{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3718ebd2",
   "metadata": {},
   "source": [
    "# Validations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be3d42a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T15:10:10.429457Z",
     "start_time": "2023-10-10T15:10:08.996737Z"
    }
   },
   "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_25 import preload_models, encode\n",
    "my_mert = \"25hz_95m\"\n",
    "_ = preload_models(checkpoint_filepath=\"/home/tony/Data/MERT/mert_test_8x_400k.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e37f87de",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T15:10:11.836172Z",
     "start_time": "2023-10-10T15:10:11.833558Z"
    }
   },
   "outputs": [],
   "source": [
    "audio = Audio.from_file(\"../audios/canon.wav\").get_segment(to_s=10.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f06559a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T15:10:12.119248Z",
     "start_time": "2023-10-10T15:10:12.038685Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250, 768)"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "id": "0e55f806",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T15:10:12.333583Z",
     "start_time": "2023-10-10T15:10:12.211359Z"
    }
   },
   "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": 9,
   "id": "a3e8e3a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T15:10:12.993896Z",
     "start_time": "2023-10-10T15:10:12.865680Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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939d9DAoK+y2a5b5WUFAgpLu7m3zuc58j8+fPJ5FIhMyaNYu85z3vIXfeeadYh2fb+LNqNm/eHJj19OSTT5L3vve9JJ1Ok1QqRZYtW0a++93vetZ5+OGHyamnnkoSiQRpbW0lf/M3f0Nee+21sv3785//TFasWEGi0Sg56KCDyO23306uu+46EjR0/PKXvySnnXYaSaVSJJVKkcMPP5x87nOfIxs2bKj6PQwNDRHDMEg6nSaWZYnlP/nJTwgA8olPfKLsPf4sMEII+dWvfkWWLl1KTNP0fDdnnnkmOfLII8u28clPfrJqajb//sP+rrvuOrHuvffeS8444wwyffp0Ypom6erqIh/60IfIunXryra7cOFC8v73v7/icfHfOOxv8+bNFfddxoUXXkjmz59PotEomTNnDvn0