__()
         self.trg_pad_index = trg_pad_index
+        self.vocab_size = vocab_size
         self.backbone = configure_backbone(backbone, backbone_args)
-        self.character_embedding = nn.Embedding(vocab_size, hidden_dim)
-        self.position_encoding = PositionalEncoding(hidden_dim, dropout_rate)
+        self.character_embedding = nn.Embedding(self.vocab_size, hidden_dim)
+
+        self.src_position_embedding = nn.Parameter(torch.randn(1, max_len, hidden_dim))
+        self.trg_position_encoding = PositionalEncoding(hidden_dim, dropout_rate)
+
+        nn.init.normal_(self.character_embedding.weight, std=0.02)
 
         self.adaptive_pool = (
             nn.AdaptiveAvgPool2d((adaptive_pool_dim)) if adaptive_pool_dim else None
@@ -48,7 +55,11 @@ class CNNTransformer(nn.Module):
             activation,
         )
 
-        self.head = nn.Sequential(nn.Linear(hidden_dim, vocab_size),)
+        self.head = nn.Sequential(
+            # nn.Linear(hidden_dim, hidden_dim * 2),
+            # activation_function(activation),
+            nn.Linear(hidden_dim, vocab_size),
+        )
 
     def _create_trg_mask(self, trg: Tensor) -> Tensor:
         # Move this outside the transformer.
@@ -96,7 +107,21 @@ class CNNTransformer(nn.Module):
         else:
             src = rearrange(src, "b c h w -> b (w h) c")
 
-        src = self.position_encoding(src)
+        b, t, _ = src.shape
+
+        # Insert sos and eos token.
+        # sos_token = self.character_embedding(
+        #    torch.Tensor([self.vocab_size - 2]).long().to(src.device)
+        # )
+        # eos_token = self.character_embedding(
+        #    torch.Tensor([self.vocab_size - 1]).long().to(src.device)
+        # )
+
+        # sos_tokens = repeat(sos_token, "() h -> b h", b=b).unsqueeze(1)
+        # eos_tokens = repeat(eos_token, "() h -> b h", b=b).unsqueeze(1)
+        # src = torch.cat((sos_tokens, src, eos_tokens), dim=1)
+        # src = torch.cat((sos_tokens, src), dim=1)
+        src += self.src_position_embedding[:, :t]
 
         return src
 
@@ -111,20 +136,22 @@ class CNNTransformer(nn.Module):
 
         """
         trg = self.character_embedding(trg.long())
-        trg = self.position_encoding(trg)
+        trg = self.trg_position_encoding(trg)
         return trg
 
-    def decode_image_features(self, h: Tensor, trg: Optional[Tensor] = None) -> Tensor:
+    def decode_image_features(
+        self, image_features: Tensor, trg: Optional[Tensor] = None
+    ) -> Tensor:
         """Takes images features from the backbone and decodes them with the transformer."""
         trg_mask = self._create_trg_mask(trg)
         trg = self.target_embedding(trg)
-        out = self.transformer(h, trg, trg_mask=trg_mask)
+        out = self.transformer(image_features, trg, trg_mask=trg_mask)
 
         logits = self.head(out)
         return logits
 
     def forward(self, x: Tensor, trg: Optional[Tensor] = None) -> Tensor:
         """Forward pass with CNN transfomer."""
-        h = self.extract_image_features(x)
-        logits = self.decode_image_features(h, trg)
+        image_features = self.extract_image_features(x)
+        logits = self.decode_image_features(image_features, trg)
         return logits
diff --git a/src/text_recognizer/networks/metrics.py b/src/text_recognizer/networks/metrics.py
index af9adb5..ffad792 100644
--- a/src/text_recognizer/networks/metrics.py
+++ b/src/text_recognizer/networks/metrics.py
@@ -6,28 +6,13 @@ from torch import Tensor
 from text_recognizer.networks import greedy_decoder
 
 
-def accuracy_ignore_pad(
-    output: Tensor,
-    target: Tensor,
-    pad_index: int = 79,
-    eos_index: int = 81,
-    seq_len: int = 97,
-) -> float:
-    """Sets all predictions after eos to pad."""
-    start_indices = torch.nonzero(target == eos_index, as_tuple=False).squeeze(1)
-    end_indices = torch.arange(seq_len, target.shape[0] + 1, seq_len)
-    for start, stop in zip(start_indices, end_indices):
-        output[start + 1 : stop] = pad_index
-
-    return accuracy(output, target)
-
-
-def accuracy(outputs: Tensor, labels: Tensor,) -> float:
+def accuracy(outputs: Tensor, labels: Tensor, pad_index: int = 53) -> float:
     """Computes the accuracy.
 
