train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "98e00ed2-2a4f-4a18-8437-b01e8114801f",
   "metadata": {},
   "source": [
    "# Train transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fbd84bd7-38bc-4892-9c1f-cf2dc0af6961",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn.parallel import DistributedDataParallel as DDP\n",
    "from torch.distributed import init_process_group, destroy_process_group\n",
    "from torch.cuda.amp import GradScaler, autocast\n",
    "import GPUtil\n",
    "\n",
    "from transformer_implementation import Transformer, Tokenizer, TransformerConfig, DataLoaderFactory\n",
    "from utils import training_loop, plot_losses, estimate_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d80f8cc4-1ed2-4c69-86bd-702686561503",
   "metadata": {},
   "source": [
    "## Init\n",
    "### Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ab956e7-7067-48fc-816f-cc1814f2c9dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# init tokenizer\n",
    "tokenizer = Tokenizer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b63bf7f-d62b-425a-b572-e4c7175ebc92",
   "metadata": {},
   "source": [
    "### Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f985eab2-93bd-42dd-88f5-8d363341f341",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# init config\n",
    "config = TransformerConfig(\n",
    "    vocab_size = tokenizer.vocab_size(),\n",
    "    block_size = 64,\n",
    "    n_layer = 6, # 6,\n",
    "    n_head = 8,\n",
    "    n_embd = 512,\n",
    "    max_epochs=150,\n",
    "    train_data_size = 30000, # batch * 500 iters\n",
    "    max_iters = int(30000/12)-1, # dataset_size / batch_size\n",
    "    eval_iters = int(3000/12)-1, # val_dataset_size / batch_size\n",
    "    BOS_IDX = tokenizer.BOS_IDX,\n",
    "    EOS_IDX = tokenizer.EOS_IDX,\n",
    "    PAD_IDX = tokenizer.PAD_IDX,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e7b6e34-39fb-46bb-b32e-3db07169d184",
   "metadata": {},
   "source": [
    "#### Multi Process config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d5a0b842-0014-45d9-b788-ab98474ab49c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU is not NVIDIA V100, A100, or H100. Speedup numbers may be lower than expected.\n"
     ]
    }
   ],
   "source": [
    "if config.ddp:\n",
    "    init_process_group(backend=config.backend)\n",
    "    ddp_rank = int(os.environ['RANK'])\n",
    "    ddp_local_rank = int(os.environ['LOCAL_RANK'])\n",
    "    ddp_world_size = int(os.environ['WORLD_SIZE'])\n",
    "    config.device = f'cuda:{ddp_local_rank}'\n",
    "    torch.cuda.set_device(device)\n",
    "    master_process = ddp_rank == 0 # this process will do logging, checkpointing etc.\n",
    "    seed_offset = ddp_rank # each process gets a different seed\n",
    "    # world_size number of processes will be training simultaneously, so we can scale\n",
    "    # down the desired gradient accumulation iterations per process proportionally\n",
    "    assert gradient_accumulation_steps % ddp_world_size == 0\n",
    "    config.gradient_accumulation_steps //= ddp_world_size\n",
    "else:\n",
    "    # if not ddp, we are running on a single gpu, and one process\n",
    "    master_process = True\n",
    "    seed_offset = 0\n",
    "    ddp_world_size = 1\n",
    "\n",
    "torch.manual_seed(1337 + seed_offset)\n",
    "torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul\n",
    "torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn\n",
    "# note: float16 data type will automatically use a GradScaler\n",
    "ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[config.dtype]\n",
    "ctx = nullcontext() if config.device_type == 'cpu' else torch.amp.autocast(device_type=config.device_type, dtype=ptdtype)\n",
    "\n",
    "\n",
    "gpu_ok = False\n",
    "if torch.cuda.is_available():\n",
    "    device_cap = torch.cuda.get_device_capability()\n",
    "    if device_cap in ((7, 0), (8, 0), (9, 0)):\n",
    "        gpu_ok = True\n",
    "\n",
    "if not gpu_ok:\n",
    "    print(\n",
    "        \"GPU is not NVIDIA V100, A100, or H100. Speedup numbers may be lower than expected.\"\n",
    "    )\n",
    "config.compile = gpu_ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1780f83b-0772-4d0a-b29e-78d41df045bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TransformerConfig:\n",
      "\n",
      "Tokenizer:\n",
      "+---------------------+\n",
      "| vocab_size : 100277 |\n",
      "| BOS_IDX    : 100264 |\n",
      "| EOS_IDX    : 100265 |\n",
      "| PAD_IDX    : 100266 |\n",
      "+---------------------+\n",
      "\n",
      "Data:\n",
      "+---------------------------------+\n",
      "| block_size              : 64    |\n",
      "| batch_size              : 12    |\n",
      "| train_data_size         : 30000 |\n",
      "| grad_accumulation_steps : 40    |\n",
      "+---------------------------------+\n",
      "\n",
      "Model:\n",
      "+-----------------+\n",
      "| n_layer : 6     |\n",
      "| n_head  : 8     |\n",
      "| n_embd  : 512   |\n",
      "| dropout : 0.1   |\n",
      "| bias    : 0     |\n",
      "+-----------------+\n",
      "\n",
      "Training Loop:\n",
      "+-------------------+\n",
      "| max_epochs : 150  |\n",
      "| max_iters  : 2499 |\n",
      "| eval_iters : 249  |\n",
      "+-------------------+\n",
      "\n",
      "AdamW Optimizer:\n",
      "+------------------------+\n",
      "| learning_rate : 0.0006 |\n",
      "| beta1         : 0.9    |\n",
      "| beta2         : 0.95   |\n",
      "| weight_decay  : 0.1    |\n",
      "| eps           : 1e-09  |\n",
      "+------------------------+\n",
      "\n",
      "System:\n",
      "+-----------------------+\n",
      "| device_type : cuda    |\n",
      "| device      : cuda    |\n",
      "| dtype       : float16 |\n",
      "| compile     : 0       |\n",
      "+-----------------------+\n",
      "\n",
      "DDP Settings:\n",
      "+-----------------+\n",
      "| backend : nccl  |\n",
      "| ddp     : 0     |\n",
      "+-----------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b77fbb2-162b-4589-8ca0-98d80e539bb1",
   "metadata": {},
   "source": [
    "### Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba9de1b3-95be-4adb-bfc1-de3a27eb0eb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wmt14 (C:/Users/thiba/.cache/huggingface/datasets/wmt14/fr-en/1.0.0/2de185b074515e97618524d69f5e27ee7545dcbed4aa9bc1a4235710ffca33f4)\n",
      "Found cached dataset wmt14 (C:/Users/thiba/.cache/huggingface/datasets/wmt14/fr-en/1.0.0/2de185b074515e97618524d69f5e27ee7545dcbed4aa9bc1a4235710ffca33f4)\n",
      "Found cached dataset wmt14 (C:/Users/thiba/.cache/huggingface/datasets/wmt14/fr-en/1.0.0/2de185b074515e97618524d69f5e27ee7545dcbed4aa9bc1a4235710ffca33f4)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[95m\u001b[1m\u001b[4mNumber of data by datasets splits\u001b[0m\n",
      "Train\t\t: 30000\t-> 2500.0\n",