pz9Nuru7y9b7u7/7O7J06VKSTqdJJBIhhxxyCPmnf/onMjQ0FPi9+M9LnsX417/+1bOcnzc9PT0177OCwnhCI0Q50hQUFBQUFBSmFpQHSEFBQUFBQWHKQXmAFBQUFCYpisViVa9YW1tbYBsMBYWpDkWAFBQUFCYpnnrqKbzrXe+quM7dd9+NT33qUxOzQwoKkwjKA6SgoKAwSdHf349169ZVXOfII48cVfq9gsKBDkWAFBQUFBQUFKYclAlaQUFBQUFBYcpBeYBC4DgOdu3ahXQ6Pe4l9hUUFBQUFBQaA0IIhoeHMWfOnNCq7XzFccWtt95KFi5cSGKxGDnxxBPJM888U3H9n//85+Swww4jsViMHHXUUeS3v/2t5/Vf/vKX5L3vfS/p7OwkAMjzzz/veb2vr49cfvnl5NBDDyXxeJzMnz+fXHHFFWRgYKCu/d6+fXvFAmTqT/2pP/Wn/tSf+tt//7Zv317xPj+uCtDPfvYzXHXVVbj99ttx0kkn4eabb8aqVauwYcMGzJgxo2z9p556ChdddBFuuOEGnHvuubj33nuxevVqrF+/HkcddRQAIJPJ4LTTTsNHP/pRXHrppWXb2LVrF3bt2oVvfetbWLp0KbZu3YpPf/rT2LVrF37xi1/UvO/pdBoAsH37drS2to7yG1BQUFBQUFCYSAwNDWH+/PniPh6GcTVBn3TSSTjhhBNw6623AqBhpfnz5+OKK67AP//zP5etf8EFFyCTyeA3v/mNWPaOd7wDy5cvL+tXs2XLFixevBjPP/88li9fXnE/7rvvPnz84x9HJpOBadbG+YaGhtDW1obBwUFFgBQUFBQUFCYJar1/j5sJulgsYt26dVi5cqX7YbqOlStXYu3atYHvWbt2rWd9AFi1alXo+rWCfwmVyE+hUMDQ0JDnT0FBQUFBQeHAxLgRoN7eXti2jZkzZ3qWz5w5E3v27Al8z549e+pav9b9+OpXv4rLLrus4no33HAD2traxN/8+fNH/ZkKCgoKCgoK+zcO6DT4oaEhvP/978fSpUtx/fXXV1z36quvxuDgoPjbvn37xOykgoKCgoKCwoRj3EzQXV1dMAwD3d3dnuXd3d2YNWtW4HtmzZpV1/qVMDw8jLPPPhvpdBr/+7//i0gkUnH9WCyGWCxW9+coKCgoKCgoTD6MmwIUjUaxYsUKPPLII2KZ4zh45JFHcPLJJwe+5+STT/asDwAPPfRQ6PphGBoawllnnYVoNIoHHngA8Xi8/gNQUFBQUFBQOGAxrmnwV111FT75yU/i+OOPx4knnoibb74ZmUwGa9asAQBcfPHFmDt3Lm644QYAwJVXXokzzzwT3/72t/H+978fP/3pT/Hcc8/hzjvvFNvct28ftm3bhl27dgEANmzYAICqR7NmzRLkJ5vN4ic/+YnH0Dx9+nQYhjGeh6ygoKCgoKAwCTCuBOiCCy5AT08Prr32WuzZswfLly/Hgw8+KIzO27Zt81RpPOWUU3DvvffimmuuwZe+9CUsWbIE999/v6gBBAAPPPCAIFAAcOGFFwIArrvuOlx//fVYv349nnnmGQDAIYcc4tmfzZs3Y9GiReN1uAoKCgoKCgqTBKoZaghUHSAFBQUFBYXJh6bXAVJQUFBQUFBQ2F+hCJCCgoKCgoLClIMiQAoKCgoKCgpTDooAKSgoKCgoKEw5KAKkoKCgoKBQL7auBZ79AaDyiCYtxjUNXkFBQUFB4YDEbz4P9LwBdB0KHHRms/dGYRRQCpCCgoKCgkK9yA3Qx00PNXU3FEYPRYAUFBQUFBTqhZWjj5seqbyewn4LRYAUFBQUFBTqhVWgj3tfAwZ3NndfFEYFRYAUFBQUFBTqASGAlXf/f+vR5u2LwqihCJCCgoKCgkI94OoPx6aHm7MfCmOCIkAKCgoKCgr1QFZ/AODtPwG21Zx9URg1FAFSUFBQUFCoB0IB0oB4O5AfBHaua+YeKYwCigApKCgoKCjUA64ARRLAwe+iz99S2WCTDYoAKSgoKCgo1ANOgMwYcMhK+lz5gCYdFAFSUFBQUFCoB4IAxYGD30Of71wPZPqat08KdUMRIAUFBQUFhXrAPUBmDGidDcw4EgChZmiFSQNFgBQUFBQUFOpBiVWBNhP08RCmAqmq0JMKigApKCgoKCjUA1kBAlwj9La1zdkfhVFBESAFBQUFBYV6IHuAAKB9IX0c6W7O/iiMCooAKSgoKCgo1AORBs8IUMtM+ljKAoWR5uyTQt1QBEhBQUFBQaEe+BWgWAsQSdHnSgWaNFAESEFBQUFBoR74PUAA0DKdPmZ6Jn5/FEYFRYAUFBQUFBTqgV8BAtwwmFKAJg0UAVJQUFBQUKgHpSACNIM+juyd+P1RGBUUAVJQUFBQUKgHQQpQShGgyQZFgBQUFBQUFOqB3AuMQ4XAJh0UAVJQUFBQUKgHcjd4DhUCm3RQBEhBQUFBQaEeBCpAjABlFAGaLFAEaCpgy1+A/7kMGFHpmQoKCgpjhkiDD8oCUwRoskARoCmAvQ/dBLz0Mwy88Ktm74qCgoLC5EclBWhkL0DIxO+TQt1QBGgKYF8vnZFs3KkUIAUFBYUxQ6TBSx4gngVmF4D84MTvk0LdUARoCiBqZwAAxC41eU8UFBQU6gchBN9/7C089VZvs3eFIkgBisSBWBt9rsJgkwKKAE0BxJwsAIBYxSbviYKCgkL9eHHHIP79wTdwzf++0uxdoQjyAAFSOwxFgCYDFAGaAkgwAgRbESAFBYXJh55hSjh2DORA9gd/jZWjjxE/AVK1gCYTFAGaAkiCKUAqBKagoDAJMZijY1fRcjCUs5q8N6igAKlaQJMJigAd4CBWEXEw4qMIkIKCwiQEJ0AA0D2cb+KeMAS1wgBUO4xJBkWADnAUskPiuaZCYAoKCpMQMgHaO1Ro4p4wBDVDBZQCNMkw7gTotttuw6JFixCPx3HSSSfh2Wefrbj+fffdh8MPPxzxeBxHH300fve733le/5//+R+cddZZmDZtGjRNwwsvvFC2jXw+j8997nOYNm0aWlpa8JGPfATd3VMzJpsd7nf/cZQCpKCgMPkwJCtAQ/uDAhQWAmMeIGWCnhQYVwL0s5/9DFdddRWuu+46rF+/HscccwxWrVqFvXuDT46nnnoKF110ES655BI8//zzWL16NVavXo1XXnGd/5lMBqeddhr+/d//PfRz/9//+3/49a9/jfvuuw9//vOfsWvXLnz4wx9u+PFNBuSGB8RzzVEKkIKCwuSDTID2Du8HClBQGjwgKUBTc8I92TCuBOimm27CpZdeijVr1mDp0qW4/fbbkUwmcddddwWuf8stt+Dss8/GF77wBRxxxBH46le/iuOOOw633nqrWOcTn/gErr32WqxcuTJwG4ODg/iv//ov3HTTTXj3u9+NFStW4O6778ZTTz2Fp59+elyOc39GIeMW5NLs/cA8qKCgoFAnBvcnBYgQWuwQUCGwSY5xI0DFYhHr1q3zEBVd17Fy5UqsXbs28D1r164tIzarVq0KXT8I69atQ6lU8mzn8MMPx4IFCypup1AoYGhoyPN3IMBDgFQITEFBYRJCJkA9zVaALImAhaXBZ3oAx5m4fVIYFcaNAPX29sK2bcycOdOzfObMmdizZ0/ge/bs2VPX+mHbiEajaG9vr2s7N9xwA9ra2sTf/Pnza/7M/RmlrEuA9ClGgHqGC3hlpypJr6Aw2bFfKUAyASrLAmOFEB0LyPVDYf+GygJjuPrqqzE4OCj+tm/f3uxdagis3NRVgP7+v5/Dud99Etv3ZZu9KwoKCmPA4P7kAeIGaE0HdNP7mhEBEp30uTJC7/cYNwLU1dUFwzDKsq+6u7sxa9aswPfMmjWrrvXDtlEsFjEwMFDXdmKxGFpbWz1/BwLs/LB4rpOpRYDe7qU90PY0e8aooKAwJvgVoKZWgy6xKtBmAtC08tdVNehJg3EjQNFoFCtWrMAjjzwiljmOg0ceeQQnn3xy4HtOPvlkz/oA8