{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3718ebd2",
   "metadata": {},
   "source": [
    "# Validations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "be3d42a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:17.776301Z",
     "start_time": "2023-10-09T21:44:12.472851Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "# %#matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from suno_utils.audio import Audio\n",
    "from suno_utils.tasks.mert import preload_models, encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b79e6ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:22.192282Z",
     "start_time": "2023-10-09T21:44:17.778126Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at m-a-p/MERT-v1-95M were not used when initializing MERTModel: ['encoder.pos_conv_embed.conv.weight_v', 'encoder.pos_conv_embed.conv.weight_g']\n",
      "- This IS expected if you are initializing MERTModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing MERTModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of MERTModel were not initialized from the model checkpoint at m-a-p/MERT-v1-95M and are newly initialized: ['encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'encoder.pos_conv_embed.conv.parametrizations.weight.original1']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "my_mert = \"m-a-p/MERT-v1-95M\"\n",
    "_ = preload_models(\n",
    "    model_name=my_mert,\n",
    "    revision=\"8881df140a93e2e\" if my_mert == \"m-a-p/MERT-v1-95M\" else \"af10da70c94a\",\n",
    ")  # default \"m-a-p/MERT-v1-330M\", \"af10da70c94a\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e37f87de",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:22.199558Z",
     "start_time": "2023-10-09T21:44:22.194370Z"
    }
   },
   "outputs": [],
   "source": [
    "audio = Audio.from_file(\"../audios/canon.wav\").get_segment(to_s=10.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f06559a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:22.369425Z",
     "start_time": "2023-10-09T21:44:22.201379Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(750, 768)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = encode(audio, do_clustering=False)\n",
    "out2 = encode(audio.get_segment(to_s=5.01), do_clustering=False)\n",
    "out3 = encode(audio.get_segment(from_s=1, to_s=6.01), do_clustering=False)\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0e55f806",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:22.532283Z",
     "start_time": "2023-10-09T21:44:22.370390Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(out.mean(-1))\n",
    "plt.plot(out2.mean(-1))\n",
    "plt.title(f\"encode with {my_mert}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3e8e3a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:22.656138Z",
     "start_time": "2023-10-09T21:44:22.533326Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+i1/+8pdF7ethhx3GmKqPPfZYx0y/995746OPPsIbb7yBgYEB7Lzzzli1ahUARCJbW2yxBZYtW+b7+le/+lU0NDQ4JQL4sgQSoweSAEmMWIwfPx6tra3QdT3SHXQUTJ8+HQDw5ptv+m6TyOmiQfjdd9/FZptthubmZqTTaTQ2NgpNm/x7p0+fDtM0sdVWWxV1x01CUU8//TS23HJLJ7S31157IZvN4tZbb8WaNWt8DdAEtVCdWoRjjjmGCd2RsNcTTzyBDRs24K677mK+2/LlyyO9n+CDDz6AaZrM93/vvfcAwAl33XfffWhvbw8NIR555JG4/vrroaqqJ9upnFi4cCH22Wcf/PKXv8R3v/tdNDc3h77npz/9KRPCpA3U3/jGN3DTTTfh0UcfxTvvvAPTNIsOf11yySWOegdYIUYamqYxdaaIohPlOv7oo48Caxk1Njbi0EMPxS233IIDDzwQm222Wcy9lxgpkARIYsRC0zQsWrQIt912G958801P2GrdunWxi7594QtfwFZbbYXLLrsMxx13HOMDIhPkpEmTMHv2bNx0000488wznXXefPNNPPzww84Eo2kaFixYgH/+859YsWKFE/5555138NBDDzGfe9hhh+HMM8/Eeeedh1tuuYWZiE3TxMaNGzFu3LjAfd9rr71w6aWX4sMPP3QK5m222WaYOXMmfv3rXzvrBIFMokFZZ9XA1ltvja233tqznChHtAKVy+UcT0nY+wlWrVqFu+++G4cddhgAoLe3F3/5y18we/ZsJ/x1//33Y//990ciETys7rvvvrjgggswbtw4572Vws9+9jMcdNBB+NOf/oTTTjstdP1Zs2Zh1qxZwtfmz5+PsWPH4o477sA777yD3XbbzVOAMCrmzJkTed1169bh17/+NXbaaSeGAImu3/vvvx9Lly7F97///cBt/vjHP8b06dM9XjuJ0QVJgCRGNH71q1/h8ccfx9y5c3HiiSdi1qxZ2LhxI1555RU88sgj2LhxY6ztqaqK//u//8MhhxyC2bNn4/jjj8ekSZPw7rvv4q233nKIy29/+1sceOCBmDdvHk444QQMDQ3hiiuuQHt7OxP2OO+88/Dggw9ir732wsknn4xCoYArrrgC22+/PeObmT59Oi688EKceeaZ+Pjjj3HooYeitbUVy5cvx913342TTjoJP/7xjwP3fa+99sJFF12ElStXMkRn7733xjXXXINp06Zh8803D9zG9OnT0dHRgauvvhqtra1obm7G3Llzi54IK4099tgDY8aMwbHHHovvf//7UBQFN998c+yQ5nbbbYcTTjgBL730EiZOnIjrr78ea9aswQ033ADAMuc+/vjjuPrqq0O3paoqzjrrrMif/fTTTwsrFO+0006eEC6PAw88EDvssAMuvfRSnHLKKaFesSAkk0kcdthhuP322zEwMMC07aBx4YUXArC8VwBw880345lnngGASN97n332wbx587DNNtugq6sL1157Lfr7+3HvvfdCVd28nT322AOf//znscsuu6C9vR2vvPIKrr/+emyxxRbCitE0dt55Z+y8886RvrfECEZVcs8kJIYRa9asMU855RRziy22MJPJpNnZ2Wnut99+5rXXXuus41d1l6Qn82nfzzzzjPmVr3zFbG1tNZubm82ddtrJvOKKK5h1HnnkEfOLX/yi2djYaLa1tZmHHHKI+fbbb3v278knnzTnzJljplIpc+uttzavvvpq30rQ//jHP8w999zTbG5uNpubm80ZM2aYp5xyirls2bLQ49Db22tqmma2trYy6cy33HKLCcA8+uijPe/h0+BN0zT/9a9/mbNmzTITiQRzbPwqQR977LGR0q0hSNkWpYebZrwqyc8++6y5++67m42NjebkyZPNn/70p+ZDDz0UOUWbVFl+6KGHzJ122slsaGgwZ8yYwXz2vffeayqKwlQqJqDT4P1QTBr8Oeec49lHEW688UbhOVxMJejFixebAExFUcyVK1cK1wna5yj44Q9/aG699dZmQ0ODOX78ePPII480P/zwQ896v/jFL8zZs2eb7e3tZjKZNLfcckvze9/7ntnV1SXcJ79yAAQyDX70QTHNKrs7JSQkJGoY06ZNww477MAUuuRx8skn4+WXX8aLL744jHsmISFRCmQITEJCQqJEzJ49OzD9XUJCovYgCZCEhIREiTjppJOqvQsSEhIxIStBS0hISEhISIw6SA+QhISEhISExKiDVIAkJCQkJCQkRh0kAZKQkJCQkJAYdZAmaB8YhoFVq1ahtbW1Zsv/S0hISEhISLAwTRN9fX2YPHkyUzyThyRAPli1apWnA7WEhISEhIREfWDlypWB1e0lAfJBa2srAOsAtrW1VXlvJCQkJCQkJKKgt7cXW2yxhTOP+0ESIB+QsFdbW5skQBISEhISEnWGMPuKNEFLSEhISEhIjDpIAiQhISEhISEx6iAJkISEhISEhMSogyRAEhISEhISEqMOkgBJSEhISEhIjDpIAiQhISEhISEx6iAJkISEhISEhMSogyRAEhISEhISEqMOkgBJSEhISEhIjDpIAiQhISEhISEx6iAJkISEhISEhMSogyRAEhISEhISEqMOkgBJSEiMfHz2CvD81YBhVHtPJCQkagSyG7yEhMTIx5/2tR4bO4Cdj6jqrkhISNQGpAIkISExerD2nWrvgYSERI1AEiAJCYnRA1Wr9h5ISEjUCCQBkpCQGD1QZdRfQkLCgiRAEhISoweKVIAkJCQsSAIkISExeqDKIU9CQsKCHA0kJCRGD6QCJCEhYUMSIAkJidED6QGSkJCwIQmQhITE6IHMApOQkLAhCZCEhMTIhqG7z2UITEJCwoYkQBISEiMbet59LhUgCQkJG5IASUhIjGzoOfe5JEASEhI2JAGSqG3I5pUSpcIouM9lCExCQsKGJEAStYt/fAe4bEcg21ftPZGoZ9AKkKJUbz8kJCRqCpIASdQu3l8M9H4KrHuv2nsiUc+gPUCmVBQlJCQsSAIkUbvIDViP2d7q7odEfYNWgCQBkpCQsCEJkERtopAFDPvOXYbAJEoB7QGSBEhCQsKGJEAStYlsP/VcEiCJEsAoQGb19kNCQqKmIAmQRG0iJwmQRJkgPUASEhICDAsBuvLKKzFt2jSk02nMnTsXL774YuD6d955J2bMmIF0Oo0dd9wR999/P/O6aZo4++yzMWnSJDQ2NmL+/Pl4//33mXU2btyIo446Cm1tbejo6MAJJ5yA/v5+SNQJJAGSKBdoAkRXhZaQkBjVqDgBuuOOO3D66afjnHPOwSuvvIKdd94ZCxYswNq1a4XrP/fcc/jmN7+JE044Aa+++ioOPfRQHHrooXjzzTeddX7zm9/gD3/4A66++mq88MILaG5uxoIFC5DJZJx1jjrqKLz11ltYvHgx7r33Xjz11FM46aSTKv11JcoFYoAGpAlaojQYUgGSkJAQwKwwdtttN/OUU05x/td13Zw8ebJ58cUXC9c//PDDzYULFzLL5s6da373u981TdM0DcMwOzs7zd/+9rfO693d3WZDQ4P517/+1TRN03z77bdNAOZLL73krPPAAw+YiqKYn332WaT97unpMQGYPT090b6oRHnx/iOmeU6b9ffvH1R7byTqGR886p5Lz1xe7b2RkJCoMKLO3xVVgHK5HJYuXYr58+c7y1RVxfz587FkyRLhe5YsWcKsDwALFixw1l++fDm6urqYddrb2zF37lxnnSVLlqCjowO77LKLs878+fOhqipeeOEF4edms1n09vYyfxJVhAyBSZQLuswCk5CQ8KKiBGj9+vXQdR0TJ05klk+cOBFdXV3C93R1dQWuTx7D1pkwYQLzeiKRwNixY30/9+KLL0Z7e7vzt8UWW0T8lhIVARMCkwRIogTIOkASEhICyCwwG2eeeSZ6enqcv5UrV1Z7l0Y3ZBq8RLnAeICkCVpCQsJCRQnQZpttBk3TsGbNGmb5mjVr0NnZKXxPZ2dn4PrkMWwd3mRdKBSwceNG389taGhAW1sb8ydRRcgQmES5wKTByzpAEhISFipKgFKpFObMmYNHH33UWWYYBh599FHMmzdP+J558+Yx6wPA4sWLnfW32mordHZ2Muv09vbihRdecNaZN28euru7sXTpUmedxx57DIZhYO7cuWX7fhIVBEOApB9LogTIOkASEhICJCr9AaeffjqOPfZY7LLLLthtt91w2WWXYWBgAMcffzwA4JhjjsGUKVNw8cUXAwB+8IMfYJ999sEll1yChQsX4vbbb8fLL7+Ma6+9FgCgKApOO+00XHjhhdh2222x1VZb4f/9v/+HyZMn49BDDwUAzJw5EwcccABOPPFEXH311cjn8zj11FNxxBFHYPLkyZX+yhLlgAyBSZQL0gMkISEhQMUJ0De+8Q2sW7cOZ599Nrq6ujB79mw8+OCDjol5xYoVUFVXiNpjjz1w22234ayzzsLPf/5zbLvttvjnP/+JHXbYwVnnpz/9KQYGBnDSSSehu7sbe+65Jx588EGk02lnnVtvvRWnnnoq9ttvP6iqikWLFuEPf/hDpb+uRLnAm6BNE1CU6u2PRP1C9gKTkJAQQDFNGRQXobe3F+3t7ejp6ZF+oGrgb8cAb//L/f8Xa4Bk2n99CQk/LLkSeOjn1vM9Twfmn1Pd/ZGQkKgoos7fMgtMojZBK0CADINJuLj3h8DD/y/6+tIDJCEhIYAkQBK1iSzXt00aoSUAYONy4OXrgef+wBY4DIIkQBISEgJIAiRRm5AKkIQI+SH3OV3fJwiyF5iERPWR6QXu+xHwibgLRDVQcRO0hERRyHGERxIgCYAtZGhEVYBkFpiERNXxwWLgpT8D3SuAqXdWe28ASAVIolZBQmDJJvt/SYAkABgUAdIjKkAyBCYhUX0Q9ZZWcasMSYAkahMkBNZqV+6WHiAJgFOAIra1kARIQqL6INdh1Ot2GCAJkETtQS8ABfsuodUuXJnr91+/RpHXDXywth+y0kQZQZOZqCEw6QGSkKg+yPVKHvu6gO6VQG6warskCZBE7SFPGaBbrYKZHlN0HeDXD7yL+Zc+iSfeW1ftXRk5KGTc51FN0NIDJCFRffAE6K6TgMt2AN69r2q7JAmQRO2BkB1FAxrHsMvqCCs2Wnc2KzdW7w5nxKFAkZnIJmhqvRqS3yUkRhWcEBhHhLTq5WJJAiRReyAmuVQzkGqxntchAdINK/RV0GUIrGygFaDIdYCkAiQhUXUQxZZcg4QQqcnq7A8kAZKoRRAClEhTBKj+PEC67f0xpAeofNCLUIAYD5D8LSQkqgKivjoKkH1dapIASYTANE1c/8xyPDUa/CSEACXTlgoEeCtD1wEcBciQk27ZUJQHSJqgJSSqDj4ERhRcVYbAJELw0sebcP69b+OY61+s9q5UHiQDLNHoEqA6DoHptUiAnvwN8OKfqr0X8VHIus8je4AkAZKQqDp4708NKECyEnSdYHVP7RSPqjjy9l0+rQDVYwjMJj5GrRGgjR8Bj19kPd/txOruS1zQBKgoD5A0QUtIVAUGVwdIeoAkokJTFef5iK8rQxSgZFNdm6CJ96fmQmB03Y16y4rSi1CA6PWkAiQhUR3otacASQJUJ9AUlwBl8iN8ECcKUCINNNQvASLEp+ZM0KrmPqfVkXoAEwKTdYAkJOoGfAhMeoAkokKlFKDBXMQ733qFowDVtwfIqFUTtDJSCJD0AElI1A34EJhUgCSigq4lM5irs7BFXIy0NPiaI0DUZR+1oWitgCZskT1AkgBJSFQdTghMeoAkYiJbcEnPqCFAjALUX3c1XAhprbksMJoE1BsBYtLgi6gDZEgCJCFRFXiywGQlaImIyBbcgXvkh8AoDxAhQEah7sI1NWuCpjOh6uyYFhcCkx4gCYmqw+DrANnXpVSAJMKQowjQ0GhSgJLN7vI68wHptWqCrmsFqBgCJLPAJCSqDk8hROkBkoiIURUCIwpQstGSRxNp6/868wHVbCVoo44VIDoNfuULwL9OBfpDqqNLBUhCovog446p28/tcbGKCpAshFgnoBWgwfwIJ0B5u04NIT6pZosU1ZsCVKsmaDoEFjWVvFZAK0AvXms9Tp4N7Pod//cY0gQtIVF10Nch7eWTHiCJMDAeoOwI9wDlKQUIqNtiiMRvW3sK0AgJgRHkQ6qkM1lgI/zmQUKiVkFfh3mKAEkPkEQYWBP0CB/EaRM04BKgbF919qdIFGyiUdMKUL2FwEQEKIzEyTR4CYnqg/bsFaibFukBkggDY4Ie8SEwqhUGULfFEHX7J9NrzQTNeIDqTAHSYxIg0+RCYDX2W0hIjBbQBIhRgGQITCIErAl6hIfAClQzVKBuCVDtpsGPsBBYkIrFvyYVIAmJ6oAea4gCpCYAqs3TcEMSoDoBHQIbyI4SBShBPED12RG+oMsQWNkRlwBletn/JQGSkKgORApQFf0/gCRAdYPsqKwDxHmA6k4Bsh5LUoBWvQo898fydm0fKWnwBEH1gLIcASrncZSQkIgOkQeoiv4fQBKgukE2X3wa/PL1A7jx2eVMGK2mUfBTgOqLADmFEEshQA/+HHj4F8Anz5Vpr8ClwddZODW2AtTD/i8VIAmJ6kCkAFWZAMk6QFWCaZq48bmPsdPmHZgzdUzo+jmdVoDiTVr7/u4JAEDPUAE/mL9trPdWBXk/D1B9hcDKUggx02098kpGKWDS4OtMAYpLgPjjJgmQhER1oAvqAMkQ2CjExuXov+5rePi+O3HOv9+M9JYspfoU6wF6+ZONRb1v2FHgs8DqMwTmFEIsJfOITO7lNCuPOA9QwLGRHiAJidqAqBCiDIGNQtz3I7R++iT+mroI/Zloag6tABVbCVpTq+e2j4U8VwdI1azHOgrXmKbpKEAldYMv2ASlnN+9XtPgTdMnDT6CAqTYQ50shCghUR3Q406eygKrImQIrBroX+M8jRoeoT1AcUNgBIlaJ0C9q4AlV7qTHKkETS6SOjKw0j9rSSEwRwEqo1JTr2nwRkGs4ERRgNIdwNBGWQdIQqJaEIXApAdoFCLd4TyNqg6UoxlqzStA95wGvP+Q+7+jABECVD8KEP27lmSCJmRQhsDE4S8g+NgQBaixwyZAMgQmIVEV0CEwRwGSIbDRh8YO52lCz/ivR4EJgRVJgBJqjf/cq19j//coQPVDgGjfT2kKkD1olLNpab2GwHwJUIQssEY70UASIAmJ6oAedxwFqLoaTI3PiCMUVOXL8ca6SG9h0uCLDIHVvAI0dmv3uZZyvT/ksY78GzTpKckETSZ9vYzkjyYB9dQNXuT/AaIVQpQESEKiutClAiQBAFk3nXuiGY0A0QpQJm8UFVapeQ/Q2K3c5/SkVoceIDoEVrQJmu5jVTEFqJ5CYD5qaWAhRFsBImHnOjqHJCQC8ckSYNkD1d6L6GCywOybGZkFNgpBdTWfUIQCBBTXEFUtlQAZOrD+g8oZSbWUeHk9hsDKQYBoclIxD1AdKUAFH7IWSQHqsB6lAiQxUnDDAcBfjwDWv1/tPQmHaYorQVc5C0wSoGogRytA6yO9ha/iPFBEGKxkBeie7wN/nAO89OfStuMHP5WjDglQodwEqGJp8HWkABUTAstSWWCAJEASIwN0SPyzpdXbj6jgldcaqQQtCVA1QClAkxCuABV0A/wcmivEH8hL9gC9eov1+PgvS9sOgWEAfzsWePQC+38fVasOCRDt+ymaABUqpQDVaRp8MVlgHg+QTIOXGAHIU0Vhu1dWbz+igr+5LUgP0OgF5QGahHAFiG6ESjhMQY82kJvUgF82D1C5VINPngXe/ifw9O/s7fopQKQQYv34NxgPULGTLqMAyRBYUVlgWRkCkxiBoKvib/yoevsRFfzNq1SARilME8i5CtB4bGJIigi02tPcYKkhBSPaQE6bp7VypcGXiwBRShhj+AWA3U5yn9ehAkQToKhk1bsRasIvJ1Gp115gfibooAy5DGeCrqNMQgkJX9AEaP2y6u1HVPDjV414gGQhxOFGfpC5C21SsjBMQAsQZ4gClFAVNCRU9AHIR5xU6fW0ctHdck2avMmXKDwLLgZ2/577Wp0ToKLT4OlBo5zfnekGX0cKEN/ZncDvfMxn3NdkGrzESAJNgNYts24glRrO8pUKkAQAVvUA0IhsqEeEGKAbEqpTzDCqqkCrR3GywHIFAw+91YWeoQpOkPTEVci4E35DK3sx1yMBKosHqFIKUJ2GwDLd4uV+BMjpBK8A6TbrqSRAEiMBNAHK9QM9n1ZvX6KAH7tlN/hRCsr/AwCNyIVOkITEpBIqErZUlI8aAqMIkILoBOiSh5fhuzcvxbdvfCnye2KDnuALWVeN4O8K6rAZalnqADEKWTl7gdVpFhhRgJrGscv9SBwxQDe0uiRaEiCJkQCaAAGWClTL8ITAZCXo0Qn7rrSgNgAAGpQ8CoXgu3ASAmtIaEhq8RSgPOUBihOKuf0lK7Ng6SebIr8nNqhyAChkXILDx4XrvRBiWUzQFaoEPRIIkF8YjxRBbGgDFFJNXBIgiRGAHHsj7ZzrtQq/EJhUgEYZ7BDYYModxI3cUPBbCAFKqk4mV0GPNpDTGWRxlIhi0uxjgw4HFrKumdVDgOpcASraBF2hNHgmBFY/xxRD3dZj02bscj8SR8oIJBoAxR7qIiqnEhI1jfwg+79fkdBagScEZs950gM0ymAz96HkGGeRnh3wWxuA6wFKaSoStgKUj0hmaAUoDgHKRyRYJYE2tRaG3IvEEwKrQw9QORSgQsQ0+LjKGK8A+aWX1xrI+dLMKUCmIT4GtKJIPGVSAZIYCeBDYLWu5PI3cI4CJENgowu26pHVWjBkWq0fDJ7N82+hFKCk5ipAYenzAKvkRFmfoKQO5lHBK0BkkvcNgdURASqHCZpRgHy++9p3gV9NBZ78bfTt0mTh0xeBi7cAPn25uH0cThATNK8AAeIJgHidVM1VgCQBkhgJ4ENgtU6ApAIkAYAiQM0YgkWAzFwwAXJM0JobAnvgzS7scuEjePr94ErSdB2gopWISoEhQJkIIbD68QCVpxcYpcz4KUCrXrHqSn38dPTt8rVw9CzwwM/i799ww1GAIhIgRwHS3HNIEiCJkYB6U4B4AuRcm5IAjS4QAqQ2YQiWEdoIIUC0CZqEwP6+9FNsGMjh6OteDHxvnvEAFb3XFsp9svIEKDQEVj8EqFAWE3Re/JxZxx744qhjouOYSEd/f7XgmKBFBEjw/YnfR01IBUhiZIGfM2qdAPmNX1IBGmWwpcuM1oSMSRSgYBN0ThACi4osnQUWQ4noxAZckrwKOypUmXW/bu3FwqnTgughMMOoi9o19LE2zXjH3gHtzfEjOORYxCFAomrIycbo768WiAk6rgKk0CGw+iHREhK+8ITAanxM9BufpAdolKEIBYgYkhOqWwgxKmgFKE4a/G+S12KR9gzuaTjLXVhutu4JgREC5KMAFbLAlbsB136p5o27vIeqKBUoShZYMQqQSAVJ1rgCZJr+afBAiAdIKkASIwx8CKzGx0PfEL5UgEYZ7El/SHE9QJ6URg7EQ6KpiK0AxfUA/fnpj3Ds9S9iurrK+2KiIdZnh4ImQPmMG5rhi2MR/0auD9jwPrDmTeA/t5V3X8oM/lgX5QOK0gyVEKM4d4CiVPBaD4Hl+l1CI1SABN+f9gBJAiQxkkAIEOlxV/MhMD8FSBKg0QV70s+ojRgyLUIRFgIjyo2mKvEVoJghsAvvewdPvrcOeVMgTVY0BJYJD4HRePay2qlhY+hA90p2Ea8AlUqAfBUgEgKLEdoRhYFqnQAR9UdNWpWdeYgIokFngWnu8lpLBpCQiAtCgEiPu3oNgclK0KMMh98MnPkplo45CJmYCpCqKE4rjKig0+Dj1OPLi/rk0gRoyZXAJ885n/H6p93xfC6mKSiEGBICo7HpY0sJqgX87Rjgsh2Ad+9zFpUlBFaIEwKLowCJaubUuDeGEKDGDvFdo9ADZH8nRWN7y0kVSKLekecJUJ2GwKQCNMqgqkBDKzJIOR4g5IMVIDcEpjitMAjCGgDnivQAhRKgh34O3HAgAOD/nvgQX/3js7jr1c8ibx+5AXYiKtAhsAgECAg9bsOGd++1Hp/7o7OIJ4Nn/uMNPPRWV7ztRgqBFeMBEpCdQo0cSz8QA3S6XXw+EIK46lXg5Rts5zldCJG6bmqd7ElIhMGjANV6CKw2PUDV1Z9GMQzDxKBJCFBED5CiQOM6uqshDChHyT5xFJocNO9Cn5P1s25r/1d1x5hEafUH4LLAuM/2I0C1dtFTkyyv+Nz3xmosXz+ABdt3Rt+eHqEbvOMBKjENvtZNlEQBSreLz0NyLlz7JeuxdZK4ECIgFSCJ+kfdhcB8bjqkAjQ6UTBMJwSmFEIIkD2Zqqri1AEiUGMoQHF8KAUhNxa/n4R7YvlcPAQoKAtMQMaA2rvoKTIqOhaZvI5XV2zCLc9/Eq0qN/39fNPgy6QA5YeADx4FemKoeMMJUgU63SE+H3gyvOEDfwVIEiCJegdJgycEqNZvYHyzwKqrwUgFqEowTNMNgYWZoO3JNKEqniwwJUQBKrYbvNAE7fN+sn9xts8YoIFo3eB51Frcm1aABAQopxv4r6ss39SE1gbsH6YGFaIoQEV4gES/00ePW3+KBpyzMfq2hgu0AiT0AHEEMNnImqBp0iQJkES9g5ROqRcFyG//pAI0OlEwTKcXmGr7L3ozeVz68DK8v4ZVRwiHUQVZYGGWaNYDFH3/ciJu7DNxFMpBgHIDcBQmPsSh+ClAtRYCC1aAaDL6xmc9ntc9iJMGX2olaIJaLRRIFMOGVh8PUI4ldqlmzgRNK0A1+h0lJKKgkHPHg3rxAPlmgY1gArRx40YcddRRaGtrQ0dHB0444QT09/cHvieTyeCUU07BuHHj0NLSgkWLFmHNmjXMOitWrMDChQvR1NSECRMm4Cc/+QkKBfcA33XXXfjKV76C8ePHo62tDfPmzcNDDz1Uke9YLAzDVYAUmwBdeO/b+MNjH2D/y55i1iUhME3xKkDhHqAiu8GLPEA+BIgQn1itNjI8AaLOi8gKUI3d9YQoQHnKj9WfjUBYojRDdRSgEtPgax3kt9ZS/gSILg6XbBQXQgSkAiRR36DHyrrJAhuFlaCPOuoovPXWW1i8eDHuvfdePPXUUzjppJMC3/PDH/4Q99xzD+688048+eSTWLVqFQ477DDndV3XsXDhQuRyOTz33HO46aabcOONN+Lss8921nnqqafwla98Bffffz+WLl2KfffdF4cccgheffXVin3XuCgYBuUBsgjQyx9vAuCNUBhUFhifBh/HA1R0FlhIrZmCXoQC1MPWzWEmL79mqDxq7a4nwAQNsFW5B7PBJGRDfxaFfIRmqOQYxCqEWIcEiHx/LWllUvLQ8+zEoKXEhRABWQdIor5BxkqtAUg1Wc9r7WaQx2jLAnvnnXfw4IMP4qWXXsIuu+wCALjiiitw0EEH4Xe/+x0mT57seU9PTw+uu+463Hbbbfjyl78MALjhhhswc+ZMPP/889h9993x8MMP4+2338YjjzyCiRMnYvbs2bjgggvws5/9DOeeey5SqRQuu+wyZru//OUv8a9//Qv33HMPPv/5z1fqK8eCbsAphEhCYH61YhwTtCIIgZVRAaKzxAq0ApQfsk7UUAUoxsSy4UPrsXk8MLAOyNKTFx8CU6wwhqeLee0SIFHGHf1bDOT8FaB1fVns9ZvHcF3DanyRLAwthFiiCbrWQZMZ4et59hwyjQACJBUgiToGyRpONbmlSWreBD3KKkEvWbIEHR0dDvkBgPnz50NVVbzwwgvC9yxduhT5fB7z5893ls2YMQNbbrkllixZ4mx3xx13xMSJE511FixYgN7eXrz11lvC7RqGgb6+PowdO9Z3f7PZLHp7e5m/SkI3DKcVhkOAfAiEqwB5W2GE1QGK0wuMJmAMASpkrMdyeoA22k1WJ8yyHnOU70kkiwbVfqkVUJMsXwgRYD1AAwEhsGVdfcjkDeSyGXehqYuVC6KMGPnoykY9KkAkBOg3YOo59hwyDbflh1MIUXFfk5CoVxCyozW4BKjWxkIeo80D1NXVhQkTJjDLEokExo4di64ucUG4rq4upFIpdHR0MMsnTpzovKerq4shP+R18poIv/vd79Df34/DDz/cd38vvvhitLe3O39bbLFF4PcrFboJxwOk2gTDr06PUwlamAYfXQEStYASfQ4AqHTKO7nj8Jk4yPtiVYImBGji9vaO2rIuX7XX2SEf30ctIcQDRC8ayPmTkIL9Q6XADRqiQY4+BlEn9noMAfllCBLonAJk6