     Args:
         outputs (Tensor): The output from the network.
         labels (Tensor): Ground truth labels.
+        pad_index (int): Padding index.
 
     Returns:
         float: The accuracy for the batch.
@@ -36,6 +21,12 @@ def accuracy(outputs: Tensor, labels: Tensor,) -> float:
 
     _, predicted = torch.max(outputs, dim=-1)
 
+    # Mask out the pad tokens
+    mask = labels != pad_index
+
+    predicted *= mask
+    labels *= mask
+
     acc = (predicted == labels).sum().float() / labels.shape[0]
     acc = acc.item()
     return acc
diff --git a/src/text_recognizer/networks/residual_network.py b/src/text_recognizer/networks/residual_network.py
index e397224..c33f419 100644
--- a/src/text_recognizer/networks/residual_network.py
+++ b/src/text_recognizer/networks/residual_network.py
@@ -221,8 +221,8 @@ class ResidualNetworkEncoder(nn.Module):
             nn.Conv2d(
                 in_channels=in_channels,
                 out_channels=self.block_sizes[0],
-                kernel_size=3,
-                stride=1,
+                kernel_size=7,
+                stride=2,
                 padding=1,
                 bias=False,
             ),
diff --git a/src/text_recognizer/networks/transformer/__init__.py b/src/text_recognizer/networks/transformer/__init__.py
index 020a917..9febc88 100644
--- a/src/text_recognizer/networks/transformer/__init__.py
+++ b/src/text_recognizer/networks/transformer/__init__.py
@@ -1,3 +1,3 @@
 """Transformer modules."""
 from .positional_encoding import PositionalEncoding
-from .transformer import Decoder, Encoder, Transformer
+from .transformer import Decoder, Encoder, EncoderLayer, Transformer
diff --git a/src/text_recognizer/networks/transformer/transformer.py b/src/text_recognizer/networks/transformer/transformer.py
index c6e943e..dd180c4 100644
--- a/src/text_recognizer/networks/transformer/transformer.py
+++ b/src/text_recognizer/networks/transformer/transformer.py
@@ -6,11 +6,25 @@ import numpy as np
 import torch
 from torch import nn
 from torch import Tensor
+import torch.nn.functional as F
 
 from text_recognizer.networks.transformer.attention import MultiHeadAttention
 from text_recognizer.networks.util import activation_function
 
 
+class GEGLU(nn.Module):
+    """GLU activation for improving feedforward activations."""
+
+    def __init__(self, dim_in: int, dim_out: int) -> None:
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Forward propagation."""
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
 def _get_clones(module: Type[nn.Module], num_layers: int) -> nn.ModuleList:
     return nn.ModuleList([copy.deepcopy(module) for _ in range(num_layers)])
 