NBDD4WuH4QVK1YgEol4trNhwwZs27atru0cKCAyAXKmjgnacYgYNEuWisUrKExW5Es2CtI1XLAcDOWbOJaJFPhY8Ou8H5gyQu/3MKuvMnpcddVV+OQnP4njjz8eJ554Im6++WZkMhmsWbMGAHDxxRdj7ty5uOGGGwAAV155Jc4880x8+9vfxvvf/3789Kc/xXPPPYc777xTbHPfvn3Ytm0bdu3aBYCSG4AqP7NmzUJbWxsuueQSXHXVVejs7ERrayuuuOIKnHzyyXjHO94xnoe7X0IruATImEIK0HDeAp8klpz9oHeQgoLCqDCUp+OWpgEtMRPDeQt7h/JoS0Sas0NhVaA5hAKkCND+jnElQBdccAF6enpw7bXXYs+ePVi+fDkefPBBYXTetm0bdN0VoU455RTce++9uOaaa/ClL30JS5Yswf3334+jjjpKrPPAAw8IAgUAF154IQDguuuuw/XXXw8A+I//+A/ouo6PfOQjKBQKWLVqFb73ve+N56Hut9CKI+K5TqaOAiRL5koBUlCYvOA1gNIxEzNb4xjOj6B7qIAlM9PN2aFqClBK1QKaLBhXAgQAl19+OS6//PLA1x577LGyZeeffz7OP//80O196lOfwqc+9amKnxmPx3Hbbbfhtttuq2dXD0gYJZcAGVPIBD2Qc4s+WiodVUFh0mIwV0IUJdxifBeb9RPwFbwDe8exH5jjEOh6gLeHQ3SCTwS/rmoBTRqoLLADHKblEiATU0cBGsi6ZK9oqxCYgsJkxWCuhGO0t/Auey1Wj/wMANA9Tv3AvvmHN7Diaw9Vzhyt6gFS7TAmCxQBOsARtTLiuTmFPEADuRLOM/6MG807YJcO8BYghAC5gWbvhYLCuGAwV0Jao4Sk1e4HQMZNAXrotW70Z0t4dVeFQrhVPUDKBD1ZoAjQAY6o485kDGI3cU8mFoPZIq4y78NHzT+jpf/VZu/O+OJP/wbcuBjYGlDpvDAMPP4toHfTxO+XgkIDMJgtoQWUdJikiBbkxq0jPK8xVDFsHtYJnkOZoCcNFAE6wJFwJAUIU0cBGswW0QVaBJKUDvA6QHteAogDdL9S/tprvwIe/Srw+I0Tv18KCg3AUN5CSsuJ/6dpQ+OiABUsW4TOrUph81qzwLK9gDN1Jp2TEYoAHeBIEHfgMKdQFlhueABRjQ4+jjX5Q2Bf/vWr+NTdz8IOSum3GbG1AmbFPDSWPzB62ylMPQzmSmiBRIAwNC4eoN4Rd5wo2RUUoGoeoOQ0WiWaOECmt4F7qNBoKAJ0IIMQJCUCFNlfTNCODTz7A2DvG+P2EdZIj/TP5CdA9z6zDY9t6MGO/gBzJi9waQXMivkye/J/BwpTE4O5Elo099zu0gaxd7jx1aDlJqtWpdphPAssTAHSDSDZRZ8rI/R+DUWADmAU81lENFeCNWE3t4Q8x5t/AH73j8AfvjRuH0GkmZdjT/7QHx+QS0HSvCBAAbNivmwKlUAAQEN/3zoU2PJks/dEYYwYzJWQgjcEli81vhq0hwDVogD5O8HLUO0wJgUUATqAkRn2diOOwKo8s5ko9LxOH7N94/YRRs7d9mQnQIQQEfoKlOZFCCxAAbIL3nWmCjb+kd583nq02XuiMEZQAuSe23MitLRHT4N9QLKvqLICVMUDBKhMsEkCRYAOYOSGBzz/m5qDkrUfhMH63qaPQYpFgxApSORqkofA5ME40JzpVPAA8WVTLQRWZKFCVR5g0mMoV0KLZIKezwhQo31AXgWoEgGq4gECVCbYJIEiQAcwcpkBAMAIkmJZqbAf3Aj3cQKUq7zeGBArSOrXJFc/5MG4GKQA8RCYHUSApqgHiHfszg82dz8Uxgy/AjTLpP0Nu4caqwDJBKhUMQ1e6gYfhmgLW7dCQUWFpkMRoAMYhRE6+A9pbs+c4v6QEr5vfBUgQgiSlkuAyCQnQPJgHOhNsGvwANn7gfI3kSix8g/5gabuhsLYMZQrIeUxQdOMxr3DjR0/9jZSAeKvjaPKrTB2KAJ0AKOUpQQoY7SKZVaxyRdkYQQY2UOfl8ZHAcqXHLQRKe17khuAbZvgEG0HjtfeCPYmOBU8QEoBau5+TEG82T2Mjd3DDdlWyXaQKdqeNPg2Qn/T8VSAKpuga/AAGaxT/SSffB3oGPdmqArNg5WjJCBvpGGXNBgaaT4B6t/sPg+6YTcAg7kSpsElQNokv/mXHAc/iv47ZmAAT2fPBdDlXaGWLLCpNhBzD5AiQBOKfMnGR773FDQNWPev70XEGNscm3eCl0NgLdYAgMYrQN4QWA0m6EpZYEaUPoaNPY4D6Ep/aDbUL3AAgxOgoplCSaMzklKz+2L1veU+t/K0j1WDMZArYprmEiAyycM/lk0wAwOIaDb0IFNvTSGwyU0C6wYPgSkT9ISiZ7iA4YKFobyFbGHsVZAHGQFKSyGweGkABmzsbaACRAjxEKDAgqMcNSlAnAAFXJP9W4BvHUJb2Cg0FYoAHcAgeSpD22YLbBgA9oMQGPf/cIxDjHwgW0KnRIA0Z3Lf/G3bEfWc7KCMtlqywCZ5GLBuqBBYU9A74p6DuVLjCJDcCkMDQQdGGqoADeUsT4LBmCpBAxIBCrjudq6nJUA2PjSKPVVoJBQBOpBRoCTAibagxKKddml/I0CND4MNZIqYBteDoE3ym7+s2gUSoEp1gIQHaHJ/B/WCFJkCZBfGzWumUI4+qZ1EvkEEyICNONh2dTqOTdMG0T3UuGrQ/t5iFU3QpSqVoAGXAAVNSrgaW2iMT0ph9FAE6ACGVqT1Mkg0DUtjBKjZNXEmgABlRgYQ09wb/mQnQPJv5lgBx8IbLqoQGAUhIEWJ9CgVaMIwHgqQXAUa7QsA0HYY+ZKD4UJjwts9PjWpYjd4oQBVIEBmBQ8Qfz8bnxWaB0WADmBwAqTFWmCDeoCsZnuAJoAAFYe8xcc0Z3J7gOySTIDKfz+LLSsWymuOOGy2SqYSAbKL0CHdfKeoD6hoOfjgrU/iH37+4oR9Zl+msQrQUN5CCzdAG1GgdS4AYF6UKnyN8gH1jHgJUGDLGY66PEABExahACkC1GwoAnQAwywxAhRvhc0UIKeZIbBiBhjeTZ/zAWIc6hJZPgKkT3oFyP3NAtt6sGXFQnmoJ5ejyzTiuErRgQ5/8bkpqgC93TuCF3cM4tcv7Zqwz5SVlEYoQJ4aQNEWIEVbTCyMcQLUmPHMv52aeoFVJEDMHxRkguYEqDhMs8EUmgZFgA5gRGw6SBgJlwDZzSzMtY+lwCc63G7J41ANWm6ECsBNE5+kkMNeZSEwQmCCHp8eoPJo8gA8VXxARUWAAGAwy4ix5VQ29TYQjVaABnMltwZQzCVAc3k7jAb1A+MKUMTQAFRLg2f7UzENvkIdIPk65dmKCk2BIkAHMKKMAEWSbbBYGrxdauJNkIe/Og92B49xIGS6r8mqMdmzwKQQWFkoi7g3Nt0p/y49y6ZKGKxMARpoym40GwM591pvREp6LeiVFKB8aeykazArKUCxVkGAZhqUADVKAeLK1ew22t7CrqkS9ChN0HIYW4XBmgpFgJqM3760G89u3jcu247b9EYQTbbCYdkTTlMVIFYDqPMgt4/OOGToGHkvAdLI5A79yL4f4m9mK80wzQCi51k2VRQgFQID4KaQA8BIcWJU0L6MFAIrNtgEHW0BUlQ5bicDAIBMAz4DcLPAZrdRUlPZBM09QJVaYVQwQcuqrMoEayoUAWoidg/m8Ll71+Oz96wfl+0nCL0RxFLtcLgHqJlZYFwBmnbwuPbKiRVpHzAe9jMmuQdI/s0c/4AqHZvhFL2FJQmBORUVIH8IbIqaoHkIDACyDcqWqoZeOQ3ealQIjCtAbgiMV4O2G+Sh4QrQnHY6MQs1QTu2ex2N1QQNUB+QQtOgCFAT0c3k296RwrjE6JOEzpziLe1wdBoCa2o2UB8PgR0ERJgCNA4eoESJEqBMlA6WOpnkBEj+zfxVraUBVgPxDrgVyNIBDRUCA+BTgCaAAFm2g/6se841QgEaypfcIojRFqBlBgCgxaKqeWBvvFHADYFVUYDkCdtoK0GrENh+A0WAmogBabAYyjX25mRZFlrYwJFsaYPDPECBdWQmCvskAsQVoHHIAkuz2WExORMAoE/6EJj7m5URWH9ml1xWwK+uTdEQGJmiIbCBnHuuZCbAA7QvW/QIkI0zQcsKEA2BJS2m8lby6tSIouWgn6lls6spQPL11QgFSIXAmgpFgJoIeYY20GAClBl2W0EkWztAuALUrBBYMQsMs3Rc2QPU4DpAlu2glXWLdlpmAZj8ITD5Nyvra+Y/Npn0lBGgqRECswvezBon29+kPWkuBnPuuZKZAA+QXAUaaJAJOicrQGkRAos6eSSQb4gC1CtlgE1vocQlNA2eX1OaARgVeonXUgkaUMUQmwxFgJqIASlGLz9vBLLDVCIuEgPRWMINgTWLAPEu8PF2INkpeYAaS4CG8hY6NTqr0tvmAAAMTPY0eOk38xMe/wzTowD5vtspQoCKee9NxZ6iHiBZYc5MQAis11dMcKx1gGyHYDhvuZ3gYy00DMaUl2naUGWzco3gPcW6WmKie31oM1SRAp+ovFE+vlWqBA0oBajJUASoiZBJz2CusTenXIaqIFktCWiaqwA16yYoG6AByQPUWAI0kC1iGpj6lWYKEJncBMjzm/kJj7/GkbxumQI0ub+HWlHKUQI0QFIAACc70MS9aR7ksHoQAfr1i7vwr/e/gt2DjfHhlStAYyNAw3neCV7yAGmaUIG6MFS5a3uN4P6fGekYTEaAwkNgNTRCBWqvA6QIUFOhCFATIcfoBxscAiuMDAAAcholGoRdkKRZ4aA+KQUecOPnDfYADQ0NIqHR75W0MAVoshMg2bfl//38BEgpQCjlaQhsD+kEAGhT1gMkEaAAQ/I3/7AB//30Vpx10+O495ltY24s2mgFiI+JrbqkAAHCBzRNG6zctLRGcAI0PR1DRKeFEMNN0DW0wQAqm6BVCGy/gSJATcTgOIbACkwByutJuoApQFqzQmByEUTAHUAanAWW698DACggCi3ZTj9qsofAPJld4VlgALykxz/4ThECZDMCtJe0AwD0wtQkQINVFCCesTVcsPCl/30ZH/vBM9i+r7yfXK3gKfBRpqKMVQHi+9+ms/M4mqaPTAGiIbCxEyBeA2h6OgaDE6AwYlWqlQAxhcixyttdqBDYfgNFgJoIjwm6wQSolKVhoIJBwwCkkiQ7EZAzwIBxqwRdGOoGAAwbbTBYMTJzkitAmkRcyjrb12WCntxm8FphFykB2k2mAQDM0tTrueQ4xEeAvGSEECJS4z/7zoMRj+hY+3Yfrvj/nh/1Z3IFaG4HVZ3HaoIeYibuNCdAQgGiqfBdaAwBchWguBsCG7MCFHGf+yce8nWo0uCbCkWAmghZom50CMzKUQJUYgTIlWSbrQD5Q2CNVYCsoR4AQNZshxmhs7AIrIZ4BZoFR1Z9fCGvsqwwDwHK+9adGgoQYVlg3WgHwOojTbGCc8MFy5OS7leAMkVbvH7Fu5fg15efBgB4YfvAqMeiPkaA5jECNNY6QHw/WmQPECBCYF3aYEMKIXpCYEYVBahWD5D8ehkBkq7RKXZe7m9QBKiJkLM05OeNgJOnBMiKsEGDESCtGY1BrQIwtJM+71xMH83xUYAc1gg1F+2EwQiQCXvCmkGOC6QBU/OpOLY/pFlBASpb9wCFU6Q3zEHSgjxhM/Eplgk26FOU/a0wuMHY1DXEIzqWzExjQScNl7+4fWBUn8lDYJwAjbUSNCdASeL3AEkhsAZ4gHgW2PSWGEyd3hJDlSUesq+mAOmyAuTP3FQm6P0FigA1EeNZBwiMANmCALGaFc1oDDq4gz5GUkByGns+TpWgWSPUUqwTRoSSvohmTXIC5N68NF84r6y5bQUTtF1qYh+4iQTrsJ1FDINgCugUM0L7VRx/K4yRPAsvxU1oGlU9jl3QDgB4ftvAqD7TVYAokWqUAsRb+