N73kN+nHgmghAQBHQ52CFCNjYU8yLWocJSj3jxAZ5xxBhRFCfx79913K7GvReG2227Deeedh7/97W8eQkbjzDPPRE9Pj/O3cuVK33XLAZ3yAKl6tBCYpihIeAohBn9OPkYzVFq10EBNEvlgBYhM9pHbPeQzQM+n1nOiAJHJy++OQHSh1JjsO5iPrrYNBoTAhmxylFS4dQQ+IJMmRVHvAusyBEYN+iLwJmhTZwshArIhqsTIgE4Re9KgutZN0GRsSjSyy+vNA/SjH/0Ixx13XOA6W2+9NTo7O7F27VpmeaFQwMaNG9HZKa5/0tnZiVwuh+7ubkYFWrNmjfOezs5OvPjii8z7SJYYv93bb78d3/nOd3DnnXcyYTURGhoa0NBQ5m7nAdANExnbA6TpGXuZeF3aBM23wghVgGIUQtSpLKUEqJ0JUoAMwyVAUeeVTcsBmEBDG9A+xVpG0pz9QhzC4ne1JfsuWzMA4jAThcBoDASYoAdtApQC9/30HIBmZlFP3wA6yD9RfUBEAdn3LGDKF4BbDgtevxYQ5gHSc6wJ2jD8FSBJgCTqGYwCZI+XNTYWekD2OZl2+5gBVfcAxSZA48ePx/jx40PXmzdvHrq7u7F06VLMmTMHAPDYY4/BMAzMnTtX+J45c+YgmUzi0UcfxaJFiwAAy5Ytw4oVKzBv3jxnuxdddBHWrl3rKDqLFy9GW1sbZs2a5Wzrr3/9K7797W/j9ttvx8KFC+N+zYpDN0zHA6TZHiA/1YAxQccshJiN4QEqUDEyVgGyCzWKJg5TdwhQpOrGgGuAHru1e0dAelH5TXB1EAIzTQN47CJg+pdhGJsFrhuUBj+UJwSIN31739PdTxOgmApQxxbAtL2ivafaCPUAFdjq4qZO1QGyiY+qATokAZKobzgV8xN1FAKzr8VEml2eGD7RQYSKeYBmzpyJAw44ACeeeCJefPFFPPvsszj11FNxxBFHOBlgn332GWbMmOEoOu3t7TjhhBNw+umn4/HHH8fSpUtx/PHHY968edh9990BAPvvvz9mzZqFo48+Gq+99hoeeughnHXWWTjllFMcBee2227DMcccg0suuQRz585FV1cXurq60NMTofjcMEGn6gC5CpCfB8h61FQFSZVXgII/J04laJ0JgdGNSm1y4tNFXI+aBfbAGcCtXwc2vG/9P2669wIQSKI9Q3kMiPhCjV30Xxh8FnjqN8ANB4SqYb1DLFl57oP1uHTxe9Z54acACQiOZtLtMiKGtugCgVrSG5evRfBqTrN9Ezbdyhb1KECmQVWClgqQxAgCfV5r9vhZqK2x0AOn0XWKXd4cfKNYaVR05Lv11lsxY8YM7LfffjjooIOw55574tprr3Vez+fzWLZsGQYH3V5Yv//973HwwQdj0aJF2HvvvdHZ2Ym77rrLeV3TNNx7773QNA3z5s3Dt771LRxzzDE4//zznXWuvfZaFAoFnHLKKZg0aZLz94Mf/KCSXzcWdKoStKZnANP0JShBdYDidIMPm5TpsE0iqgL0n1twRvd5aEQm3AP0wv8B7z8MvPF36/+OLb13BII7/NPv+A82ZQSfXQuyr59qF+I4zxYMh+gAwJF/fgF/ePR93PrCJ44ClIxggk6Y1DpRQ2Dkd1RV6wTif4NaBO8B+v5/gB+8Doyfaf2v57wmaFMSIIkRCGEIrMYJkCkgQMkmq2J7FVFRC/bYsWNx2223+b4+bdo0T9gknU7jyiuvxJVXXun7vqlTp+L+++/3ff2JJ56Iva/DDd00kSFZYDAAPeefBcaEwPhWGGG9wKJ3g/dVgIJM0M/9EXNzy7GLui8MY2v/jdO/c/cK67FxjBUTpiEIdT367lr8v5Tm7ftRAxd9JpeDiD5EMYRvGsyhMcWaAp//aAO2HGsNCk2azvafFRAcRgGKSghpBQiwCFA+uCFv1WFwhuaGFuuPTADPXuZWxQXsEBjnG1JkGrzECADdNLpeQmDkmktQBKjK6g8ge4FVDQXdxADSMEx7UM70+E6aTB0gLuZl+nRoJ2AUoBghsIRCK0ABJmhbHUohH9xrjJ68SR+wdLtXffDpDqyLTtUaUIB6+sXEIYohvHvQu/+fbhrCkJ0hllZ5DxC1/vuLgX+dihaDVj1iKkBEEUk2+q9bK6AHfRr0HeXQJvd5UBq8JEAS9QxaASIWAtrzVosQhcCa/bOyhwuSAFUJhmnCgIoektUzuMG3Tk+QAlTQQwhQDA9QgakDRCtAASEw+84jAT2YYInuUEQEyMfkWhD1JquBu56eAT8CFD7Jdg9693/lxkHKBB3gAbr1v4FXb0YThqjXoxIgTk2pshExEvzqANG+Hxq0B4gQH6J4SQI08jDUXd3Pf+8h4JnLhqfGlk4pm7RnsgbGQ1+Qa06jxprm8GSqSkMSoCqBkI2NZqu1YHADQyDW9mWwqnuIWVfkAQpLt2Z6gcUIgSVEJugAApSEHrx9Uc2edId38uUmOOKT0WkCRDLHaqD2RU//kHB5FAVok0AB2jSYd9LgNdPHA+SnfMVNg6dDYLUOvzpA6Xbx+tIEPbLx9KXA81dbz99/BPj1NOC5K6q3P7cdDjxyDvDRE5X/LIPKiKQVlbogQNT1K0NgoxeELGwERYAoAvHVK57FgsueQiavs3WA7CywDvThG9rjaDQGEIRcrEKIEdPgaa+FTWwS0IOzwESTdrqdvSMAPCGwlZsshUWnDUA11ACwb1CsAEVpC7JJoAABQF+mAMCEZlivD9r1opyBj5QR4BG3ECJRgOqhMjTvASLY9URg91O8mWyMCZorhFjLoQKJcPSuBh49D3jwZ9b40/U6ABNY/Xq19wzY6HNtlhOiStBAbWeCOQSIDoFJBWjUgig3mygFiEZXbwZ9mQJ6hvJuKwyqDtA1qd/j18k/4Wzz6sDPyebdwT5OK4xEUAjsu0+7xMW+GJNKIdgDJFJrGjsswkOrPlwIbMUGQoCoiY9kDtTAHU/fQEa4vBAhBEb3A2tLu8fgg7X9SKEAxfZ3DRCbNSE4a98WbzCyAsR5gPj31SIh8vMANY8DDvglsO3+7HJTeoBGLOixJNvv/s5R62BVEnmxIlxW0HWAFMW9JmpgPPSF0AMkCdCohSEIgYmQKxiOmmBVgrYI0FzVajdykPq872eYpsn0nCrZA0R7Krg78QT04O0LFaAO+82UCZcLga3YaBEgxgOUrCEC5KMARQmBDVHklD50n3UPIQ13kO81bcWLDPBr3xFvMKqywSsjPAGqRYUkrBdYx1Rufd0b6lOlB2hEgC4Imu0JDw0PJ3LDkE1JrgUSTqqHTDBhFpgkQKMWjgLkhMA2CtfLFtzQktULjP3J+s20bwXmTN5gVJ2wQoWdS3+PE7V7AYTUAVJUz0QUGgITeYAa2qxHOguJ83gQAsRkgTkKUPUHvP5BsQJk6uEkIkP1DuO9XE02ASpAcwpmOgO/rwJUZBo8T3hqsVeYI/v7EKAx09j/TboVBp8GX4MKl0R0FCiVJdvn/s41MB4wbR4qBVoBAlxSUdMESKQASQ/QqAVRS8IUoEzeAEn0UgUm6CE0+Iae+HYLgQpN/1pMfu1y/CJ5G9LIsh4g3gStah7PRTJUAeIuzlSrO5k1tLrLOWLlGMHN6ofARERzYEgseZsR9i1DKUA8eWxUrPdnlbSrfpFtlhoC49Pg60IBIqqVTyuMMZwCRKcF8x6gvx8PLH+6/PsoMTzIUzcd2T6XHNdCCGw4FSC1DhUgGQKTALwKkDkgJkDZguGEyxKCZqgDZpppd8G8xhGgQIWGCgu0YdDrATJNOFX5hCGwQjwTNJ29E0CAiImbVYBa7G0O3wV/2u2v4oDLnka2wJKDgSGxAqREGIzpStC8Z4goQDm1ATlSr5Rss3uleINFm6DrQAHi73p5dGzJ/m8Y/pWguz8Bbjq4/PsoMTzgFSC9lhSg4QyB2ed1PbTDECUxSAI0ekHIwgbTCgOZA+sBWNlXv0lcg69rTwCwPECOCVpVoKleBciPeHgVoKAdcgePXSaY6Gxz77QzQwNYtYmSdhXVDZ/YCPcAcSGwxg73ebrNfc6FwMh3YzxAJAtsGC/4f/5nFZat6cPzH7GhyiE/AhRFAbLJlGGYnt+GeIBySqOrfpHfyE/pKaYXmGh7w6EArX7d6gvX9Ua09XnfAw/eAxRkgna2Kb1AdQlaAcr0ujcG1SJA9Hk0HASITwioh3YYZG6g29U0javOvlCQBKhK0H2ywA5Wl+DwxJP4bdLqmZYtuAUGNcXbDHUQDb61gAgBIqQpsE4PpVh0KP1QKRXgnRVrcOgfqZCBonjuxK06QP6b91ycjAJEESBOWSKkqppZYHToK8ER0MFM6QqQ6PcjIbCc2uCSP6NgD7Y+v2PkbvBULzCyXdHrlcQbd1p94V6/I9r6vJ+HR7oN+PZDQOdO1v9MIUSNfSTY8EG8fZaoDTAKUC91Y1AlAkQrpsMSAuNqYtVFCMw+RuO2cZf5+fmGEZIAVQmEADl1gIYsArSZ0susR4fANBUeD9CAmfZVgEgIjKRYB1dqdgePNvQzk2LSyKB7kFJwFM0bAlNCKkHzag3JAAO4EBh7h08qXRfoUzU5vCboLFVMkidAmay4GKMSYd9IFpjo92uCRazyahp5EgLT84E+H/P1O6xqtGHwKECG+PVS8dbdwO1HWWEKHuT4RFXx/NLgaWy5OzD1i9ZzI8ADRLDq1WifLVFb8HiAqhwCo6+XYVWASCHTeiBA9hjTsSVw4mPAD16r7v7YkASoSuAVIDU/iAbkWPMx7BAY0wpDQYLqEj6INAo+HiCiALU1JpnPFO+QO3i0m/1QqIs6jRybFq+oQhN08PaDFCCKAHEhjlpQgGivDt+KxCiIB13F4I4ZhdYGi9Bk7SwwUc2gNIgCRJmgjWACpLz9L6sa7SdLfNcB4PUAbTtf/HqpuPM44N17gSd+5b8PUX/DsDR4AtrX5FcIkUASoPoErwA5dYAiJgGUG/Tn5oYhC4xPCPBTgGop25EuoTJljjdrs0qQBKhKIKSmD43O5D4GfdC48AarAFkhsGa4d0BRQmBtaetCCbweKPm4zexjJsEkClDp/fIxQful4wOIToC4Cc71ANEmaFIJepgIUJ4mBOx3NHwUjO+svQjPNfwvWuG9IySENFABUixlKa82Iu9kgRWiDfKrQ+6unCwwe7sH/x74yvnu6+X2AIlCTXHv2v1aYfCgqz2HeYBWvRLtsyVqC7wC5NQBqpICYg6zAhQlBPbMZcBvtwHW10iYl84griFIAlQFGIZJkREFAwmLDIxV+jyqAe0BImnwrYp7kZnwV3b6M9YE0E4UoBghMIUmQEqB3S9VE9cBikOAaBM04wHiCJC9SVYBIllgwyN50wQoTzWfLegGVL5fl41puffRqWzCNspnntda7ZAk2a7QA0TqAPEKUBR1ZtPHwa/zTUIbxwBf/IGbTVLuLDBRo8q41Xv9WmHwoIsdegohcsOdXzadRG2j4JMGXwshsOHwAHlM0DYBom/GHjkHGFxvPdYCTG7MqRHU1t6MEvBEYUC1CEC7MsAWIIQVJiERLk2x0uBbqQ7gGozQNHgy4UYNgbUafYzSIFSAOEOpFQLz33zRITCiAJkiAjT8ITD6GGYKhuf34pFWvPtIFKAMpwBpqoIWWzEiBCivpVFgPEARyEn3J8Gv86EhAqcydJkJUKbHu8yIGQKL4gEC2I7vYQpQLab7S4SjwGWB6SWGwN5fDNzzg+LbWDAeoOEIgfFp8AEeoFohHGTOq5X9sVFbezMKcN/rq/GrB95llmVVqxJyI7JQFZZF5HQ2BJZQFSasosEISIO3LkwSAgMCMsGoO/FWs5cZTFIoeD1A3OSphXaDDzJB+ytARB3RRSEwIz8sce4MowAZzPJkCAFqgPeulPweGU4BOl57AG+mv4OF6vNOFlhBbUTejOYBcrDxo+DX+V5gBDR5KCeEBChuCCyiB4gJgRGip7KvEYzUlhh9XcA799RmQctyIM9Xgi5RAbr1v4GlNwLP/qG499PXpMjwX27wNbGC0uDDQsbDBV51rhHU1t6MAjzw5mpc98xyZllOsUIPjchB40NgedYErakKWhR3AFBhBHiArAulvYkiQH6EgRo8Wow+KNTkkBIqQJwJWolRCbqhDdh8V+p/fw+QowCJTND8diuEIZ+KzZm8zhjSRRASoEY7BGYrS7odVjtL+wsA4MrUH9xWGFr0LDAHGz4Mngx8FaAKdUsPJEARfj/TpErphwzoQhO0nwI0QgnQAz8D7vgW8NHj1d6TyoAJgfXGO5eCEBY69gN9Teo5cdufcoKvBJ0grXJEBCjlXVYpvHAt8OevAEObrP/fvAv4bKn1XHqAJACgKeU9AbJ2r6dGJctWYIblAaIVIEVR0KG6A4AG00kV5zHgKEAuqfD16TAhsG7mJTYEpgjrAIV7gOxBYfa3gJ8uBzaj6kEEhMB0U6AAJYeZAOVoBYgmQAYSSnwFiHiyMgUDpmkibysyTkkEUB4grZEiQDl38Au6kzLywSoQ740hUCjyUE6IwgJxFCB6ggkbQOn+Zs6x8skCG6kEqK/LeuxdXd39qBR4BajUEBhBse/nr5dKq0B8X7ygEFiYYlpO/OcW4NMXgZUvARuXWy1n/nGi9Zr0AEkAQFPKe0JmbQXIk24OKwvMKYRo/1rtKusBEqVRA940eCCg+C118bQVNjEvJRTDVTrICSzIAgsMgZGJLpHyFsCKoAAJm6HS260ghvI6zk3ciNtTFzBZX5FCYAIPUGvaLUuQ101HVXobLincXFkHwCJAOZEHiCaBIqx/z/+1MA/QcBADxwMU4fej1wnzABEVizZBOwoQ931HKgEiCklBXKSz7uFRgMpkgi6W+POKqUjxLCd0TgGqdAgsqs2AJqJ2ZwMMrLO3wWWe1ggkARpmpJPeEyDjhMCynhBYrmA4Co9qd7NuU9kQWFgrDNoD5KvSUHc/LUav5+UGxR5cCAESmaCjhMBEkizdCkMVK0BMCCyRdvdjGBSgTF7HcYmHsbv6DsZ0PeMszxb0UBO02APkkryhvO78vkNKo7N8D/UtAICeSLsEqJB1B9uwKqoZ72/ogG+GSuDXHb4c4M+NOCZoRgGK6AESEqBR0gqDhGCGIyW7GvAoQGWqBF2sAsRfL8OmABECFNALLOyGIQwvXw9cvDnwyXPh6xICaRTcc488Sg+QBBASAvMUQjStEJjphsAAoF3hFaCQStCN7qQRxQMkgjOR+ypAIa0wyKAsuiOhTdAKW2lZFylAWnJYy7/TITCFGnyHckYED5B3/5obEs5vmc27BSQ1ygCfsJ/rWiPypigEFnIn5Tf5mab/3Zhfc9RikUi7z3P97GtxfBv0xBRaB4gicY7SRUg7e26NXAXIPkfzI0wBGuoGllzFenX0nFt8kCa9xaDY9/LEqdIEyGOC5sZCeowvVQG694fWtXvDgeHrkuNn5F2SahQsYsa336kRVL8ZxyiDiABlFOsEblRYBUiDYYXADBMp5LHNvw8Dps+zTND2Oa7C8PUA9XF1gICALLCQiYhUJnYmSREBilJniNyt0CBp7YAnFVXYDFVLWtspZCobAssNAI9dhLbsLu4ySn6PEgJLCxSglKYinVAxkNMtBchmjnwRTAAwEnwILGI2lC8B4uo50fBrjVEsyG8EWL3u6FBnnOq99Dphd5A0iQs7ViOWAI1QBeiBnwGv3+5dPkQ1KNbzxRttiyVAHg9QgPpaDvDKptMKg7SXoYhvqQSoeQIwsNZ6PrgRaBobsF/kmtbZcy8/6K86Vxm1tTejAMIQGFwPEE+AcgUDhmliD/UtNK1dCiz5I9oQzQM0kBOEwCKkwdMomKqzbwCCQ2CBHiCiAAlCYAlqGedbINs0TFoBSg1PB+QnLgaevxKHvXo8tUOUByhKCEzgAUomVDTaRHiIUYC8x09nssCyMQiQT00TepDnByPHP1MmBYgmLnazX89rUX4/ugYQr+LwYEzQnNmbJ8sjlgDZ11CxdW1qFR8sFi8fpAhQKWGwokNg3PuCws/lgG8laEJ8qd+91BBY2yT3+dv/DF6XCYFR+5Afkh4gCQtCBQhiD5BKKUB9pusPmYI1znNNEXuATNN0QmAt6YQzb/hngXkv/rypIQfrAvJ4gDzNUAvRFKBESFomN2iTbZqgJj41QVU/rWDKadeb3mWMCdpAsogssJSmOER4KKc7IUw+AxAAjEQTlwUWsSKyrwJE7a+vAlQBAjTAE6AiPEBRMlqYStDc+wocIRARoE0fu2m89QpyTfDft97hZ/ynz+lSFOFyeYD0CqfBeypBczeD9E1k2A1DGGhD94rng9c1aALEKUDSAyQBiAnQkElCYDnGB2KFwCyFwKB+qunGx85zFQaTmk2QLbjLmxsSjoE6ShaYswiqE35xPUD2BSVQgIIEINcDFI8AEXLHFIhkFKAKhsBEkzM1uEWpA7TP1q2Y0dmKI3bdwlmWSqhotAlQJu+GMDVFRIAakQX1XfnaNn6IpABV2AMUSQGKkQYfRc4n56cp6AbPk2X+e3avBC7fGfjN1uGfU6swzZGrAKV8CBCNUsaDcmWBVboApScNntQBspfTv3up+0KrWWE3K4afAkSFwGQdoNGN8Cww94TVYCCbN2CYYNLjm5Bh1hEpQET9AYDmVAIaIUC+WWDegaMAzVEf3BAY8QAJ6gBF6QYfRoAKYgLE3McMlwlasG2FDoHlw0Ngm6VNPHja3liwfaezLKmpznmQoTxAqo8HyDFBF+gQWI0rQHTxQsDqS0SjGAIUZfCkfUx8uj9PCHgFaMUS8fJ6glFw93+kEaCGlvB1SgqBlckEXenzR+eUTV4NpxWgUmojmSbrZwq7VhkPEB8CkwqQBMR1gAYN6wS2Ep7di1CFgZxuERxReATw9wCRFPjmlAZNVdwiv0O94roOgpPboBQgp6eVEwJjT51EWCsMxwQdQoA4mdslVdS21eEiQN5jQhOgbMEINUFrtmKU1NzjldRcBYj2ACUECpCZaBRXgg5TgPyaMtKDsycNvoweIH4yoX0aQPEeoDCITNCKjwIEsNdCqYX0agH8xDOSwCtAooSKkkJgZTJBV/o84itB8yGwfJkIUH6IfX/Ytshx0PPsjWxuQJqgJSwEeoCULKP0EAVIN01oPl4TvzpA3YPWQNDRZBEFTVEwTVmNKX/awUpt5CEYOHRojvrgSYP3hMAK0SpBJwSDFgAsug7Ycg9gv7PZt5ESAAwBUocpBBZMgKIoQJphrZ/UXA0rlVCRTtEKkB3mEyhAZrKJygLLhashCdsr5hsCowmQXyuMMtzB8oMlr0jFKYQYxwMkMkH7eYAAlhBWq5t4OUGTvJFGgHgPUGOHd524E345CDD/vmELgfm0wigUEQJb8xbwyHms54fPZgtVgAJM0EZtmqBlGvwwQxQCG4TtAUIOKU4BIq0w+AKJBJpPGnz3kN0HzE6BVxUF2ymfQjFywOrXvBsSSMc6VEd9cAhQQBp8pG7zfj6OHf/b+uN3y/7aCZUz8/kpQIUcsPFDYPyM0g2AAjOjEtMDpOrW3Vgy4d5rpDQVjUnr/6G87pwTot/YTDZyWWDupP7KhP/CF9bejZypIUUIctNYoPeziCEwv2aoJQzg+SHguv2BSTuzy/k6QM7dYgwTdFjxR4BVsfgQmEgBMnT39bDwiWlafzVWy4QBHf4YaSZo/gYh3Q70r2GXxVWE4ygcvtvgrttyt5Lh4RcCEylAUffl//awHgc3AF+1m8Ly2WxhxyfIBO0oQCWOyWVGDV/JIxMiBWiQmKCRRZKaUEkavG6aXIFEABN3BODfDLV70LoYOuxGqKqquBOs6EQWKUCK5obA+DR43gOk6MEV06OaoPl9sDeaVLmN+zUA/NfJwFW7A68J6oXEhWAwVQ2aAIWHwFSbMKW4EJgoC8z5fRra3Q0kmnzrAN075Yf4avYC/LrwTXd9UqcjzAQtuhNTy+ABeuufQNfrwKs3s8v5kJzjF8iHl9ovJguMaZ9BFCBBYcA4CtDfjgGu+EJtFxgcyQoQ//ul273rxFXx6LGwaBM0rwBVOgTGp8FzajijAMXcl67X3ed8S49QAuTnARr0b79TZUgCNMwIIkBpJYe0ypqgrYaZghTpzh2cdXRByKKHU4A0hgAJLnRhCIxSgMrWCsMnBOb3NuKP4c9UvxDYG3daj89fFetzxB/uPSaazhZCDGuGqgo8QHQWmFXmwDZBkzpATWOcdROaipwpSoNPYEhX8bo53c0SA4DGEAIUNBCVQwHyUx1ItV6COHfecTxATosUmgAFDLo0AQrbj3f+DWxaDnz0RPh+lBth5R7IeUGThBFHgLgbknSHd524Ez59npTNA1RpEzRfCZq0wiB1gGJ4gPgxm77JyHIEKIxcRqoDVFuUo7b2ZhRAGAIzXAWoUXVPWFUxMGgXM/SER8Zu7SwXpcG7HiA3BOaoSKKLQhgC0/zT4D0m6EJpITABaFO1hzfyxb94jJka+XN8EUaAIpigXQLkSr9Jvg6QzilATePc96uKGwIrsK0wcgVrfaZKtqMACTqwA5VXgPze6yFAdO2WsPTaGGnwjgJEnRdBvgM/BSiIzA/3Xez9PwUu3sLqsA0AK15g20GsWwb8eivgyd8EK0DrlgHP/L5+iRGvANGVxQlKUYDK5QGqdAjMuQniCyGKPEABZMwwrHD1bUe4ywpZ4NavA0tvFITAomaBBdUBkgrQqEaDR8qgQ2A5NCicApQnbRKoi2rSbGCLuQAABWagCbq90dq2qrgqUj6fx+oebhAUZoHRJmguDV7hPUDWtv1bbYSYoAWgQ3tJPnRMJkN6wKczjdq3jPw5vhAck6TuHrcoJmiFeIA01gOUprLAPCGwaXs66yZoAsQVQszrdr8w+jIOVYAC7sQcBaiEO1i/CcivFxgQnQDFSYMXhcBE8FOAgkjgcN/FvniNdf08cynQuwq44QB20lp8jnW3/vhF7OTHE4bHLwIeORd478Fh2e2yg1fBko3edUrxABVrgq9aCIwoQJwaHlUB2vA+8OmLwHsPuMu6Xgfefxi45weuCZooTEHbookW3QsMsMLfUgGSAABFYALrJwqQkkNacS9CWvVxQmBb7Q2c9IRz8fs1Q+0eYj1Amqo4xQRXb+rDvIsfg0nf5doXT950Jxk6BOb1AHlN0EBAnaGodYAo0NtK8mcq6R9GKwvr33efRzHMhkGgLiU8BCjMBG17gBLiEBibBUZ+432srLjvPgVNVahCiHQWWMJRgJw6QQClAIWkwYvIhNMKowQC5DdIerLA6ImnEiEwaiIMDIH5KFGesEZA/aThQn4I6PnU+n16PxPvDxMC4445uaOvdLPOSoG/HkVjSdw6QHGIuO82uOul0llg/PXg+CEFFcCDSEuYp46cL0SRDrpO6etFeoAk4mDAcAf2FriDFp0W7UyOasIKQ9l3uppioKALPEAkBEZlgRGSQrwmvRnvxT8At4O3QZmgva0w+EKI1rZ8fUCF+ASIVrY8WWCkgzydqrmBIkC8X6AYCAbEpBGvGapS8KkDlLKzwHI6dJ0rhKioVkbcpJ2heRQgigCJFCAyWIWaoAMUoJJCYH4KUIAHKLICFMMEXYgaAvNJg/a0N6D2sVoyfn7IJS+5AXff6dAg/b2NAudxidGAthbBK1oiNTmMTHvWp46PyCQfBcMZAjPNcBN0VAUo7HrKcgQoiFzyoUSPB4ga22oItbU3oxSDpjuANZvuRMEoQCQ0Rk5a+27dNwtsiPUA0SZoQoQ29HODJYB+uLIy6wEKboZqpWGb4a024hAgkyZAghRYgM1UoBWgchRIFCghLAEywkNg9qDa3KChOaWhvTGJhoSKRrsg5mBOoABRd0maqrgKj55jWmE4ChBdzYKEwPSceDIIuhMrRysMXw8QHwKL4wGK4R/zhMAUb9o6fQ76eoB4AhTRVF1J5AddAmTqlg/o3fvYiYyfxPMCNaDSCkWlwIfARASoFAWo2L6CQWphucEokXwrDIEHiN63Dx8DXvxT9M8iYytRlYNChPR+iVphyF5gEn4oGAoyNglqNMQEyHnOeXD8WmGQNHjiAdJUVwEifqL1/dTEY5/c/SZNgOgQWHAdIGdfTBPXPvUhLn7gHfZFhwAVZ4J+dMzhGDJTWLbF160FDgGiFKByEyABeAUoLAuMyNINCQ1/+595+Nt35yGhqWhpsI7fQM41j6v8bwxL+WIKITryt+ooQKomCIEB4jBYkBmxLAqQzx1noAk6YoG1KMSDkB0SDqDJwaH/ZxXTO/Jv7jJfDxD3Peh9rKYCRBPJP8wGbj8SePvf7jJ+EhcRoHpt9cF/N2El6BI8QOVSgCpKgKjP8muFwShA1L7c/F/A/T8GPiEtX0L2kw+BBX0vjwLkVweotihHbe3NKIVumhiC92JWGQ8QNwnYj34KUA+nACkKKAXIelxPK0D2wEErQIbimqA9HiDBiZyADl038cv738U1T36E5eupSY8MTEWaoAcaJ2HH7J/x3IxfWAvSdgiMVoA2Lfd+XgRkCzpufHY5Pl4/ELpuiiZABZ2p2ySCUsg48u/2k9vxuU4rc6VJoABp/G8MLgsMcAdpSgFKJilSme6A0zlNFAartALEk5lkk/VoFNiwJDNghhCgUtLg6e85+0jgzJXA9H2pgom0AkTvH+/rqIEq0flBINvvXU57Y/hJnFYDyDEZCQrQ3O8BCYGaHDcExk/ccd8PeI9nJUNg9HkYpw4QHepd9679WggRjhMC428k/HqBSQ+QBI9swcAQvBez0ARNWD+lAPEeINM0PWnwGpUG7ypA3hDYgOl6gHSqGarrASJp8N74cRIFh3gBcPfLNIszQZNGqArQoKkoIOFkPglDYPRFF4MAXfnYBzj3nrcx/9InQ9dNGbQJOjwEBkAorTfbBGggW6Aavnrj5IwCBLjfkSJAKj0RJNMu6YitAKnsOsWAHySJVwtg1YtKeYCcEFhO/B4yAIu+a5AJOui14QLtAfJdJygEFlAGo9Zhmi7RO/0d4MBfAYm0d724RJUn7MWoQMPZDZ4Jxfq0whApQHxICghXAgc3WI+OCTqiB0jnFCCZBSYRBNMEhsxgBSipcgRIpQgQpwANUKpCBxMC4xSgPq8C1McoQKrAA+QfAktAx8ZBd6Jw9ou+cIrwAGmKgoRdR8epeUQIEG2CLjKl9ZkP1rP7C/gOYikz69xNRTFBWxv2DqrNVAjM4wGiyImmKMjRhQ7JwKK6ZDCZpCb5RKObHixUgALMiCJVJC74CTiZdkMVJAzGd4oPDYFxab9BUDkC5BeuEqX8MwpVgAeoWgpKfgjIhRAgvn8TPRE5akAdKkB0l3tyfpejGSpPBovxAQ1nGjwTArPPYTKmGnlL1REpQPSNIglthZ0H/WutRxJWD0yDp7aV6wfTtkTWAZIIQ0agAKUo42+CN8gqxATtrQNE/D+phIq0nT+uKoozwRIFaJ3AAzRAeYAM0K0wgitBA1Y16E0UAcrkdXT1ZHD0n55xVyoiC0xVFSTsLCqn71mDIARWpKFRWMDRJ4tKhekQmmwpClADUYB0RylTnRR197LUVAUGVDfTy1GANMcDpNFhxWQaSAUoQI4ULbj0y1EIkT9uasLdH0KAgjKsROCbmgaBJzZ+kruI7AWlDzMG6WH00NChi9xAuAI0tIn9n1ED6jgERt9EEOVHGAIrkQBle4A/zwfu+3H0bfBEopIEk64CTdR42ldp5MUKED1O9q1mX/PDgHVjiEa7Mn0gAaJe4xMe8oOAQN2uBdTW3oxiiDxA41NZTFeseh8JJUgBYgfkbioFntQdUlU3kywhDIHZJmjeA+SXBeajAHUzBMjAxQ+8gzc+WUutFN0DRDdCJb20vCEwPwUoeFLdNJDD2t6MvU0BAQqSwvND0A0TOd0IrQPkty3SEoUOgQlN0LbyVSBhMEJqqErQDQnqt0g0lhACK0czVO4z1YRbs4lUpw4iFyLE8QDx56UfaRISIOp68GT2BGSIVRL0sckPiT1ANDwEaISYoGl1jig/lQiBvXU38OlLwEsxsqU8ClAFj68TDqauBVoJ03PhChAhQGHnAVEbyVir54H3Hgbevd+7Ln1N8CSdToCoMQ+Q7AZfBaQ0N4OHYMj03s1cYf4KaAC+nj0bSYVXgGgPEDuB8wZogHiAbMVAMaHAYNPgSRYYOA8QMUF76gAJCJBSwKYBd0DJFHR0D+Zdo7CixroAmBCYXQcob3AEKNdnxZy1BDuYBUyqpmni4CueQV8mj+fO3M9HAfIpJAgAuQFkE5YClQzLAgOEBKjFVoCyBQNZ4uURGAVVm8DmkLBIKOUBImSwKUHtfzIdEgKLYIIuRSHgv6uaAFLN1vNckQQolgeIu6eLpQD5ZM8AnAdoGAkE/bmFIe/dNQ+eABWq6AEyTetYlWPSc8z/SVe9FKnJpSpAfWvE6wVugyfLwxACo1Ufpg5UTtwNng6N9q5mXwsDGWuNPHCbnYV7xko3EQVgjwFPgOj/ZTd4iZSgHUZGoAARLNKecgkQ58FRBWnwvTYBaku7F4ZKpcEDFnESpcHTJmhTpABxBIxGkleAcjqSmoIGhVy0MTvBkyahQSEwwL24GUOrfwhsXV8Wn3UPoTdTwLKuXo+CBiC4X1J+yGlREi0EJlKA3Mmc/F5iE7StfJF7lZzrASLEqYkWRmpOAdIiEKCodYCKIUA+7xFVvWYUIO6coLODKt3skvlc7tjQ7V5E4F9nEgOGOQR286HAVfOKbzFBg1xDtOpT7jpAADBEHb+oxVSHMwuMb4QKWNcYbf4PVYBW2a9FPI/psZaAHx+DCBBN2qUHSIImQKTAsSgLjKBFySBBOoWLssA4AkTUJfpzNMoDRN4nDoE1uYsoE7THAxTBBJ0p6EioqqsAxe4Eb79NVZByTNAkLpZyJ3qHAEULga3Y6E7S763pF5YRCFSA8gPI5K0LPsgEnSHmZYEHKJVQnQapvRnr2ApN0ET54kNgVBZYk0btv6qWoACVIQuMN0GrCfd3ckzQPLkooweI/15+ngORAiTKmBLtY7VCYADbAkMETwisSiZoQwc+egJYvyx8n6PAKaNBjZPCStAlEiCaQPo1FOYxrIUQfYqC0u0wRJWgM93ussEN1pgUVckUNZ1VNWDDh8CfvmzVoaKPI7El0CUwCKQHSGL+zAkAgMntabTbrSr6TUE820Yzhqg6QKwHSFVMFHT2giMqSUKjiZabBQZYZGUwpzvd5kWFEA0k/Fth+CpAVAgsbyChKRQBil4EEaBM0IqrADF+Hd4IHTELbOUmmgD1eUKI1gcFKEC5QUd9ScH/c3pBvC/ibREjdO+Qtd+KgJy4BCjJbMukTNC5xvHshnnCQaPSzVCDPEB+ClBYyCBWHaAyeYA8+1glEzRPDntWBq8faIIexlYYTBp0RCIRBJECVGwWmGEAK563/FT8+v1UCCwXcBPEbI9TuCt5fEUKEMDWAmJCufa5SitAANDXFT8ERsM0gAd+Cny2FPjb0ey2CHEk6fM0aswDJAlQFXD2Idvj7INn4c7v7YGOJuuiWYcO3/WblQxlgubqmAAwOAJEiAPdP0tV2Y7yTi2gPnuAtS8sOg1ep0JgDgLqACVQ4AiQjqSmOiTBjGGABuA0Q02oitNLi6l5RBuh6R45gDuZrXwJeP8RRu5dscElJO+v6Y8fAlt6A/KD3QAoYihAHyFAPhlppBYQ8WypAnJCCFCOU4BMqE6CUG/HTPw8fwKu3eI31gInBCb4DkYAAapYFlipIbAieoH5/U8Q5gEKaoUxnFlUcSsbR1GAhiOElys3ARIUUi02BPbKjcD1C4C/fM17LtIEM0gFZj6TU7grSZD91FBCvvQchMZ3DwFaHe08phVcfj9oQ75oW3RVeoIaU4CkCboKaGlI4Nt7bgUAaLMVoHVmh+/6rRjyEiBqYDe5i7ggIEB0LzDALbLoFC4UeoBUAQEiny+uBL1xwB2wh/K6lcFlK0CmmkQcCxwhcpqqOOGiPEOAKAXIE1bJW39/+Zp1RzJ+BnD8A0DTWI8CFCcNHgDwxp1oT0yCgt3c7wbF9fDYGFCarOxPn4wykgnWE+gBsr+3mbAKPNv7pVNKR1NKw1X6fvhq02RrQVAILIhMlEMB8pigNUEafJEEqJweIFEhREYBCiBAw6oAxfXPEI9cu5XSXRAoQMMRAqPDR2HG7Sgg30MLIUBRKjn/5zbr8bOXveciTRSiEjdyrhAVphohMHJcBjewJNiPAPWuApo55ViEZJP4JsIo+JugCRraLNWWJqXSAyRBo8MhQAKZ0YYVAhNXggYAnQ+B2Xd4JIUasMJItAJEtudkVdkn6QClAGkwkTN5AhQQAlO8afCaqiBlm6ANNZ4J2ikQqFJEgCYrdDVo0aTav8YdiNe9C6x6FQCwkvIAre3LYsOAYAIOIkAAEj2fOOQHAHKqe9w+2ef3ODL3c2QVe1DyU4BICMz2AAWFwLKOAmTtVwHuOqSxqqNkOQqQYAAn5nDR5FGOVhiBITB7IqxoFhjvAYpTCJH6zT96HLj3h+4kWDUPUJE97Ro7rEd6Eh/OZqj055ZFARKct8X2AqPfFxSuiqwA2ceThOeGxQTNEyD7//cXs5/vECCuQGamO9p+Jpss1Z+/9kydNUeLlLdE2kvUakwBqq29GYVoj6AAtSiUAiSoxKwXOAVIJ8oJG0pJMAqQdfLnbS+LKASmQRcoQNFN0Nm8jpxuOB4gI8oERsGg0uCTtqHb2V+ArQbNT6J6lo3nA7j43tfx3po+hgD5QjD4Patvj5u1wwAA6uAGNzMOQEFzlbMNWx6I54wdkFdIk0I/D5B1DAdz1m+hBJmgCRG1SQ0pjKgoQEOCy5BL2vsiIl4klCDKyCtHM9SgEBg5pn7qymdLgYf/nzeLJFYdoFLS4Knj9ej5wMvXA0/+2t7namWBFZlB1TLReiTmV9McXg9QuUNgIuIuKoQYJQRGbyPo+Eb1ABEikRgGD5BfRiT5TssesB4339XeNx8PUG4w2nVO1GT+2jN01hxNiiby++RpRVNblKO29mYUgtTqWRvkAULGa4JmFCA+BGa3SFA5BUhxB25CqAqGyfhnBqkQmAad7UMFUARI5AHSnfRwwPIAZQsuASoo8RQguhJ00r5wmIwt2gQtUhW4mh4fr+3Gfa+vxmq7AOK0cYLYNoFAAcoghZeUWQCARGY9GpzMOA06pW7ppu3bIQqQz0BKp8ID4jpACccDpDH7pdv/pzQ3m8w5NlqA8iQykzo7QEhBkQTINEvzAP3py8BzfwCe+T37eikKUCgBMt1HUahy7TvsPgLVNUFHRdsk63HQDofQk12lFIp8Blj+tHXtlT0EJiJAgnM4SggsEVUBihoC403QlVSAfK4ForRseN963G4Bu2+EABFiTHdoDwJRk3klxzTYY0eKK9JIpNnrr8bUH0ASoKqjI4IC1KAUqDR4rwJkeAiQ650h4ENg5HlOZ0/kHBVa0WB4FaCAOkB8TZyhvI5cwXBCRQUlpgJkuApQQugBokNggrRlTgFKQscnGwZgmkBjUsMWYwMIkEC10aFivWEXQMxscA3Q3J1O3r6shhTifeEUjZ7PgBf/hLFJ+u6TInbUQKGSEJhJUuqtSbpgWuukEqpTK8ghQOROVESAROnEzucSBajICb6QBTgfFFQNSBICFDEEtuFD9v+KeIDsa4NMAn5Eg5BXxgNUByGwtinWI6lrMxwK1gM/AW462FLPKhYCC8kCi6IA0epnEAGKnAVGPEDDYYIWVIIG2O+kpYCt92XXJwSotdN6zA1E20/i3+OvI0NnbxhEpQ4SDex+1pj/B5AEqOogJuhsQB0gwG1j4SpA7k+X9wmB0WnwmgpPGrz1XoMZ3GnCkygiBEYjk7eqHDsEKKbn3qkETWWBCU3Q2V5xajV3V5KA7vh9xjannGrMQggUoAI0rDUs0pXKbkQjCYElGlhFzlaAhlRSp4gjQDccCNz/Yyxad5WziDaoC03Q3LEr2JduSlPddhlOjSR7khAVgwxSgJQSFSBRqI9WgLJ+BIib5GlzJb1+UVlgfoUQOb+Tn+eLqAB1Y4K20UoUILuj93C08njlL9bjc3+oQBYYMUHTdYBElaAjEEb63A86vrE9QMMZAvMxQQOWykPCU3wvMHJe5OOGwAQeIPqa6RUoQFqKfZ9UgCR4kDT4MDSapBQ8UWAUmHZOlZ8C5M0CYytBO+tSgwZNUjToAhO0vU3ByZzk+mJl8jqyeR0pWynJK8XVAaKzwJiaPU7qZ0F859fzKbt/SsEtYKgpaEwF3JEIBj8DKtYblqFXNXVMUOzwQiINxf5dCtCc/c44BIgLAXR/AgDYofcp96vQBEhggqaVOQDQTTsEJlKAtAAFiCwTeYBKTYMXkQg1wXq1AK8CYeTZpp983ZFS6gBFLYTo1zyXTOT0+TWsafD2fk3cMd772uyMwEGRAlSpCZrK8cxXSQGKFAKjzv0gwhSVAJmcAlTNEBhgGeB5Px9PgHKD0U3Q/PbJdiMpQFzF6hqDJEBVBjFBh65n2pMHw6itE0rn+orpgiwwRWHT4FOqnQXGhcDyTAhMpACREJzYA0QjY/e5IsTIs60Q0ATIaQlBe4DIPhgF9zvQE2D3CmZ7SegYsglQQlOdOjxCCCbEAlQMGRqQ7gAATFFs41+iwSFAOkWAhlSifIg7eCcNdwBRGQVIRIDY84SE2VIJ1VnHVYCCPECCiYT/3GIVgjACNNRtPYpCYPQk2cARIEcBijCAFtsM1a/5LZkEq50FJqrGG4T2LazHoU22x4/ODKrQ/rdv7j7PldkDpIsIkGDsjGSCprZBSPnso7zrxS6EaO9PJc8PXwWIInWNY6ibmYLlzSLHjxDjfMQQWNInBGbqbJFNoQeoQXqAJILR0RSNAHWYNoOnTkSTTLq8AqQLFCCOALWliKfGVYDypgb6Lk4zdc/EG9wMlSNAOZ0JgeXM4hQglcoCy9FZYAwBsj+bLtpFFCB7cEiigCE74yqhKk4dHiEEE6IOzVJZ7PoZUxQ7vJBIQ9GsbRlQHSUm6xcCs0EI0AL1RXxVe859QVAIMc8pcXnTDYERdcypZxQUAouSBl9WBUhzU7JJRpIoBEZCNYA3tKH7DPoiFNsM1VcBIiEwap+rEQJLpIBtvhLtPWoC6NzBem7q1t1/sR4mQlqjgCZA/Wvd52VVgKhzQ9RYM0rIkJ7MiTKSaAC+9Q9WZYxrgibXXSVDYORYktAUAX09N45hx0YnA0wBWibY24kaAvNTgAw25N27yvterYF9n/QASfCgFaBsosV/PaPbekIP8PZz3xCYxk6ktELTao8jed31ABW4MIvlAfIJKfi0wqCRKejIFnRHAcqaGq5+8kPMu/jRSKnoBuUBSokqQdN3OfRdGLn4SVVX2xCaQMHJUktqqicLi4FgQtRNFbphwmyxCNBkuAqQan9mAapDRLKq/Xv6ECDAajJ7Teoy/Cb5J+/3gqAZKtk9+3dJaq4C5LQJcUJgAnlflE1DUKoHyFcB6rCeZ3rYdGwCngB5GlTaoUaynSAUWwjRp1SBUAGqRjNULQX819XArK8B+54V/J4xW1m+K2I+H9zAhcAi/r5P/Br49VTgvYeD18sNWuc4rayseZN6vUKtMESI1AqD+v6EHKhJYJv5wE8/BPb+qbUsrgna8QBV8PwgNxH8tcCEwCgFyNTd8FTLBLcmV+QssCAPEHWTKAoXJngPUG11ggckAao6OigCNNg42Xe9wBAYN6ARkpDgssDoMEurowC5ITB+kg1OgxeZoEUeIFcBypoJ/OqBd7G6J4NfPfiu73clYJqhJgRZYM5djs72yOF78th3pinojgcooSlOHR4hBAoQMR6bTTYBEihAtAcoq3HNWgWgawk5oMgl+Qn536FgWC9YTVWt/XIVIBICE4R1HA9QkAJU5AAuGgTVhKsAkTL9HgJUYJtQ8j6OQZtoNm8Wvg+eZqgRCyH6KUDkGFa7F5iWsr7/4X8BZhwU/J5x061H0opgaFORBOiX1uO9p/mvY5rAb7YGLt6cPc+73nCflyUNPqB+FY0oITCmcSenrKfb3cynuCZosm+VDIERRY5cUwT0cUl3sArQxo+s52OnsyUpYmWB8QpQwf+mwdknLgQmPUASPNooAvTUTr8Cxm0DBDWMEJjKDD4LTJgGz2aBtZDemnQIjFN7NBQ8oRdRGj6BRwEiWWC2CTpjuu/pHQofqHS6EKIWFgKjzIH8IGkToAQKrgdIDTFBCyZEw75cjCZrIqY9QKqdoq2bilOHKasFe4CsfRIMQtSxVRQFmqp4CFCeSoN3FCCD8wCJDJ5R0uCLzgITEC5SCZpsO9MtLlkQpAANrLMeo5Tuj1wHiKTB26TRzwPk7GO10uAF4T9RbyYaY20C1DjGehzcyB7TuPvvRw4BdiJc9567nPxmQA0qQNQ6hFDQJRaSFEmIguE0QZP99ShAvAeI+j4bPrAex25FVYmPGwITpMHnQ66ZRFpmgUkEI510B+g1DdOA/10K7HyE/xsYU5lNgPheYHYoJMmFwDTKo+MSIDcE5lGATEEdIEEILG8TGxJiI56UoTwbAssY7nuy+fC7D2LmptPgc3QWmJPpQHmANI4Apdsd2Tep0ARIbIImtYfEJmjr8z7LW4bUyQ4BSjsEqADNqewcjQAJBiFOKtYUxfM7BHqAii6EWKoHyEcBUhTWCB0aAqOb2ubcu/SmIhSgUj1A9D46+1cFEzR9TocRoHFbW4+kG/fQxuIUIIJAAkRty88zU6lK0ML9KTYERl1fsRUg4gEahjR4EgILUoDoEBgArLeLI47dilKAomaBBYTAwhSgRErWAZKIDocUBJ0oAgVIMQ2moadQAeI8QC1JKnPIxwOkQmSCJt3gKTJjr0O2T3xNpBI0CYEN6e6+ZwrhFx+JdqmqghRpheHrARKEwABLNXBM0Lpzw5/wSYPXTX8CRKovX7vUkvpThFAmGqBRafCkuWk+4UOAKPJB+qQ5EPz2mqp4DOSsAsS1wggqhBiUBl9yFphIAbK36Rihfap20wSIvosnyxXVVTSCwN9l+ikGUbPAACskWLU6QIQA0QpQo3hdAqIAkRCYxwMUc4IOPDYRzOGV6gUmQhQFiF6H9gAREIJZdCHEGlCA6HFkva3Mjd2aVYBiZYEJ0uDDFCCtQSpAEtExZ5o9wAf1S2FiqtZ6GgwmNOSkwXNZYLQHiCSf5XS3DQZRcsztDgQAPDVmkb8HiLrAMnYRx6Q9mbdRBChHpcEPGu73Il6cILiVoOGYoH09QHQILMENBrY6Q9cpSnJp8A3I4d7Uz6Hcd7q1QJgFZu3DBpMr1EfVATJMBb1DdtabZhsO9SxLRqg7eL52ktBbpXoVoBylAJHf2WmGGlgIMSANvlQFSKR0Od6KDutR1ITR4AgQ/fnE/9M0Lvi6IOAJpN+E6SmEGDCY5/pqIAQWRwEiITBCgDaWRuCCVJUoZKqc3eDDCFCYkgcEe4AAqndd3FYYpBv8MJigeQWIH/Po7+MoQFuz6lapIbBICtAo9gBt3LgRRx11FNra2tDR0YETTjgB/f3BF0Mmk8Epp5yCcePGoaWlBYsWLcKaNWxLgxUrVmDhwoVoamrChAkT8JOf/ASFgvhCfPbZZ5FIJDB79uxyfa2y49kzvoybvr0b9phuS/xBChD1mmI/V2FYLS1s5H0KISaYOkDWOnwILKkpUA7/C/A/z+L5MV8VECBvHSBCgHgFiGQlEQVoUHf3fSgCAXIrQavFe4DSHc7/tAKWUBU0USbofdTXsIP6MRKv3GAtEAykpv3d6Yax1sZcs18BGvrs7u4FeqKiiyGSARZAs8pNLoLfXlUVTyHEHGWCTvBFIoNCYEGhBFGD0DggbRdo0OZSwA6BhXmAqGMSx/8DeElSwkct4b+riCwSZHpqwwRNoCW8xSJptNnp6I4JemPl6gBF2VZZQmACIigCfePS1wXcdRKw8kV2Hfq3JL874wEqUgEajjR4RwHi1FBGAepgCRAhKmO2cv1N+cFo++lngg4jP4DAAzTKssCOOuoovPXWW1i8eDHuvfdePPXUUzjppJMC3/PDH/4Q99xzD+688048+eSTWLVqFQ477DDndV3XsXDhQuRyOTz33HO46aabcOONN+Lss8/2bKu7uxvHHHMM9ttvv7J/t3JiSkcj9tmOGuCDmLIgBKbBwIvLN+Lht7oAALqgFYaigKkETcI3Bd1wGj5mkbSIRiIFdO4ARVVgQkWOMi+7WWDutkmfKp4AuZ/lJUCZSB4gQoDANPx0fDq+BIirimpfvLRJ20qDd/fHoC8FqjEm/d0Nm5z0mzwBSrt+LKhOCExRE+6AQ2fIUMRoy2Y+BOa9JBOq4i2EaCtASUYBihECC6oDVOwEPxhAgEJDYBvZ/wkGbGJE/CxRQJNIP8WAJ0BBE3mmtwbS4LkJaNH1wG7cWDrjYOCQy91rkxwzPgQWRcHiFTG/0FKUSVTPiUsyxIGICNIgvzlNgO79IfD6HcB1XP0k0W9digfI6QY/DCGwYjxAgHUuNHa43w2IRkz9CiFGea+nDlDtBZwqtkfvvPMOHnzwQfz5z3/G3Llzseeee+KKK67A7bffjlWrBEWTAPT09OC6667DpZdeii9/+cuYM2cObrjhBjz33HN4/vnnAQAPP/ww3n77bdxyyy2YPXs2DjzwQFxwwQW48sorkcuxF9n//M//4Mgjj8S8efNC9zebzaK3t5f5qxoieoAUigCd+JeX8d1blmJtX8YJhQQVQkzazVVbBj8FHjkXAPAv/YuO14a8B+D6lAlCYOT1pA8BImGevkLMEBhdB4jaLyfbiRwLU2dLxNMp3ul25yKkw00JTWFCYIzSpeecAXcQVKjIUYC4EATV9bgADb0Z63MSquJW8KXDQ9RgNTXNd08Xe4A8ITDD9QA1pRLYafN27DClzd0fwLq7pVtMAMFp8Hz5/LggChBtVibfhw6BCU3Q693/6c+PqwDRnwn4+2U8BChgIs/2Vr8XGD/xbzsf+Py32GUH/hqYc5z7Px0CY1p5RCAtfDiTVuhoRFU7ooaT/ECXuRCBkA+auPFNdQlE+yz0ABUbAqsQAdLzbjiR9wDR52fjGOuOlyYcHVOtR1oRDUjOcECuH56ARzk2njpAoygEtmTJEnR0dGCXXXZxls2fPx+qquKFF14Qvmfp0qXI5/OYP3++s2zGjBnYcsstsWTJEme7O+64IyZOnOiss2DBAvT29uKtt95ylt1www346KOPcM4550Ta34svvhjt7e3O3xZbbBHr+5YVgQoQXQjRDYEB1ly3oT8nNEFbvcBoAmQ933H9/UB+AAOdu+J6/UDHawNYtYMA1+Rsfaa3DlDGMUFbA0E6oTGEJQXr4uzLu/sThQCRkI5KpcEDVBiM9qwEhsDs/VPoEJjKmKAZhSU34NxJDsIlCob9eX0eBchthqpDdVL8NVUVEyBqAp3SwN1lCu6SNFXBkMlOgCQE1pBQMbmjEf8+dU/c+p3d7TcE9DqqZCFEMkmSjtOAVwGis8AICeMVIHqyjlMDiIA+hlFN0IEKUAmVlEtFkPLBhyX4/8kxG1gXPwuMr11Fp7XTEJEJUcHKUsNgPMngQX7nQsYl/X7ritQsUQgsdh2gCqfBOxWd4Q2B0vsqUm3I+qpKEbwI3iyiYPPEk4xnQSFJ6sYQwOhSgLq6ujBhwgRmWSKRwNixY9HV1eX7nlQqhY6ODmb5xIkTnfd0dXUx5Ie8Tl4DgPfffx9nnHEGbrnlFiQS0fpPnXnmmejp6XH+Vq5cGel9FUHQiSIMgbl3+YM5XZgGz2eBETIwNmM15uzecn8YULn3WI+MOuLUAaI8QCarADUkVaQpAkSUlyEqDT6vU6EsHxiCbvDkvcw+8JWg+RCYfZGmGBM0qwAp1DFErt8hCkMmRRTs36Xf4wFyL3QdKnptD5CvAkRNoBMT3MTgowDxn0kUoKQmiKvT5IYPg1WyFQYhMS3Udc97gGgFiNxd6nk2dEFPqsUoQEwILIQAGREUoEwvqtcM1ScEJlrG/99ij5P9a9l9jkLgSiFAY7fyLiuVAPkRwUMut8I7X7e9e6CaO/upRUIFSGCCLmSi/dZ8JehKEWTi/2lo85qS6ePrNKymlVBKtSbXXTkUoKAedXw3+JFggj7jjDOgKErg37vvhlf5rRR0XceRRx6J8847D9ttt13k9zU0NKCtrY35qxqieoCIAqS4isJgruCEwPhCiBq1Xsp+Pi5r9csaaLYk0oaEQAEyBQoQEwJjPUANCVZdaVTFvcD6ssHyOd0MlfwBVCaY0AOksZM7VRWVJoCaqiCd9JI0a0ddBWiACoFldWv9ITRANyniQZmgdWhOFpjqR4CoAXi8yt2FCWTihKpggCNAWcoE7QEd3vIoQKQQYkAIrNgBnITAWqgbFE8WWI87YZBBWc+xRK1UDxATAouqAAURIF4BGsYQmFMBWURY+SKl3P/kdxjayCoEUTxMGY4A9fsRIMG5YreeAQCk7PO/1EwwJwTGTcRzjgN+8iGwJWVzIGTaTwGKGgIDoqlAw1UI0a8NBiAmmPT5QF8HIl+iH/zqADk9yZq8vwlBooGrA1R7ClA0eYTCj370Ixx33HGB62y99dbo7OzE2rVrmeWFQgEbN25EZ2en8H2dnZ3I5XLo7u5mVKA1a9Y47+ns7MSLL7KufpIl1tnZib6+Prz88st49dVXceqppwIADMOAaZpIJBJ4+OGH8eUvfznOVx5+xKwDRIe2BnO6QxxodWC7ia3MegnFAGBifNZSuvqapwFYz0yohAAx4SFBHSCSBUYKLTYkNKbAY6NqAKa30GLvUB7tjUkM5goYyOoY38oO8nQlaMBK+R4ydCoEFiELrLHDuXPi0+AVKiuBKUiY7Rd6gAact1uKTDvswZEyQReoEBirAFGDDTVAdoC7CxMMEqqqoN9kJ3KHAGmCc0VVrYHH4JQVgJocRBMqp4rExaCIAHF1gOgQGBmU9TxnMi5VAaJDYBE9QEGkL1ujITB+GT/hk3RoowD0Ut26I3mASlCAaAWwZTywsa+MITDBlKUo9rFQYA00GUtx9JuYwxSgZKO7rdxgsMpBb6/SJminDYYoA1Cgpvt54YgROhuBlKZ8QmA5e9xKpK3vnROFFWvfAxSbAI0fPx7jx4cPRvPmzUN3dzeWLl2KOXPmAAAee+wxGIaBuXPnCt8zZ84cJJNJPProo1i0aBEAYNmyZVixYoVjZJ43bx4uuugirF271gmxLV68GG1tbZg1axaSySTeeOMNZrtXXXUVHnvsMfz973/HVlsJ5NlaQ5ACRJ9EilsHiGAopzshIo3yC31t9hToD2tw5mwYGI8epM0hQFHR2zgZXgJkPbIeII35bICqA0QpQOmEu58NSsEaS7jTrXswjy3GAnv86jF0D+ax9Kz5GNdCeW44L1NSUzCUh5vy7+sBovY33e4MvowJWmVDR0wbDyqde5AKgdGZYn1oogiQqwAZUJ3901TFkqsBTgFyP6vV4CYanzpAXgXINUELQQYlTwisQgpQIefe5Ys8QOSudcVzwAa7LgkhJ4UhMAM4PUGV7AGKmgUWZILuYwkabyyvJEStMAj4ZR5PkAo0TwD6VgG9n7rLI4XAOGIehwA1UwSIFK+sVAiMQFGsybgwRJH8GCEwel3RtoLgkLMKV4IOUoD2/QWw4gVg9++5yxhS1+R9Xo4QWDId7MtiPECjKA1+5syZOOCAA3DiiSfixRdfxLPPPotTTz0VRxxxBCZPtpp+fvbZZ5gxY4aj6LS3t+OEE07A6aefjscffxxLly7F8ccfj3nz5mH33S2D5/77749Zs2bh6KOPxmuvvYaHHnoIZ511Fk455RQ0NDRAVVXssMMOzN+ECROQTqexww47oLm52XefawaBCpC3sBRd4HAgV3AUoATnD9GogS+p6NhKse8K27dwwlxJJnU+yATt9QARFSWVUJGmQmAkDZ5XgLqHrEGte9Aa5F9d0c28TszcqsqGe4JDYEkuC6zDWY8mOXSJAHrfATAZL7QJmq6UzaTCa64JukClzUfxADUWupn9EFeCVj0KEDFBCz1AgH8/sKCCcqV4gAhpVFQ2XMWboAF3MiWDKz85iipBFx0C81GA+EKIQd9ZpyqNh61bbgSaoH369NEgakwvlXkbZf/5ENjAevF6/GSvaG79IYAyupeaBu8TAqPBNwH2W5dvtgv4H8soZNHxABEFyKgMSR7aZD3yKfCA5bv64RvAvJPdZb4KEFeh/gvHAv/7inebHVtSJmgfApRoFKvJgHU8aHI0EjxAcXDrrbdixowZ2G+//XDQQQdhzz33xLXXXuu8ns/nsWzZMgwOunHW3//+9zj44IOxaNEi7L333ujs7MRdd93lvK5pGu69915omoZ58+bhW9/6Fo455hicf/75lfwqw4uYHiCvAuRNgwfAXMwaDExTbTP6uOmOapHi+ocBnHdHmAXmVYC+oi7FbxNXoxEZtxs8V8umezDPVHbWuMncrQRNJnubABWICZpM2AVqgNR8TdAJhQ6BsZ/FdLK3J10DClMCQKcJEK3IMB4g7vgJPUBUPabsJmY/RNWONdVrvM7qbhaYEBo3GQDWoKwHeEpKUYAIUWkcw07W5Hwdt403jEUGZd5nQSZVw3AnYtFdrx+KqgMUcNdu5Gs0BEZPLgnxHTYJR9IEqBgFyM8Lw5OpRAOwjZ3Fm+5wr9EoLSqCEBQCIyDnU6keIIAqCREhHOxkgVHbqIRPLEgBEiFMASKKrZb0ZpUd8Vfge8+545GvByjtr8p5QmAjwAMUB2PHjsVtt93m+/q0adNgckw5nU7jyiuvxJVXXun7vqlTp+L++++PvB/nnnsuzj333MjrVx2BWWBeBcjPA6TxBIgarBKKga0UQoC2cXw14SEw/zpAhEQ0JDV8pf+vmJJ4Gw8ZuzoEKG+yxK5nKI+BrH9YSjfZ70H2Lafb34NRgKhl9DlFpcEzChBHNJKKVwHKIcnsM01uGAUokXaOi4cAkXg7rXJQA7CW4YoHCtPgVQyhAaaiQrEH1oweFgIjxRCpO286HFZuBYj4fxrHiglQQytw2pvAn/YF1r5tLfNr6UCOT7YHTmhMdNfrB/oaieoBCiRAheqZoANDYPRx9pnsiQLU85m7LFIavJ1ynWqxJko/BYc/blrKasVx6lJLCfrHCeL14iJKJWi+FpBvFpiAjHkUoBhV0XkTNGB933IrHo4HqCPa+sx1QCnIKS4EpmjefW0cw3qfPFlnNnlKNLJtOGgk+F5go0wBkigS1MlomByJETBqPgRWcEzQ3M9LDUJJM4e9VNsrNW4bR4lJhtUBctLgRXWAXAWo1bAurhYMOcSIr2bcM5RHP0WAdC4tnohDrgfIJkCOAiToBaYl2aJrDa0wFHEhRBpsCGyjvb8pRvVhCJBHAbL2hQ6TJVTFnZh8KvEqfHaMTxYYoKCguYQhax8bz2/svEnQD0wPIUClKEBOEcSx/rJ3Ms02NPXL0CKTHRnwE43hPaBoKNxnCtexf/8oBEjnFKCaCYFRx9lP7XAUoLgEyJ4cSejRr8cWf9zI77TZNta5QN+klAIntT0oBMYpQPRYybQCCfEAAfFqYvEmaP7zygWiwqVaoq3vmwbPZYEpqnfc8RDCIA9QkAI0SusASZQA6mSkPSgAIoXARGnwAJiLcutlf8YO6sfoRzMeVXbHhn5rgGPS4EkILKQQYpbUAaKzwAxrYm9Ssk4hRN4D1NWTwUDW3adsgb3bMkzeBM17gKgQGN0Nnu7hoyjQFW8avDcE5lWAskgypIf29/TyChCVBk+gqSpVHTZiITpRHSB7si4kXf9aRg9IgwfcQYmeuOjnwgm1hCwwogA1jeMyFbmBlJbawxQgv7L/YaBDQaGFEG0yHfSd6fMLqH4vMAJVFXryGBACRE/kUSZ1EnokYcvIChA/Xtnnf34IeOVmYNMn4Z8d9DlBITCPB4haN09VXA9rhQHEq4ruhMCo36gSYVK61EcUeDLbbKS4607V/BUwAj8TdKIx2Jiu+twM1QgqGgKTKBLUiTKEFFpA+ThoFq0SBchVTgayutMLLMmfxHToRbcGhAvyR+KOuz51wk/CEBjjAfIW2fIoQJqCZN4mQMggYRIFiD3d3u3qZRQgvjo0UYRUJw0+Yh0gzq+gqwkkwSpAGh8CYzxARAFiCZDBKEDUIEKlweugUus1RXwHHDSoCk3QNgHSXAJECiHyoTz3wwUNUek2GCK/SFk8QLwCxBOgDve5n0GZHCun8WOHeD0/+En/NPgJLlQBorPAhlMBCgiBAdYEo2cDFKAJ3mVxWmGQ7DtfAsQdC09mmn2cn70c6Flp7e/ZPobqIEQxQQd5gAoZoKGF3Raznz77HYXs8iZooDKZYHwLoDD4ESD+xkNRBQQoxGBPUuiDFCB+O1IBkogEWgHisn+EhRBpBShfcLrBMwqQYdX94fGS8TkAbsZVca0wWA9QWslDse+Ym5CFZlrPCQHacqx1Ab67uo8hQLwCRDdDBSgPkKcOkM56gOi7PcBRgJICBYjsC62ikck8YyaYkNa4NnfgYD1AbCFEAlVRWJWKIGgCFTVDJY1gE+7n5+xmqB6Vz3mTIATmtMHwIQXFeoCe+yPw2AXW86Yx3F0fN5DSao6fP4dMUI4CNEa8nh+KaoUR5gGiCWyNKED0cl8P0ETvsij7T8IjpK9b3BCYs3/2fvWstNcv0gwddhzozyYeIJq8MAqQvc90KIknbnFCYKZAAarEOUL2O6qXxtcEzV13iuolOKEhMMoDJAmQRFlBnYyBIbAYhRD9Bnim1QOAZIKuHi0KgWnsI7ytMJoM19fSpLgEKG9a+z5rUhtSmoq+bAHvdbnZJlkfBYjPAsvxCpCps3eIvAJkE68klQVGlJPbTpyL7395G7TTh4EJgbnf80cLZuLmE3bDzpu3cx4gsQna8gBRJI0gaLIVKDrkdyio7k7qDgHy2Y4oBOa0wQjpqB03xPPaX93nnTsHp74yITAfcsIrQHFDYPSxjlsJWjRIGwVOAaqBZqgEJCTkFxoSKUCxQmC2ByiOCZpGkGITFYYO5+bNT+kCvB4get+YViv2MR1D1YTjz9NYWWCCPmWVUAlN6iYvCuixhL4OROoOf97z63iOu+lu1288IdsmkARIIhKoE2XIQ4DoE8pWgOhWGFk3DZ5RB3wuyCGwJy9dWThYAUo4n0+qJZMQWKPpmpCbkHHCS4RINTcksO1E6+7rpY/dLKi3VvXi+BtexBufWhkobhaY9ZmuB4hLgwfYuP+Emcx3ytv7yfRCs8nh5mOacPr+n0MHfZhtQ2/GTDIhrZbGNPbadjySmoo+3zR42gNEE6CIJlpfEzSQpwhQwb50Fb/iYmEhMOFnx7jrpUEI5yF/AHb87+ghMK3Bh3BwClDcEBgd4oicBSbI5KG3Z1Q7DT4gBEY/8iCFOGlEUfjIb0rUt6gEiFeAok7WQaB/z6Dt8R4gXwJkf/8xU6nt8goQZ5IPAl2EVXTDUy6U5AFqEi8HrDFHUbgiuyGKEEGYAjSa6wBJFAlaATJjKkB5qhAifQfgpwBxBEvkAWK8O2Ti0BLAgl/iCuVIbDStdElCMNK6S4DaFfc5MUGnkypmTrIG5pc/cevg3P7SSjy+bB0O+eMz1i5zITCvCZraLzK5qxpw4G+AXU8Evvs0AEC3s8D4Zqg0mErQ9qCXNVNMCMzpKaYpGKBDk3QrDJNSgEQeIJ9QpPsZ3kGCmNHfWONOQsQDpIYRIF2QBu+XUaXGuOulQUIOk3YSGB8DQmCqJh48CzngP7cBny31vicK6O/sSxw4vxN5FN3NGnkuhFmFEJjfb0a+n98ElWr2LoviT+HDRFFDYJ72HOUgQPTvGTDZ8h4gmoTkKQJECNWYae6ychRCVDRKOaqEB6hMJmgPARIY6T0hMZ/PTDTUdQhMmqBrERT7Hjd2LNBDvSaQFPkQGOkGn4gQAuOLE6ao95CJl2R5eT5/9//BtQ8+hG3wDgDXSJzW3bBWB9xwGCFSDQkNW4+3BtaNA+I7y7dW9bgmaJUt+ufxAAGs8bF5M2Dh79yXTG8WWGAdIGp/dbp2EaVEMWFBKg2eNkqzHiBuovWDyANkf39arcubhAD5bEdUCDGoEzz92XEVDqe6tKBsfpACRPq2kfcrqkUu+ruAf35P/J4ooCdMP4LolwYvUoA4T9nwpsGHmKDJcj+iQfe1Iojy+5JjSAhUVBN0JRQgeuwKDIFxHiBGARJ4gOiu9R4PUMRwMH1Doyas611HhbLADHbfwuCXBu9nVCf7Dnh/N7/K1loyBgGSCpBEFFAkY8YWXONYAUunTdCDWTcNPuExQbOw1CV2ghA1QxWGwGzohumoJAmSBq+7pOeLm7v7SytAW28W3JLkludXCDxAPllggDj1lbzkmKAjKkA2skjCYLLurO2kNJVVxRJpoM1q77LaHOsuFtUBCps8A7LAMhQRdTxAvgpQQCHEUAWoSAJEfAZBd5K0B4jv25b0OSdie4Ai3H37eYBEx4avglwrlaCB8BCYonjrxkRp1UCIVygBipgGXwqcEJjiPZ9oBHmAHGO06f5+jALEe4BISYiQ35o+F1RaAapkCCyqB4gmQI3i5UA0BcjvfFGTwTW6pAdIIjaoSVBJ8Y59b5yWVoAGsgWQeoIJTQXu/wnw5/nCUva8/weIEQKzUaAIECERybyrACWy3QAAQ9EcdaQhoXk6v/N4e3WvpxK0xwRNH4sAAkR8OZbKIwgPgqsDZCOLJExBTZuEprA1jbQk8PmjkTvmPlyjH+IuVkUhsJDJWVQHiBAgpi2HtczfAxSQBebrASoiDd40XYXEUYAElaAJmBBYgp0g+fokBHEVIL9wDQ1PHSCuoSWNHE+AhqkZqmG4v5+vCTrJPoogCoOFKhskBGa/txDVA1TBEFiQ0gCEeICGvMtoEzR/PKKGwOjtqVrxNxFR4JigoypAdD0smgD5ZLwJbvbcz/Y5X7RE8LnHhMNrj27U3h5JsCe4J6si2APUR6WVa9CBF68FPn0JWPG852M8BmtESYPn2lUYJvJEASIEqOASINLAz1Dd75FOqpgQQoByBcPT0iPpCYFRheBoDxCHPEWUyD7ylaBFIbAsUlAFEm5CU/G+sTm1XAG0BJJbfRF5xT1WQg9QESGwtb3Wd8tQvxchh/4hMKIAUSGwSihAeg5MRggQIwTGeYD86gLFVoAipFr7maCFClCVQmBDrj/OlwSS4xuktBTjA3IUIFI7p0gPUFlCYCFhQIIoHiB6f+kMOb5cQOQQGK0AJeJ5h+IirgJEm8eDPEBOCCyggKnfceCbT3ter20PUO3tkQTHxH3itYCwDhCNVD9V/l6QDswUOCSrCSpBMx4gikyYpsmGwAi5oNs7OATI/ayGhIqxzSlfewYA5Aq6pxJ0ijdB0/sT0AE6T31PEgaLogDlzAR7vO2L+Xv7TMd6tOPXW98InPaGuyuKgnQipA5QaAjMe0m+02WlJGcoIkpECP86QORumLpzD/UAFTF40+QgIQqBBVSCzg9FC4HFVYCiwFMIUVDLhcATAhsmE3S/3auvcax/qjHZ3yClRUiAQn5jkQdIpHx5CiFWIA1ejzjxR/EAMRllSeBnHwM/es97jMjgZBjWd3zj70DPp97PpD+DMUFXggDFVIDosCVdD8uv6nVQCMwvcUNLhihAte0BkiboWoSg4akDmjUIFCAA2FxZi7+lzkfDfVQ6uCCGH64AWY9ZnxAYCbUVwJqMtVyvZ7umRitAGhKainHNDVjfL76zzOmGtxJ0gkuDB6wLzMgHNkCkM7mS0DEEgQIE7x1xFkkoWgLO4bWP9w5T2vH6ufujtSHhUcQaUxqG7HpGCZWqsKpHJECCwa170Bq06ZpNhByGhsAYBYiYlcuYBeZsX6Em4wAFiL4TzfZVRgGKgigeoETa+n68AjRcHqD+NdZja6f/OlqIBwgQ944K+g503R2aGOh5LxEbljT4qCGwCB4gJmSV8C+ySSs5L18P3P9jS+n4f2vZ9TwKEKf4lhN0tlkUMGSPurHy63smuLn2fDYPksjgB+kBkogN+uQLYteCZqgA8A3tCUxWNkJd8ay7UOCLCPMAEfiZoInZmnRMJwRIyXoJED1ANyStbQSFwfIFU9AM1S7MSFeMJgNOkAmaIUAFZlsECYGKlkUKis9dUVs6KSQfaer4CT1AZOLxGwwEg9tl35gNgPMAGRFDYEwaPJlIypgFRshBstElg4wHKIDA5wbYwVikVAAVUoB8CBC97w6JrFIIrM8mQKJqzgTk/IrrAQpr/eG8lyJPojBYmAm6HB6gqCGwSB4gzrTsBzoE9uFj1nPR92dM0CoqGgKLWwhR1PJD9H5Bg+voHqAQE7SsAyQRG0EnomA9XgFaYwruamglwAZfBRpgFSCieAubocKdhAu8yThD5+3boC4SEiYKMkJbCpBd0NGvEjTgHivH4Os9XnnDS9K8IbAABcj5rPCBJ52k/EZBJmg1IS7SJxgkDv38FLxz/gEMASK/TWgdIGEl6DJ6gBxVyUdiDzpmOU4B8iNAw6IACTxAforUcIfAggiQowAFTC5CAhTU/JX2jlDGdJEROswEPawhME71FCpAVNPkoBg8nQUW9Hvz7Smc91WwFUbkEJiPb8uvDpASNO8EZIFFDoHVHt2ovT2S4E7EZKhaoCnsxSYiNiIFKBOiAJFTnvEKURcf6R+WpxSWBHS3jD4NaqKLogDlCgZ0zufihMBEChC52EVZYIbpZG2Rdhh8CEyYBWYmodIEKIL0TBMgTVVcQsZ7TRRNPMH6/NbppIrnzJ2sTaTHevxRHgRWgg5rhVGkAuRsRwHaplgEL0i9UbkaIqLjMXHH4DvMYqH6KUBcCEyEYSNAdrilNYgAhfQCA3yywAJ+Y6aStlvkU5gK7/EAVaIOUEg7EALic8wLTNA8KQrbL1rJCfq9SdPYhlZ2u7Vmgqbh5ysN8gCN21a8LS2OCVoqQBJRQKsTquZ/wgvqAAFgWmM4EClAIg9QjBAY6TpfoDxCCehuI0UKCk2AoihABYOqBB1ggibHJsgDJErV16IoQAkuBBZFAQoJgdGDWAwCpCgKelITsXvmCnx89POO+uZ7E0sGJfou0CErPunm5VKAAOD4+4ETH3M7cNP46h+BKXOAfX7KmaC5/drzdODER6PvSxzwCpCoEnS1FaC+CApQ0SGwiARI1aiq4hFCYJ40+HIoQCHtQJzPDlCAyLIoXeUB9vwI+r1Jph7xEomaH5cLTiHEiNO2LwHyqwNEqVj8wDJjIXDAr4DPLeS2FeYBoglQgOJWJUgCVIvgpUg/5szVASJEQaRmCD1ApkABYkJg1iRLh8Be+6wX2YK1faIA6dT+JaELQ2BKkgqBRVGAdMPxGKlcHSCPCRoI9gDphlPLyM0CEytAq1t3dJZlkYKaCMqM8KIxFRICI4OpqooVhoDPaG5IoAvj0G82OAb0WCEwkVpDw/kdzei1bvy2OWYaMHGW+D1fONoiR62d7ETEb6NpbGXUHyDAAxQhBDZcHiCiAEUKgZXRA0QrLorifkZBkAkWWgixDHf9ZQmBcR6gsP2KGgIbtHsZNtkFUGupEGLkEBinAIm2ryjA7t8Dps5jl2tJnwxFxbst6QGSiASmDlBACMzxAFmD0pimlP2/SAESmaC9kwudBk/GOloB+tPTH+Nf/1kFwPUA0cUCEygIQ2BqwqsAtaaD78IyebEHKFsI8ACVqAAtb3IJUA4JqDFNfHQavFgBooyMIiUmQCZubrC2NZDVHXLqGwIj+03fBZJ0bl8CRPeOiziAO7WFfMJFYaDvHrUU+/sVu00HETwegVlgfgrQcBEgWwEKygIjxCduGnyUEJizbfuY/Hk/4LqvsP6WYekGH9UEHUEBirotJwQWcjPgpwBV1ARdRBYYDb9WGKKK0Dw8TVK5MHbTOOtxz9O826pBD5BMg69F8AqQ3wnJZYFt1pLC+v6sDwHyhsCEHiDNe5LSHiAdKtb0WNtyFRqryrMKw2o4SkJgatIZcNSEVwFqbgi+kDN2OjnZJTcNXpQFNsT+T0E3TOTNBKC4RIf3AGn24PJRchvsYS+brGxARp/k/awAMCZoYTNUyjQpqM0UqADZ6tJgruB4gHyzwEReBHIX7Gc2pu/k9Fy0DJ5CiKoUBnow1lL2OUPISIkEKEia5wvdCU3QNeIBClSAoniABGHIIILrqGH270+OSbbXKqo6uAFoGc+uS+AxQQ9jGnyQB8hRgCKqKHRGZFEhsFpQgCJmgfHEJ8irw49PGhcCO/RqIN0GTNnF+1nSAyQRCXwWmF8JcS4LjHhqHAK0w38DU/e0nosIkCAE1sCYoL0hMAUmMnYIjO46TxQWq/u7fcfU6pIHpbEdm7U0QFGA9kbrc7eZ0Or5/I4m97OGHAJEmpByvcAA78XM/f/Ue+vw8FtdjlGbhMB45USzl3fn3Pe/b2yOnowg3BYAmgAJCyHSd3ExPEAA0JSyFaCcTnmAfBiQqB5JmAJEK1KC1ilCkMmm2FBVJRWgQALE9XoKSoPnMRwhsNygeyMRSICK9AAFTeqEcDgKEHcc6XMuzARdFg8QlbkVhEgKUFQCRBGZQAJkh8AahyMEFrcOkF8IjPcA8SGwoPIA3PgkSmTYcnfqvJQKkERcRPYAsQrQ+BZCgKhJlpyAEesAJQVp8HQILAEDWTs0VaBMyjoSgJnHOMUetLUU0DQG6Flh/T9xR1wz9wtY359ziNo2E1pw9bfmwDBNnHzrKwCAloYE+jMFFAwTQzlOASJp8KIsMAJqwM3rBo65/kUAwPdSJAtMB0wgyZFKogD15k3snf095ijv4UFjV3wtu8xdKVIWmLtdphBi1DT4wBCYrQBlC+Fp8ApHvACKAAWYoEnxv9wA0LyZd52ez4B3/g18/ltW5gtRgIoOgSXZ5/SA6afAFLNtHn5p8FEKMw6HAkSKICYa3QwjEZxmqHHT4CPUAXIKW6bEr4u2UwkFyCGnJYTAiAIU1U+kUgphrYTA4laC9oMnC4wzQUcoveKA7wbPv17jHiBJgGoRTBZYwv/E4UzQm/EKEB0+i+gBEqbBMwSo4FGANFWBiQSgA2NA0kLbWOl90k6YM3Ws5/MO2KET3YNuem1zKoFUQkUhpzsKkKrwJmiBB0jwP+0VKnAKkCcEZpPG3iywwpyIFaZ1192TNeFk+UcxQfNp8J5u8FQmh2iCDWgYSBSg/qwbAvPtBi+q6hxmggYsclTI+CtAz1wKvPRna+Db9TuuAlTOEBhBsaQq2WTt/9Q9/Nfx9QBRn8l/vpayW0IMAwH60M5+G7tVcPbMuOn2elv7r1NyCIyv/hxAgCqRBh+5EjRFgEyzRAUoYgiMmKAdAlTBStBxCyH6ISwEFtcDRJ8fQYp8DSpAtbdHEt5K0H6qAJcG35a2TjaXAFEp9FE9QAITNFNJWdEdc3JBdwlQusEafH7+JbvBYLqdPeE7XXMxDzps1JjSnH0gShNfBygnygIT/E8rRZ6GrRqvANkhMJ4nBrUlCfkujAfINCwyQg9iwhBYuAl6MKc7zVB958ZiQmCAqxbwHdAJyIA/aN/5lqwABYXAigyrnfQksOcPga9e4b8OOXAeD1CAAkS+Y6VDYIYOPGfv+5zjg9fd7bvAKS8Gr1cuEzT/OuCd6PmilSLVJmqGoWd/QiZ+ohiahvUeYTPUiCboqFlgRAEa1iywiErKgb+1Hvc7h13Of3e+iGPQ9kU3nPQ17CFItU2ApAJUi+An3RAFiBCgFnuCdNLglWACJKwELfAA0dk0SRQwaBML1wOkONlSm6fsiTPdBnR/4m6Y8gPxoH1HzQ2aG+rSxVlgwQqQHwEKToMnBKjHJkDNKQ1jW1L44RdnAou92/YDHQJjPECANenQg5jojjZgkCAm6IFcwU2D93NBCwlQSB0gwJ308wPi18ndOPEXlKwA0QSIC4H5ZWGFYfx2wPxzg9ehs3yAaIUQk42WL6fSCtCrtwCbPrZ8JZ8/KnhdVQXGfy54nbh1gHiSwJ+n9DlFtrPDImDzXYEtuTRp0dhl6NEM9vznhZEW+rcrZHxaYUQkEUwILI4JmlMWywkjpgI09yRg+0PZrveAv2oeyQMkCoFRx51XsGucANXeHklErwRtn2x7Th+Df3xvnpNW7hRCpMNngjRxYS8wgQeIRhIFJztLp1OxyYA2uMF6bGgDuldQ38lfxlcUxSFeTXYIjIarAAX0AnP+dwdJer2CSQgQ8RWJQ2CDurX8oB0n4emffhk7bDHO/7ME8G2FAViDryEgp8z++w8+TUQBotPg45igcyEeIPo1PwWIECBCqP0KIUYFr/gwIbAK1QACvH3PRJOsnwJUyTT4dcuAB8+wnu95mn/GXhzEDYHxhQf5EJhIAZoyx6oT45lcBaQlbngocgiMOl/0PPs5ZBsOiSh3IURbAXKuO3JeGd5musUirgka8JIfICAEFsUDJBhvg5ofSwIkERueLLBgBWjLjjTmTB2LljSnAKmae6E7bRDcQSKsG7wICeguAbL9JcxETwhQuh3Yam/rOX9XKABpItqc0jz74FaCtr5vcBaYe6xyujvI01lgSU3xZE8RBYhUtSbhprhpnJ5WGPT76UHZ73cNCoFRClBoM1TRnWiYCRpwJ1w/BYicR6QvVBRfURCCQmDFbjMKfCtBU9eElgJTS4jsTyUVoBf/ZP1OW+0NzDu1PNsU/d6BITByjvqEwEQeIL9JUxgCi0kgo1ZvVjX3d9WznFJlcNsqVxYYIUAd3Pvsz77zWOB32wFv/RN4934ruaBYxA2B+YE/jnwWWGAaPDc/8M1Qg0Jg0gQtEQkxPUBkQGl1PEAkPkKboMmdesqZ3ERp8HRIxRRIQEkU3CwwygPkXFSD663HdBtwwMWWnL/LCf7f1UZDUgMyBTQKFCCnEnRClAbvf8FlBSGwhKKzjVA3fgS8dB1acuvs9aztkWPpbl8JNCgT+HqAAGsAoytBl6AAkRBYpDT45/4IdL0O5PqtZWEmaMD/rpUPgZWqAHlCYMOtAPEeIOp7kP0h33k4PECkivq2C8o3YZQaAvMoQDSxCJmURed4XAWIN2UHQWuwwl18yF+UhRmEKApQIedeU35ZYO/823q881jrcd6pwIKLwr+HCHELIfrBc9Nof9eiCiFydYDqTAGSBKgW4ekFFqwAkcGstcEasJg0eD4LjBrgRQoQDVEI7DNzM6cVBl0HyBksHQWoA2jfHPjSGYGfQeAUR6RM0ASebvCBIbBgD1AKBdb/85evMaE6YvhuaeAIUMS4O5MFxnuADJ2N44sGhIBQYYudBt+fdSeQ0EKIRgF4+Bfsa4EKUEgIrOwKEJcFRh+TYj1AUeAphCioA0RuIAgBGg4FKGrjzzgoNg3erw4QowCF+FKEBCiuAhQxBEbWKQx5z1+eAIWRqSjNUIn6A8Vt+suHwBrHUOvBLW5ZDOIWQvSDby+wCFlgcdPgy1EHqoKoPUomEb0OEHe30cJngSkUeSJ3RNTJuvUkyt8Sgm/mfoFL8/+Ne4w93CwwulkpuWgGKA9QDJAWEk0NiYAQGGuOBuC9WDUfDxCVBcakwNM+JbgKUEuaGwwi3nXRhm5NVSxCQ5MRuhJ0zBAYSYPvowiQbyuMoHTcQAUoJARWbg8QT4Bo1j2sCpCgFYaagDAtv5wEiFfa9IhZSnHgF4bKZ8TVgp19sM+hKFlgFSVAEUNggPtd+ePKe72ihsCC6gDR4S9eRXGuc/v/WYdaj7pb8iM2ivEAieCXBVaMCZovhOgplEgngQxTBfUYkASoFsF4gJL+oReumq0bAqPuypxGhl4F6NdH7I6/nrg7jth1C+Hm6ct+ibE9/qAfBgOqtxK0pggUoHgEqCFJTNBeBYgQFrcVRlAaPO0Bci840gw1gQISAT4nYpZu4T1AEe+66F5qTqiNJiO0jC0ayAJbYdgEKONOQL4hMEcdFAw6gWnwEU3QPBEqVwiMJmwl9wILADlunkrQnKGTKczYyL6nVHz0BHBRJ/D0Je6yOGpHKchngN9uA1w+2/sar0JFqQNUSQ9QnBAYIbB8HSvym0XuBk+dH74KEFcDCKBuSonnyN73DnuMLZYAGQZAWxtKgd+YyT8K3ytKgw84lvRnxS1/MAyQBKgWEVsB4tPgA+oAUSdwS0sr5k0f5ykKSGD4nLDEBM0qQKT5pk200u3iffYBUYBEITAy8ZPQlW6YDvmKmgZfMK31UtCR9I0buUqR4wFqnQQ0tAPjZ0T6HvS2NXJcRQpQESboJhICy0QJgXFmTGcHm4IL6zkeoLAQGJ8GXyYFiN7fSsrnjMfDdCcsmnRVWgH69/etx0fPd5dVigAtug7Y68fA5M9b/69fBuT6gN5PWU8PEB4CE3qA/BSgcmSBxQgL+ilAcT1AUdLgh7qtR5oA8XWAyOcRZbVYAkSTxghexECUqxCimrTGElo15Y8VQ4BqTwGSHqBaBNMNPmFNwqv/412Pu9hI+EUVEiB7wqJJjT3ZJXwuqK3HC9Jn4ZqLmSww/u4sZgiMGHxb00lPCMwhdtR+6obpzbIC4JsGbxMbDXqgAkRCYIR0oaEFOO31yB4XetuO14hukEhXghYNNAFGQeIvGsy5g2HsEFjY93AKIUasA+QUQixTFhg90AcRtVLhECCTVXQ0PgQmUIDKlQYv+v0rEQIDgB3/23pc/qT1SE9khSFAo9pt8IpLJA9QyE0a8/4iPUCRQmD2vvIh3LgeICUCAXIaAVOeOk/rG/tYEWW1aAWIuo7LXgna/q5RTND0XCGqE8X/tvRvJgmQRCTQg5OiAQsvsS7ouf/Drsd5gEg4hC2EyHmA6AnQZu5+/aQO3nESVncPYWJbGqfd8R9neaACRBAzBPY/+2yNia0N2HfGBDzyzhrmNZKSrlFKlasABZigdS8BSiq6r+JFr+d4gABvddsAJKltO8fVGRTzbCXomCEwQnboLDjfXmDkM/kBN8gADVCFEKOaoEtVgAJCYJUEfYfPhN2CCjOWOQtMSIAqHAJzJjvqvMkPsf3GeAWI92LF8QCVJQTGeZKCQAisRwGK6wGKUAlaRFb5LDBHAbKvu0KxBIhWgEqcthXFOg/4rDInDT5AYeIVIPK+ji2BTC8wZhq7Pk2YJAGSiAT6pFEUoH0KcOw93vV8yq6zhRDJxWkThvGfA7b+ktXo0h4Ej//iNFz/7HIc9vkp3G4o+O4+07Gqmx1MMnkDpmmKs8AIYobA9pi+GfaYvhkAthp1SlOd/+nsLd30I0DuBSrqBaZB5xqhKqDdTiRbzPEAxURSqACJQmAaMPtI4PELgY6pbtXsgMGHkB2aAIW2wgAXxgxTgJxCiFFN0CUqQPTvp6Uq32aCgC6EyITdaAWIqqMFUApQmbwM1SBA5PqgibHHiM3tg6cOUJwQWIkm6P51bmJFqSZouj9Y5BAYR4BM073oBLXVGOJkUOpRsowKUKkmaMA6TgVyo8wp1VELIRJCqijA/75qHSveL8ag9jxAkgDVIpgTPMivwXU/tpEQNUMlUDVgPtsbZouxTXj3ggOYDCbmLYJZNqcbXB2g0kJgzFu51hgEdLhH12N6gPyywBINzPEj63U0FTcBbTOhBemkirFNKbemEt0Qlc7kaJ8C/HwV0LsK+OMu4u9DgXx/g/aAhzVD5RGmADmFEMPqAFVCAUoNnwJEe4D8FCA6iQAofyVo0W/EZ2CVG+R7FwIIEK+48Dc3sbLARB6giMcvNwD8bhv3/6hp8OS9os+NbIL2CYGZhvsaXzEbcI8Df145ITBB1p0fcgPAdfsDW+0D7PUj72eUAlGB17hp8PQx1BIIpRM1aIKWBKgWQZ9kQXIkKXGf7WMWC5uhOtsW/+R0AT/P7gjm2EzeYHqBlaoA0aBVFDoURbd9KDheGv+6E/4EiI5jswRIh4qEqjiNZeMindTwn7P3Z4kJ4wHiCselmrnByP/3FrW9CO0GzyM0BBZggjZNSgGqkAeoGgSInuAYBSjJeYDKbIKmrxk9bzfxrLQCREKjVNffAq8AcZWg+RBYHA+QiMhF/Y17V4VviwchsITUKSpb6iBsfwn8ssAM3auiJTjV0Pks6jgVY4Je+w6w5k0rs3bP06jPKEPukqjJM0+EROCL9MaBDIFJRALjAQpQgAgB4u522EKIfNGr+PKpqOFmNq87HiBV5AEqQQGiTdCOGdn+HFWxFBChB0jRmCwe1gNkh9Ggs4UQEw0ASWgyNQAKxjSn/NPLI8BDJpk0eIqcEtADScDvIxr3wkNgHEoxQdODd9kUID4LbLhCYCIPkOK9m6ePY6LMafD0ti/fmQ2vVDwERk3OvgqQXxZYiXWAoiponpoycUzQNoFPpN3ndLgzbPJmssAo5YLedycEJuiGbug+ChBFPMNAts+30CkHVMGYEykNni7SG3NfJAGSiITIChCZrPqZxQmmEGI0BShwdwSzrKUA0VlgtFeiuSQJn/YA8V4cTVVg6KZDvpjv09DKMAJRM1QhAbJBsufGFhn+8gXjARJUzo3YL4fP+FKUCK0weET1AIkUoEKWfW4Y7oBetAKUZJ8PGwGyjxtNgHhjOl/jJFnmEBj9Wb2f2cvsc79SJQDIZ9K/pa8HKEo3+AqmwfNqSZwQmEOAGtzntCpTbCsM+vwUpec7ITCdLRdArrs4ITCiTOv58hVBJBCpznELIY4ABUjWAapF0CcZnZ3Bo0GsAKm0B4g/SQNNamKIwizZgu6oMB4PUIysKRFSjAfIS4AAiOsAcaoTGwJzFaAkEwJzj4+mWNsc01zmyccJO1B3cnytJwf+yhP/O/iGv/jt0wj1AAUUQqQnpELWLXoJFP+bVzsEZtAESIMnzZepA1TmVhiiiYZsu+IKUBAB4kJgvHmVPg+KMkFHPH4FTi2JMuFqXAiMDmkyIeiIafB8CIwmvw75p0NgVCVouho0OXfihMBov13ZFSDBTVckE7SPBygKapAASQWoFqGqwNeutIhN22T/9VI2Ocr1W4OKquLbX9wKiRcFvcAI+IyOCBCJUJm84agwHgWodVLsz6ARpABZtYAMcQiMI4uiNPiEYrAmaIHiMLa53AoQPZgKPAgRi4XxoUhfAzS/fRphoaqgVhhMCCwL9K22njePL16x8LTCGO4sME4B4pU5phI05QGiw1WVQKUJEG2C9jQO5UJg/KStx/AAqSrrwwFiKECcWhJl8ucVIL5GjR6RSPgVQmQUIIEJmq7ATqtNZD/ipME7IbCcOHReCrQABShIZWIUoLghMGmCloiKz38rfB26yWF+AGhoxc8PmoGh1W3AKkBYbbiI/koipSHDKEAqezfQPsWzfhwwHqAGdv+JAuQbAqNAK0C6Y4IusIUfBZJ0+QmQTxo8/zqAoFRR/ncInH/5Wh8EYXffQQoQHwLr67Ket3YGbzMI1aoDxBAganLh65yIPEBAeQgQr3DQqHQILDANnsuU4q8RUQgsaNJUE+znRSW5pYTAyPmr2aSWXHuxu8Hr7P7SZIiQGb50AnmfQSlpfmRShE+eA576HbD5Lu62nGKQFQyBRTFBSwVIomaQbHTvrnIWAUpoKlpTVF0HjwIUf3IXe4B0TgGiPqdd3FssKhoCQmAJTwjMnwBlmW7whAAZ2H4yFSoTDEhl9wCRwc8oUJWg/RQgfwIUSwEi29W5ySbMhElCZEbemviYTCX6WJlAz0rraSmKn1NMzS6rP1yDpKgQIn/D4KkDRKlnpo6SHQS88kKjqnWAuDT4QAUoAqFQk1zYLCoBKiIEluAUIMfXxRGgsG0xZmZqfw1BCEzkATIKrNpEbjyjEKBXbwU+fBTY+JG7zMlqqwABIudE60TrsWWC//t8Q/cRIAmQRFmhKFYmWLYXyPYDZP6nDXM8Sy9CARLNs1kqDV7TuCywthIVIIoAtXIESHUUIMrnRMBVnxYpQAtmjsPC+du6KwkUoDGVVIDoStAE9GAcMEjwJmjfNhj05/KTSJDqALAeofwgoFHlDPj3bvrYemyZGLzNIJByCSWUTSgKfiEwfoDXfBQgQy9dpQlUgCqcBs+oeX5ZYPY+0Eoz/ToQjQBpCYC+zIYlBGZ/J6Li6VnOBB1CJFTB+QFwHiB7W3ztKIAjW1QIzNTZVHoRcnZZk0y3u8z5PhXMAtvpCOtanrpHwPukCVqiluCkwlOZYH6eBqCogVU00WYKnAJEf06pIbBYCpC/YZz2ABEFKAmDzZwSKUAV8wD5hMBok1UQAYoTAuM/gyDsDjTR4O7Ppy+xihQ/IXWvsB5LUYBaJwKHXg0s+pP1/352kc55pxa/zSgQVYKmW8cAXhM0owCVYTAPVIAqHQILMkFzIbDZRwHbLnA9h0wzVAGh58G/FvXYeUzQcZqhEgWIUsHpcGeYkkITZJHnid4/OgSmUWFDxgPkp6QKQMJ3mV7qs8pNgATjTyIFbLcgOPHGN3QfBbXnAZIEqN4hSoWnjbZl8ACFpcFrvAm6bfPYn0EjqfkTIG8WWJAHyB2sdJOQEG4SHxYCRO4KdQjTWZnjGxQC4/4PDYEJBvkwBUhRXKXjlkXAsvvd13g1ibTvKMUDBACzvwlM/7L1fM8fAv/7CrD/haVtMwyMAkT3Z1NYU6ivB6gMZu2qKED2dwusA8Q1DE02Akf9DZj7Xft1kQIU5AHiyFyxafBRjrnTC4wKgdE3ICIFVgQmBEZ9X0YBEpigyecXspwHqMH7Pj/QdYucZYQAlWnKjlh6w4OS0uAlAZIoN0Sp8IEKUDEEyLssyytA9GRcRhO0NwssiAD5h8CIAsQaOA3hoDqmYnWA8uETRgwFKFIIjMcuxwe/B2AzwLrecJ97QmCEAJWW9cdAUYBx0yubYQVwdV64TCa6MJyoDhB5X6kIUoDiGkyjIlIdIJ92EbS6QRDJA8S9FtkDxN2sDG4Mf48nBMYRoCiEjX6d7xUnygKjbypJOEzPUh4g7jwKqwUkKkJa9hAYfQMWgwCVYoIu9UapApAEqN4haofhqAyqgADFH1hFxfYyecPpx6WpKjs4NQeY6CIgSh2gSFlgutcDxMr34oGoollgYemsQSZo7ncI4z/MsenYEvjeEmDmV0PeBGDKLu5zOjzH37kO2b95DQ5soXAIkKBBZtM4a1JId/grQOUo2OinAClcPaJywqlJRafB25Pr4EbgqnnA+w9Z//MqFE3kCaJ6gGhEVoCo49PS6aqEQSDjW45WgGhfTsSCguT88KhQdBaYwATtKEBU7R7NNvgTwhCmwoqKkJbbBE1f13FuNopJg//m7Za/aM8fRv+cYYI0Qdc7hB4gSubVuDvVIkJgIniywHq73BdLHLxTPs1Qrc+yXovkARL0AmMGXx8purJp8CESfIwssNB2HfRglWoBJs4K21ML/309cPtRwJo3WHXAb+AupwI0XGC6dnOT+Df/CgxtAprGVk4BMk1/BahS4S8gOAts6Q3A2rep/fC5eSI3EaYZLaTEKwWR0+BtojXra8Ci66NNuAk+BEZ5gPzqcIkgUsrINvj9o38vJ9sr6606raXs7MqIHiAa5FwplwJEF1yNEwIrRjn63IHWXw1CEqB6R1gIjB9syjS4ZvNcJeihTWXZLsCmwbc2sIOn6lGAwgnQRf+1A2YP9gFPgr179ZGigxrDFgXGAxRSNyVkYtVUxTnu4QqQJn4ehjFTgW32swgQPQEIB27FKoRYbxBmgdnHaMoX3PWYbD3q5qFUAqTn4ev3qiQBCgqBhfXeIv+Ta4gmA4EeoCJDYLTJOKraQGdbkc9mmhHHbIbKk9SwStCEJNIKkNNUNmWFl8NCYKIipE4IrBIKUBwPUJHKUY1ChsDqHcQEnfUzQXMDR5kUoGyBqwS93/+zQgRf/n8lbzuluRekVwGyLjojggeI1AHacmwTtt98M2thiAL0X58vzb8khDAN3m/QCTYK0j6gwFYY9Ofyz6OA9C6iU6RFBKh5fPyKsLUAoQla8JswBCjFmmNLQTUywIBgBYjP/uH3g/cA0ddSrBBYzEKIcQghv890aYM4ITBRxWzApxK0IATGe4Do9cJqcfGeLHpZ2QgQNXYE9ZvkEbVPZZ2gDkcuCQZhafD8nWoRJmgRMnndyQJTVQWYMgc4c2VZBu9kwr04Rc1QAbEH6P73+nHQdHdd4gFKaSpAssD0YAL0+2/MLmHPfUD28aGfU8vie4AAO7pIxvBYBCjm75KgslkIRCGwUmoAVRNMIcSAMA45n9UE3LYOeukKUDUywABqYqd/V5uMpUIIEE3k6Uf6NeFn8mnwMUNgcfoX8uMb7wGKHQILUIAKAgJE9pX3ANHrBYXADF1Mjgvl9gAVGwITVJCuY9T/NxjtIHdtDAEitS5U78RXRDNUETy9wICy3bnS3Rr86wB5CyFe8tRqDOXcAYqEwFIJla3GTKBTMfp9zwJOfr4s+++BaIApNgRGDVyhVqtSFCBi+M2HKUDj4m23VuBXCJEHuX7IxEpnB5WCailATh0gOg1e4DkBBFlgZAKPqQAVnQYvMBmHwUPa6DpAPmUoRPAzQdODU5gCJPIAAcEhML/fotxZYEWHwKQCJFFLEIXAggb0cilAVI2d0HTsmKBvTppS7MXpUYCoi7DPbEKuYKDRfg9DgAqCDBYygKXbgX1+UsZvwEE4sfqZoIMJEG2EDg+BUQNUXOncUYCoiVqkWjRtFm+7tQJy3mxaDtxxlPU86HciE2vZQmDVVoCo3zVvP+ezIv1CYEIPUFAIjCdAcUNgMQihKHON7tAeVQESHSdAXAcoITBBM3WAOAIU9NuLDNBAhT1AMgQmUa9IBZmgBRdLiQqQqgCGCby3ph/Tx1vkK1FmAjSjsxWH7DwZk9rTnjCPpxAidTfVj0ZkCzoAa8AkIbCGhAoYnHwPFOcxKAbCidVnIJu4feCmaLIZqReY88aYqoLjAaImAD8PUD1CdOxEv4nGTV60clQKqtEHDBCnspPwCv/7ekzQJIWeD4EpwXIkf1wjm6AFzUbDwHscmRBYHBN0hCwwYSVoKszFd54X1VHiITJAA+VXgJgssBhEZoSZoCUBqnc4BIiqA8SkpnInaYkK0Be32QxPv78e73b1oqPRuqA1rbx3Aoqi4Ipvfl74mocAUZLxIBqYBqiOAqRp3sEboNJYKxhy8Ns+Lzt/9yngs6XA9v8VvClq0AlvhVFKCMxO+c6HEaB6DYFFDEs6GTwkBFYuAlSFTvCA+Dwgk6vOhab80uANLgQWdm4VnQZfJhN0WCV2EchEz/9OwkrQIQqQxp1DQR6gaihAsd5XpHm6RiEJUL1DmAZPl/bnTtIS7y4ntqWxzYQWfLC2H0s+2gCg/ApQEBJ8CIzJmFDEBCihAvlqKkCiiZVbNmln6y9sU8UqQMUSoJEeAqMRZIJ2QmB1rgCJyJVvCIwPJ/lkgYUSoGILIRZjghaFwIowQas+BEhYCZr2AFE+Hz8P0PIngTfutIrXfv1GdwwH/D1AZTdBl4G8jAACVP/fYLRD6AGiK0HzvcBKG1yTmoLdtx7LLCu3BygIGl8IkRswsrY3yTBMhySxJmiBB6iWQmAhYNLgw44739k8DpICAiRUgEY4AXImL/vuvdxp8BN3tNS/L57mvjYcITAa5BrydF/nPUBc+CwqASraA1SMCZoPgWnFpcH7ZYGF3UAxhRB9QmAvXA28dRfwwWJg1avs9ofNBF2GMVsSIImqI9ADlPBeMCWGwDRVwbyt2UmvqgrQ5NnM60T1odtgpBJUSxBhJdcKh8DimKBDQJOe8DT4EgiQEwILqQQ9ohSggDpATjp8ubLAqCJ6k3YG2qj6UxUNgQm2beSt8BdPgPgQmKMAEQ9QTEOx83kxm6GWHAKjSGvkZqgkC4wPgdnjikFlDzIeICrMpfsoQDT4mj+1HgJjN1KGbVQXMgRW7xDVAQqSeUu8u0yoKra2zc/OJodTAdJsDxAhONP3wx1b/RL/96418JAQGB0KS2kUAdJrRAEqUspmEruGwwMUVgm6bk3QcUNgFTJBk+MsqiZcCfgVrSwMeUNgfs1QS/YARTx2ojo7YQgKgdGNTcOMv35Eg5A+mhgxWWDUc6Lm8OcQDV7xCVWAaogAjQAFSBKgeocmUDb8BiY1Gc/xL/o4VUFbIzugJSrVuFH0+bbqoZN6gYqCJQ174GNzFQBKAaIIUFJTWB8AQS15gCIiVgisLFlgI7QOUNTfhDdBlzsNnmw3IcgkqgT8yEo+I1CA/DxA9nlQ8RBYEddnYBZYgaqRFtEEzYPcXNLXgqgOEMA2ZOXXI/AoQD5ZYOX2AJUDIyALrP4p3GgHn9ZKF+riQ2BluHtIaAraOQI0nAqQpxAigO4hd+DOciGwVEK1QkX03St5bx2GwNRYIbASfvuolaDTHfG2WyuIqgCN28Z+tEuMO1lgwRW7Q1EtBcivInh+MEIdIJ80+FAFqMQQWCwTtKgQIuUBilsJmgchbwU/AkQ9J2QmiABt+AC483hgxQvW/9UohFjNbVQZFfsGGzduxFFHHYW2tjZ0dHTghBNOQH9/f+B7MpkMTjnlFIwbNw4tLS1YtGgR1qxZw6yzYsUKLFy4EE1NTZgwYQJ+8pOfoFBgL6hsNotf/OIXmDp1KhoaGjBt2jRcf/31Zf+ONYGg8vSKyl4wZbh7SKoqmlMaE34ZTg+QpxAigO5BmgBZAxRRgBpIij59HH4/C/jnycOoAInS4Iu79OjMr/AQGB0vK6ESNJnsRQpQvd4FCusACY7R5nOA094AFv7efh8hQOVWgKhO88OdBQZYhMxjgvbxABGiNLDOeqSzmEQY1jR4UQismF5gERUgNcm1laCq7+c5AiQicq/fYRmiX7zG+t9PASKZejIEVlZULAR21FFHYfXq1Vi8eDHy+TyOP/54nHTSSbjtttt83/PDH/4Q9913H+688060t7fj1FNPxWGHHYZnn30WAKDrOhYuXIjOzk4899xzWL16NY455hgkk0n88pe/dLZz+OGHY82aNbjuuuuwzTbbYPXq1TCMEmP2tQpnUCpYExVfnp4+Sctw8UwZ0whFscJghHgQX85wIOF4gGgC5E7MfAgslRAQoL7VwAePAJvvYv1flSywIk3QDAEahl5gMK3BPtEQXLum3iAavP2OZ8eW1DplzgIjxIdREYahGSqP/KAgBBbSDHXli9bj5C8Ef6YnBBbXBB0jcSMwDT6k7QkNv8md9wCJmksnGoBc3iUzQR6gfvsGP9NjPYalwcsssLKiIgTonXfewYMPPoiXXnoJu+xiTTJXXHEFDjroIPzud7/D5MmTPe/p6enBddddh9tuuw1f/vKXAQA33HADZs6cieeffx677747Hn74Ybz99tt45JFHMHHiRMyePRsXXHABfvazn+Hcc89FKpXCgw8+iCeffBIfffQRxo610rWnTZtWia9ZG6AHNDrLgbxWpt4tfzpmFzz34Xp8fc7mAIB2igBVXQEShcCCCBDA3vFWPARWPg9QvFYYJZigiQcIsI5VoiG4gFu9QaQA+N19M+8rcyHE4VaA/IhwIScIgQn8NIBLIj61CdAWuwV/5syvAiuWWOHSj59mw/RBcEzQpbbCKKIOUFivvqCxg+xDFA8QuaZIGRO/LDByzMtFOsqiANWp+kuhIhRuyZIl6OjocMgPAMyfPx+qquKFF14Qvmfp0qXI5/OYP3++s2zGjBnYcsstsWTJEme7O+64IyZOdDtQL1iwAL29vXjrrbcAAP/+97+xyy674De/+Q2mTJmC7bbbDj/+8Y8xNMSZzThks1n09vYyf3UB+gI0Cl4FqEzde78yayLOOWR7JOyQUlva/dzQibiMIJ9l2GEZ3TDRQxEgQnwGc9ZxaCAEiB+o8pm6zAKji27HqwQd8/O0FJw0VyK/k+M19YvW45RdPG+rG4iuhUyEa77czVCFHqAqhMD0rDup7/G/wE8+8oaBaAVILwCfvWL9H0aAttoL+J9n3PX8FKDHLgRu/brrMSpLCIyvA0Q1ig5CWBaYqA0GAfktSWZuEAEiIOv6KUDOfkkPUDlREQWoq6sLEyZMYD8okcDYsWPR1dXl+55UKoWOjg5m+cSJE533dHV1MeSHvE5eA4CPPvoIzzzzDNLpNO6++26sX78eJ598MjZs2IAbbrjBd58vvvhinHfeebG+Z02AviBogy9gXfj0IFbGE7at0f3cahRCJApQXybP+FGJB+i9NVZrkGmb2Sn7/MChZ6lBrBom6NKzwEJDYKUUQlQUa3IuDLmTNTlee/wvsNeP3BBiPUJ0LWQjECByTMuuAA2XCdrnvCtQdWtaJ4uz+2gP0Nq3rUm7oQ0YPyPiZ1Pp6CI89Vvr8aMngG3nB4eZ/BDUCsPUYyhAET1AIl8P2Yd8gAKUaGQzLLN97Hv8UIleYEVvov4JUKxvcMYZZ0BRlMC/d999t1L7GgmGYUBRFNx6663YbbfdcNBBB+HSSy/FTTfdFKgCnXnmmejp6XH+Vq5cOYx7XQIYAkQrQIIGheUy0AFMJliiGh4gmwDRBmjAVYBe/9SKqe84pd16QVG8qgsZdKqhABUZTmJCYKFp8NT3LWZS5atBk31ONgLb7Aek2+Nvs1ZQrAJE3ld2D9BwpcEHKEBGSEiYLDcN4NOXrOdT5kQfV2glhgdzPLlGx3HOXUVhj1/TOLEJOtQDFFYHKECdIr9lkAeotZN9Ty4kBEYgTdBlRSw6+aMf/QjHHXdc4Dpbb701Ojs7sXbtWmZ5oVDAxo0b0dnZKXxfZ2cncrkcuru7GRVozZo1zns6Ozvx4osvMu8jWWJknUmTJmHKlClob3cH55kzZ8I0TXz66afYdttthZ/f0NCAhobSqiRXBQwBCql0WsYaEkwIbDjrABEPkG2C3jTIEgniAXrjM4sA7TCFmqTVBKBTA201CRD57JgYtmaogLcaNF/Ztp5RrAJU7maoyRoJgRUy3u7lQe9d+471OHH76J9NV2TmQV8PfNPQuNXr1YT73smfB1a94n6u46WJWb2awIwTAiMKkL0t+vi1TgI2LXf/Jx4gv27wzn7VUAiMrl5ep4h1NMePH4/x48Mrv86bNw/d3d1YunQp5syZAwB47LHHYBgG5s6dK3zPnDlzkEwm8eijj2LRokUAgGXLlmHFihWYN2+es92LLroIa9eudUJsixcvRltbG2bNmgUA+OIXv4g777wT/f39aGmx0jPfe+89qKqKzTffPM7XrQ8QZcPU7QZ8ZBArn/FWBLoYYjVaYZA6QLT/B7AIUCav4/211oCyI02AtCRbwdUhQBUOgYkk/yIJUPHNUIv47flq0A65rvDxGg6IjkccBahkAmSTymGvA+RXCToX7rmhf/d1ttI/dusYnx0QPiRZUPTrxZigATaMNH5GkSZon2vLUYCimKCJB4grpgl4FSDiwQpTgMpmgi5hzD7ir8BHjwNfOKY8+1JFVOTWfebMmTjggANw4okn4sUXX8Szzz6LU089FUcccYSTAfbZZ59hxowZjqLT3t6OE044Aaeffjoef/xxLF26FMcffzzmzZuH3XffHQCw//77Y9asWTj66KPx2muv4aGHHsJZZ52FU045xVFvjjzySIwbNw7HH3883n77bTz11FP4yU9+gm9/+9tobGwU73C9g6l0GnAXV0azMh0CG14PEJsF1pth5fRcwcA7q3uhGybGNacwqZ3KruEHveFSgERx/Yk7FLUp1gMUsnIpafAARYDsyTqIXNcbRNfCtl+J8L4ypcGT8EiyyXqsdiXoOCEwwCVApEBkFASFwGj1jRCfUpMUUi1W8Ua6D2BUE3RoFlhIGjwg8ABRx6/NmwmNbJ/7HnJe8KiFNPgZBwEH/bbyN47DgIpp2bfeeitOPfVU7LffflBVFYsWLcIf/vAH5/V8Po9ly5ZhcNCdHH7/+98762azWSxYsABXXXWV87qmabj33nvxve99D/PmzUNzczOOPfZYnH/++c46LS0tWLx4Mf73f/8Xu+yyC8aNG4fDDz8cF154YaW+avVBlI2wUu9lDYG5p86wpsFzWWC9HgVIx1urrMF0hyntbLVkngQMGwGivGf/+wqw/n1g6ryiNqXF8gCVUAgRoDxA9mBPzq0RQYCoY7Prd4Cx04Gdjwh/X5CKEQeEAKVskz4dSqmkwuYbAsuFh8Do5aQIIqmUHQWihsQEtPpWyFg1zYoxQdMgNxnCStAxq1cTeLLARB4gexnxeUXxAAHWeETGpObNgO4V0fcrLkaAf6ccqBgBGjt2bGDRw2nTpsHkysmn02lceeWVuPLKK33fN3XqVNx///2Bnz1jxgwsXrw43g7XM4Qmv+ELgVWjGSrxAPVxClC2YGDlRotU801bPYMeueus9J0MrQCNmx7vrplD8a0wivEAUdWggej+iXoAPQF0TAXmnRzvfaWmwZNzghCgaofAoihAit1Tj5wHibSVMRb5syMqQHqOXafY47HFrtYjrdpFVTFDs8DIsRIQIE8/MvuzaGLbOsn7vly/GzZrHu9DgGooC2wEQNLAkYCoMe5yKkCMB2j4TiPXA0RCYHY1ant5tmDg025rwp7SwYU8+YHUIUAVVoBSIa0CYoBOuIsVAvPrAh4Evh9Y1Cq69QD6WkjGCI1XKgSmatHqxZQK30KI2WghJ/r9Y6d7M00DPzughhKvADHNRmMqQPPPBbaYC+z9E/tzaYtAxFYYoVlgAeqUbxNZyn/YwpZzAWAZoYkZummzePsVF34htlGGETCSSbgXWB5OCqnQA1TGOkB0FtgwpsHzdYBICGyzlhTW9GaRKxhY22cNNJuP4S7yanmAvnAs8PEzwOcOKnlT8UJgJSpAfEf4qOGDegB9LaSa/dfzvM8+5iWHwDgFCLAUlVx/hbPAuN+OJFAUsuEhMMC6iSDnw7gYBmjyWYBPFhhNgKgaXUD847HnD60/gorUAQpQy0Qd6QE2FN44xvu+oY0uSWr2STYql4q/94+B5U8CO3+zPNurU4yAkUzCU6KeXsasV846QNXxAHkVIOs7j29twJreLLIFA59tsgaazcdwd/a+HqAKh8BSTcARt5ZlU6oSIwRWSiFEgDJz2l6GoPBqvYGe4OIoQEE+ljjIcx4gwO4h1T+8IbBUs0U+ooTA+PePjRnKDfQAdbvPC1RVaiiln2/kc2lVqdg0eOKDC0qD55c5hREpAiQ65/qoIsGiQpRB+xUXzZsBJy8pz7bqGDIENhJA19dwJN4YzR6LAK0ADWdLGJUnQLYCNL7FGnT6Mnms77cGJ08IzFMNephaYZQRbDf4GIUQi8oCIwrQCCdAiRgEiExcYRV7w8CHwAB34hzOEBj5fLoSdNC5QpOjyZ+P+dkBHiA6BKZn2RBTqQMMCdPRqlIlK0Hzy8hnNXa4y0QhKEKAEmkg1Sr+/JGgvtYQ5NEcCSCDkpGHY26rdCFEygOkDKOhLuFJgychMGvyWL7emliaUxo6mvhu1n4F3uqHAGlMHaCQlUuuA0Q8QIQAjVATdBwFiExcpRAgQ3eP6f9v79yjo6jv/v/eS3aTEJJNyF0g3BRELmqQmAe1PpAfF3koFp6naPm1oBSKBa/IqfS0WG17sPY89qiles5jf+JzSrXFn9YjD1op1yMGKKlUBc1PKAoCgQqGJOSe/f7+mMzsd2ZnL7OZndn5zud1Ts5uZmd3v9+d23s+V60FCLA2DT7QPx++F1i875e7lwPA6H9N7bt162JpXGDJjMXo9/IWoFQFkJFK0Mp39Z+Hrl0MfHFIqqKuawE6Iz0GB0f2haxc9b5GAshU6NcUAd4FJh+4ukHQ5hn8srN8+OFt49De3YeSwdZV0PZpCiHKWWDF/WOQW2NcUZgT7SJKpsJthmNZN3iAiwHSCCARTsLeFGOAZMGQqGCdlssXgBemA9csAG5+SP+79WoCmY32JoC3ACkusCS2rz/beCuUeG1EOrUCyETrrLy/9g7ABebxSnFfWguQbhaYTkd6QNqut/8meiwysgUokBc59oKD1QKI0tdNhX5NEUi2EKLJrosVt4zGA3VXmfqZiYiyAHWoLUAyUe4vILZp30kWIE7zGEqDT0XkKa0w+uuyiBQEzWOklk1Wv2BJ1LJAy6n9wFefAUf/FHF/wRP5jQHgpgckgZSou/pAiHKB9R8nvAUoGXfpKIPWH0DtqtcSKwjajGNTFjt9Rlxgmtfl7a6tBK13LMSKAdIu035Hy1npMZgHXDkTqJoGVN+lXke0Y89mSACJgGLiTZDmKYDrwhcjDV5rhYrKAANinzzSecdtMl5DLrABFkLkK0HzWU8ixAABwMOfAg98qI7NSESqFqC2/t6IHV+piyDyInbSN4H/eNGYS84o2v1AsQB1JhcEff0SyfJz25Opf3eiNHiVO84E66y8v6pigBI1Q9VcGuXtLo9dPs8mlQWmdy72RMcBtfYLoMBgoLAKuGsbMH5+4s8iUoYEkAjo1blIcyFEu5BrDvWFGbp6+9DZI12Yi/PUd4pXaDPAgNim/eyQmUNMK7zby1gafArbPouzAPF37QLsRwCAvFIgNNzYe5QYIIMWILlyckdzpNidEdebWWgv2PIY+CDoeKJj3tPA2n8Y/90AdSuMtn8Cx3ZEsqr4XmC9miDogaKbBZbg0qfdx+XtLo83HM8CFKMOkBat0G3/UnoMcgHQUZ9FFiAzIQEkAkoQdIJS77y53aH4+vfYvjBT4n88nmgXmKoHmEysk4fRWAYb8aVcCTqFO2n54tjdps7cEcCSmDLKb5KiBQgs4uqwoxid3DxZJksnCDrevuLxpFZUE1CnwT9/E/C7BcAnb0rLooKg0xED1C+qPN7EmWVRFqD+7a5YgOKEGsSqA6QllqUvyBVO1YpRNx97aYAEkAgoJ5YY3eDn/qdU7v+2X1o/NpPhCyHK8T95QT9ystQnhrJ8PQEU48QezDd1jOnEmAtsgEHQciquVgC5+S401SwwPnuq5Qvp0cQK4Ybgt58S6N6dnAtsQN/LBUG39Qf8ntgrPcZ0gZkhgOQYoH5RlYyI0AogxQKUhADS/n4xBVAMAczvF1EWIBJAZuLiM5lA8MGFHh0L0A3flf4EgC+EKFuA8rOzEPCrT1j6AkgAC5CROkADLYSoWDsuq+M23CyA+N/ECLILDAAune7/LJvaEcjNk4HIfHiXXrq2r/y5vLUnVCXFLvLfr7IAmREDpLEAJSMitOtoY4DiVc2OCoKOJYD6z1E5hVJsmExcFxgJIDMhC5AI8K0wlFotYm5aH5cFJgdAD872I6gRQOV6AkjvROQLRE5EDsBYKwyf/vNkkU3xXW1ixgClQsoWoPOR5y2n1Z9lNXoWILkHFZC+rEhZkDd/HlkmV6Lm6TWYkZYIrQUoGYEXZQHSZIHJ51kjrTC0yNtf2xhVJYCStCYRKSHmVdJt6AZBi3mgyBagcJihpaPfApSjtgBl+TzICegFgev8Jg6y/gDQtMJItPIA0+BVLjCuwriVpb8zjYFmgQGcBciGIGhAI4B0LFppc4HpHJN9PZGWNDK9XZzAMOE8FhUDlIwLLIEFyMwgaG0ZhnguMIoBMhUSQCLAl5hPttmfQ/EqFqCwYgHKz85CwBfZlUO5Me5gBRBAPj6zPd2FEBULUKtYVaAHQip1gHo6gG7uIn/plPRolwDiBY4i6HgXWLoEkM4+yMctyhitSZQIbR2gZDrYJ8wC04m1lDEaBF16tXqe8YKgK6+NOWTCOCSARIDPAhOpWq8O6hggWQD54eeUQaG2BUY8HBQADWgqQae7G7x8J8oHQQu6XyVNKhYg3voDZIALjDs+5DHIAs3jTU4gpIKeeO7j0u9leAuQGfubkgbfE3scWhJlgSkxQDrnmrwy9f+xrFiy66tgGFA0MrKcd4F5fUDJOMkau/R/gPzKxGMnksblZzNBkA/wUweA47vUywSDjwGSawBla9xdhbEsQHpNGJ1mATLkAhtgIUT5TjTcG4l5EXS/SppUYoD4AGggsh/a5gLTSYOXSWsfshguMO1x2ct3pk+DCyylGCADWWBal1as77t1HTD8RmDcvwGN24Av/5+0XNsIdcVu6fuCg6M+ghgYLj+bCYJ8gH30fyPLBA2C1hZCBBAVAB1bAOlUoM12lgVI3Qw13b3AuAu0XKguXdYBp6BkTbVL7pBkfg8+BV7vs6yGd6toa9Gky/0FxBBA3foCKJ6FJdXvNRIeoD22omKA4gRBZ+dL1h25snOsOeQWARMWSs+HjI4s511gQHorg7scl5/NBEG375eY2pa3AHX3ShYgbQp84aAYJxwBLEAej4V1gHx+wN9/8lUEkJj7VdLwFpPejuTeo3WB6X2WlXh1YoBkzLC4xPxenc/WE0B9vAXIRAEkk0ocW6wssFhiircCJXPM8OuTpccySACJgAWNTzMFvy+SBdbVL4CCfvVcYwZB61mAHBYDpGqFke5mqEDkblQWQK4PguYEQ7JxQC1npMecIvVyt7nA8sqkavQeH3D9d6Rlui6wzvTEACn/p3DZkzu8M20QdIzjqnBE5HkyopIXQHYVyHQhJIBEwEUWINnt0xtm6OqRBZC0G8+bXAmf14P/fWOV/pt1LUChdAwzbfBZYAlbYagKIaYoXOSTcUdz/+eIuV8ljdcbsYolmwl28bj0OHSKenlGuMA0AiidLrDcImDVAWDtMSB/qLSMtwD5uarU8QoNGiVKAKXwmUofsyS6wQPqoOZkvq+Ic4HZtV+4EJefzQRB7+5eVAsQlwXW3acWQE8vuha/WDgRuYEYu7VeF2qHucC8hmKABlgJGuAsQM0D+xyRCORK7q9kLUAXjkmPV0wBPn0nsjwjXGCai206XWBAxDIin7P4LDD5dzXbBaa1WqZixdTGESWyUBXyAiiJOQwuB8bfLsU/DSoxPj4iJehsJgK6wYU61g4B8HF1gLRB0F6vJ7b4AWJYgBzsAktkvx1oM1QgkpFCQdARsgYBuJBcJhhjwIVYFqAM6AXmDwLwAGD9r6XRAsQju9r6eiJWlcAgoP2C5Gbq6TBvPNrzYyo3h1oLUKJCjWXXcN+XTNaZB/jmS8bHRQwIEkAioHeAybVGBEOOAepTucCSPKHJRcx4HGYBSr0bfIqHumwhIAtQBL3igbFoOy/VUfJ4gaE3SKKnu7/tRG5R/PemC59mv/AHpbgbIL0xQKoxyAKIc4HxglD+jcywZGv3WSssQCVjgRnrpTnRTUPGQmczEdC7S2o+af04LMCn4wLTZoHFRM8C5DB/u9dIM1SVAErxQkJB0NEYqQUku79CwyVr43f/AnzyP9KFsfK69I0xHvz5wuOTmncqAsiiS4L8PX09EXeXKsC8X1ya2QxV+T8FQSKXFYnKAoszvpvXGP8ewlJIAImA3l2IXG5fMGQXUJgBHd36dYBioieA/M5phApo6wAlWFkWPV5/6v27KAg6GiMd4WUBJAe5ll4t/dmJ1jLoDwBd8v9Wu8D4IOigNJ5wb+S3TYsLzOA+7AtwFqB+K3KiIGjCEZBtTgT07u6v/rr147AAP3f31i4LoCz3CKCUWmEM5CIi1yRRXGB0ykjJAqStDmwnKheYV30MpKsRatQYeAHENXCWxyK7wDKhDpA3SycGqP/RKosZkRZo64mA9iQxawMw+Q57xpJmfL7IRb+9WxI0AV+SJ7TK64Czh6Xn4+cDbf9UBys6AHUrjAQCKDRcqjTL1yQxSkDjAqM7XmP9wGRXNJ8WbTdRLjAu7scyASRngfWorSnyWLrSGANk9DN9fp0YILIAiQBtPRHQHoQ1K4W9U/d7eQFk0AL0vx7rLz//70DZ+HQML+2ou8EnWNnjAf79/wzsC5UgaBJACkY6wnf1NxnNpHpTUS4wrnu5nS4wlQXITBfYAIOgfYHYWWB0PDga2noiwJ8kfAFhxQ+gdvsYjgHKLpAyMxyM12PABWYGFAQdjRELkHwhz6Rge97K47XLAqQjgHxZkYrLZgZBa/dZoxYgb1a0BcjMXmWEbYh7pXQT/AHtC8ZeTwB4F9Bl2QWWrAASAENp8Gag7UxNd7zGYoAyUQDFtQBZlQXGucD4vlraGKC0VIJOwQWmZIFpW2HQDYGTcc+VQ2SiCpuJi9frURKawv2125KuAyQAhrLAzEDbmVpg62LSyBfp3q746wGRC3km9Xfizxcer8YCZHUdoB61O8mXBgvQQIOgVVlgSXSDJxwDnc1EgD8IHZbVlAp+zZU/aReYAHiM1AEyA+2FmyxAkZuMpARQBlqA5POFxyfFidmeBca5k+Sx9PX/tqZYgExwgUXFAFEQtAi458ohMi6yAAHRsS+ucoEZ6QZvBlpBTSf8yDHW51ABpJRH6L+o89XQ7c4C056/MiIIWpMFFg5H6gFRDJCjobOZCPB3NC4QQFItoEhbCzdZgNTd4K34Qs0JnoKgI9YLuXpyLMLhSKZYJrrA5Mdb10musH9+Akz8D2vGoFcHyKcjgMyoszPgGCBNFhhfT4xigBwNCSAR4O9CXCCAtBYgN8UAGWqFYQZRd+Tu+a1josQAdcdfjw+SziQLEO8CA4DiMcDC/7J4DHwMEF8HKE0WII+Xs9oMMAtMJYDoEupk3HPrLDIqF5j4MUBaAZTls8IUkhn4jFSCNuULNUGxJIASu8BO/w3o+IprleEBsnIsGVpSyKLCzoB2xQWmrQOkFUAmCAyPB/Bzv79hF1iWOgtMFmzya4RjIQEkAvxJwqosDhvhL/xBv9eadPAMwatKg7fgC6MEEN3xxg2CPnMY+K9/BV77njoDLJP2Ub5HnF3ELISodYGZJDB4AWrYBaa1APVxn0XHg5MhASQCPndZgPwaAeQmfFa7wEgAReOLI4Dk3l9fNmZmADQQ7QKzZQz9+xXri7gS02UBAtSd5o1+pjYLTHGBecgi6nDcdfUQFZdlgfFZXwEXxf8A2jpANsQAURB0pFqxngCSm8ZevpC5AkhxgdlpAeIsO3KslF4MUDosQB6Dlz2tBYg6wQsDCSARcFkMUCgnclJ0mwVI3QrDgi/UXoDojje6Vg2P3DKkuxXouCg9zzgBpEmDtwPestjT0b8sK40WoAG4wEbdqm8Bovgfx0MSVgRUWWDixwAV5EbmmHQjVEGwvBVGVFYOCaCIC0wnC6yjOfL8q8+lx0xKgQciLnM7t6VXzwLkS08dIEBjAUpy3vf/Hfi8Hpj0TeCrz6RlLEyNUAWCtqAIqOoAiW8BKsyNnBQDlphBMgd1N3grBJDWAkSnjEgQtE4dINkCBADNsgDKUAuQne5Mr1f6ftandoFpdy8z6gABGgtQkp9ZOEL649+j7V1GOBp3XT1ExWUusEKVBchdJyHLXWAej/ounARQ/DR4OQYI4CxAmSaAMiAGCIi4wWQXmDcrPXWAAE0QdArnDH6bKzFA5AJzOiSAREDVC0z8IOhQrntjgCx3gQHqfYqCoLlK0HFigADOApRpLjBZANm8LbWNT73+6Bu4TAiCBristXBku9stIIkB466rh6io6gC5QABREHTU87TCX4TsvmhmAnw3+L5eqTiejG4MUKZZgHzqR7uQ9yvFAuSLjmE0a4wDCYIG1DcBcnsTs9xzhG246+ohKi7rBVY4iHOBuUwAqdPgrfpSbp+y+6KZCSgXaQY8cy3w31+PvMa7wHoyPA3ebmue1gXmy4q2AJnlZhpIJWhAfQx0czFLhKOhLSgCqiww8WOAQnwMkMvqAHmt7gYPqFOW6aSvPsYunZL+wmEpsJd3gclkmgAqGSdtx7Jr7B2HYgHi6wAF9NcZKKkEQavGwfUTkyt8UwyQ46GzmQiogqDFT4NXZYG5zgIUeW5dDBAJIBV6bubuNinWR1cAZVgMUPEY4OFPgeyQveOICoLWiQHKlCBoQNruvR1qwUY4GtqCIsDfJbmgF1gox70uMHUMkEVfyu9TdrtNMgGvV7r48V3Bu1qlRxaOXj/TLEAAkFtk9wi4YHJeAFkQA5TqPuwPSGNVgrbpWHA6JIBEgD8QXWCWDQ2KzJExGwdiA5Z3gwc0LjA66QOQLBWyKwSQnrM+/XUzUQBlAno1pqzIAvOmeNMkW/5kAUSVoB0PCSAR4E2xLrhADQ5G5tveE+OiIyi8BcgyFxgJoGi0ltauNv20eCDzXGCZgl6jXUvqAKV42fNrBBC5wByPu/wHosKfJFKpceEw+At/R3dvnDXFw54sMIoBikJrqehqicT/5JWpfzOyAOmjF/Cctl5g3PZK1QUWFbRNFiCnI/7V0g2oLEDuukC1d7vLAmR5N3iAgqD10MaqdLdFUuBDVcC8p/tf8AChYVaOzDnoNdpVFd30pu6u0mJWEDRAMUACQWczEVDFALnroHSbAFK3wrDBBeYCC2NSaF01Xa2RAOjsAuDabwFj6oD2C0BouPXjcwJRLjCNBchMC4tZQdAAucAEgragCPCWAJcdlCWDxS/8yKNuhWHVl5IFKAqtq6arDejr7w6fE5Ie80qlP0IfvSBoXliaGWSssgANMAhadoFRELTjods50XBJmvLm79ag7upSPPZ1m4u5WYzPllYYFAQdhVYAdbdGYoCyC6wfjxPRC4JWWYBMFNsDLYQIUBC0gNAWFI3yiXaPwBKmjSnGtDHFdg/DcvibV8tcYOm6KDmZqCDo1siF0e4Cg05Ba0HxpVEADbQVBsAVbqRCiKJAW1AU1h6XTsKDy+weCZFG7MkC47MMyQIEQD8NXrYAyS4wIj6WusAG2AwVIAuQgNAWFIVBxdIfITT21AGiZqhRRMUAkQvMMLp1gPySyGZ96QuCTrV6qjzebooBEgWKASIIB2FLGjx/oqe7XomoGCAuDZ5cYMmhlwUGRH5bn5kxQFwQtBysbhR5XD2UBi8KJIAIwkHY3w2eTvoA9NPgyQJkDL06QEBEaJhpAeIFa19Pap/hozR40UibALp48SIWL16M/Px8hEIhLFu2DG1tbXHf09nZiVWrVmHIkCHIy8vDwoULce7cOdU6J0+exNy5c5Gbm4vS0lKsXbsWvb3qasCbN2/G5MmTkZubi4qKCtx99924cOGC6XMkCKvh434sS4OnIOho9FxgHc3Sc4oBSg5tixB535LFpZn7Gn+w9MVoWZIIeZv3dkqPVAna8aRNAC1evBhHjhzB9u3bsXXrVuzduxcrVqyI+54HH3wQb775JrZs2YI9e/bgzJkzWLBggfJ6X18f5s6di+7ubrz33nt46aWXsGnTJqxfv15ZZ9++ffjOd76DZcuW4ciRI9iyZQsOHjyI5cuXp2uqBGEZHo9HEUFeywohUhB0FOQCGzhaAeRLowuMJ1bPtkRE9SmjmwGnkxYB9PHHH+Ptt9/GCy+8gJqaGtx000149tln8corr+DMmTO677l06RJ++9vf4qmnnsL06dNRXV2NF198Ee+99x72798PAHjnnXdw9OhR/O53v8O1116LOXPm4Kc//Sk2btyI7m7Jr1tfX48RI0bgvvvuw8iRI3HTTTfhe9/7Hg4ePJiOqRKE5QzJC8Lv9SAvaNEJmIKgo9HGr1z+MmIZIBdYcgQHq/+XBUU6XGA8qbrAtO1P0iXQCMtIiwCqr69HKBTClClTlGV1dXXwer04cOCA7nsaGhrQ09ODuro6Zdm4ceMwfPhw1NfXK587ceJElJVFUr1nzZqFlpYWHDlyBABQW1uLU6dOYdu2bWCM4dy5c3j11Vdx2223xR1zV1cXWlpaVH8EkYm8uPQG/PeyqSjIscgET5Wgo9FagDou9j/xAMF8y4fjSIIxXGCKBShdAihVC5BO1hrhaNIigJqamlBaqi4B7/f7UVRUhKamppjvCQQCCIVCquVlZWXKe5qamlTiR35dfg0Apk2bhs2bN2PRokUIBAIoLy9HQUEBNm7cGHfMGzZsQEFBgfI3bBg1MCQykwlXFOBfRltY8sBPQdBRhGP0oMvON6+Bp+hohWI6Y4AAYMgY6XH8/NTeHytrjXAsho7URx55BB6PJ+7fJ598kq6xJsXRo0dx//33Y/369WhoaMDbb7+Nzz77DCtXroz7vnXr1uHSpUvK36lTpywaMUFkOGQBiibcq7+c4n+SJ1YQtFxl2+x9beW7wH2HgcrrUnu/1upHx4LjMbQF16xZg6VLl8ZdZ9SoUSgvL8f58+dVy3t7e3Hx4kWUl5frvq+8vBzd3d1obm5WWYHOnTunvKe8vDwqlkfOEpPX2bBhA6ZNm4a1a9cCACZNmoRBgwbh5ptvxs9+9jNUVFTofn8wGEQw6K7GmgSRFKpu8GQBAqAWQB5vpBM8ZYAlT8wYoP79zWwXWFYOUDQy9fdHBUHTseB0DAmgkpISlJSUJFyvtrYWzc3NaGhoQHV1NQBg586dCIfDqKmp0X1PdXU1srKysGPHDixcuBAA0NjYiJMnT6K2tlb53J///Oc4f/684mLbvn078vPzMX78eABAe3s7/H71tHw+aUdlqVYAJQg3Q3WAouEDaYODqQZQKsSMAZItQBnmYooKgs6w8RGGSYuz+uqrr8bs2bOxfPlyHDx4EPv27cPq1atxxx13oLKyEgBw+vRpjBs3TrHoFBQUYNmyZXjooYewa9cuNDQ04K677kJtbS1uvPFGAMDMmTMxfvx4fPvb38bf//53/PnPf8aPfvQjrFq1SrHezJs3D6+99hqee+45/OMf/8C+fftw3333YerUqcp3EwRhAHKBRcNbgELDI8/JBZY8UTFA/ZcjeX/LNLFNafDCkbYtuHnzZqxevRozZsyA1+vFwoUL8cwzzyiv9/T0oLGxEe3t7cqyX/3qV8q6XV1dmDVrFn7zm98or/t8PmzduhX33HMPamtrMWjQICxZsgSPP/64ss7SpUvR2tqKX//611izZg1CoRCmT5+OX/ziF+maKkGIDQVBR8MLoKppQNOH0nOyACWPNgZIRrYAZZqFRWsByjQLFWGYtAmgoqIi/P73v4/5+ogRI6JcUtnZ2di4cWPcjK2qqips27Yt7nffe++9uPfee40NmCAIfcgCFM3VXwcOb5asP1XTgAPPS8spBih5tIJCuzzTBAbFAAkHnc0IgogPf+KnIGiJq2YB390hpVbz1qBw2L4xiYJiAcqwy5M2CyzTLFSEYTJsDyMIIuNQdYMnAQRA6i01dEr08u74/Q6JJFBigDLs8kSFEIWDKnYRBBEfaoaamH/7FVA0GvgXcr0bQ6efnZwen5Vr7VASobUAyZYqwrHQ2YwgiPhQGnxiptwt/RHGyMoBetrVyybfCbSeBarvsmdMsdBagPIpq9jpkAAiCCI+FARNpAt/drQAKqwC5j1tz3jiobUAkQByPOQCIwgiPrzooSBowkyycuweQfJos8Dy9LsaEM6BBBBBEPEhtxeRLpwUR8MnAwwqiZ3GTzgGEkAEQcSHv0hRp3PCTJxkAeJdYIP1e0oSzoIc+gRBxCc7H6h7TGr4mVNo92gIkXCUBYiz+OSV2TcOwjRIABEEkZibHrB7BISIONUClDvEvnEQpkH2bIIgCMIenCSA+CDoQcX2jYMwDRJABEEQhD0Mv9HuESQP35qDLEBCQC4wgiAIwh5q75XKLIyeYfdIjEEWICEgAUQQBEHYgz8ATLvf7lEYp2Ky3SMgTIAEEEEQBEEkw91/BlpOkwASBBJABEEQBJEMTopZIhJCQdAEQRAEQbgOEkAEQRAEQbgOEkAEQRAEQbgOEkAEQRAEQbgOEkAEQRAEQbgOEkAEQRAEQbgOEkAEQRAEQbgOEkAEQRAEQbgOEkAEQRAEQbgOEkAEQRAEQbgOEkAEQRAEQbgOEkAEQRAEQbgOEkAEQRAEQbgO6gYfA8YYAKClpcXmkRAEQRAEkSzydVu+jseCBFAMWltbAQDDhg2zeSQEQRAEQRiltbUVBQUFMV/3sEQSyaWEw2GcOXMGgwcPhsfjMe1zW1paMGzYMJw6dQr5+fmmfW4m47Y503zFx21zpvmKjWjzZYyhtbUVlZWV8HpjR/qQBSgGXq8XQ4cOTdvn5+fnC7GjGcFtc6b5io/b5kzzFRuR5hvP8iNDQdAEQRAEQbgOEkAEQRAEQbgOEkAWEwwG8eijjyIYDNo9FMtw25xpvuLjtjnTfMXGbfOVoSBogiAIgiBcB1mACIIgCIJwHSSACIIgCIJwHSSACIIgCIJwHSSACIIgCIJwHSSACIIgCIJwHSSALGbjxo0YMWIEsrOzUVNTg4MHD9o9JFP4yU9+Ao/Ho/obN26c8npnZydWrVqFIUOGIC8vDwsXLsS5c+dsHLEx9u7di3nz5qGyshIejwd/+tOfVK8zxrB+/XpUVFQgJycHdXV1+PTTT1XrXLx4EYsXL0Z+fj5CoRCWLVuGtrY2C2dhjERzXrp0adQ2nz17tmodp8x5w4YNuOGGGzB48GCUlpbi9ttvR2Njo2qdZPbhkydPYu7cucjNzUVpaSnWrl2L3t5eK6eSNMnM+dZbb43axitXrlSt45Q5P/fcc5g0aZJS7bi2thZvvfWW8rpo2zfRfEXatqlCAshC/vCHP+Chhx7Co48+ir/97W+YPHkyZs2ahfPnz9s9NFO45pprcPbsWeXv3XffVV578MEH8eabb2LLli3Ys2cPzpw5gwULFtg4WmNcvnwZkydPxsaNG3Vff/LJJ/HMM8/g+eefx4EDBzBo0CDMmjULnZ2dyjqLFy/GkSNHsH37dmzduhV79+7FihUrrJqCYRLNGQBmz56t2uYvv/yy6nWnzHnPnj1YtWoV9u/fj+3bt6OnpwczZ87E5cuXlXUS7cN9fX2YO3cuuru78d577+Gll17Cpk2bsH79ejumlJBk5gwAy5cvV23jJ598UnnNSXMeOnQonnjiCTQ0NODQoUOYPn065s+fjyNHjgAQb/smmi8gzrZNGUZYxtSpU9mqVauU//v6+lhlZSXbsGGDjaMyh0cffZRNnjxZ97Xm5maWlZXFtmzZoiz7+OOPGQBWX19v0QjNAwB7/fXXlf/D4TArLy9nv/zlL5Vlzc3NLBgMspdffpkxxtjRo0cZAPbXv/5VWeett95iHo+HnT592rKxp4p2zowxtmTJEjZ//vyY73HynM+fP88AsD179jDGktuHt23bxrxeL2tqalLWee6551h+fj7r6uqydgIpoJ0zY4x97WtfY/fff3/M9zh9zoWFheyFF15wxfZlLDJfxsTftslAFiCL6O7uRkNDA+rq6pRlXq8XdXV1qK+vt3Fk5vHpp5+isrISo0aNwuLFi3Hy5EkAQENDA3p6elRzHzduHIYPHy7E3E+cOIGmpibV/AoKClBTU6PMr76+HqFQCFOmTFHWqaurg9frxYEDBywfs1ns3r0bpaWlGDt2LO655x5cuHBBec3Jc7506RIAoKioCEBy+3B9fT0mTpyIsrIyZZ1Zs2ahpaVFddedqWjnLLN582YUFxdjwoQJWLduHdrb25XXnDrnvr4+vPLKK7h8+TJqa2uF377a+cqIuG2NQN3gLeLLL79EX1+famcCgLKyMnzyySc2jco8ampqsGnTJowdOxZnz57FY489hptvvhkfffQRmpqaEAgEEAqFVO8pKytDU1OTPQM2EXkOettWfq2pqQmlpaWq1/1+P4qKihz7G8yePRsLFizAyJEjcfz4cfzwhz/EnDlzUF9fD5/P59g5h8NhPPDAA5g2bRomTJgAAEntw01NTbr7gPxaJqM3ZwD41re+haqqKlRWVuKDDz7AD37wAzQ2NuK1114D4Lw5f/jhh6itrUVnZyfy8vLw+uuvY/z48Th8+LCQ2zfWfAHxtm0qkAAiTGHOnDnK80mTJqGmpgZVVVX44x//iJycHBtHRqSLO+64Q3k+ceJETJo0CaNHj8bu3bsxY8YMG0c2MFatWoWPPvpIFcMmOrHmzMdrTZw4ERUVFZgxYwaOHz+O0aNHWz3MATN27FgcPnwYly5dwquvvoolS5Zgz549dg8rbcSa7/jx44XbtqlALjCLKC4uhs/ni8oqOHfuHMrLy20aVfoIhUK46qqrcOzYMZSXl6O7uxvNzc2qdUSZuzyHeNu2vLw8Kti9t7cXFy9eFOI3AIBRo0ahuLgYx44dA+DMOa9evRpbt27Frl27MHToUGV5MvtweXm57j4gv5apxJqzHjU1NQCg2sZOmnMgEMCYMWNQXV2NDRs2YPLkyXj66aeF3b6x5quH07dtKpAAsohAIIDq6mrs2LFDWRYOh7Fjxw6VT1YU2tracPz4cVRUVKC6uhpZWVmquTc2NuLkyZNCzH3kyJEoLy9Xza+lpQUHDhxQ5ldbW4vm5mY0NDQo6+zcuRPhcFg58TidL774AhcuXEBFRQUAZ82ZMYbVq1fj9ddfx86dOzFy5EjV68nsw7W1tfjwww9Vom/79u3Iz89X3A6ZRKI563H48GEAUG1jJ81ZSzgcRldXl5DbVw95vnqItm2Twu4obDfxyiuvsGAwyDZt2sSOHj3KVqxYwUKhkCrK3qmsWbOG7d69m504cYLt27eP1dXVseLiYnb+/HnGGGMrV65kw4cPZzt37mSHDh1itbW1rLa21uZRJ09rayt7//332fvvv88AsKeeeoq9//777PPPP2eMMfbEE0+wUCjE3njjDfbBBx+w+fPns5EjR7KOjg7lM2bPns2uu+46duDAAfbuu++yK6+8kt155512TSkh8ebc2trKHn74YVZfX89OnDjB/vKXv7Drr7+eXXnllayzs1P5DKfM+Z577mEFBQVs9+7d7OzZs8pfe3u7sk6ifbi3t5dNmDCBzZw5kx0+fJi9/fbbrKSkhK1bt86OKSUk0ZyPHTvGHn/8cXbo0CF24sQJ9sYbb7BRo0axW265RfkMJ835kUceYXv27GEnTpxgH3zwAXvkkUeYx+Nh77zzDmNMvO0bb76ibdtUIQFkMc8++ywbPnw4CwQCbOrUqWz//v12D8kUFi1axCoqKlggEGBXXHEFW7RoETt27JjyekdHB/v+97/PCgsLWW5uLvvGN77Bzp49a+OIjbFr1y4GIOpvyZIljDEpFf7HP/4xKysrY8FgkM2YMYM1NjaqPuPChQvszjvvZHl5eSw/P5/dddddrLW11YbZJEe8Obe3t7OZM2eykpISlpWVxaqqqtjy5cujxLxT5qw3TwDsxRdfVNZJZh/+7LPP2Jw5c1hOTg4rLi5ma9asYT09PRbPJjkSzfnkyZPslltuYUVFRSwYDLIxY8awtWvXskuXLqk+xylzvvvuu1lVVRULBAKspKSEzZgxQxE/jIm3fePNV7Rtmyoexhizzt5EEARBEARhPxQDRBAEQRCE6yABRBAEQRCE6yABRBAEQRCE6yABRBAEQRCE6yABRBAEQRCE6yABRBAEQRCE6yABRBAEQRCE6yABRBAEQRCE6yABRBAEQRCE6yABRBAEQRCE6yABRBAEQRCE6/j/rOCJOnlZTzsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(out.mean(-1)[25:25+125])\n",
    "plt.plot(out3.mean(-1))\n",
    "plt.title(f\"encode with {my_mert}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dd064657",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:48.981117Z",
     "start_time": "2023-10-09T21:44:45.590702Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at m-a-p/MERT-v1-330M were not used when initializing MERTModel: ['encoder.pos_conv_embed.conv.weight_v', 'encoder.pos_conv_embed.conv.weight_g']\n",
      "- This IS expected if you are initializing MERTModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing MERTModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of MERTModel were not initialized from the model checkpoint at m-a-p/MERT-v1-330M and are newly initialized: ['encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'encoder.pos_conv_embed.conv.parametrizations.weight.original1']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    " my_mert = \"m-a-p/MERT-v1-330M\" \n",
    "_ = preload_models(\n",
    "    model_name=my_mert,\n",
    "    revision=\"8881df140a93e2e\" if my_mert == \"m-a-p/MERT-v1-95M\" else \"af10da70c94a\",\n",
    ")  # default \"m-a-p/MERT-v1-330M\", \"af10da70c94a\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f8e125a2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:49.099584Z",
     "start_time": "2023-10-09T21:44:48.982572Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(750, 1024)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = encode(audio, do_clustering=False)\n",
    "out2 = encode(audio.get_segment(to_s=5.01), do_clustering=False)\n",
    "out3 = encode(audio.get_segment(from_s=1, to_s=6.01), do_clustering=False)\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c9cb0e2e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:49.233166Z",
     "start_time": "2023-10-09T21:44:49.100605Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(out.mean(-1))\n",
    "plt.plot(out2.mean(-1))\n",
    "plt.title(f\"encode with {my_mert}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a154eae2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:49.361138Z",
     "start_time": "2023-10-09T21:44:49.234493Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(out.mean(-1)[25:25+125])\n",
    "plt.plot(out3.mean(-1))\n",
    "plt.title(f\"encode with {my_mert}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce900c11",
   "metadata": {},
   "source": [
    "# Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bae5870a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T02:07:02.299512Z",
     "start_time": "2023-10-10T02:07:02.297960Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6e588f40",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T02:07:05.600505Z",
     "start_time": "2023-10-10T02:07:02.606262Z"
    }
   },
   "outputs": [],
   "source": [
    "import gc\n",
    "import os\n",
    "import tempfile\n",
    "import time\n",
    "import random\n",
    "\n",
    "import funcy\n",
    "import numpy as np\n",
    "import tqdm\n",
    "import torch\n",
    "\n",
    "from suno_utils.tasks.data_loader import load_audio_mp\n",
    "from suno_utils.tasks.mert import (\n",
    "    SAMPLE_RATE,\n",
    "    EMBEDDING_RATE,\n",
    "    encode,\n",
    "    encode_files,\n",
    "    preload_models,\n",
    ")\n",
    "from suno_utils.utils.s3 import read_from_s3, download_s3_files, upload_s3_files, check_s3_file_exists\n",
    "from suno_utils.utils.text import write_jsonl, read_jsonl, get_file_ext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8c5078db",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T02:07:09.255312Z",
     "start_time": "2023-10-10T02:07:05.603202Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at m-a-p/MERT-v1-95M were not used when initializing MERTModel: ['encoder.pos_conv_embed.conv.weight_v', 'encoder.pos_conv_embed.conv.weight_g']\n",
      "- This IS expected if you are initializing MERTModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing MERTModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of MERTModel were not initialized from the model checkpoint at m-a-p/MERT-v1-95M and are newly initialized: ['encoder.pos_conv_embed.conv.parametrizations.weight.original1', 'encoder.pos_conv_embed.conv.parametrizations.weight.original0']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    " my_mert = \"m-a-p/MERT-v1-95M\" \n",
    "_ = preload_models(\n",
    "    model_name=my_mert,\n",
    "    revision=\"8881df140a93e2e\" if my_mert == \"m-a-p/MERT-v1-95M\" else \"af10da70c94a\",\n",
    ")  # default \"m-a-p/MERT-v1-330M\", \"af10da70c94a\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51949a6b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T02:07:30.689594Z",
     "start_time": "2023-10-10T02:07:14.696758Z"
    }
   },
   "outputs": [],
   "source": [
    "p_read_jsonl = funcy.partial(read_jsonl)\n",
    "metas = read_from_s3(\"s3://suno-data/datasets/bundles/v2/music_sample/metas.jsonl\", read_f=p_read_jsonl)\n",
    "random.seed(6006)\n",
    "random.shuffle(metas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "76d5aa6d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T02:07:33.525254Z",
     "start_time": "2023-10-10T02:07:33.522181Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': '8fd3edf8-14f2-407e-85a1-69ba9b71d697',\n",
       " 'original_id': 'Little-zaint-one-day-lyrics',\n",
       " 'tags': ['Rap'],\n",
       " 'text_segments': [{'text': '[Intro]\\n\\nFeels good to be\\nBack behind the\\nMicrophone Little\\nZaint let me hear\\nYou say feel so\\nUnstoppable\\nLittle Zaint let\\nMe hear you say\\nYour ready ok\\nLet me hear you\\nSay Let´s go\\n[Vers 1]\\n\\nSince 1985 I’ve let the\\nPen be my guide been\\nFor a while my only\\nWay to fight wish\\nThat I´ll one day\\nMake my family\\nProud know It´s\\nAll about taking\\nOne step at a time\\nI see a sign right\\nBefore my eyes\\nCan´t deny my\\nLife would make\\nYou cry but look\\nAt me now I´m\\nStill fighting for\\nWhat is mine\\n\\n[Chorus]\\n\\nI´ll be there one day\\nJust need to find my',\n",
       "   'start_s': 7.62,\n",
       "   'end_s': 47.51},\n",
       "  {'text': '\\nOwn way before I’ll\\nChange the game\\nKnow this is my\\nFaith but I’ll find\\nA way to make it\\nOne day\\n\\n\\n[Vers 2]\\n\\nSometimes I dream\\nAbout reaching for\\nThe top won´t stop\\nEven if it´s hard\\nStill feel it in my\\nHeart cause Savvas\\nGonna make you\\nFeel the heat try\\nTo read everythin´\\nIn between so\\nBelieve that I´ll\\nOne day explain\\nWhat runs through\\nMy veins and how\\nIt all started just\\nSaw my dream\\nRight before my\\nEyes',\n",
       "   'start_s': 47.51,\n",
       "   'end_s': 84.34},\n",
       "  {'text': '\\n\\n[Chorus]\\n\\nI´ll be there one day\\nJust need to find my\\nOwn way before I’ll\\nChange the game\\nKnow this is my\\nFaith but I’ll find\\nA way to make it\\nOne day\\n\\n\\n[Vers 3]\\n\\nI remember when I was\\nOnly a young kid and I\\nFell in love with the\\nMusic so many people\\nThought I couldn’t do\\nIt but look at me now\\nI’m doing it everybody’s\\nAlways gonna have an\\nOpinions about what\\nYou aught to do but\\nThey don’t know you',\n",
       "   'start_s': 84.34,\n",
       "   'end_s': 122.83},\n",
       "  {'text': '\\n\\n[Bridge + Chorus]\\n\\nFeels like I´ve slept\\nFor an eternity just\\nTo find the right\\nOpportunity to\\nMake history\\nTonight',\n",
       "   'start_s': 131.29,\n",
       "   'end_s': 140.07},\n",
       "  {'text': '\\n\\nI´ll be there one day\\nJust need to find my\\nOwn way before I’ll\\nChange the game\\nKnow this is my\\nFaith but I’ll find\\nA way to make it\\nOne day',\n",
       "   'start_s': 147.01,\n",
       "   'end_s': 167.91},\n",
       "  {'text': '\\n\\n[Outro]\\n\\nTo be able to do the\\nThings you love is a\\nGift even if it takes\\nA while remember\\nThat you´ll one day\\nMake it to the top\\n\\nI’ll find a way to\\nMake it one day\\nOne day one day',\n",
       "   'start_s': 173.97,\n",
       "   'end_s': 193.8}],\n",
       " 'duration_s': 194,\n",
       " 's3_filepath': 's3://suno-data/datasets/harvest/genius_hq/audio/8kqkyBaGqrg.webm'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8kqkyBaGqrg.webm\n",
    "metas[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c1fd403",
   "metadata": {},
   "source": [
    "## let's download all the first once and for all  -- TODO: stratify the cluster inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d05f0410",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T02:07:45.487706Z",
     "start_time": "2023-10-10T02:07:45.486229Z"
    }
   },
   "outputs": [],
   "source": [
    "chunksize = 250\n",
    "tot_steps = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5736c106",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T02:07:47.323492Z",
     "start_time": "2023-10-10T02:07:47.321538Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "463759"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(metas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1b584dbd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T02:07:49.620228Z",
     "start_time": "2023-10-10T02:07:49.618599Z"
    }
   },
   "outputs": [],
   "source": [
    "chosen_metas = metas[len(metas) - tot_steps * chunksize : len(metas)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ea00c7db",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T23:17:16.841044Z",
     "start_time": "2023-10-09T23:17:16.790672Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n"
     ]
    }
   ],
   "source": [
    "filepaths = [\n",
    "    fp\n",
    "    for m in chosen_metas\n",
    "    if (fp := m.get(\"s3_filepath\", m.get(\"audio_filepath\", m.get(\"filepath\"))))\n",
    "    is not None\n",
    "]\n",
    "print(len(filepaths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7f345a47",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T23:33:47.941539Z",
     "start_time": "2023-10-09T23:17:18.282565Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  downloading audio...\n",
      "downloaded in 989.7\n"
     ]
    }
   ],
   "source": [
    "tmp_dir = \"/app/suno/data/mert_cluster\"\n",
    "t0 = time.time()\n",
    "if filepaths[0][:2] == \"s3\":\n",
    "    # if filepaths are on s3 then load them to a temp dir first\n",
    "    tmp_out_filepaths = [\n",
    "        os.path.join(tmp_dir, f\"audio_{n}.{get_file_ext(filepath)}\")\n",
    "        for n, filepath in enumerate(filepaths)\n",
    "    ]\n",
    "    print(\"  downloading audio...\")\n",
    "    confirmed_downloads = download_s3_files(\n",
    "        filepaths,\n",
    "        tmp_out_filepaths,\n",
    "        chunksize=100,\n",
    "        n_cores=16,\n",
    "        joblib_backend=\"threads\",\n",
    "        silent=True,\n",
    "    )\n",