@@ -36,9 +50,17 @@ class _ConvolutionalLayer(nn.Module):
         activation: str = "relu",
     ) -> None:
         super().__init__()
+
+        in_projection = (
+            nn.Sequential(
+                nn.Linear(hidden_dim, expansion_dim), activation_function(activation)
+            )
+            if activation != "glu"
+            else GEGLU(hidden_dim, expansion_dim)
+        )
+
         self.layer = nn.Sequential(
-            nn.Linear(in_features=hidden_dim, out_features=expansion_dim),
-            activation_function(activation),
+            in_projection,
             nn.Dropout(p=dropout_rate),
             nn.Linear(in_features=expansion_dim, out_features=hidden_dim),
         )
diff --git a/src/text_recognizer/networks/util.py b/src/text_recognizer/networks/util.py
index e2d7955..711a952 100644
--- a/src/text_recognizer/networks/util.py
+++ b/src/text_recognizer/networks/util.py
@@ -39,6 +39,7 @@ def activation_function(activation: str) -> Type[nn.Module]:
         [
             ["elu", nn.ELU(inplace=True)],
             ["gelu", nn.GELU()],
+            ["glu", nn.GLU()],
             ["leaky_relu", nn.LeakyReLU(negative_slope=1.0e-2, inplace=True)],
             ["none", nn.Identity()],
             ["relu", nn.ReLU(inplace=True)],
diff --git a/src/text_recognizer/networks/vit.py b/src/text_recognizer/networks/vit.py
new file mode 100644
index 0000000..efb3701
--- /dev/null
+++ b/src/text_recognizer/networks/vit.py
@@ -0,0 +1,150 @@
+"""A Vision Transformer.
+
+Inspired by:
+https://openreview.net/pdf?id=YicbFdNTTy
+
+"""
+from typing import Optional, Tuple
+
+from einops import rearrange, repeat
+import torch
+from torch import nn
+from torch import Tensor
+
+from text_recognizer.networks.transformer import Transformer
+
+
+class ViT(nn.Module):
+    """Transfomer for image to sequence prediction."""
+
+    def __init__(
+        self,
+        num_encoder_layers: int,
+        num_decoder_layers: int,
+        hidden_dim: int,
+        vocab_size: int,
+        num_heads: int,
+        expansion_dim: int,
+        patch_dim: Tuple[int, int],
+        image_size: Tuple[int, int],
+        dropout_rate: float,
+        trg_pad_index: int,
+        max_len: int,
+        activation: str = "gelu",
+    ) -> None:
+        super().__init__()
+
+        self.trg_pad_index = trg_pad_index
+        self.patch_dim = patch_dim
+        self.num_patches = image_size[-1] // self.patch_dim[1]
+
+        # Encoder
+        self.patch_to_embedding = nn.Linear(
+            self.patch_dim[0] * self.patch_dim[1], hidden_dim
+        )
+        self.cls_token = nn.Parameter(torch.randn(1, 1, hidden_dim))
+        self.character_embedding = nn.Embedding(vocab_size, hidden_dim)
+        self.pos_embedding = nn.Parameter(torch.randn(1, max_len, hidden_dim))
+        self.dropout = nn.Dropout(dropout_rate)
+        self._init()
+
+        self.transformer = Transformer(
+            num_encoder_layers,
+            num_decoder_layers,
+            hidden_dim,
+            num_heads,
+            expansion_dim,
+            dropout_rate,
+            activation,
+        )
+
+        self.head = nn.Sequential(nn.Linear(hidden_dim, vocab_size),)
+
+    def _init(self) -> None:
+        nn.init.normal_(self.character_embedding.weight, std=0.02)
+        # nn.init.normal_(self.pos_embedding.weight, std=0.02)
+
+    def _create_trg_mask(self, trg: Tensor) -> Tensor:
+        # Move this outside the transformer.
+        trg_pad_mask = (trg != self.trg_pad_index)[:, None, None]
+        trg_len = trg.shape[1]
+        trg_sub_mask = torch.tril(
+            torch.ones((trg_len, trg_len), device=trg.device)
+        ).bool()
+        trg_mask = trg_pad_mask & trg_sub_mask
+        return trg_mask
+
+    def encoder(self, src: Tensor) -> Tensor:
+        """Forward pass with the encoder of the transformer."""
+        return self.transformer.encoder(src)
+
+    def decoder(self, trg: Tensor, memory: Tensor, trg_mask: Tensor) -> Tensor:
+        """Forward pass with the decoder of the transformer + classification head."""
+        return self.head(
+            self.transformer.decoder(trg=trg, memory=memory, trg_mask=trg_mask)
+        )
+
+    def extract_image_features(self, src: Tensor) -> Tensor:
+        """Extracts image features with a backbone neural network.
+
+        It seem like the winning idea was to swap channels and width dimension and collapse
+        the height dimension. The transformer is learning like a baby with this implementation!!! :D
+        Ohhhh, the joy I am experiencing right now!! Bring in the beers! :D :D :D
+
+        Args:
+            src (Tensor): Input tensor.
+
+        Returns:
+            Tensor: A input src to the transformer.
+
+        """
+        # If batch dimension is missing, it needs to be added.
+        if len(src.shape) < 4:
+            src = src[(None,) * (4 - len(src.shape))]
+
+        patches = rearrange(
+            src,
+            "b c (h p1) (w p2) -> b (h w) (p1 p2 c)",
+            p1=self.patch_dim[0],
+            p2=self.patch_dim[1],
+        )
+
+        # From patches to encoded sequence.
+        x = self.patch_to_embedding(patches)
+        b, n, _ = x.shape
+        cls_tokens = repeat(self.cls_token, "() n d -> b n d", b=b)
+        x = torch.cat((cls_tokens, x), dim=1)
+        x += self.pos_embedding[:, : (n + 1)]
+        x = self.dropout(x)
+
+        return x
+
+    def target_embedding(self, trg: Tensor) -> Tuple[Tensor, Tensor]:
+        """Encodes target tensor with embedding and postion.
+
+        Args:
+            trg (Tensor): Target tensor.
+
+        Returns:
+            Tuple[Tensor, Tensor]: Encoded target tensor and target mask.
+
+        """
+        _, n = trg.shape
+        trg = self.character_embedding(trg.long())
+        trg += self.pos_embedding[:, :n]
+        return trg
+
+    def decode_image_features(self, h: Tensor, trg: Optional[Tensor] = None) -> Tensor:
+        """Takes images features from the backbone and decodes them with the transformer."""
+        trg_mask = self._create_trg_mask(trg)
+        trg = self.target_embedding(trg)
+        out = self.transformer(h, trg, trg_mask=trg_mask)
+
+        logits = self.head(out)
+        return logits
+
+    def forward(self, x: Tensor, trg: Optional[Tensor] = None) -> Tensor:
+        """Forward pass with CNN transfomer."""
+        h = self.extract_image_features(x)
+        logits = self.decode_image_features(h, trg)
+        return logits
diff --git a/src/text_recognizer/networks/wide_resnet.py b/src/text_recognizer/networks/wide_resnet.py
index 28f3380..b767778 100644
--- a/src/text_recognizer/networks/wide_resnet.py
+++ b/src/text_recognizer/networks/wide_resnet.py
@@ -113,6 +113,7 @@ class WideResidualNetwork(nn.Module):
         dropout_rate: float = 0.0,
         num_layers: int = 3,
         block: Type[nn.Module] = WideBlock,
+        num_stages: Optional[List[int]] = None,
         activation: str = "relu",
         use_decoder: bool = True,
     ) -> None:
@@ -127,6 +128,7 @@ class WideResidualNetwork(nn.Module):
             dropout_rate (float): The dropout rate. Defaults to 0.0.
             num_layers (int): Number of layers of blocks. Defaults to 3.
             block (Type[nn.Module]): The default block is WideBlock. Defaults to WideBlock.
+            num_stages (List[int]): If given, will use these channel values. Defaults to None.
             activation (str): Name of the activation to use. Defaults to "relu".
             use_decoder (bool): If True, the network output character predictions, if False, the network outputs a
                 latent vector. Defaults to True.
@@ -149,9 +151,14 @@ class WideResidualNetwork(nn.Module):
         self.dropout_rate = dropout_rate
         self.activation = activation_function(activation)
 