      "Validation\t: 3000\t\t-> 250.0\n",
      "Test\t\t: 3003\t\t-> 250.25\n",
      "Total\t\t: 36003\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "36003"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading dataset\n",
    "dataset = DataLoaderFactory(config.block_size, config.batch_size, tokenizer, config.device, config.train_data_size)\n",
    "len(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b89467cb-b399-4906-b89c-fb9d554e7dd4",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "690b87c3-c7dd-4518-b63f-6d388702ce5a",
   "metadata": {},
   "source": [
    "#### Initial gpu usage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b05af4f3-8a3a-4804-97ae-8f60755268df",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_gpu_memory():\n",
    "    GPUs = GPUtil.getGPUs()\n",
    "    gpu = GPUs[0]\n",
    "    return gpu.memoryUsed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99fcd21e-1172-4cbb-8c7a-8330331704bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial GPU Memory Used: 364.0 MB\n"
     ]
    }
   ],
   "source": [
    "print(f\"Initial GPU Memory Used: {get_gpu_memory()} MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d8c7c5a8-3fd1-4861-83d3-713d88291dbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Encoder parameters: 70.22M\n",
      "number of Decoder parameters: 76.52M\n",
      "Total number of parameters: 146.74M\n",
      "num decayed parameter tensors: 100, with 146,789,376 parameters\n",
      "num non-decayed parameter tensors: 32, with 16,384 parameters\n"
     ]
    }
   ],
   "source": [
    "# Create model\n",
    "model = Transformer(config)\n",
    "model.train()\n",
    "model = model.to(config.device)\n",
    "\n",
    "# for Accumulating Gradients for Larger Effective Batch Size. If enabled=False scaler is a no-op\n",
    "scaler = GradScaler(enabled=(config.dtype == 'float16')) # Initialize GradScaler\n",
    "\n",
    "# create a PyTorch optimizer\n",
    "# optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate, betas=(0.9, 0.98), eps=1e-9)\n",
    "optimizer = model.configure_optimizers(weight_decay=config.weight_decay, learning_rate=config.learning_rate, betas=(config.beta1, config.beta2), eps=config.eps, device_type=config.device_type)\n",
    "\n",
    "# compile the model\n",
    "if config.compile:\n",
    "    print(\"compiling the model... (takes a ~minute)\")\n",
    "    unoptimized_model = model\n",
    "    model = torch.compile(model) # requires PyTorch 2.0\n",
    "\n",
    "# wrap model into DDP container\n",
    "if config.ddp:\n",
    "    model = DDP(model, device_ids=[ddp_local_rank])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3426d9ca-cd7b-4ab8-b667-eff4b9adfa25",
   "metadata": {},
   "source": [
    "#### GPU after Loading Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f428744-3d2f-4df9-8414-56e75ae5acbd",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "85c6838c-b9fc-4490-8187-c83cbb292128",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU Memory Used After Loading Model: 996.0 MB\n"
     ]
    }
   ],
   "source": [
    "print(f\"GPU Memory Used After Loading Model: {get_gpu_memory()} MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "76fade97-efa1-4b69-aac7-00cce079c58f",
   "metadata": {},
   "outputs": [],
   "source": [
    "CALCULATE_MEMORY = False\n",
    "if CALCULATE_MEMORY:\n",
    "    # Get the initial memory usage\n",
    "    initial_memory = get_gpu_memory()\n",
    "    \n",
    "    batch = dataset.get_batch('train')\n",
    "    \n",
    "    for i in range(5):\n",
    "        n_batch = next(batch)\n",
    "        xb = n_batch['inputs']\n",
    "        yb = n_batch['targets']\n",
    "\n",
    "        # autocast means part of the model run in FP16 to save memory\n",
    "        with autocast():\n",
    "            # Forward pass\n",
    "            logits, loss = model(xb, yb)\n",
    "        \n",
    "        # Calculate the memory used\n",
    "        memory_used = get_gpu_memory() - initial_memory\n",
    "        \n",
    "        print(f\"Memory used for a batch {i}: {memory_used} MB\")\n",
    "        \n",
    "        # Make sure to zero out the gradients and free the memory\n",
    "        model.zero_grad()\n",
    "        torch.cuda.empty_cache()\n",
    "    assert \"a\" == \"b\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa3d15a-f2c0-41a8-81da-4a0fed68bdaa",
   "metadata": {},
   "source": [
    "**Memory History: Before Quantization 5006.0 MB**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f97e48a-b435-4d6e-97b7-416e67aef6f7",
   "metadata": {},
   "source": [
    "### Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4bb80bbc-39c7-4cbb-869a-9557cb968411",
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      "C:\\Users\\thiba\\.virtualenvs\\transformer-LWcVpt7F\\Lib\\site-packages\\torch\\utils\\checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
      "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping\n"
     ]
    }
   ],
   "source": [
    "losses_list = training_loop(model, ctx, optimizer, scaler, dataset, config, saved_path = \"./out/transformer-train2.pth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96236360-9fa5-42d4-9beb-e802ba9c9cdf",
   "metadata": {},
   "source": [
    "### Plotting losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ac7486a-daa4-439a-ba76-d33dd9c61589",
   "metadata": {},
   "outputs": [
    {
     "data": {
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zhvT0dCZOnHjJ+yxdqxdffJG33nqL77//nldeeYWIiAgyMjL45JNPCm1Y74L+vsiLs2fP0r59e26++WZuueUWbrnlFh588EFmzJjB/fffD1hHIho3bszQoUPp378/Dz30EE8//TRjxozh+++/t3+9PvjgA5YsWZLr82SG/ctt61pl9nLvvfcSGhqaY/6F1w8mJyfnaWCUzO3OnDnzotfl+fv7X2m7BaIofF+JyOUpJImIw/Xs2ZPq1aszfPhw+2lyAA0bNnRgV+edOXOGxMTEXE9BystpSW3atKFZs2bcd999zJw50z79ciOGXcrx48fp06cPHh4e2Y4mNWvW7Iq2M2vWLPub71GjRhEdHc38+fPt82+//XaOHj3K8OHDs633+uuvX1XPYB1NyDrqXPXq1bMdJct83hUrVvDQQw9lm165cuVsR9ny8gY66/P37duXChUqZDualHk6Z9YBAwra8ePHadu2LU5OTtleQ269pKamsmDBAhYsWICTkxNffPEFY8aM4c033+To0aMA9tENp0+fjoeHB2vWrOG1117j+++/t58ymJqayvLlyy/b26W2dTlNmjRh1apV9sceHh7UqlWLhQsXAtj7PXPmTJ56yauzZ88SExODi4tLnrebeVQrK19fX3uYytwHzZo1Y+XKldmWa9asmX1+5te3devWV92/iBQ9Ot1ORBwu8y+rWf+SWqZMGcaNG+eolrLJyMhg2bJlDBs2jFq1atmnN27cOMd1LLnJ7fUBjB8//qp7WrhwIWXKlGHs2LH2ac7OzjzxxBNXtJ25c+cSHx/PuHHjuOWWW/jrr7+yXX+RW+/XXXcdXbp0ueKely1bRkpKSo4en3rqqRzLpqen5/h63X777TmGaM4MiHkZ+nzhwoW4urry+OOPZ5v+9NNPk5GRkefry/LDwoULqVWrFnfeead9mouLC0888QSxsbGsXr0aIEd4NMbY38SXLVs212Xi4+MJCAiwzz979iwrV67k0UcfxdvbO0cv1atXt39+uW1dziOPPIKr6/m/v44dO5YyZcrYv7ZLliwhOjqaF198MdtyufVyJTIyMvjzzz8ZMWIErVq1ytN277vvPipUqGB/fPvtt1O7dm17r9u2bSMsLIwxY8ZkG31xwIABtGzZkn/++QewThddvXo1Dz74IPXq1buq/kWk6NGRJBFxuA0bNhAREcGMGTP47LPPMMbwn//8p0idfvLaa6/Rv39/1q9fz5dffomLiwuPP/44e/fuzXXghawOHjxIQECAfajpmJgYRowYcU3XIMyfP59169bx3nvv0aBBA/bv38/w4cOv+Hqd+Ph45s6dyz333APkvCnwggULGDFiBHPmzOGff/6hYcOGjBkzhv3792d7g5kX4eHhfPjhh7z44ossWLCAhQsX0qFDB2655ZYcQ6kvWLCASZMm8cMPP7BhwwbatGnDPffcYz8Skeno0aNERkYyZswYYmNjiY+PZ/PmzQQFBeV4/vnz57NixQrefvttGjRowO7du+nfvz/Dhg1j8uTJ2QZpyA99+vTJcR8ssILpN998w6OPPsr06dPp2LEjQUFB3H777XTr1o3x48fbj3R99913VK1alRUrVnDq1Cl8fHx44okn2Llzp/36pf3797Nq1Sq2b99OREQEnTp14vbbb2fq1Kn253zsscdYt24de/bs4dtvv+XYsWN4eXnRpUsX6tatS/v27fO8rUtxc3Nj+fLlzJ49m2bNmjFu3DjWrl3LvHnzAIiNjWXs2LHMnDmTHTt28Ouvv3L27Fnq16/PoEGDWL9+/RUH/UwvvPACvXr1YvPmzXz77bfs37+fqlWr4ufnR9++falWrVq25SMiIli3bh3Tpk3Dy8uLp556iiNHjvDtt98C1ql/EyZMYPr06axevZpffvnFPgR4YGAgkydPtm/rySefZN26dezYsYNvvvmGwMBAGjRowKBBgy77+0FEii6HD7GnUqlKXl1sCPA9e/bkunyXLl3Mhg0bTHx8vDl16pR57733TL9+/YwxxvTo0cO+3MWGAM9tuOULh6S+2BDgU6ZMybFuYGBgtiGpAdOrVy+zfft2k5SUZI4cOWIefPBB88EHH5iEhITLfj2aN29u/v33XxMTE2POnDljvv76a/uQ0lmHr542bZqJjY3NsX5uvVepUsXMmDHDREVFmcjISDNjxgzTrl27PA8Bnlm33HKLMcaY4ODgHMNuA+aFF14wgYGBJjEx0Wzfvt0MHDgwx37I7et94RDggHFycjKvvPKKCQ4ONvHx8WbFihWmZcuWOb7ebm5u5oMPPrAvt3btWnP99deblStXmpUrV2Z73sGDB5u9e/fah2PPfO259ejh4WE++ugjc+rUKZOcnGwOHTp00e+dvH5fXFiZ35MXc8899xjA1KhRw3z//ffmzJkzJikpyezevTvHfhs+fLhZvHixCQ0NNUlJSSYoKMh8+eWXxsvLy77Miy++aDZt2mQiIiJMfHy82b9/v5k4cWK2obgB07BhQzN9+nRz+vRpk5ycbE6ePGnmzZtnhg8ffsXburAy93X37t3NV199Zc6dO2diYmLMzJkzTZUqVXIs36NHD7No0SITGRlpEhISzJEjR8wPP/xg/Pz8LvuzcKmqUaOGmTJlijl+/LhJTk42p0+fNkuXLjUPPfRQtuc2xpg777zTvP322yY0NNTEx8eb+fPn5zqE9x133GG2b99uEhMTTXh4uJk5c6apXbt2juVatmxp/vzzTxMREWESEhLMgQMHzOuvv57jZ7hatWq5fu2y/pyoVKoiUQ5vQKVSqYptzZkzxxw+fNjhfahUjqzMN/odO3Z0eC+Xq8yQNGLECIf3olKpim7pmiQRkTy68NQpX19fBg4cmO1CdRERESn+dE2SiEgeHTt2jOnTp3Ps2DF8fHwYO3YsKSkp/O9//3N0ayIiIpKPFJJERPJo8eLF3H333Xh7e5OcnMzGjRt58cUX83xDWRERESkenLDOuxMRERERERF0nyQREREREZFsFJJERERERESyKBXXJNWuXZvY2FhHtyEiIiIiIg7m6enJ6dOnL7lMiQ9JtWvXJjg42NFtiIiIiIhIEVGnTp1LBiWHhqTu3bvzf//3f3Ts2JHatWszbNgw/v77b6sxV1feeustBg4cSKNGjYiOjmbZsmW88MILhISE5Pk5Mo8g1alTR0eTRERERERKMU9PT4KDgy+bCxwakjw8PNi9ezc//PADc+bMyTbP3d0dPz8/3nzzTXbv3k2VKlX49NNPmTdvHp07d77i54qNjVVIEhERERGRyyoyQ4AbY7IdScpNp06d2Lp1K/Xr1+fkyZO5LuPm5kbZsmXtjzPTYsWKFRWSRERERERKMU9PT2JiYi6bDYrV6HaVKlUiIyODqKioiy4zceJEYmJi7KXrkURERERE5EoUm5BUtmxZ3n//fX755ZdLpr53332XihUr2qtOnTqF2KWIiIiIiBR3xWJ0O1dXV2bPno2TkxNjx4695LIpKSmkpKQUUmciIiIicq2cnJyoXLkynp6eODk5ObodKaaMMcTGxhIVFYUx13ZFUZEPSZkBycfHh969e+u6IhEREZESpEaNGjz88MM0b97c0a1ICXHw4EG+/fZbzp49e9XbKNIhKTMgNWnShF69ehEREeHolkREREQkn7i6uvL2228TFxfHF198wZkzZ0hPT3d0W1JMubi4ULNmTUaOHMnbb7/NuHHjSEtLu6ptOXwIcF9fX/vjhg0b0q5dOyIiIggJCeGPP/7Az8+PW2+9FRcXF7y8vACIiIggNTXVUW2LiIiISD6oVasW5cqV48MPP+Tw4cOObkdKgGPHjhEREcHLL7+Mt7c3p06duqrtODQkderUiVWrVtkfT548GYDp06fz2muvMXToUAB2796dbb2ePXuyevXqQutTRERERPKfs7M1hlhycrKDO5GSJPP7ycXF5aq34dCQtHr16ktenKcL90REREREpLAVmyHARURERERECoNCkoiIiIhIERAYGMj48ePzvHyPHj0wxlCpUqUC7ApGjx5NZGRkgT5HUaOQJCIiIiJyBYwxl6xJkyZd1XY7d+7MN998k+flN2zYgLe3N9HR0Vf1fHJxRXoI8JLGydMJE28gw9GdiIiIiMjV8vb2tn9+55138sYbb9CsWTP7tLi4uGzLu7i45Glo8/Dw8CvqIzU1lbCwsCtaR/JGR5IKUbtlTWgaVJ8Kfu6ObkVERESkyHIvV67Q60qEhYXZKzo6GmOM/XHz5s2Ji4tjwIABbNu2jeTkZLp160ajRo2YO3cuoaGhxMbGsmXLFvr06ZNtuxeebmeM4b///S9//fUX8fHxHD58mMGDB9vnX3i6XeZpcf3792f//v3ExsayaNGibKHOxcWFTz/9lMjISMLDw3nvvfeYPn06c+bMuaKvwZgxYwgICCA5OZmDBw9y7733Zps/adIkjh8/TlJSEsHBwXz66af2eWPHjuXw4cMkJiYSGhrK77//fkXPXRh0JKmQeLetzlG/U8S6JuC2pQx1P6nJqQlnQPdLExEREbFzL1eO+MVrC/15PQZ0JyEpKd+299577/Hcc89x7NgxIiMjqVevHgsXLuSll14iOTmZ++67j/nz59OsWTNOnjx50e1MmjSJ559/nv/7v//jiSeeYNasWfj4+Fz0GiF3d3eee+45/vOf/5CRkcFPP/3Ehx9+aA8xEyZM4J577uGBBx7gwIEDjB8/nmHDhrFy5co8v7Zhw4bx6aef8tRTT7Fs2TJuvfVWpk2bxqlTp1i1ahUjRozg6aef5q677mLfvn14e3vTrl07ADp27Mhnn33Gf/7zHzZs2EDVqlXp3r37FXxlC4dCUiEJ9Q+nbb8mVJ7lyZraOzj17Bm8hlUlbFgE7HV0dyIiIiKSn1599VWWLVtmfxwZGYm/v3+2+bfddhtDhgzh888/v+h2pk+fzq+//grAiy++yPjx47nuuutYsmRJrsu7ubkxZswYjh07BsDUqVN59dVX7fOfeOIJ3n33XebOnQvA448/zsCBA6/otT333HNMnz6dL7/8ErDudXrDDTfw3HPPsWrVKurXr09oaCjLli0jLS2NkydPsnXrVgDq169PfHw8CxYsIC4ujhMnTrBr164rev7CoJBUiPxXHcGzqTtjfxrBL4P+JaxxBK67XCjzriuJryaDcXSHIiIiIo6VkJSEx4DCP7KQn0eRALZt25btsYeHB6+99hqDBg2iVq1auLq6Ur58eerXr3/J7WQNVgkJCURHR1OzZs2LLh8fH28PSAAhISH25StWrIi3tzdbtmyxz8/IyGD79u32G/vmRYsWLXIMMLF+/Xr7qYK///47Tz31FMeOHWPx4sUsXLiQ+fPnk56eztKlSzl+/Lh93uLFi5kzZw6JiYl5fv7CoGuSCllsfAJf3vYngx7uyqAz3UhzSSfx5WSqvF7R0a2JiIiIFAkJSUmFXvktPj4+2+MPP/yQ2267jRdffJHu3bvTvn179uzZg5ub2yW3k5qamu2xMeaSgeZKly8Ip06dolmzZowbN47ExES++OIL1qxZg6urK3Fxcfj5+XH33XcTEhLCG2+8we7duwt8GPMrpZDkILNmLCbohtNM2DYagPgXEqGBY3sSERERkYLRtWtXpk+fzty5c9m7dy+hoaE0aNCgUHuIiYkhNDSUzp0726c5Ozvj5+d3Rds5cOAAXbt2zTata9eu7N+/3/44KSmJBQsWMH78eHr27MmNN95ImzZtAEhPT2f58uVMmDCBtm3b0qBBA3r37n0Nryz/6XQ7B9oXeIzUEWn02OXH6io7qDarEue6apx7ERERkZLmyJEjDB8+nPnz52OM4c033yz0IzwAU6ZMYeLEiQQEBHDw4EGeeOIJqlSpgjF5v+7jgw8+YPbs2ezcuZNly5YxePBghg8fTt++fQFrlD0XFxc2b95MQkIC9957LwkJCRw/fpxBgwbRqFEj1qxZQ2RkJAMHDsTZ2ZlDhw4V1Eu+KjqS5GCHT5zghk/bUDbDjXM3RuN8t5OjWxIRERGRfPbMM88QGRnJhg0bmD9/PkuWLGHHjh2F3sf777/PL7/8wo8//sjGjRuJi4tjyZIlJF3BKYd///0348eP57nnnmPfvn08+uijPPDAA6xevRqAqKgoHn74YdavX4+/vz99+/Zl8ODBREREEBUVxfDhw1mxYgUHDhxgzJgx3H333dmOQhUVpiSXp6enMcYYT09Ph/dysapUoYKZsP8+g8GUjyxrqOL4nlQqlUqlUqkKunx8fMyPP/5ofHx8HN5LaS0nJydz8OBB88Ybbzi8l/yqS31f5TUb6EhSERAdF0fYxEhaxDcksXIyZSdf+gI+EREREZGrUb9+fR566CGaNGlC69at+fLLL2nYsCE///yzo1srUhSSiogf5//Ds8vvASB5dAoUvXtqiYiIiEgxl5GRwf3338/WrVtZv349bdq0oW/fvhw8eNDRrRUpGrihiMjIyGDay/N5pNNtfFN7DmWnlSG5ZSqkOLozERERESkpTp06Rbdu3RzdRpGnI0lFyPo9u+k0vSVeKVVJbpwKjzu6IxERERGR0kchqYh545PvePnIfwHwfMLdwd2IiIiIiJQ+CklFzKmzYYR+eY5y6WWJbZAAHR3dkYiIiIhI6aKQVAT9OvdfbgvvCUC5hzXSnYiIiIhIYVJIKoKOBp+iy/Y21oO7gDIObUdEREREpFRRSCqign46jXdyNZIqpcAAR3cjIiIiIlJ6KCQVUX+sWME9Z6x0VFan3ImIiIiUOCtXrmTy5Mn2x4GBgYwfP/6S6xhjGDp06DU/d35t51ImTZrEzp07C/Q5CopCUhF1IiyU9uubApA6IA2qOLghEREREQFg3rx5LFq0KNd53bp1wxhDmzZtrni7nTt35ptvvrnW9rK5WFDx9va+6GsQhaQibdufB2kf25SMMhlwp6O7ERERERGA77//nn79+lGnTp0c8x544AG2bt3Knj17rni74eHhJCYm5keLlxUWFkZKSkqhPFdxpJBUhP2+ahn/CR0IgNt/NXqDiIiIlBLuDqgrsGDBAs6ePcv999+fbbqHhwd33HEH33//PVWrVuXnn3/m1KlTxMfH4+/vz1133XXJ7V54up2vry+rV68mMTGRffv20bdv3xzrvPfeexw6dIj4+HiOHj3KG2+8gaurKwCjR4/mtddeo3379hhjMMYwevRoIOfpdq1bt2b58uUkJCQQHh7O119/jYeHh33+tGnTmDNnDs8++yynT58mPDycqVOn2p8rL5ycnHjllVc4efIkSUlJ7Ny5k5tvvtk+v0yZMkyZMoXTp0+TmJhIUFAQL7zwgn3+pEmTOH78OElJSQQHB/Ppp5/m+bmvVN5flRS60+FnabCqNi6+LqR0SoUmwBFHdyUiIiJSgNyBeAc8rweQkLdF09PT+fHHH7n//vt5++237dPvuOMOXFxc+OWXX6hQoQLbt2/n/fffJyYmhkGDBjFz5kyOHj3K1q1bL/scTk5O/PXXX4SFhXH99ddTqVIlPvnkkxzLxcbGcv/993P69GnatGnDt99+S2xsLB988AG//fYbrVu3ZsCAAfaAFR0dnWMb7u7uLFmyhI0bN9K5c2dq1qzJd999x9SpU3nggQfsy/Xq1YuQkBB69eqFr68vv/32G7t27eK7777L09dt/PjxPPvsszz66KPs3LmTBx98kHnz5tGqVSsCAgJ48sknGTJkCCNHjuTEiRPUq1ePevXqATBixAiefvpp7rrrLvbt24e3tzft2rXL0/NeLVOSy9PT0xhjjKenp8N7uZoaO/R2MzC8q8FgeMPx/ahUKpVKpVLlV/n4+Jgff/zR+Pj4nJ/ujnHIP/cr671Zs2bGGGN69Ohhn7Z69Wrz448/XnSd+fPnmw8++MD+eOXKlWby5Mn2x4GBgWb8+PEGMP369TMpKSmmVq1a9vk333yzMcaYoUOHXvQ5nn32WbN161b740mTJpmdO3fmWC7rdh566CFz7tw54+7ubp9/yy23mLS0NFOzZk0DmGnTppnAwEDj7OxsX+a3334zv/zyy0V7ufC5T506ZSZOnJhtmc2bN5upU6cawHz66adm2bJluW7r6aefNgcPHjSurq5X931lq7xmA51uV8T9uWYF94ZYo9y53u8CTg5uSERERKQgJWAd1SnsyuNRpEyHDh1i/fr1PPjggwA0btyYm266ie+//x4AZ2dnXn75Zfz9/Tl37hyxsbHcfPPN1K9fP0/bb9GiBSdPniQkJMQ+bePGjTmWGzlyJOvWrSMkJITY2FjeeuutPD9H1ufavXs3CQnnvwjr16/HxcWFZs2a2aft27ePjIwM++OQkBBq1qyZp+fw9PSkTp06rF+/Ptv09evX06JFCwCmT59O+/btOXToEJ9++in9+vWzL/f7779Tvnx5jh07xjfffMOwYcNwcXG5otd5JRSSirgzkRFUXFGBSmkVSKuXDt0d3ZGIiIhIAUtwQF2F77//nhEjRlChQgUeeOABAgICWL16NQD/93//x/jx43n//ffp1asX7du3Z8mSJbi55d+tXW644QZmzZrFwoULufXWW+nQoQNvv/12vj5HVqmpqdkeG2Nwds6/OLFz504aNmzIK6+8Qvny5Zk9eza///47AKdOnaJZs2aMGzeOxMREvvjiC9asWXNF10RdCYWkYmDOv6sYecZ2od7Dju1FRERERCyzZ88mIyODUaNGcd999/HDDz/Y53Xt2pW///6bWbNm4e/vz7Fjx2jatGmet33gwAHq1auHt7e3fdoNN9yQbZkbb7yR48eP884777B9+3YCAgLw8fHJtkxKSsplj7gcOHCAdu3a4e5+fgSLrl27kp6ezqFDh/Lc86XExsYSHBxM165ds03v2rUr+/fvz7bc7NmzeeSRR7jzzju5/fbbqVLFuhdOUlISCxYsYPz48fTs2ZMbb7zxqoZazwuFpGLgrzUrefDUEOvBKKBgvhdERERE5ArEx8fz22+/8e6771KrVi2mT59un3fkyBH69etHly5daN68OV9//TVeXl553vayZcs4fPgwM2bMoG3btnTr1i3bIBGZz1G/fn3uvPNOGjVqxBNPPMFtt92WbZmgoCAaNmxIu3btqFatWq5HmWbNmkVSUhIzZsygVatW9OzZkylTpjBz5kzOnDlzZV+US/jggw+YMGECI0eOpGnTprz77ru0b9/ePkpd5sAMzZo1o0mTJtxxxx2EhIQQFRXF6NGjefDBB2nVqhUNGzbk3nvvJSEhgePHj+dbf1kpJBUDkbExRKyM4c4z/aw99oGjOxIRERERwD7c95IlS7JdP/TWW2+xY8cOlixZwqpVqwgNDWXu3Ll53q4xhttuu43y5cuzZcsWvvvuO1566aVsy8yfP5/JkyczdepUdu3axY033sibb76ZbZk///yTxYsXs3LlSsLDw7n77rtzPFdiYiI333wzVatWZevWrfzxxx8sX76cxx9//Mq+GJfx2Wef8fHHH/PRRx+xZ88eBgwYwJAhQwgICACso0jPP/8827ZtY+vWrTRo0ICBAwdijCEqKoqHH36Y9evX4+/vT9++fRk8eDARERH52mNWDh/ZpCCruI9ul1n39rvFHNs017iluVojsNzs+J5UKpVKpVKprqUuNQqZSnW1pdHtSpHfVi6FQHji9J3WhA+BghvQQ0RERESk1FJIKiZS09J4bfq3vHTiQSqneEJr4H5HdyUiIiIiUvIoJBUjPy1dROjRc0w68ZA14U2scf1FRERERCTfKCQVIxkZGbz6w9eMO30HDeNrQy3gOUd3JSIiIiJSsigkFTN/rlnBvsPH+F/Qk9aE/8MKSyIiIiLFjDEGoMBuCCqlU+b3U+b319VQSCpmjDG8/P2XjAjvzfWRra3T7d6+7GoiIiIiRc65c+cAaN68uYM7kZIk8/spPDz8qreh2F4MLdy0no179zC54tPcWOW/8AAwC1ju6M5ERERE8i4+Pp5Vq1YxcuRIAA4ePEhaWpqDu5LiytXVlebNmzNy5EhWrVpFQkLC1W8rH/uSQvTSd1+wsvVXPHpyOF/X+wu+B9oAsY7uTERERCTvpk2bBsCdd97p4E6kpFi1apX9++pqOWHdMKnE8vT0JCYmhooVKxIbW7ISxNKPPueGzq1p2HIo4dWi4GtgjKO7EhEREbly7u7uVK9eHScnJ0e3IsWUMYbw8PBLHkHKazZQSCrGrmvRis1fTmdFxa306TDOmtgfWOrQtkREREREiqS8ZgMN3FCMbTmwj5n/LqR3TGfu3NvPmvg9UNGhbYmIiIiIFGsKScXc/331GVFxsXwf+QoVQzygHvCxo7sSERERESm+FJKKubCIc7z03Zd4ZJTnl8C3IQP4L3CLozsTERERESmeFJJKgK/m/cn2QwcYmNqV/huutyZ+C7g7tC0RERERkWJJIakEyMjIYNwn75ORkcEc8yFlg92gDvCIozsTERERESl+FJJKiC0H9vHtgrm4Z5RjYtD91sTngXKO7EpEREREpPhRSCpBXvzuC85GRTIx/X48z3pALeAhR3clIiIiIlK8ODQkde/enXnz5hEcHIwxhqFDh+ZY5vXXX+f06dMkJCSwdOlSfH19HdBp8RARE83zX03BzZThzTOPWhNfAMo6tC0RERERkWLFoSHJw8OD3bt389hjj+U6//nnn+fJJ59kzJgxXH/99cTHx7NkyRLKltW7/ouZsWQBOw4fZMy5EXhGuFvXJj3g6K5ERERERIoXUxTKGGOGDh2abdrp06fNs88+a39csWJFk5iYaO688848b9fT09MYY4ynp6fDX2Nh1Z29+xmzaqt5d/djBoPhOIYyju9LpVKpVCqVSqVyZOU1GxTZa5IaNmxIrVq1WLZsmX1aTEwMmzdvpkuXLhddz83NDU9Pz2xV2vyxegWBIcE8FXU37hHloD4w2tFdiYiIiIgUD0U2JHl7ewMQFhaWbXpYWJh9Xm4mTpxITEyMvYKDgwu0z6IoPT2dj2f/TDlTlqcC77Ymvgi4OrQtEREREZFiociGpKv17rvvUrFiRXvVqVPH0S05xA+L5nEuOoqXEh6kbEQZaAjc6+iuRERERESKviIbkkJDQwHw8vLKNt3Ly8s+LzcpKSnExsZmq9IoISmJL/7+E/eMcjxyaLg18SXAxaFtiYiIiIgUeUU2JAUGBhISEkKfPn3s0zw9Pbn++uvZuHGjAzsrPqb89RtJKcm8kzaOMhGu4AuMdHRXIiIiIiJFm8OHAG/Xrh3t2rUDrMEa2rVrR7169QD45JNPePnllxk8eDCtW7fmxx9/5PTp08ydO9eBXRcfZ6Mimb54ARUy3Bm5t6818VnH9iQiIiIiUhw4bAi+Hj16mNxMmzbNvszrr79uQkJCTGJiolm6dKlp0qRJgQzzV1LLt049k75iszm7bqlxSnCyhgTv6fi+VCqVSqVSqVSqwq68ZgMn2ycllqenJzExMVSsWLHUXp/0x+vvM6JHb3q7jWVll23wD3Cro7sSERERESlcec0GRfaaJMk/H/w2E4CpGc9DBjAIaOnQlkREREREiiyFpFJg8/69rNuzi5ZpDWm+vYE18RmHtiQiIiIiUmQpJJUSX837C4B3Yh6zJtwLXPyevCIiIiIipZZCUinxx+rlnIuO4jbXnlTx94SywBOO7kpEREREpOhRSColklNSmLHkHwAePWy7uexYwMNxPYmIiIiIFEUKSaXItwvmAvB61UdwOeYCVYAHHdqSiIiIiEiRo5BUihw8EcTqXTtwc3Gj/9rrrYlPAy4ObUtEREREpEhRSCplvlkwB4DJFZ6GcKAhMNyhLYmIiIiIFCkKSaXMn2tWEB4dRbPqDfD9u5418TnH9iQiIiIiUpQoJJUyySkpzFhsDeDwSux/IRG4Duju0LZERERERIoMhaRS6Nt/rFPu7mk7APfZ5ayJOpokIiIiIgIoJJVKh04cZ9Wu7bi4uHD/gVshAxgCNHN0ZyIiIiIijqeQVEp9M986mvRCx/thvm3iMw5rR0RERESkyFBIKqUyB3CoV9OLLovaWBPvA2o6tC0REREREYdTSCqlUlJTmb54AQAvNngANgHlgMcc2paIiIiIiMMpJJViX8/7C4CB13elxndVrImPAeUd15OIiIiIiKMpJJViAcEnWbJlI87OzjzlejccA6oBDzi6MxERERERx1FIKuW+nPcnAA/fMgyXT23fDs+g7wwRERERKbX0VriUW7BxHSfPhFGjchWGH+0FEUBj4DZHdyYiIiIi4hgKSaVcenq6fTjwJwfcBV/YZkxwXE8iIiIiIo6kkCR8989cUtPS6NamPS3+bggJQGegn6M7ExEREREpfApJQmjEOeasXQnAE91Hwje2GS87ricREREREUdRSBIAvvzbGsDh3n634PF5eUgGbgK6ObQtEREREZFCp5AkAKzatZ0DxwPxdPfgvlaDYLptxkuO7EpEREREpPApJIld5tGksUNHwPtAGjAA6OTIrkRERERECpdCktj9+O8/xCcm0qaRL90qtIefbTNedGRXIiIiIiKFSyFJ7KLj4vh5+RLAdjTpXSAD655JrRzZmYiIiIhI4VFIkmy+/PsPAG7v0YcaoVXgL9sMHU0SERERkVJCIUmy2XnkEJv278GtTBn+O3AovG2bcSfQ2JGdiYiIiIgUDoUkySFzAIdHh9yGs78z/AO4AC84tC0RERERkUKhkCQ5zF65jHPRUTTwrs0t1994/mjSaKCeIzsTERERESl4CkmSQ1JKMtMWLwBg7JARsBFYAZQB/s+RnYmIiIiIFDyFJMnVV/OsU+5uuf5GGnjXPn806SHAy2FtiYiIiIgUOIUkydXR4FMs2bIRZ2dnHh0y3DqStAkoDzzj4OZERERERAqQQpJc1Je2o0n/HTgEtzJlzh9NGgtUcVhbIiIiIiIFSiFJLmrBxnWcPBNGjcpVuL1HH1gA7AI8gScd25uIiIiISEFRSJKLSk9P55v5cwAYO3SENfEd28zxWGFJRERERKSEUUiSS/run7mkpqXRrU172jTyhT+Bg1in2411cHMiIiIiIgVAIUkuKTTiHHPWrgRsR5MygHdtM58ByjmqMxERERGRgqGQJJf1xd9/APCf/gPxdPeAn4FArKHAH3JkZyIiIiIi+U8hSS5r9a4d7A86RoXy7tzb7xZIA/5nm/k81k1mRURERERKCIUkyZOv5v0FZBnAYRpwGqgH3OeorkRERERE8p9CkuTJj//+Q3xiIm0a+dKtTXtIBj60zXwBcHFcbyIiIiIi+UkhSfIkOi6On5cvAbIcTfoaCAd8gTsd1ZmIiIiISP5SSJI8+9I2gMPtPfpQs0pVSAAm22a+CDg5qjMRERERkfyjkCR5tvPIITbt34NbmTI8eMsQa+LnQDTQChjqwOZERERERPKJQpJckS/mWkeTxgwZjrOzsxWQptpmvuywtkRERERE8o1CklyR2auWcS46Ch/vWtxy/Y3WxE+AeKAjcLPjehMRERERyQ8KSXJFklNSmLZ4AQBjh9gGcAjHGsQBdDRJRERERIo9hSS5Yl/N+xOAW66/kQbeta2JH2INC94NuMlRnYmIiIiIXDuFJLliR4NPsWTLRpydnXl0yHBrYgjwg22BlxzVmYiIiIjItVNIkqvyhW048P8OHIJbmTLWxP8BaUB/oIujOhMRERERuTZFOiQ5OzvzxhtvcOzYMRISEggICODll3XRS1Hwz6b1nDwTRo3KVbi9Rx9rYhAw3bbAO47pS0RERETkWhXpkDRhwgTGjh3L448/TosWLZgwYQLPP/88TzzxhKNbK/XS09P5ev5fAIwbevv5GW9gXZvUE+jrgMZERERERK5RkQ5JN954I3///TcLFy7k+PHj/Pnnn/z7779cd911jm5NgO/++ZvUtDS6tmlH28ZNrIkngS9tC+hokoiIiIgUQ0U6JG3YsIE+ffrQpIn1Brxt27Z069aNRYsWXXQdNzc3PD09s5UUjLCIc8xZuxKAsUNHnJ/xDhAHdAZuc0RnIiIiIiLXxhTVcnJyMu+++65JT083KSkpJj093bzwwguXXGfSpEkmN56eng5/PSWxerT3M2bVVhO7aLXxdPc4P+8NDAbDPgzOju9TpVKpVCqVSqXy9PTMUzYo0keSRo4cyT333MOoUaPw8/Nj9OjRPPfcc9x3330XXefdd9+lYsWK9qpTp04hdlz6rN61g/1Bx6hQ3p17+91yfsZHQATQErjHQc2JiIiIiFwlhye6i9WJEyfMuHHjsk176aWXzIEDB/I9Laquvh6/baQxq7aaPdN+zT7v/7COJh3DUMbxfapUKpVKpVKpSneViCNJ7u7uZGRkZJuWnp6Os3ORbrvU+fHff4hPTKR1w8Z0a9P+/IypWDeZbQg87JjeRERERESuVJFOG/Pnz+ell15i4MCB+Pj4MGzYMJ555hnmzJnj6NYki5j4eH5evgSAccOyDAeeCLxp+/wVwL2wOxMRERERuToOP+x1sapQoYKZPHmyCQoKMgkJCSYgIMC8+eabpkyZMvl+SE11bdWhSTNjVm01yUs3mJpVqp6fVwbDUazT7l53fJ8qlUqlUqlUqtJbec0GTrZPSixPT09iYmKoWLEisbGxjm6nRNv4xQ/c0LINL377Oe/Omn5+xgjgD6ybzLYGAhzSnoiIiIiUcnnNBkX6dDspXr6Y+wcAjw4env26sT+BJUBZYIojOhMRERERyTuFJMk3s1ctIzw6Ch/vWgy+sXv2mY9jHUkagG4wKyIiIiJFmkKS5JvklBS+mT8HgKduvzv7zADgf7bPP0GDOIiIiIhIkaWQJPnqi7//IDUtjZ7tO9Let2n2me8CgUB94GUHNCciIiIikgcKSZKvgs+e4fdVywAYf+HRpERgvO3zZ4FmhdmZiIiIiEjeKCRJvvvkj18AuLt3f7yqVss+c76t3IDPC7szEREREZHLU0iSfLf14H427PWnrJsbY4YMz7nAk1hHlfoAd+ecLSIiIiLiSApJUiAyjyaNHTKCsm5u2WcGAW/bPp8CeBdiYyIiIiIil6GQJAXir7UrOREWilfVatzVu3/OBd4HtgPVgG8LuTkRERERkUtQSJICkZ6eztQ5s4FchgMHSAPuA5KAW4EHC7E5EREREZFLUEiSAvPdP38Tn5hIe9+m9Gjvl3OB/ZwfCvwTwKfwehMRERERuRiFJCkwkbExzFjyD3CRo0kAk4G1gCcwDXAqpOZERERERC5CIUkK1Gd//QrAkBtvonGdujkXyADuB+KAXsAThdebiIiIiEhuFJKkQB06cZx/Nq7D2dmZCXePzn2hY8Bzts/fQzeZFRERERGHUkiSAvf2T9MAuH/Arfh418p9oa+BJUB54A+gYiE1JyIiIiJyAYUkKXAb9/mzdNtmyri6MnHU/Rdf8EHgNNAa+B1wLZT2RERERESyUUiSQvHGjO8AeOCWwdSr6ZX7QqexhgOPA/oDXxRScyIiIiIiWSgkSaFYt2cXK3Zsxa1MGV641NGkncBdQDrwMPB8obQnIiIiImKnkCSF5o0fraNJ/x04hDo1al58wX+Ap2yfvw/cXsCNiYiIiIhkoZAkhWb1rh2s3rWDsm5uTLj7vksvPBX41Pb5TOCGAm5ORERERMRGIUkKVebRpIdvHUatatUvvfAzwDygHLAQ6FzAzYmIiIiIoJAkhWzFjq2s27OLcm5lL37fpEwZwChgPVAFWAZ0LfAWRURERKSUU0iSQvf69G8BeGTwMLyrVrv0wvHAzcBKrHsnLQF6FWx/IiIiIlK6KSRJoVu2fQsb9/lTvmy5S490lykeGAQsBjywTr0bUJAdioiIiEhpppAkDvHqD18DMGbI8IvfNymrRGAoMBfrGqW/gWEF1Z2IiIiIlGYKSeIQy7ZvYeXObZR1c2PS6IfztlIKcAfwG+AG/AE8WWAtioiIiEgppZAkDvPit18AcP+AW2lazydvK6UB9wDfAi5Yw4R/A5QpkBZFREREpBRSSBKH2bR/D/PWr8HFxYU3Hng07yumA48AT9s+fxhYClxmDAgRERERkbxQSBKHevn7L8nIyODO3v1o79v0ylb+BLgViAZ6AFuAlvndoYiIiIiUNgpJ4lB7jgXwy/J/AXj7oXFXvoHFQBfgKNAI2ATcmX/9iYiIiEjpo5AkDjdp+tekpacx8IaudGvT/so3cAC4DlgBeAK/Al9hjYInIiIiInKFFJLE4Y4Gn+L7f+YB8M7DV3E0CSAC6A+8CWQAj2IdVbrCM/hERERERBSSpEh448fvSEpJpnvbDtx8XZer20g68CpwMxAGtAO2Y42GJyIiIiKSRwpJUiScDj/L1Dm/A/DxuKdwK3MNY3ovA9pjnX5XAfgJ+B4of61dioiIiEhpoJAkRcbbM38gLOIcLRs04vm77ru2jYUC/bCOLKUDDwJb0eh3IiIiInJZCklSZETFxTJ+6kcAvPyfB/N+g9mLycC6RqkPEAK0wgpK91/bZkVERESkZFNIkiLltxVLWbR5A2Xd3Pj62Yn5s9HVWNcnLQHcgWnAj1in4omIiIiIXEAhSYqccZPfJz4xkZ7tO/LALYPzZ6NngVuAF7FOv/sPsAfrKJOIiIiISBYKSVLkBIWeZtL0bwD4cOx4alSukj8bNsC7QE8gEGiANcjD10DF/HkKERERESn+FJKkSPrkj1/YeeQQVStWYvJjz+TvxtcBbYCptsePAHuBAfn7NCIiIiJSPCkkSZGUnp7Owx++TXp6Ovf0G0D/zjfk7xPEA08ANwEBQD1gETAdyKcDVyIiIiJSPCkkSZG1/dABPvvrNwC+fnYinu4e+f8ka4G2wEdYo+GNBvYBQ/L/qURERESkeFBIkiLtlR++4tjpYBp41+bjx54qmCdJBJ4DugIHgFrA38DPQLWCeUoRERERKboUkqRIi09M5P73XicjI4OHBg3j1i7dC+7JNgEdsAZ3SAfuBvYDdxXcU4qIiIhI0XNVIalu3brUqVPH/rhz585MnjyZhx9+ON8aE8m01n8nH82eBcB3//cS1StVLrgnS8YaJvx6rCHCawK/YJ2W17HgnlZEREREio6rCkk///wzvXr1AsDLy4ulS5dy3XXX8fbbb/PKK6/ka4MiYJ12tzfwKF5Vq/HVM/l0k9lL2Y4Vil7BGuShG7ANa2CH2gX/9CIiIiLiOFcVklq3bs2WLVsAGDlyJHv37qVr167cc8893H///fnZnwgAySkp/OftV0lNS2NEj97c0++Wgn/SVOAtoBnwo23aaOAw8BLgVvAtiIiIiEjhu6qQVKZMGZKTkwHo27cv8+bNA+DgwYPUqlUr/7oTyWJXwGFes91kdur4/6NuDa/CeeJgrHB0HbAe8MAKT7uBHoXTgoiIiIgUnqsKSfv27WPMmDF069aNfv36sXjxYgBq167NuXPn8rVBkaze/+VHNu3fQ+UKnkx/YRIuLi6F9+RbsU67uxsIAZoDq4BpaBQ8ERERkRLkqkLShAkTePTRR1m1ahW//PIL/v7+AAwZMsR+Gp5IQUhPT+e+d14jISmJPh078+XTLxR+E78CLYAvsO6tdD9w0PZR40WKiIiIlAjmasrZ2dlUrlw52zQfHx9To0aNq9peQZWnp6cxxhhPT0+H96LKvxrarYdJW77JmFVbzesPPOq4Xq7HsAtj/7cfwz0YXBz/NVKpVCqVSqVSZa+8ZoOr+rt3uXLlKFu2LFFRUQDUr1+f8ePH06xZM86ePXs1m7yo2rVrM3PmTMLDw0lISMDf35+OHTUWc2n397rVjPvkfQBeHf0QY4aMcEwjm4FOwP8BkVhHmH7Cuint/YCrY9oSERERkWtzxQlsyZIl5tFHrb/eV6pUyYSEhJgTJ06YhIQEM2bMmHxLepUrVzaBgYHmhx9+MJ07dzYNGjQw/fr1M40aNcr3tKgqnjXp/oeNWbXVpK/YbG7r3sux/XhieAHDWc4fWTqGYRyG8o7/WqlUKpVKpVKV9rqCbHDlGz979qxp2bKlAcx///tfs2vXLuPk5GRuv/12s3///nx7Ee+++65Zs2ZNYX0hVMW0vnpmojGrtprEf9eZm9r5ObwfPDA8iyGU82HpDIZXMVRz/NdLpVKpVCqVqrRWgZ5u5+7uTmxsLAD9+/fnr7/+whjDpk2b8PHxuZpN5mrIkCFs27aN2bNnExYWxo4dO3jooYcuuY6bmxuenp7ZSkq2xz79H3PXraKcW1n+fvtDmtdv4NiG4oGPgIbA48AxoAbwOnAC+Axo4KjmRERERCQvrjiB7d692zzxxBOmbt26Jioqytxwww0GMH5+fiYkJCTfkl5iYqJJTEw0b7/9tmnfvr15+OGHTUJCgrnvvvsuus6kSZNMbnQkqWRXObeyZu2Ub41ZtdUc/ulPU7lCEdrfLhhGYtjG+SNLaRh+xtChCPSnUqlUKpVKVUqqQE+3GzFihElOTjZpaWnm33//tU9/4YUXzMKFC/PtRSQnJ5v169dnm/bpp5+aDRs2XHQdNzc34+npaa/atWsrJJWSqlG5ign6dZ4xq7aaJR9MMS4uLg7vKUf1xrAYk+3fvxj6FoHeVCqVSqVSqUp4FWhIAoyXl5dp3769cXJysk/r3LmzadasWb69iKCgIPPtt99mmzZmzBhz6tSpgvhCqEpAtfNtauIWrTFm1Vbz0binHN7PRasdhp8wpHI+LO3AcBcaPlylUqlUKpWqgKpAr0kCCAsLY9euXdSuXZs6deoAsHXrVg4dOnS1m8xh/fr1NGvWLNu0pk2bcvz48Xx7DilZdgcc5v73XgfgmZH3cN/Ngxzc0UXsBu4FfIFPsa5j6gD8AhzBupbJ3WHdiYiIiJR6V5zAnJyczCuvvGKioqJMWlqaSUtLM5GRkebll1/OdmTpWqtTp04mJSXFTJw40TRu3NjcfffdJi4uzowaNSrf06KqZNXrDzxqzKqtJunf9eb6lq0d3s9lqyqGl7FGwcv8F47hLQy+RaA/lUqlUqlUqhJQBXq63TvvvGPCwsLMmDFjTJs2bUybNm3M2LFjTVhYmHnrrbfy9YUMGjTI+Pv7m8TERLN//37z0EMPFdQXQlWCysnJyfz15v+MWbXVnP5zkalbw8vhPeWpymEYg+EI2a9bWo/hEQyVikCPKpVKpVKpVMW0CjQkBQcHm8GDB+eYPmTIkCu6XqiIfSFUJawqlHc3/j/8Ysyqrebgj3+YmlWqOrynPJczhuEY/sEaCS/zXyKGXzB0LwI9qlQqlUqlUhWzKtBrkqpWrcrBgwdzTD948CBVq1a9mk2K5Lu4xAQGvfAUx0NDaFbfh6UfTqVqxUqObitvMoC/gEFAXeA5YA9QDrgLWIN1XdMjgIeDehQREREpoa4qJO3evZvHH388x/THH38cf3//a25KJL+cPBNGn2fHEXIunLaNm7D4f5/h6V7MUkUo1s1p2wJ+wNdYAz20tX1+CvgE6AI4OaZFERERkZLECeuQ0hW56aab+Oeffzhx4gQbN24EoEuXLtSrV4+BAweybt26/O7zqnl6ehITE0PFihWJjY11dDviIC0bNGL1p19TvVJl1vrvZMDzT5KQlOTotq5eZeB+YBzQJMv0UGAe8DewHEgu7MZEREREiq68ZoOrOpK0Zs0amjZtypw5c6hcuTKVK1fmr7/+olWrVvznP/+56qZFCsr+oGP0f+5xouJi6d62A3Pe/ICybm6ObuvqRWEdPWoG3II1dHg04I11Ct4/QDgwG7gHK1SJiIiISJ5c1ZGki2nbti07duzA1dU1vzZ5zXQkSbK6oWUbln40lQrl3Vm4aT3DX32e5JQUR7eVP8oAPYBhwFCsa5kypQGrgblYR5lOFnJvIiIiIkVAgR5JEimuNu3fw+CJz5CQlMTAG7oy7+2PKF+2rKPbyh+pwDKsG9HWAzoBbwF7AVegDzAFOAFsB14B2jikUxEREZEiTSFJSp1Vu7Zzy4TxxCUm0L/zDfzz3id4lC/v6LbyX9Yg5As8izUqXjrWABBvAP7AMazwNAyo4ohGRURERIoWhSQpldbs3sHN//cEMfFx9OrQiUXvf0qF8u6ObqvgHAU+xjodzxt4AOu0u0SgIdbRpzlY1zFtA/4HDEDDi4uIiEipdEXXJP3555+XnF+5cmV69Oiha5Kk2Li+ZWsW/+8zKlfwZOM+fwY8/yQx8fGObqvwuAP9gL5Ab6DlBfNTgS3AClttRCPmiYiISHbOQEWgElCB3G9JEgcEFWJPF5HXbHBFIemHH37I03IPPvhgXjdZ4BSS5HI6NmvBvx9MoWrFSmw9uN8+Cl6p5A30wgpMvYFGF8xPBnYBW7HC01bgEPk4/IuIiIgUWT5Y1zxnVnOsYOSZh3UXY43I62AFEpKKI4UkyYt2vk1Z9tHnVK9UmZ1HDtHvucc4Fx3t6LYcrwHnQ1MfoFYuy0QD64CVttoFZBROeyIiIlKAWmL9/98XuBGofpnlE4FYcn8fsBIYla/dXRWFJBuFJMmrVg0bs/yjz/GqWg3/o0fo++xjnI2KdHRbRUsjoDNwne1jR6xT9rKKwhogYg8QgHU9VAAQUmhdioiIyJVyA1oDHbCuYe5Lzj+OpmAN+rQd6xpmf+Ac1h9Mo7FO0y/iFJJsFJLkSjSv34AVk7+kVrXq7A86Rp9nxhEacc7RbRVdLkBboCfWEaebsA675yYB2I/1i3WHrfaga5xEREQKU3msQZsaAY2BdljBqBXWPRezSsT6w+dyYBWwGysoFWMKSTYKSXKlmtStz4rJX1C3hheHThyn9zNjOR1+1tFtFQ8uWL9ouwFNsH75+mKdw5zbeC5pWNc0HQAOZvl4DOsvUiX6t5OIiEgBc8K67ccgrNPmmpD7qfOZzgE7gc1Y914sgQM2KSTZKCTJ1WhUuw4rPv4SH+9aBASfpPfTYzl5JszRbRVfrljXN7XD+mXd0VaXOrc5AysoRdkqHCtA7QX22SqqYNoVEREpllyB+lh/sBxoK+9clovCOh0+EOv/051YZ3icLJQuHUohyUYhSa6Wj3ctVnz8JY1q1yEwJJheT4/leKgurMlX9bAuCm2BNUJOZnnlcf0QIAyIuKAOYI2+p5H3RESkpPIG+mNdJ+yLdfZGA3KeuREL/AssxBpc6Ril+o+MCkk2CklyLerW8GLF5C9oUrc+J8JC6fX0GI6dDnZ0WyVfWaBylqqCdXpAS6xzplth/aXscmKwLizdAhzGOo0gPMvHCEr4b0ARESm2nLBOYy+DFXzKYA2sMMBWHS6yXiLW/3nLgX+AtRSLARUKi0KSjUKSXKta1aqzYvKXNK/fgOCzZ+j9zDgOnzzu6LbEE2gKVAOqYgWpqkBNrNP6OgEel9lGPNbIPLuwTjXYCRzB+o+onK3KYv2mPEyxv1hVRESKGCesMyhacv5sima2qpiH9bdiDa19kPMjyoZQwt/dXxuFJBuFJMkPNatUZfnHX9C6YWNCzoXT55lxHDge6Oi25FJcsE7ju85WdbECVXXbxypXuL1kzl/MmlnH8qtZEREpFZyANlhDbPe0fax2BeuHYZ06txhYCmhcqSumkGSjkCT5pXqlyiz76HPa+TblbFQkQ158lk379zi6LblaLljnb3fIUn6cH0wiGUiyVebpfxc6S/bQtAVrsAkRESm9KmD9/9IY69TwrOVLzj/SxWPdEuOgrQ7Z6gzWKLBpWKfLpaGbtecDhSQbhSTJT1UrVmLJB5/RqVlLklKSefD9N/ll+RJHtyX5qRxWQLrwN2Mj4Pos1QErPF3oKNYIfFkrzbZ+5j0pGmH9Rzcf+BvrGikRESk6nLCu/ymPFWKyljtQG6iT5aMP1vDavlx6iG2AOGAd1n2HVmHdPzAtn/uXi1JIslFIkvzmUb48P730BsO69QTgtenf8Pr0bx3blBQ+N6A92YOT71VsJw3rfPI/gLlYfzkUEZHCVwnohzVs9i3kPnR2Xp3F+qNZEHAiSx3HurG6QpHDKCTZKCRJQXB2dubdhx/j+bvvA+DnZYt58H9vkpyiK/tLtapYf3m8sFyw/rM8lqWqASPIOTrRWayLbzMrEGuUviSsEYuSgAQ0kISIyJUqA9yANbBPFayBETLLG+uPXVmHz47DGgnVw1buWeaFAaeBYNvHU1gD/xzB+t2tU6+LLIUkG4UkKUgPDhzCV89MpIyrKxv3+XPbK88TFqFzp+QKNMYKS7cDna9gvWis0/X+xLqANyn/WxMRKTa8sU5lTrygqgN9sY4Q9cS6XuhSDmDdT2ghOYfOdsIKSiloSO1iTCHJRiFJClrP9h358433qVqxEsFnzzD81efZcmCfo9uS4ijzYl/fLNUA6z/l8pwflrwy2S/8jcP6D90f6zd61org/LVR+hUoIiXNTcATwDBy3kQ1N2eB1UAo1lH66CwfN2EdvZcSTSHJRiFJCoNvnXr8/faHtGzQiOSUFMZOfo9pi+Y7ui0pqZywThnJPALlk8f1gjg/glI4EIkVoiKw3jgcwRq0QkSksHlgDXzghvU7ztn20Qnr91IC1pGhBKzreW7DCkdts2wjEOuUuvK2cretswZYhjVkduYfk6TUUkiyUUiSwlKhvDs/vvgat3XvBcDnc37n6c8/JjVNV2dKAesIDAW8OP+mwtlW3lj35KiTh+2kYQWlPVnqGNaFxjH53rWIlEaVsH4vNcAa/KY91rWZTbB+Z12peOAnYCrWEfMLlfh3unKlFJJsFJKkMDk5OfHSvQ/y5n/HALDWfycjX5tIqK5TEkfLHFSiDdYpfVVs0zKrFpe+wW40Vlg6AURh/WU3axmsU11csf6S64p1pGoZ1lC3GmRCpHRwwTpVuCXQyvaxEVYw8sI6ZfhizmCFnszThTPvCeTG+dOO3bHC1FHgc2Aa1u8kkTxSSLJRSBJHGNSlG7NeepNKFSpwLjqKRz56h7/WrHR0WyKXVhsrRGWGqVZYf+2tfol18iIe614gi7GuBQjHuo4qjhL+P5BIEdYM61TdAKxTcS+8SWkNoDfQB2vUtziskdyCsUZyO411ilxdoF6Wj43I/R5yWUXZtuMP7AJ2ArvJ+y0Q3NAfXuSqKSTZKCSJozSt58Mvr7yFX9PmAExfvIAnP/uQ2IR4B3cmcoXcyX7HeE+sN0HlbB8z3xBdeGf4RsDNXPrGivGcP0p14dDnLpw/4lUFa8CKGKzhz49gnQqoEaZEzqsEdMU64nIO6w8S4bbPvbECT29bZT0FNxnr5yrzesWuZL/W50rFYY0Stw/rnkCHgRCswRLC0LWP4lAKSTYKSeJIZVxdee3+R5hw9324uLgQFHqa/7w9iXV7djm6NZHC0wYYgBWY/LDuSeKSD9tNx/oL+BHOB6fMz4+T8y/jWblg/dW7NtYphKfyoR+R/FYe654+XbCO8J7D+t7OrBCsn6/eQC+s6xPz+rOVhPWHhoa258nNLmAF1sAHrljBKrNqY/2R4xRw0lanbNs8QQl/dynFmUKSjUKSFAVd27Tjx4mv06h2HTIyMvjfrzN59YevNKiDlF7lsIY8r4B1pKgh54c9b2x7nIJ1XVNmRdmWbYp1kfel7neSjvWG8myWiuf86UD1yD5ccATW6T7+to+htuWzVpzto07zkYtxw/ojQAWs79eoiyznDXS3VX3O3yw6s8phneLWjrwNa53VYayjNdVtVRUrOKUDW4HlttqAdUTH2dZDc1vVArYBK7GOKomUMApJNgpJUlR4unsw+bGn+e+goQDsPHKIe99+lf1BxxzcmUgx5c35wNQky+e+XPri8ExJWGGoLlf2RjSN88EpBesNaNZKt233NNZ1F6dtj1Owjm6l2z6mYYW3U7YqzDNxy2Kdqnipo20FxRvrtRbUf8mVsa6nqY4VlAO59GmZ7lgDCmSe1plZ5cg+TH4E1vdMI6zreTLL17Z8RayQlFUk1pGVY7Y+qmHd16fJFbyeYGAj1nU7lbCuI8qsWlhHlJZjHfFZifX9lpUz1tckmcL9HhMpohSSbBSSpKgZ1q0n3zz3IjUqVyEpJZkJX09lyl+/YUyJ/lEUKTxOWG96a1xQnlinBAVivWkNxfof0A1rBK62WH+5b4P1htnjgrrcxejXKgorLJ3j/MAWmUevynB+dDAv2+dlsY4YZF7rEWJ7HE/2oxJpWEcKfDkfIutgBaTMe2SF2z7GYYW4dNt66VjhLh7r/jTxl/j8wmmZB8qbYx0xucn2MfO+XgexjlhswzrCcc722mpmeZ3lyH5tTThWUKmLNahIZtXn/D6/MKikYwWJI1jXuzlhHUmsZ1uvao49ce0SsMLXxWRgHbFci3Xdjhvn7+2TeerbDqxwdKlTQTNDuYjkmUKSjUKSFEVeVavx/f+9zKAu3QBYum0zD7z/BsFn8zq0j4gUOldyBic3coYKN6wQU9tWdbDe+Ltival1tn0sg/XGvi7WEYKSJgXra3JhWEgnf65Ju5RorHBVk0uflpkpnuyndkZihbHKZB8q3wMrZB8GDtnqMFZ4i7FVHFYIcscKcI1s1RArtK7FOtUt+hpfo4hcFYUkG4UkKcoeHTKcj8Y+hUf58pyLjmL0e6/zz8Z1jm5LRAqbJ1aYqov1xjzzei0P27xUrKNEYZwfISwJK4zVylI1OX8/mcwqg3U0IuvogcewgsqFR9vcOX/aYGaoK2ub7pHLx9w+vzAAJQKbsMLBGtvn5bEGGeiMNTBBJ9vrDbugErFOUauepdxtryfIVlkHMTiDFViyjp7mzfkjaL5YAeYE1lHFzI96eyBSaigk2SgkSVHXpG59Zr38Jp2btwTgo99mMfHbqRrUQUSKp6yhqhxWENFgFyJSROQ1GzgXYk8ikosjp07Q9fH/Mvn3nwF49s57WDflOxp413ZwZyIiVyEZ63S1zKNXCkgiUgwpJIkUAalpaTzz+WSGvPgsETHRXNeiFTu/+4m7evd3dGsiIiIipY5CkkgRMn/DGjo8fC8b9vpTuYInv7z6Ntu+/tE+wIOIiIiIFDyFJJEi5kRYKD3GP8Jr078hLjGBjs1asODdyWz+cjoDrrvR0e2JiIiIlHgauEGkCKteqTLP3Xkvj982Eo/y1s0z1u/ZzYP/e5PDJ487uDsRERGR4kUDN4iUAOHRUbzwzVQa3j2UD3/7icTkJLq2aceOb2by0KBhjm5PREREpETSkSSRYqROjZpMf2ESfTteB8Bfa1by8IdvExGjuxKKiIiIXI6OJImUQMFnz9D/ucf5vy8/JSU1leE39WL397Po1aGTo1sTERERKTEUkkSKGWMMH/72EzeMe4BDJ45Tt4YXKyZ/ydy3PqRbm/aObk9ERESk2FNIEimmdh45hN8j9/L1vL8AGNqtB2unfMvGL35gRI/eODvrx1tERETkauiaJJESoGk9H565YxSjBwyinFtZAI4Gn2LCN1P4c/UKB3cnIiIiUjTkNRsoJImUIDUqV+Hx20by2LDbqVapMgC/r1rGY5/8j7NRkY5tTkRERMTBNHCDSCl0NiqSSdO+pv6dg3nzx+9JS0/jjp592T9jNnf17u/o9kRERESKBYUkkRIoISmJV3/4is6PjmZXwGGqV6rML6++zZy3PqBeTS9HtyciIiJSpOl0O5ESroyrKxPvuZ+X//Nfyri6kp6ezvyNa/lq3l/8u3UTxpToXwEiIiIidiXydLsJEyZgjGHy5MmObkWk2EhNS+ONGd/R8ZH/sGLHVlxcXBjWrSeL//cZR3+ew4RRo6lZpaqj2xQREREpMorNkaROnToxe/ZsYmJiWLlyJU8//XSe1tORJJHsmtdvwCODb+P+AbdSxbMiACmpqfy1diVfzfuT1bt2OLhDERERkYJRoo4keXh4MGvWLB5++GEiIzVCl8i1OHgiiGc+n0ztEQMZ/e5rbNznj1uZMtzVuz+rPvmaAz/+zvjb77YHKBEREZHSpliEpM8//5x//vmH5cuXX3ZZNzc3PD09s5WI5JSUksyPS/7hxsf+S/uH7uHLv/8gNiGe5vUb8Mnjz3D8t3m8OvohKpR3d3SrIiIiIoWqyIekO++8Ez8/PyZOnJin5SdOnEhMTIy9goODC7hDkeJvd8Bhxk1+n9ojBvLoR++wO+Awnu4evP7AowTM+otxw+6gjKuro9sUERERKRRF+pqkunXrsm3bNvr168eePXsAWLlyJbt27broNUlubm6ULVvW/tjT05Pg4GBdkyRyhUb06M07D42jaT0fAAKCT/LqD1/z19qVJKekOLg7ERERkSuX12uSinRIGjp0KHPnziUtLc0+zdXVlYyMDDIyMihbtiwZGRmX3IYGbhC5eq4uLvx30FBeu/8RvKtWAyA6Lo55G9bw+6rl/LttkwKTiIiIFBslIiRVqFABHx+fbNOmTZvGwYMHef/999m3b99lt6GQJHLtPMqX56nb7+bRwcOz3YxWgUlERESKkxIRknJzudPtLqSQJJJ/nJycuKFlG+7o2Yfbe/RRYBIREZFiRSHJRiFJpGBkDUx39OxD3RrZA9PPyxcz5a/ZHDge6MAuRURERM4rsSHpSikkiRS8zMA0sldfbu/RO1tgWrptM5/9+RsLN6+/7DWEIiIiIgVJIclGIUmkcDk5OdGzfUcev20kQ7vehIuLCwDHTgcz89+FLN+xlU3795CaZUAWERERkcKgkGSjkCTiOD7etRg39HYeGjSUqhUr2afHJyaybs8ulu/YyqLNG9gbeNSBXYqIiEhpoZBko5Ak4njly5bljp59GXBdF3p36ISXbTjxTHsDj/LL8iX8svxfAkN0A2gREREpGApJNgpJIkVP64aN6dPxOvp27Ey/jtdT1s3NPm/jPn9mLVvMj0sWEpsQ78AuRUREpKRRSLJRSBIp2ipVqMDw7r24u8/N9O7QyX4NU0x8HNMWLWDKnN84GnzKwV2KiIhISaCQZKOQJFJ8eFWtxp29+vHo4Nto2aARABkZGfyzaT1T58xm1a7tpKSmOrhLERERKa4UkmwUkkSKp36drmf8iLsY1KWbfVpSSjJbD+5nrf8u1u3ZxYZ9/kTHxTmwSxERESlOFJJsFJJEircmdevzxPCRjOzZN8eAD+np6cxZt4r3f/6RbYf2O6hDERERKS4UkmwUkkRKjiZ169OtTTu6tWlPtzbtaFrPxz5v2fYtvPfzDJZv3+LADkVERKQoU0iyUUgSKblaNmjE83f9h1F9B1DG1RWAbYf288PC+SzZupFjpzWcuIiIiJynkGSjkCRS8tX38uaZO+7hoUFD8Shf3j49IPgkS7Zs4t9tm1i5c7uGFBcRESnlFJJsFJJESo9qlSrx34FDueX6G+naup396BJY1y/tDDjEmt07Wb17B2v9dxEZG+PAbkVERKSwKSTZKCSJlE4VyrvTq0Mn+ne+nps730CTuvVzLLP90AH+XLOC31ctJyD4pAO6FBERkcKkkGSjkCQiALWr1+Cmth3o0d6PHu38aOHTMNv8XQGH+X3VMuauW83BE0FkZGQ4qFMREREpKApJNgpJIpKbmlWqMvjG7tzRow99OnbG1eX8qXkJSUnsP34M/6MB7DkWwK6Aw6zds4v09HQHdiwiIiLXSiHJRiFJRC6nasVKDO16E3f07EuPdn64lyuXY5kTYaF88fcffPfPXM5FRzugSxEREblWCkk2CkkiciWcnZ1pXLsubRo1pk0jX9o09KVHez+qV6oMQFJKMj8vW8KUv35jV8BhxzYrIiIiV0QhyUYhSUSuVVk3N+7s1Y8nh99Jx2Yt7NOPnDrB8h1bWb59Kyt3bdMRJhERkSJOIclGIUlE8lOXVm15YvhIbu/RJ9sQ42AN/rBky0YWbFzHxv17dA2TiIhIEaOQZKOQJCIFwdPdgx7t/OjTsTO9O3SibeMm2eZHxESz2BaYFm3eQFScfv+IiIg4mkKSjUKSiBSGmlWq0rtDJwbe0JWB199INds1TACpaWks2bqRX1csZd76NcQmxDuuURERkVJMIclGIUlECpuzszM3tGzNoBu6MfjG7rRp5Gufl5icxMJNG/h7vXU/poDgU0TGxjiwWxERkdJDIclGIUlEHK15/Qbc2bsfd/e+mWb1fXLMj4iJJiD4FAeOBzJn3SoWbd5ASmqqAzoVEREp2RSSbBSSRKQoaefblLt69+fGVm1oXLsudWrUzLFMZGwMf65Zwc/LlrB69w4yMjIc0KmIiEjJo5Bko5AkIkWZe7lyNKpVB9869ejWpj139e6XLTiFnAtny4F97As6xv7jgewPOsbBE0EkJic7sGsREZHiSSHJRiFJRIoTZ2dnurdtz6g+A7i9R2+qVqyUY5mMjAzW+u9i+uIF/LF6OXGJCQ7oVEREpPhRSLJRSBKR4qqMqys3tm5L64aNaenTiFYNGtGqYSOqZxk5Lz4xkb/WrmTGkn9YuXObTs0TERG5BIUkG4UkESlp6nt5M6rPAEYPGETz+g3s089GRbJixzaW79jKsu1bCAwJdlyTIiIiRZBCko1CkoiUZNe3bM3omwdxV+/+VPGsmG1eYEgwq3btYOvB/Ww9uB//Y0c0ap6IiJRqCkk2CkkiUhq4urhwXYtW9O14HX38OtOlVVvKuLpmWyY5JQX/YwFsObCPtXt2stZ/F6fDzzqoYxERkcKnkGSjkCQipZFH+fJ0b9OBG1u3pXPzlnRu1oJqWa5lynTsdDBr/XeyevcO/lyzgpj4+MJvVkREpJAoJNkoJImIWBp416Zz85Z0adWG7m3b08G3GS4uLvb5cYkJzFj8D1PnzObgiSDHNSoiIlJAFJJsFJJERHLn6e5Bl1ZtuKltB27r3pOWDRrZ5y3dtpmpc2azaf9ezkZFYkyJ/q9CRERKCYUkG4UkEZG86e3XmSeGj2Rwl+7ZjjClpqURFnmOkHPnOB1+lk379zJ71VKOndboeSIiUrwoJNkoJImIXBkf71qMG3o7o/reTO1qNXB2ds51uW2H9jN75TJmr1rG8dCQQu5SRETkyikk2SgkiYhcPVcXF7yqVqNW1erUqladBt61uLVLN3r7dcLV5fzoeTsOH2TJ1k0s2bqRDXv9SU1Lc2DXIiIiuVNIslFIEhHJf9UrVWb4Tb0Y2bMfPdv75RgAYtWu7SzespEFG9fpKJOIiBQZCkk2CkkiIgWrRuUq9O98A/07XU//zjfgXbVatvl7jgUwf8Na5m9Yy5aD+8jIyHBQpyIiUtopJNkoJImIFB4nJyfaNPLl5s43MPCGrnRr0y7baXmRsTEEhpzm1NkzBIef4dTZM5wIC2XHkUMcOB6oUfRERKRAKSTZKCSJiDhOFc+KDLiuC0O63sSA67pQuYLnRZeNiotl8/69bNjnz8Z9e9h99AhnIiMKsVsRESnpFJJsFJJERIoGVxcXWjZoRN0aNalboyZ1qlsffevUpWPTFniUL59jncjYGA6eCOLgieMcPBHEziOH2LDPn/jERAe8AhERKe4UkmwUkkREij4XFxfaNvKlS6u2dGnVhhtatqZRrTq5Dj+elp7G9kMHWeO/kzW7d7Juzy6i4vT7XURELk8hyUYhSUSkeCrr5kaTOvVoXr8Bzes3oIVPA7q0akPDWnWyLZeRkcGeYwH20LR2zy7CIs45qGsRESnKFJJsFJJEREqWejW96N62Aze17cBN7TrQwqdhjmUOnTjOip1bWbFjGyt3beNcdLQDOhURkaJGIclGIUlEpGSrWaUq3dq046a2fvRo70fbRr45TtPbFXCYFTu2smLnNtbs3klsQryDuhUREUdSSLJRSBIRKV0qV/Cke9sO9PbrRB+/zrRp5Jttflp6GlsP7rcdZdrOlgP7FJpEREoJhSQbhSQRkdKtZpWq9Gzfkd4dOtGnY2d869TLsczhk8fZeeQwO44cZMfhg+wMOKRT9ERESiCFJBuFJBERyaq+lze9OnSid4dO9Gzfkfpe3rkudzw0hB1HDtrDk//RI5w8E1bI3YqISH5SSLJRSBIRkUupXqkyHZo0w69pczr4NsOvaTOa1K2f67JxiQkcOnHcft+mfUHH2H74ACfCQgu5axERuRoKSTYKSSIicqUqenjQ3rcZfrbw5NekOU3r1aeMq2uuy4dHR7Ht0AG2HzrA9sMH2XZov446iYgUQQpJNgpJIiKSH1xdXGhUuy7N6/vY7tvUkLaNfGnTyDfX8HQ2KtIKTocPsP3QQfYFHSMwJJi09HQHdC8iIqCQZKeQJCIiBcmtTBnaNPKlU7MWdGzagk7NWtC6YeNcg1NqWhoBwSc5eCKIgyeOs/Xgftbt2cXZqEgHdC4iUvqUiJD0wgsvMHz4cJo3b05iYiIbNmxgwoQJHD58OM/bUEgSEZHCVtbNjbaNfO2hqUOTZjSr54NH+fK5Ln/oxHHW7tnJWv9drNuzi2Ongwu5YxGR0qFEhKRFixbx66+/snXrVlxdXXnnnXdo3bo1LVu2JCEhIU/bUEgSEZGiwMnJiTrVa9pP12vdsDE3tm6b4z5OAKfDz7LWf5c9OO0NPEpGRoYDuhYRKVlKREi6UPXq1Tl79iw33XQTa9euzdM6CkkiIlKUVfGsyI2t29K9TXu6t+1Ap2YtcCtTJtsyUXGxbNjrbw9O2w4dIDklxUEdi4gUXyUyJDVu3JiAgABat27Nvn37cl3Gzc2NsmXL2h97enoSHByskCQiIsVC+bJl6dy8Fd3btqd7m/bc2Lotnu4e2ZZJSklmd8ARgkJDOHEmlBNhoZw8G0ZgyGn2BR0jXYNDiIjkqsSFJCcnJ+bNm0flypXp3r37RZebNGkSr732Wo7pCkkiIlIcubi40LaRL93bdrAHJ6+q1S66fEx8HOv3+rNm905W797BtkP7SU1LK8SORUSKrhIXkr744gtuueUWunXrRnDwxS9o1ZEkEREp6ZrUrU/bxr7Uq+FFvZpe1Pfypn5Nb5rWq0/lCp7Zlk1ISmLjvj2s8d/B6t072Lx/H0kpyQ7qXETEsUpUSJoyZQpDhw7lpptuIigo6IrW1TVJIiJSWjg7O9OmkS83te1Aj3Z+3NSuAzUqV8m2THJKClsP7ed0+FkyjCE9PYMMk0FGRgaBoadZvGUjWw/u10ARIlIilZiQNGXKFG677TZ69uxJQEDAFa+vkCQiIqVZC5+G9sDUo50ftavXuOw656Kj+HfbZhZv2ciGvf6ci4kmJiFe1zqJSLFXIkLS559/zqhRoxg6dCiHDh2yT4+OjiYpKSlP21BIEhEROa9xnbrc2KotFT08cHZyxsXZGWdnZ1xdXOjYtDn9O9+Q45S9TLEJ8UTFxREcfoZZSxfz47//EBMfX8ivQETk6pWIkGRM7q3df//9zJgxI0/bUEgSERHJOxcXF65v0Ypbrr+RAdd1oaVPI9zLlct12fjERH5evoSv5v3JjsMHC7lTEZErVyJCUn5QSBIREbk2ri4uVKpQgcoVPKlcwZMbWrZmzJARtG7Y2L7MtkP72bDXn0MnT3Do5HEOnggi+OwZB3YtIpKTQpKNQpKIiEjB6NamPWOGjOD2Hr0p6+aWY35cYgInwkIJDj9LcPhZTp0NI/jsWQ6dPM6ugMNExsY4oGsRKc0UkmwUkkRERApW9UqVubVLd1r4NKBZPR+a1fehce26lHF1veR6QaGn2XnkEDuPHGZf0FFOhIVx4kwoZyIjCqlzESltFJJsFJJEREQKn6uLCw1r1aFeTS/qVK9Bneo1qVvDqtYNG9O4Tt2LrpuUkszJM2EcDwvlaPApAoJPEmD7ePT0KRKTdZ8nEbk6Ckk2CkkiIiJFT6UKFWjv25QOvs3o0KQZTerWo35Nb2pVq46zs/NF10tLT2P1rp3MWbeSuetW67onEbkiCkk2CkkiIiLFRxlXV2pXr0H9mt40rFWbxrXr4lunLr516uFbpy5VK1bKtvzWg/uZu24Vh04e50xkJGGREZyJjCAqTv/ni0hOCkk2CkkiIiIlR6PadRjWrSe3de/Jja3aXvSoU0pqKhv37eG3lUv5c80KXeckIoBCkp1CkoiISMlUs0pVhtx4E/06XUft6jWoWbkKNatUzXEz3PT0dFbu2s5vK5ayK+AwZVxdz5eLK1FxsewMOERySoqDXomIFBaFJBuFJBERkdLFrUwZ6tX0YsiNN3FX7/5c16LVZddJSU1lV8BhNu3fw6b9e9l8YC/HTgcXQrciUpgUkmwUkkREREq3hrXqMLJXX0bc1JualauQmp5Gatr5qlOjJt5Vq+VYLyoull0Bh9kVcJidRw6x4/BB9gUdw5gS/dZJpERTSLJRSBIREZHLqe/lTZdWbbihZRtuaNmaDr7Ncr1BbmRsDGv9d7F69w7W7N7JzoBDpKenO6BjEbkaCkk2CkkiIiJypVxdXGjh0xC/ps1tw5Q3xa9pcyqUd8+2XGxCPCfCwjgXE8W5mGjOxUQTHh3Nueio85/b5p0IC9U9nkQcTCHJRiFJRERE8oOLiwsdfJvRo70fN7XtQPe27aniWTHP62eOuLd8x1aW79jClgP7SNNRKJFCpZBko5AkIiIiBcHZ2Zlm9XzwrlqNahUrUb1SZapVqkS1ipXOP65YiWqVKlGjUhUqVaiQbf3YhHi2Hz5IREwM0fFxxMTHEx0fR3h0FBv37dGpfCIFQCHJRiFJREREioJGtevQt+N19PHrTG+/zlSvVPmSy0fHxbF2z05W7drBql3b2R1wWEeeRK6RQpKNQpKIiIgUNU5OTrRt3ISWPg2p5FGBih4eVPKoQCWPCvh4e9OtTfsc93tKSEpi++EDbD6wj822YcpPnglz0CsQKZ4UkmwUkkRERKS4cXZ2pl3jJvRs35Ge7Tte9Pqn5JQUQiPOERpxjpCIcEIjznE8NJQjwSc4cuokAcEnSUhKcsArECmaFJJsFJJERESkuHNycqJpvfpc36I1N7RszfUtWtO2sS+uLq6XXTf47BkOnAhix+GD7DhykB2HDxEQfFL3e5JSSSHJRiFJRERESiK3MmXwqlKVWtWqU6tadbyrVqN2tRo0rFWbJnXr0aROPapd5LqnmPg4dhw5xMZ9e1i/dzcb9voTGRtTuC9AxAEUkmwUkkRERKS0quJZkSZ169G6YWM6Nm2BX9NmtGvchPJly+VYdn/QMbYe3E9cYiLpGemkZ2SQnpFBSmoq+48Hsmn/Ho4Gn3LAqxDJPwpJNgpJIiIiIue5uLjQvH4Drm/RihtbtaVrm3Y0r98gT+uGR0exaf8eNu3fy84jh9gbeJQTYaEF27BIPlJIslFIEhEREbm0apUq0aVlW9o29sXNtQwuLs64urji4uyMe9lytPdtSsdmzSnnVjbHurEJ8ewLOsbewKPsC7R9DDpGyLlwB7wSkUtTSLJRSBIRERG5dmVcXWnXuCk3tGzNDS3b0KZRY5rXb4BbmTK5Lh8RE20PTHsDj7Ev6Ch7A49yLjq6kDsXOU8hyUYhSURERKRguLq44FvHuuapVcNGtG7YmNYNG9OkTj1cXFxyXSc04ly2I07Wx6PExMcXcvdSGikk2SgkiYiIiBSusm5uNKvnY4WnBo3sHxvXqXvRdU6eCcP/6BF22+swR06dJCMjoxA7l5JOIclGIUlERESkaPAoX54W9RtmO+rUqkEj6tX0ynX5hKQkAkNOc+psGKfOnuGk7ePhkyfYeeQQcYkJhfwKpLhTSLJRSBIREREp2ipVqEDrho1p26gJ7Ro3oZ1vE9o2aoJ7uZxDlWfKyMjg8KkTbD90kO2HD+B/LIAjp05w6uwZHX2Si1JIslFIEhERESl+nJ2daVSrDvW9vKlX04u6NWpSt0ZN6tf0pnXDxtT38s51veSUFAJDTxMQfJKA4FMcDT5FQPApAoJPEhR6mrT09EJ+JVKUKCTZKCSJiIiIlDw1KlfBr2lzOjZtTsemLWjh04BGtepQ1s3touukp6cTGHqaLQf2sX6vP+v27GJv4FEdeSpFFJJsFJJERERESgdnZ2fq1qiJb516+NapS+Pade2f+9apl+vpe9FxcWw+sJfg8LNEx8cRFRdLdFwc0fFxHDgexI4jB0lJTXXAq5GCoJBko5AkIiIiIgDeVavRqmFjbmzVlq6t29KlVRsqelS45DqJyUlsPbifdXt2s37vbjbs9ScqTu8piyuFJBuFJBERERHJjbOzM60bNqZz85ZUr1SZyhU8qeThQSWPClSrWAm/ps2pUblKjvX2HAtg/d7drNuzm3V7dnE8NMQB3cvVUEiyUUgSERERkavVtJ4PXVu3pVub9nRt3Y5m9X1yLHMuOorT58IJjThHiO1jcPhZDp4IYn9QIKfOhjmgc8mNQpKNQpKIiIiI5Jcalatwoy00dWvTjo5NW1DG1fWS68QmxLM/KJADJ4I4djqYoNDTBIWGEBR6muDwsxo4ohApJNkoJImIiIhIQSlftiyNa9fFu2o1e9WqVp36Xt60qN+QpvXqXzJEpaalcfjkCfyPHcH/aID9o44+FQyFJBuFJBERERFxFFcXF3zr1KNlg4a08GlIA+9aNPCuTQPvWtSv6Y1bmTK5rpeckkJYZARhkRGERpwjNOIcx8NC2HMsAP+jARwPC8GYEv02vkAoJNkoJImIiIhIUeTs7Eyd6jVo1aAxbRv70rZRE9o29qV5/QZ5OoVvz7Gj7A08ypFTJwgIPsWR4JMcO32KxOTkQnoFxY9Cko1CkoiIiIgUJ2VcXe2n7nlVOX8Kn2+durRp5EtLn4aXvGnuyTNhBASf5Mipk9k/Bp8kOSWlEF9J0aOQZKOQJCIiIiIliauLC03q1rcHJt869WhS16oqnhUvul56ejqBoac5cDyIA8cDOXA8iIDgkwSFhnD6XOkYQEIhyUYhSURERERKi6oVK1mBqU49e3jyrVOPpvXqU7mC50XXS0lN5cSZUPuoe4Eh50fgCwoNIeRceIm4BkohyUYhSUREREQEvKpWo0X9BrTwaUgLH+tjQ+/a1Pfyvuw1UMkpKRwPC7EFpxACQ04TGBrMkVPW6XyxCfGF9CqujUKSjUKSiIiIiMjFOTs7U7taDRrWqp1t9L0G3rVoWKs29Wp64epy6RAVci6cwydPcPjUCQ6dPG77/DjHTgeTmpZWSK/k8hSSbBSSRERERESunouLC3Wq16CBd+1sQapx7To0qVsf76rVLrpu5nVQS7dtZtzk9wux69zlNRtcOhKKiIiIiEiplp6ezomwUE6EhbJm944c8yt6eNC0rg9N69Wnad362T56unvgW6cee44FOKDzq6eQJCIiIiIiVy0mPp5th/az7dD+HPO8q1ajWX2fYnfvJoUkEREREREpEKER5wiNOOfoNq6Ys6MbEBERERERKUoUkkRERERERLJQSBIREREREclCIUlERERERCQLhSQREREREZEsFJJERERERESyKBYhady4cQQGBpKYmMimTZvo3Lmzo1sSEREREZESqsiHpJEjR/Lxxx/z+uuv4+fnx+7du1myZAk1atRwdGsiIiIiIlICFfmQ9Mwzz/Dtt98yffp0Dhw4wJgxY0hISODBBx90dGsiIiIiIlICFemQVKZMGTp27MiyZcvs04wxLFu2jC5duuS6jpubG56entlKREREREQkr4p0SKpevTqurq6EhYVlmx4WFoa3t3eu60ycOJGYmBh7BQcHF0arIiIiIiJSQhTpkHQ13n33XSpWrGivOnXqOLolEREREREpRlwd3cClhIeHk5aWhpeXV7bpXl5ehIaG5rpOSkoKKSkphdGeiIiIiIiUQEX6SFJqairbt2+nT58+9mlOTk706dOHjRs3OrAzEREREREpqYr0kSSAjz/+mBkzZrBt2za2bNnCU089hYeHB9OmTbui7WgABxERERGR0i2vmaDIh6TZs2dTo0YN3njjDby9vdm1axcDBgzgzJkzeVo/8wuhARxERERERASsjBAbG3vR+U6AKbx2HKN27dqX/CIUFk9PT4KDg6lTp06R6Eey0/4purRvijbtn6JN+6fo0r4p2rR/iq5r3Teenp6cPn36kssU+SNJ+eFyX4TCFhsbqx+2Ikz7p+jSvinatH+KNu2fokv7pmjT/im6rnbf5GWdIj1wg4iIiIiISGFTSBIREREREclCIakQJScn89prr5GcnOzoViQX2j9Fl/ZN0ab9U7Rp/xRd2jdFm/ZP0VUY+6ZUDNwgIiIiIiKSVzqSJCIiIiIikoVCkoiIiIiISBYKSSIiIiIiIlkoJImIiIiIiGShkFSIxo0bR2BgIImJiWzatInOnTs7uqVS54UXXmDLli3ExMQQFhbGnDlzaNq0abZlypYty9SpUwkPDyc2NpY//viDmjVrOqjj0mvChAkYY5g8ebJ9mvaNY9WuXZuZM2cSHh5OQkIC/v7+dOzYMdsyr7/+OqdPnyYhIYGlS5fi6+vroG5LF2dnZ9544w2OHTtGQkICAQEBvPzyyzmW0/4pHN27d2fevHkEBwdjjGHo0KE5lrncvqhSpQo//fQT0dHRREZG8t133+Hh4VFYL6HEutS+cXV15b333sPf35+4uDiCg4OZMWMGtWrVyrYN7ZuCk5efnUxffvklxhjGjx+fbXp+7h+jKvgaOXKkSUpKMvfff79p0aKF+frrr01ERISpUaOGw3srTbVo0SIzevRo07JlS9O2bVuzYMECExQUZNzd3e3LfPHFF+b48eOmV69exs/Pz2zYsMGsW7fO4b2XpurUqZM5duyY2bVrl5k8ebL2TRGoypUrm8DAQPPDDz+Yzp07mwYNGph+/fqZRo0a2Zd5/vnnTWRkpBkyZIhp06aNmTt3rjl69KgpW7asw/sv6TVx4kRz9uxZM3DgQOPj42NGjBhhYmJizBNPPKH944AaMGCAefPNN82wYcOMMcYMHTo02/y87IuFCxeanTt3muuuu8507drVHD582MyaNcvhr62416X2TcWKFc2///5r7rjjDtO0aVNz/fXXm02bNpmtW7dm24b2jWP2T9YaNmyY2blzpzl16pQZP358Qe0fx39BSkNt2rTJTJkyxf7YycnJnDp1ykyYMMHhvZXmql69ujHGmO7duxuwfkEmJyebESNG2Jdp1qyZMcaY66+/3uH9loby8PAwhw4dMn369DErV660hyTtG8fWu+++a9asWXPJZU6fPm2effZZ++OKFSuaxMREc+eddzq8/5Je8+fPN9999122aX/88YeZOXOm9o+DK7c3epfbF82bNzfGGNOxY0f7MjfffLNJT083tWrVcvhrKil1qTfhmdWpUydjjDH16tXTviki+6d27drm5MmTpmXLliYwMDBbSMrP/aPT7QpBmTJl6NixI8uWLbNPM8awbNkyunTp4sDOpFKlSgBEREQA0LFjR9zc3LLtq0OHDnH8+HHtq0Ly+eef888//7B8+fJs07VvHGvIkCFs27aN2bNnExYWxo4dO3jooYfs8xs2bEitWrWy7Z+YmBg2b96s/VMINmzYQJ8+fWjSpAkAbdu2pVu3bixatAjQ/ilK8rIvunTpQmRkJNu3b7cvs2zZMjIyMrj++usLvefSrFKlSmRkZBAVFQVo3ziak5MTM2fO5IMPPmD//v055ufn/nG95m7lsqpXr46rqythYWHZpoeFhdG8eXMHdSVOTk588sknrFu3jn379gHg7e1NcnIy0dHR2ZYNCwvD29vbEW2WKnfeeSd+fn65Xq+nfeNYjRo1YuzYsXz88ce88847dO7cmc8++4yUlBR+/PFH+z7I7fec9k/Be++996hYsSIHDx4kPT0dFxcXXnrpJX7++WcA7Z8iJC/7wtvbmzNnzmSbn56eTkREhPZXISpbtizvv/8+v/zyC7GxsYD2jaNNmDCBtLQ0Pvvss1zn5+f+UUiSUuvzzz+ndevWdOvWzdGtCFC3bl0+/fRT+vXrR3JysqPbkQs4Ozuzbds2XnrpJQB27dpF69atGTNmDD/++KODu5ORI0dyzz33MGrUKPbt20f79u355JNPOH36tPaPyFVwdXVl9uzZODk5MXbsWEe3I4Cfnx/jx4/Hz8+vUJ5Pp9sVgvDwcNLS0vDy8so23cvLi9DQUAd1VbpNmTKFW2+9lV69ehEcHGyfHhoaStmyZe2n4WXSvip4HTt2xMvLix07dpCamkpqaio9e/bkySefJDU1lbCwMO0bBwoJCclxasOBAweoX78+gH0f6PecY3zwwQe89957/Pbbb+zdu5effvqJyZMnM3HiRED7pyjJy74IDQ3NMXKni4sLVatW1f4qBJkBycfHh379+tmPIoH2jSN1796dmjVrcuLECfv7hAYNGvDRRx8RGBgI5O/+UUgqBKmpqWzfvp0+ffrYpzk5OdGnTx82btzowM5KpylTpnDbbbfRu3dvgoKCss3bvn07KSkp2fZV06ZN8fHx0b4qYMuXL6d169a0b9/eXlu3bmXWrFm0b9+ebdu2ad840Pr162nWrFm2aU2bNuX48eMABAYGEhISkm3/eHp6cv3112v/FAJ3d3cyMjKyTUtPT8fZ2fpvXvun6MjLvti4cSNVqlTJ9hfz3r174+zszObNmwu959IkMyA1adKEvn372q9ZzqR94zgzZ86kbdu22d4nBAcH88EHH3DzzTcD+b9/HD56RWmokSNHmsTERHPfffeZ5s2bm6+++spERESYmjVrOry30lSff/65iYyMNDfddJPx8vKyV7ly5ezLfPHFFyYoKMj07NnT+Pn5mfXr15v169c7vPfSWFlHt9O+cWx16tTJpKSkmIkTJ5rGjRubu+++28TFxZlRo0bZl3n++edNRESEGTx4sGndurWZM2eOhpgupJo2bZo5efKkfQjwYcOGmTNnzpj33ntP+8cB5eHhYdq1a2fatWtnjDHmqaeeMu3atbOPkJaXfbFw4UKzfft207lzZ3PjjTeaQ4cOaZjpAt43rq6uZu7cuebEiROmbdu22d4nlClTRvvGwfsnt+UvHN0un/eP478gpaUee+wxExQUZJKSksymTZvMdddd5/CeSltdzOjRo+3LlC1b1kydOtWcO3fOxMXFmT///NN4eXk5vPfSWBeGJO0bx9agQYOMv7+/SUxMNPv37zcPPfRQjmVef/11ExISYhITE83SpUtNkyZNHN53aagKFSqYyZMnm6CgIJOQkGACAgLMm2++me2NnfZP4VWPHj1y/b9m2rRped4XVapUMbNmzTIxMTEmKirKfP/998bDw8Phr62416X2jY+Pz0XfJ/To0UP7xsH7J7flcwtJ+bV/nGyfiIiIiIiICLomSUREREREJBuFJBERERERkSwUkkRERERERLJQSBIREREREclCIUlERERERCQLhSQREREREZEsFJJERERERESyUEgSERERERHJQiFJREQkC2MMQ4cOdXQbIiLiQApJIiJSZEybNg1jTI5atGiRo1sTEZFSxNXRDYiIiGS1aNEiHnjggWzTkpOTHdSNiIiURjqSJCIiRUpycjJhYWHZKioqCrBOhRszZgwLFy4kISGBo0ePMmLEiGzrt27dmuXLl5OQkEB4eDhff/01Hh4e2ZZ54IEH2Lt3L0lJSZw+fZopU6Zkm1+9enX++usv4uPjOXz4MIMHD7bPq1y5Mj/9fzv38wrbH8dx/PW9FmpkYYGFBTs/8itFyhKJhM2sJIpkdjYWsvDrLxDjV0m4U1LCxq+abGg2LChN5MeGzTRIGAm9vwvfppnu93v71r3uzL09H/Wqzzmfzznn02f37pzP+fpVgUBAoVBIp6enamtr+5S1AADEBkUSAOC3Mjw8rOXlZRUVFcnj8WhxcVE5OTmSJIfDoa2tLd3d3am0tFROp1NVVVUaGxsLX9/V1SW3263p6WkVFBSooaFBZ2dnUc/o7+/X0tKSCgsLtb6+Lo/Ho5SUlPDz8/LyVFtbq9zcXLlcLgWDwV+3AACAX8IIIYSQeMjs7Ky9vr7aw8NDVHp7e02SmZmNj49HXePz+cztdpsk6+josJubG3M4HOH+2tpae3t7s7S0NJNkV1dXNjw8/J9zMDMbGhoKHzscDjMzq6mpMUm2trZmMzMzMV8rQgghnxf2JAEA4srOzo5cLlfUudvb23Db5/NF9fl8PhUXF0uScnNzdXh4qFAoFO7f29tTQkKCsrOzZWbKyMiQ1+v97hyOjo7C7VAopPv7e6WlpUmSJiYmtLy8rJKSEm1vb2t1dfWbOQEAfm8USQCAuPL09KTz8/NPuffz8/P/Gvf6+hp1bGb68uXjC/XNzU1lZmaqrq5O1dXV8nq9crvd6unp+enzBQDEBnuSAAC/lfLy8m+O/X6/JMnv96uoqEgOhyPcX1FRoff3d52cnOjx8VGXl5eqrKz8oTkEg0HNz8+rpaVF3d3d6uzs/KH7AQDiC2+SAABxJTExUenp6VHn3t7edHNzI0lyOp3a39/X7u6umpubVVZWpvb2dkmSx+PR4OCg5ubmNDAwoNTUVI2OjmphYUGBQECSNDAwoMnJSQUCAW1sbCg5OVkVFRVRP3f4nsHBQR0cHOj4+FiJiYmqr68PF2kAgD9HzDdGEUIIIdLHjxv+jd/vN+njpwoul8u2trbs+fnZLi4uzOl0Rt0jPz/fvF6vhUIhCwaDNjU1ZUlJSVFjOjs7ze/328vLi11fX9vIyEi4z8yssbExavzd3Z21traaJOvr67Pj42N7enqyYDBoKysrlpWVFfO1I4QQ8vPy1z8NAADinpmpqalJa2trsZ4KAOAPxp4kAAAAAIhAkQQAAAAAEfjcDgAAAAAi8CYJAAAAACJQJAEAAABABIokAAAAAIhAkQQAAAAAESiSAAAAACACRRIAAAAARKBIAgAAAIAIFEkAAAAAEOFvQyeQXSK/0wMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Call the function\n",
    "plot_losses(losses_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c737bd4-e48a-489e-b8b4-276da31c5b72",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c786cd2-80d4-47fb-81c2-88c5cd31cdf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Evaluation - test:   0%|          | 0/249 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thiba\\.virtualenvs\\transformer-LWcVpt7F\\Lib\\site-packages\\torch\\utils\\checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
      "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
     ]
    }
   ],
   "source": [
    "test_loss = estimate_loss(model, ctx, dataset, config, ['test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fa47d53b-ce30-4cfb-92de-b5979258cf92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss = 5.894730\n"
     ]
    }
   ],
   "source": [
    "print(f\"Test loss = {test_loss['test'].item():4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eb879fc1-d275-47e9-8ce0-7bc29e07dffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), \"./out/train_model.pth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b955616-b592-413b-918f-7d18ffcd1caa",
   "metadata": {},
   "source": [
    "## Find specific exemple from overfitted dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "088a9991-a210-49af-acc3-17c63055651e",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = dataset.get_batch('train')\n",
    "nb = next(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6073b7aa-9768-410a-ae37-f8856efab837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This is an issue which, as you will understand, will have to be addressed at IGC level and is an issue that only a Special European Council can authorise the Portuguese Presidency to pursue, because this kind of issue falls outside the institutional framework in which the forthcoming Intergovernmental Conference will take place.'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb['translation']['en'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cee51a0e-4e78-4a08-ac06-b840c690b518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"C'est une question qui devra être étudiée, vous le comprendrez, au niveau de la Conférence intergouvernementale. Il s'agit là d'un thème que seul un Conseil européen extraordinaire peut autoriser car c'est une question située hors du cadre institutionnel dans lequel s'inscrit la prochaine Conférence intergouvernementale.\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb['translation']['fr'][0]"
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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