    "    time.sleep(5) # make sure things close\n",
    "    local_filepath = [\n",
    "        filepath if b_confirmed else None\n",
    "        for b_confirmed, filepath in zip(confirmed_downloads, tmp_out_filepaths)\n",
    "    ]\n",
    "else:\n",
    "    local_filepath = filepaths\n",
    "print(f\"downloaded in {round(time.time() - t0, 1)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb76d9ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T02:08:38.511684Z",
     "start_time": "2023-10-10T02:08:38.510258Z"
    }
   },
   "source": [
    "# Convert audio and encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e0c74dd8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T02:11:45.298915Z",
     "start_time": "2023-10-10T02:11:45.294475Z"
    }
   },
   "outputs": [],
   "source": [
    "# load first minute of each file\n",
    "def _load_audio(filepaths, sample_rate, max_duration_per_file_s=None):\n",
    "    with tempfile.TemporaryDirectory() as tmp_dir:\n",
    "        t0 = time.time()\n",
    "        if filepaths[0][:2] == \"s3\":\n",
    "            # if filepaths are on s3 then load them to a temp dir first\n",
    "            tmp_out_filepaths = [\n",
    "                os.path.join(tmp_dir, f\"audio_{n}.{get_file_ext(filepath)}\")\n",
    "                for n, filepath in enumerate(filepaths)\n",
    "            ]\n",
    "            print(\"  downloading audio...\")\n",
    "            confirmed_downloads = download_s3_files(\n",
    "                filepaths,\n",
    "                tmp_out_filepaths,\n",
    "                chunksize=100,\n",
    "                n_cores=16,\n",
    "                joblib_backend=\"threads\",\n",
    "                silent=True,\n",
    "            )\n",
    "            time.sleep(5) # make sure things close\n",
    "            local_filepath = [\n",
    "                filepath if b_confirmed else None\n",
    "                for b_confirmed, filepath in zip(confirmed_downloads, tmp_out_filepaths)\n",
    "            ]\n",
    "        else:\n",
    "            local_filepath = filepaths\n",
    "        download_duration_s = round(time.time() - t0, 1)\n",
    "        # remove Nones\n",
    "        safe_orig_idx, safe_filepaths = zip(*[\n",
    "            (idx, fp) for idx, fp in enumerate(local_filepath) if fp is not None\n",
    "        ])\n",
    "        print(\"  loading audio...\")\n",
    "        t0 = time.time()\n",
    "        audio_arrays = load_audio_mp(\n",
    "            safe_filepaths,\n",
    "            target_sample_rate=sample_rate,\n",
    "            max_duration_s=max_duration_per_file_s,\n",
    "            num_workers=32,\n",
    "            force_threads=True,\n",
    "            debug=True\n",
    "        )\n",
    "        load_duration_s = round(time.time() - t0, 1)\n",
    "        # merge back into None list\n",
    "        out_audio_arrays = [None]*len(filepaths)\n",
    "        for idx, arr in zip(safe_orig_idx, audio_arrays):\n",
    "            out_audio_arrays[idx] = arr\n",
    "        assert(len(filepaths) == len(out_audio_arrays))\n",
    "        if len(filepaths) >= 10:\n",
    "            assert(np.mean([arr is not None for arr in out_audio_arrays]) >= 0.5)\n",
    "    return download_duration_s, load_duration_s, out_audio_arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9b08ddca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T02:11:48.813037Z",
     "start_time": "2023-10-10T02:11:48.811621Z"
    }
   },
   "outputs": [],
   "source": [
    "train_data = []\n",
    "val_data = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7e9b694a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T02:34:18.632243Z",
     "start_time": "2023-10-10T02:34:18.621939Z"
    }
   },
   "outputs": [],
   "source": [
    "# if downloaded already, can directly go here :D \n",
    "tmp_dir = \"/app/suno/data/mert_cluster\"\n",
    "local_filepath = [os.path.join(tmp_dir, fp) for fp in os.listdir(tmp_dir)]\n",
    "local_filepath = sorted(local_filepath, key=lambda x: int(os.path.basename(x).split(\"_\")[1].split(\".\")[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "eb78c40a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T03:08:12.157977Z",
     "start_time": "2023-10-10T02:34:18.961790Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                                                          | 0/20 [00:00<?, ?it/s]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|█████▋                                                                                                            | 1/20 [01:38<31:10, 98.43s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1/20: 0.0s downloading, 4.7s loading, 88.8s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|███████████▍                                                                                                      | 2/20 [03:17<29:41, 98.97s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 2/20: 0.0s downloading, 4.9s loading, 89.6s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 15%|████████████████▉                                                                                                | 3/20 [05:02<28:43, 101.40s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 3/20: 0.0s downloading, 5.3s loading, 93.9s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██████████████████████▌                                                                                          | 4/20 [06:44<27:07, 101.74s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 4/20: 0.0s downloading, 5.1s loading, 92.2s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|████████████████████████████▎                                                                                    | 5/20 [08:29<25:45, 103.02s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 5/20: 0.0s downloading, 5.2s loading, 95.0s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|█████████████████████████████████▉                                                                               | 6/20 [10:09<23:47, 101.94s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 6/20: 0.0s downloading, 5.0s loading, 89.9s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 35%|███████████████████████████████████████▌                                                                         | 7/20 [11:53<22:13, 102.54s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 7/20: 0.0s downloading, 5.0s loading, 93.7s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|█████████████████████████████████████████████▏                                                                   | 8/20 [13:33<20:20, 101.68s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 8/20: 0.0s downloading, 5.0s loading, 90.0s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 45%|██████████████████████████████████████████████████▊                                                              | 9/20 [15:10<18:22, 100.23s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9/20: 0.0s downloading, 4.8s loading, 87.6s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|████████████████████████████████████████████████████████                                                        | 10/20 [16:50<16:41, 100.15s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 10/20: 0.0s downloading, 5.0s loading, 90.1s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 55%|██████████████████████████████████████████████████████████████▏                                                  | 11/20 [18:27<14:55, 99.46s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 11/20: 0.0s downloading, 4.7s loading, 88.5s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|███████████████████████████████████████████████████████████████████▏                                            | 12/20 [20:11<13:25, 100.68s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 12/20: 0.0s downloading, 5.1s loading, 93.3s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 65%|████████████████████████████████████████████████████████████████████████▊                                       | 13/20 [21:52<11:44, 100.69s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 13/20: 0.0s downloading, 5.0s loading, 90.9s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|██████████████████████████████████████████████████████████████████████████████▍                                 | 14/20 [23:35<10:08, 101.36s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 14/20: 0.0s downloading, 8.3s loading, 89.8s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 75%|████████████████████████████████████████████████████████████████████████████████████                            | 15/20 [25:21<08:34, 102.98s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 15/20: 0.0s downloading, 6.6s loading, 94.9s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|█████████████████████████████████████████████████████████████████████████████████████████▌                      | 16/20 [27:03<06:50, 102.64s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 16/20: 0.0s downloading, 5.2s loading, 91.5s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 85%|███████████████████████████████████████████████████████████████████████████████████████████████▏                | 17/20 [28:44<05:06, 102.23s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 17/20: 0.0s downloading, 5.0s loading, 91.2s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|████████████████████████████████████████████████████████████████████████████████████████████████████▊           | 18/20 [30:30<03:26, 103.27s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 18/20: 0.0s downloading, 5.3s loading, 95.2s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▍     | 19/20 [32:10<01:42, 102.22s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 19/20: 0.0s downloading, 5.1s loading, 89.7s encoding -- 0.7h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [33:53<00:00, 101.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 20/20: 0.0s downloading, 4.9s loading, 92.8s encoding -- 0.7h processed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for n in tqdm.tqdm(range(tot_steps)):\n",
    "    filepaths = local_filepath[\n",
    "        -(n + 1) * chunksize : len(local_filepath) - n * chunksize\n",
    "    ]\n",
    "    download_duration_s, load_duration_s, audio_arrays = _load_audio(\n",
    "        filepaths,\n",
    "        SAMPLE_RATE,\n",
    "        max_duration_per_file_s=8 * 60,\n",
    "    )\n",
    "    t0 = time.time()\n",
    "    print(\"  embedding audio...\")\n",
    "    encoded_arrays = encode(audio_arrays, do_clustering=False)\n",
    "    # transpose\n",
    "    encoded_arrays = [arr.T for arr in encoded_arrays]\n",
    "    timing_encode_s = round(time.time() - t0, 1)\n",
    "    n_hours_processed = round(\n",
    "        np.sum([arr.shape[0] / EMBEDDING_RATE for arr in encoded_arrays]) / 60 / 60, 1\n",
    "    )\n",
    "    # subsample audio array for better diversity\n",
    "    stacked_arr = np.concatenate(encoded_arrays, axis=1).T\n",
    "    idx_list = list(range(stacked_arr.shape[0]))\n",
    "    random.shuffle(idx_list)\n",
    "    keep_idx = np.array(idx_list[: int(stacked_arr.shape[0] / tot_steps)])\n",
    "    stacked_arr = stacked_arr[keep_idx, :]\n",
    "    if n == tot_steps - 1:\n",
    "        val_data.append(stacked_arr)\n",
    "    else:\n",
    "        train_data.append(stacked_arr)\n",
    "    print(\n",
    "        f\" {n+1}/{tot_steps}: {download_duration_s}s downloading, {load_duration_s}s loading,\"\n",
    "        f\" {timing_encode_s}s encoding -- {n_hours_processed}h processed\"\n",
    "    )\n",
    "#  1/20: 25.0s downloading, 4.5s loading, 61.6s encoding -- 13.0h processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "67b92052",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T03:09:27.595710Z",
     "start_time": "2023-10-10T03:09:27.593675Z"
    }
   },
   "outputs": [],
   "source": [
    "# TODO: why is the above ~2x slower than before? torch version?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cb95ea16",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T03:09:51.735144Z",
     "start_time": "2023-10-10T03:09:28.370766Z"
    }
   },
   "outputs": [],
   "source": [
    "np.save(\"/home/tony/Data/MERT/mert_95M_75hz_norm_val\", np.concatenate(val_data, axis=0).astype(np.float32))\n",
    "np.save(\"/home/tony/Data/MERT/mert_95M_75hz_norm_tr\", np.concatenate(train_data, axis=0).astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d758e1d8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.380361Z",
     "start_time": "2023-10-09T21:44:25.380353Z"
    }
   },
   "outputs": [],
   "source": [
    "# import torch\n",
    "# x = torch.randn(20, 100, 40)\n",
    "# n_x = (x - x.mean(1).unsqueeze(1)) / (x.std(1).unsqueeze(1) + 0.00001)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29942363",
   "metadata": {},
   "source": [
    "# Faiss clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b76ae3f8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.380952Z",
     "start_time": "2023-10-09T21:44:25.380944Z"
    }
   },
   "outputs": [],
   "source": [
    "# conda activate faiss\n",
    "# ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aedeebae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T14:33:45.835119Z",
     "start_time": "2023-10-11T14:33:41.749743Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3434092, 1024)\n",
      "(36897, 1024)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n",
    "\n",
    "import numpy as np\n",
    "import faiss\n",
    "from sklearn.metrics.pairwise import paired_distances\n",
    "\n",
    "model_name = \"330M_75hz\"\n",
    "X_arr = np.load(f\"/home/tony/Data/MERT/mert_{model_name}_norm_tr.npy\")\n",
    "y_arr = np.load(f\"/home/tony/Data/MERT/mert_{model_name}_norm_val.npy\")[::5]\n",
    "\n",
    "# model_name = \"v2_25hz\"\n",
    "# X_arr = np.load(f\"/home/tony/Data/MERT/mert_{model_name}_tr.npy\")\n",
    "# y_arr = np.load(f\"/home/tony/Data/MERT/mert_{model_name}_val.npy\")[::5]\n",
    "\n",
    "print(X_arr.shape)\n",
    "print(y_arr.shape)\n",
    "# (1144610, 768)\n",
    "#   (12299, 768)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e8492b0f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T14:33:56.771572Z",
     "start_time": "2023-10-11T14:33:48.547999Z"
    }
   },
   "outputs": [],
   "source": [
    "x_mean = X_arr.mean(axis=0)\n",
    "y_mean = y_arr.mean(axis=0)\n",
    "x_std = X_arr.std(axis=0)\n",
    "y_std = y_arr.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ddc9906",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T14:33:56.777443Z",
     "start_time": "2023-10-11T14:33:56.773516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.554562 , 5.9677773, 7.0578976, 6.0912848, 4.722813 , 4.9881277,\n",
       "       4.8236613, 5.38047  , 6.3868456, 4.41745  ], dtype=float32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_std[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "694889ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T14:33:56.825251Z",