-        self.num_stages = [self.in_planes] + [
-            self.in_planes * 2 ** n * self.width_factor for n in range(self.num_layers)
-        ]
+        if num_stages is None:
+            self.num_stages = [self.in_planes] + [
+                self.in_planes * 2 ** n * self.width_factor
+                for n in range(self.num_layers)
+            ]
+        else:
+            self.num_stages = [self.in_planes] + num_stages
+
         self.num_stages = list(zip(self.num_stages, self.num_stages[1:]))
         self.strides = [1] + [2] * (self.num_layers - 1)
 
diff --git a/src/training/prepare_experiments.py b/src/training/prepare_experiments.py
index 6e20bcd..21997af 100644
--- a/src/training/prepare_experiments.py
+++ b/src/training/prepare_experiments.py
@@ -14,7 +14,7 @@ def run_experiments(experiments_filename: str) -> None:
     for index in range(num_experiments):
         experiment_config = experiments_config["experiments"][index]
         experiment_config["experiment_group"] = experiments_config["experiment_group"]
-        cmd = f"python training/run_experiment.py --gpu=-1 --save '{json.dumps(experiment_config)}'"
+        cmd = f"poetry run run-experiment --gpu=-1 --save '{json.dumps(experiment_config)}'"
         print(cmd)
 
 
diff --git a/src/training/run_experiment.py b/src/training/run_experiment.py
index 0167725..2c9a196 100644
--- a/src/training/run_experiment.py
+++ b/src/training/run_experiment.py
@@ -256,7 +256,7 @@ def run_experiment(
     # Load from checkpoint if resuming an experiment.
     resume = False
     if checkpoint is not None or pretrained_weights is not None:
-        resume = True
+        # resume = True
         _load_from_checkpoint(model, model_dir, pretrained_weights)
 
     logger.info(f"The class mapping is {model.mapping}")
diff --git a/src/training/trainer/callbacks/wandb_callbacks.py b/src/training/trainer/callbacks/wandb_callbacks.py
index df1fd8f..20414df 100644
--- a/src/training/trainer/callbacks/wandb_callbacks.py
+++ b/src/training/trainer/callbacks/wandb_callbacks.py
@@ -110,7 +110,9 @@ class WandbImageLogger(Callback):
             if isinstance(self.targets[i], list):
                 ground_truth = "".join(
                     [
-                        self.model.mapper(int(target_index))
+                        self.model.mapper(int(target_index) - 26)
+                        if target_index > 35
+                        else self.model.mapper(int(target_index))
                         for target_index in self.targets[i]
                     ]
                 ).rstrip("_")