     "start_time": "2023-10-11T14:33:56.778370Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.5577035, 5.9814835, 7.0419974, 6.0882034, 4.7146254, 5.0282454,\n",
       "       4.8062806, 5.4323816, 6.3929725, 4.4077325], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_std[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fc998ee1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T15:03:21.378477Z",
     "start_time": "2023-10-11T15:03:21.376304Z"
    }
   },
   "outputs": [],
   "source": [
    "DO_NORM = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a21d1807",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T15:12:50.629984Z",
     "start_time": "2023-10-11T15:03:21.942020Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculating codebook 0...\n",
      "train codebook 0...\n",
      "\n",
      "Clustering 3434092 points in 1024D to 4196 clusters, redo 3 times, 50 iterations\n",
      "  Preprocessing in 1.90 s\n",
      "Outer iteration 0 / 3\n",
      "  Iteration 49 (172.06 s, search 162.78 s): objective=1.31979e+11 imbalance=1.099 nsplit=0       \n",
      "Objective improved: keep new clusters\n",
      "Outer iteration 1 / 3\n",
      "  Iteration 49 (350.87 s, search 332.21 s): objective=1.31983e+11 imbalance=1.102 nsplit=0       \n",
      "Outer iteration 2 / 3\n",
      "finish train codebook 0...search 502.88 s): objective=1.31965e+11 imbalance=1.095 nsplit=0       \n",
      "val score: 195.787\n",
      "train score: 194.491\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "# https://github.com/facebookresearch/faiss/blob/edcf7438bb1862d544db53ed20c60795bdb45e3d/faiss/Clustering.h\n",
    "n_codebooks = 1\n",
    "n_clusters = 4_196  # 1_000\n",
    "train_scores = []\n",
    "val_scores = []\n",
    "input_X_arr = (\n",
    "    X_arr.copy() if not DO_NORM else ((X_arr - x_mean) / (x_std + 0.0001)).copy()\n",
    ")\n",
    "input_y_arr = (\n",
    "    y_arr.copy() if not DO_NORM else ((y_arr - x_mean) / (x_std + 0.0001)).copy()\n",
    ")\n",
    "X_arr_resid = input_X_arr.copy()\n",
    "y_arr_resid = input_y_arr.copy()\n",
    "y_preds_prev = np.zeros(input_y_arr.shape)\n",
    "X_preds_prev = np.zeros(input_X_arr.shape)\n",
    "centroids_list = []\n",
    "# models_list = []\n",
    "for n_codebook in range(n_codebooks):\n",
    "    print(f\"calculating codebook {n_codebook}...\")\n",
    "    faiss_model = faiss.Kmeans(\n",
    "        d=X_arr_resid.shape[1],\n",
    "        k=n_clusters,\n",
    "        niter=50,\n",
    "        nredo=3,\n",
    "        max_points_per_centroid=1024,\n",
    "        seed=n_codebook,\n",
    "        verbose=True,\n",
    "        gpu=True,\n",
    "    )\n",
    "    print(f\"train codebook {n_codebook}...\")\n",
    "    faiss_model.train(X_arr_resid)\n",
    "    print(f\"finish train codebook {n_codebook}...\")\n",
    "    # score preds\n",
    "    y_cluster_preds = faiss_model.index.search(y_arr_resid, 1)[1].squeeze()\n",
    "    y_preds = faiss_model.centroids[y_cluster_preds]\n",
    "    y_arr_resid -= y_preds\n",
    "    y_preds_prev += y_preds\n",
    "    val_score = round(np.mean(paired_distances(input_y_arr, y_preds_prev)), 3)\n",
    "    print(\"val score:\", val_score)\n",
    "    val_scores.append(val_score)\n",
    "    # start stuff for next round\n",
    "    X_cluster_preds = faiss_model.index.search(X_arr_resid, 1)[1].squeeze()\n",
    "    X_preds = faiss_model.centroids[X_cluster_preds]\n",
    "    X_arr_resid -= X_preds\n",
    "    X_preds_prev += X_preds\n",
    "    train_score = round(\n",
    "        np.mean(paired_distances(input_X_arr[::100], X_preds_prev[::100])), 3\n",
    "    )\n",
    "    train_scores.append(train_score)\n",
    "    print(\"train score:\", train_score)\n",
    "    centroids_list.append(faiss_model.centroids)\n",
    "    # models_list.append(faiss_model)\n",
    "    del faiss_model\n",
    "    print(\"-\" * 10)\n",
    "codebooked_centroids = np.stack(centroids_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "78ba358d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T15:12:50.783788Z",
     "start_time": "2023-10-11T15:12:50.631998Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(train_scores, label=\"train\")\n",
    "plt.plot(val_scores, label=\"val\")\n",
    "plt.legend()\n",
    "plt.title(\"Semantic clustering scores vs Boosting rounds, MERT 95M 75HZ model\")\n",
    "plt.xlabel(\"ncodebook\")\n",
    "plt.ylabel(f\"kmeans elucidian distance (normed:{DO_NORM} input)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f42adc4c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T14:53:40.801166Z",
     "start_time": "2023-10-10T14:53:40.798758Z"
    }
   },
   "source": [
    "95M 75HZ default with norm\n",
    "- val score: 23.675\n",
    "- train score: 23.624\n",
    "- val score: 22.901\n",
    "- train score: 22.846\n",
    "\n",
    "NOTED\n",
    "- More clusters (like 4196 vs 1000) definitely help -- if the data is large enough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "698c7d8c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T15:12:50.920520Z",
     "start_time": "2023-10-11T15:12:50.784999Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do norm False\n"
     ]
    }
   ],
   "source": [
    "print(f\"Do norm {DO_NORM}\")\n",
    "if not DO_NORM:\n",
    "    np.save(f\"/home/tony/Data/MERT/cluster_centers/{model_name}_4196_default\", codebooked_centroids)\n",
    "else:\n",
    "    np.save(f\"/home/tony/Data/MERT/cluster_centers/{model_name}_4196_normalized\", codebooked_centroids)\n",
    "    np.save(f\"/home/tony/Data/MERT/cluster_centers/{model_name}_4196_mean\", x_mean)\n",
    "    np.save(f\"/home/tony/Data/MERT/cluster_centers/{model_name}_4196_std\", x_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f62438ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "BREAK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0464b234",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T14:55:33.990715Z",
     "start_time": "2023-10-10T14:55:33.985366Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroid_saved = np.load(f\"/home/tony/Data/MERT/cluster_centers/{model_name}_normalized.npy\")\n",
    "np.array_equal(centroid_saved, codebooked_centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cc074ce0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T16:52:06.821209Z",
     "start_time": "2023-10-10T16:52:06.809852Z"
    }
   },
   "outputs": [],
   "source": [
    "# files = [f for f in os.listdir(\"/home/tony/Work/tony/MERT/train_log\") if \"8x_400k_encode\" in f and \"prev\" not in f]\n",
    "\n",
    "# import shutil\n",
    "\n",
    "# for f in files:\n",
    "    # os.remove(os.path.join(\"/home/tony/Work/tony/MERT/train_log\", f))\n",
    "    # shutil.copy(os.path.join(\"/home/tony/Work/tony/MERT/train_log\", f), os.path.join(\"/home/tony/Work/tony/MERT/train_log\", f.replace(\".log\", \"_prev.log\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcfdeee1",
   "metadata": {},
   "source": [
    "# Check clustering output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9f884f2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.387882Z",
     "start_time": "2023-10-09T21:44:25.387875Z"
    }
   },
   "outputs": [],
   "source": [
    "from suno_utils.tasks.mert_25 import ClusterModel\n",
    "from suno_utils.tasks.mert_v2 import preload_models as mert_v2_preload_models\n",
    "from suno_utils.tasks.mert_v2 import encode as mert_v2_encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab7418fe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.388516Z",
     "start_time": "2023-10-09T21:44:25.388509Z"
    }
   },
   "outputs": [],
   "source": [
    "_ = preload_models(\n",
    "    checkpoint_filepath=\"/home/tony/Data/MERT/mert_test_8x_400k.pt\",\n",
    "    centroids_filepath=\"/home/tony/Data/MERT/cluster_centers/normalized.npy\",\n",
    "    device=\"cuda\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a177faca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.389394Z",
     "start_time": "2023-10-09T21:44:25.389387Z"
    }
   },
   "outputs": [],
   "source": [
    "_ = mert_v2_preload_models(\n",
    "    centroids_filepath=\"/home/mikeys/bundle/2x1k_centroids_mert.npy\",\n",
    "    device=\"cuda\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23451002",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.390053Z",
     "start_time": "2023-10-09T21:44:25.390044Z"
    }
   },
   "outputs": [],
   "source": [
    "x = audio_arrays[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de42f7e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.390749Z",
     "start_time": "2023-10-09T21:44:25.390741Z"
    }
   },
   "outputs": [],
   "source": [
    "x.reshape([-1,]).reshape([1, -1]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4da17918",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.391439Z",
     "start_time": "2023-10-09T21:44:25.391430Z"
    }
   },
   "outputs": [],
   "source": [
    "audio_arrays[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ef5f4c7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.392116Z",
     "start_time": "2023-10-09T21:44:25.392108Z"
    }
   },
   "outputs": [],
   "source": [
    "encoded_array = encode(audio_arrays, do_clustering=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64ee2de3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.392784Z",
     "start_time": "2023-10-09T21:44:25.392775Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.from_numpy(encoded_array[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "026bebf4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.393361Z",
     "start_time": "2023-10-09T21:44:25.393354Z"
    }
   },
   "outputs": [],
   "source": [
    "len(encoded_array), encoded_array[1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf4ae30e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.394152Z",
     "start_time": "2023-10-09T21:44:25.394144Z"
    }
   },
   "outputs": [],
   "source": [
    "np.array(encoded_array_cluster[0]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f79c60e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.394708Z",
     "start_time": "2023-10-09T21:44:25.394700Z"
    }
   },
   "outputs": [],
   "source": [
    "encoded_array_cluster = encode(audio_arrays, do_clustering=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e33a7999",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.395497Z",
     "start_time": "2023-10-09T21:44:25.395490Z"
    }
   },
   "outputs": [],
   "source": [
    "len(encoded_array_cluster), encoded_array_cluster[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc7ed381",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.396066Z",
     "start_time": "2023-10-09T21:44:25.396057Z"
    }
   },
   "outputs": [],
   "source": [
    "np.array([encoded_array_cluster[0]]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f36928c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.396880Z",
     "start_time": "2023-10-09T21:44:25.396872Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.from_numpy(encoded_array_cluster[0].astype(np.int32)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5bba161",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2885bc85",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.397454Z",
     "start_time": "2023-10-09T21:44:25.397446Z"
    }
   },
   "outputs": [],
   "source": [
    "encoded_array_cluster_normalized = encode(audio_arrays, do_clustering=True, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fce57d61",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.398257Z",
     "start_time": "2023-10-09T21:44:25.398249Z"
    }
   },
   "outputs": [],
   "source": [
    "encoded_array_cluster_75hz = mert_v2_encode(audio_arrays, do_clustering=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dfe41f4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.398825Z",
     "start_time": "2023-10-09T21:44:25.398818Z"
    }
   },
   "outputs": [],
   "source": [
    "encoded_array_cluster_75hz[0][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2b7be97",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.399477Z",
     "start_time": "2023-10-09T21:44:25.399469Z"
    }
   },
   "outputs": [],
   "source": [
    "encoded_array_cluster[0][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6d14a92",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.400197Z",
     "start_time": "2023-10-09T21:44:25.400189Z"
    }
   },
   "outputs": [],
   "source": [
    "encoded_array_cluster_normalized[0][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a435caad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.401077Z",
     "start_time": "2023-10-09T21:44:25.401069Z"
    }
   },
   "outputs": [],
   "source": [
    "# Codebooks 2x 1k:\n",
    "#   score: 5.953\n",
    "#   score: 5.745\n",
    "\n",
    "#   score: 12.57\n",
    "#   score: 12.20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b92374cf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.401762Z",
     "start_time": "2023-10-09T21:44:25.401754Z"
    }
   },
   "outputs": [],
   "source": [
    "# save cluster centers\n",
    "# np.save(\"/home/georg/notebooks/gpt/data/cluster_centers/mert_v2_25hz_1x10k\", codebooked_centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2564eea1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.402441Z",
     "start_time": "2023-10-09T21:44:25.402433Z"
    }
   },
   "outputs": [],
   "source": [
    "# !aws s3 cp /home/georg/notebooks/gpt/data/cluster_centers/mert_v2_25hz_1x10k.npy s3://suno-data/georg/tmp/mert_v2_25hz_1x10k.npy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "507e4272",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.403119Z",
     "start_time": "2023-10-09T21:44:25.403111Z"
    }
   },
   "outputs": [],
   "source": [
    "codebooked_centroids[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c517acd6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e2cf7999",
   "metadata": {},
   "source": [
    "### embed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9a3df44",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-09T21:44:25.403711Z",
     "start_time": "2023-10-09T21:44:25.403703Z"
    }
   },
   "outputs": [],
   "source": [
    "modal run ~/code/glockenspiel/suno_utils/suno_utils/scripts/gpt/modal_encode.py \\\n",
    "    --embed-type 'mert' \\\n",
    "    --data-type 'music_sample' \\\n",
    "    --version 'v2' \\\n",
    "    --chunksize '500' \\\n",
    "    --min-duration-s '30' \\\n",
    "    --max-duration-s '800' \\\n",
    "    --output-name 'mert_v2_25hz_2x1k'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50fc5e07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb5fbf08",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee5d4f12",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40e335f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d092649",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a296a4ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54c66630",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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