pR5gDDY0BDYjFZZAapSB6hSJ3gA0HWA9V8s8+KpStD7DRQBahIIIb40+AYTIJZdQJhsrHEFqBk+kP4t9LF9AU1jBcatF5iZowTISUyDyQkQ7PC01skAecbo8/xYdShA1hQhQBqrBJ0jMQwRToAGmrdDTcCAr6yG3wM0xAhQS9wt5nfs/HYAwPPb6y8cSQgpV4AaYoImiDtskhTjBIhngY3dA0QIQc+IpAAxD1B4CKxGDxDgGqErhsAUAWomFAFqEjJF23Px+iXrsULnBIgNGhozBI9rX6zh7vJGlAAwsI0+ti9wl41TN/hogXWCT3bBMF0PUOiMbjJAIj26L4Rp+VubVGiFMVUUIJ2pijnEMASWBTnFFSB/JWhRYyfmhmqOW9gBgCpA9abEDxcsFNk1Nre9MSbowVwJMZSggxEpXwisSxsaswdoKGehaNFtTE/HYLIssHATdI0eICC8FpDHBK1CYM2EIkBNgt/z0+gQmFGiBEjnBIhdjOPmAep+DbjlGODnF5e/xglQx0J32ThVgk6UBgAAerpLhP1M2GJwnpSQQmC6PwTmV4AqFEJsah+4CYQuVesd5ArQFPMAcXW5M0UnPn4TNM8AS0sK0OGzWhEzdQzmStjc620nUg29LIzUEjPRnqSfOdY6QEO5ElogTZAi7LdkDVE7MQR7jD6jnhE6/qTjJuIRA2Y1EzSfsPEJXCWEtcPwpMErBaiZGHcCdNttt2HRokWIx+M46aST8Oyzz1Zc/7777sPhhx+OeDyOo48+Gr/73e88rxNCcO2112L27NlIJBJYuXIlNm7c6FnnzTffxAc/+EF0dXWhtbUVp512Gv70pz81/NjGAj5A8boTA9kinAZmKpkWHcD0RCsASQEar0KIT99G/TxbnihPOQ5SgMapEnSKNUI10zOFEZEqQJM3BKY5chq890bm1OUBmhomaMOmx51ItU55BWhOOw3VjPhCYMP5cgIUNXUcPbcNALC+Th9QX4aeW9NaokhEDQBjD4HRTvDsHI62UF8NIHyEhkYQt4dC3l0b9g65VaABeEzQgSpYPQpQWDsMTyHEYWCMBSgVRo9xJUA/+9nPcNVVV+G6667D+vXrccwxx2DVqlXYu3dv4PpPPfUULrroIlxyySV4/vnnsXr1aqxevRqvvPKKWOfGG2/Ed77zHdx+++145plnkEqlsGrVKuTz7mB/7rnnwrIsPProo1i3bh2OOeYYnHvuudizZ894Hm5dGPINUA4pz9QYC6I2JUAmI0A6i0ePCwHK7gNe/gV9buWBoR3e1we20kdPCGx8usGnHXqji7fPEDOwCGyUxjhTbCqc2hUgIn2fxEeAnAb7rfZXRGw6S29Jp10FaKoSoDY60ciGhcDiEc9y1whdnw+oV+qnFTfpbaVgOWOa1A3KChBPgQcAI4JilO5nC5vwjBbC/8MIEDdBAyH9wHweoIqhQhECq0CAiAOURl98UmFsGFcCdNNNN+HSSy/FmjVrsHTpUtx+++1IJpO46667Ate/5ZZbcPbZZ+MLX/gCjjjiCHz1q1/Fcccdh1tvvRUAPdluvvlmXHPNNfjgBz+IZcuW4cc//jF27dqF+++/HwDQ29uLjRs34p//+Z+xbNkyLFmyBN/4xjeQzWY9RKrZ4CGvWa1xxCP0Z2ikDyjGCFAkQWd0emQcPUDrf+RVG/o2eV+v5AFqoAJECEEH6wSfap8lBiBdIyhZk7cYomxc93uAbJ+/wJEIkFP0EaApogBFHHrcrek2DGFqmqD5WMKLEmaLtoeMjAQoQABw7ALXB1QPerkClHIVIGBsqfCeEFisxfNaKU7bnKTtfaPePgD0s/3moUJuggZCUuEFAYrhX+9/Baf9+5/CE1jCTND+iYgKgzUN40aAisUi1q1bh5UrV7ofputYuXIl1q5dG/ietWvXetYHgFWrVon1N2/ejD179njWaWtrw0knnSTWmTZtGg477DD8+Mc/RiaTgWVZuOOOOzBjxgysWLEidH8LhQKGhoY8f+MJHgJrS0TRnqAXXyMzweIOnVVEWxgBMikZaHhfLNsC/vpf9HmEhRt6JQJUygEjtDoz2iUPkKgE3TgClMkMI6nRwSU9baanGqs1iW/+cvVnvwLkD4HJRmfbp645U6QQYpQRoPa2NgyRqRkC41lg3JAMAFkpJCWywGJ+AtQOAHhjz1CZalQJQgFKxxA3dACUPIzFCF2wHG8ITIIVp5lg6TEqQPw7SUaZX1B3FaDA0hl8vIok8Mjr3dg5kMNru0LuFUEKkGMDxEcKVSZY0zBuBKi3txe2bWPmzJme5TNnzgwNRe3Zs6fi+vyx0jqapuHhhx/G888/j3Q6jXg8jptuugkPPvggOjo6Qvf3hhtuQFtbm/ibP39+fQdcJ/gA1Z6MoD1JL5RGtsNIEDpziic5AaKzEaPRIbA3fw8MbgcSncCxn6DL+iRP1sB2+hhNAwnp+x+HZqjDfbsBAAUSQTzV7ilGZjfYazQa9I4UcNNDb2LnQH2Zb7Lvx09g/SEwR8qq8xMgMhUKIdol0futo71d1AFycvWndk9m8MnUjNY4+D1dNkK7HiBvCGx2WwKzWuNwCPDSjtpJY1+GEaCkCf3uVfhF7CsAyJiM0EXLkRSgtOc1K059QK3OwKi3DwB5VqsoEaGqlUyAAn2DkgeIJ1YM5kKuK2GCll6XyVCM2hNQGN/JtkI4DrgsMEIIPve5z2HGjBl44okn8Oyzz2L16tX4m7/5G+zevTv0fVdffTUGBwfF3/bt28d1P7lE3Z6IoDXBCFDYhVQnbIegBVQBSqTbAQA6M0E3XAF69k76uOKTwKyj6HM5BCaHvzR3cHErQTdQAeqnStOA1ko/S1aAis3PgPrvtVvxnUc24od/2VzX+/RKCpA/BCaFvRy/AjQVCJDkp5jW0SHqADnZgSbtUHMwII0vKabyjEgEaKTAPUBm2XtHUxCxd5gpTvE8sONZHK9tQAtyYzJCF+1wBciO08lUyhlbGnlOKECUABmyAhSUCi95gAosfT504hpkgpaf86KwKgTWNIwbAerq6oJhGOju7vYs7+7uxqxZswLfM2vWrIrr88dK6zz66KP4zW9+g5/+9Kc49dRTcdxxx+F73/seEokEfvSjH4XubywWQ2trq+dvPCEGqGQE7YnGKkCZXA5xjZWRT9OBgvfFMhrZGX3v68DmxwFNB46/BJi2hC6XQ2BBBmjAJUB2oWGNKguD1Fw/pFPVC7oBm53i9n4Q/nmbpRaPBHTmrgS5+rP/9/P3ApNJDzdE50iUrdv872DcwepQ2URDayqJgkmVAyc3tUJgPMmiLRERYa6slAkWlAXGcRzzAa2vwwjNFaDpUfd8TKA4pmrQJdtBKsQDhAZltWbZ/sWZAqRpWuV+YKUAAhTqAQoIgcmTEK6Iq1pATcO4EaBoNIoVK1bgkUceEcscx8EjjzyCk08+OfA9J598smd9AHjooYfE+osXL8asWbM86wwNDeGZZ54R62SzdADUde+h6boOZz/qCM3VnrZkVITAGuUBygwPiOfxFA+BcQWogUoIV38Ofz/QPh+Ydgj9f3C7Wy8jyAANeEvJ+0vFjxIl1gg1Y7aLZRboAO/sB0UAe3t78WnjASTzPXW9T5fS4P0Knl/VkTO/OAEaAfWBkKnQDZ4pQFnEkYxFYEdZGYgpZoLmN+X2ZESoGyOBIbDKClCtBRF5FeiuqHudxbXCmBSgkk3QgmAFSNMb42nkCpBs3Oap8JWywIgZEwUUQyeuQQ2o+VinR4A4m2QrD1DTMK4hsKuuugo/+MEP8KMf/Qivv/46PvOZzyCTyWDNmjUAgIsvvhhXX321WP/KK6/Egw8+iG9/+9t44403cP311+O5557D5ZdfDoCy889//vP42te+hgceeAAvv/wyLr74YsyZMwerV68GQElUR0cHPvnJT+LFF1/Em2++iS984QvYvHkz3v/+94/n4dYF1wQdEYXDGkWAcowA5RB1iwHyzuj1DBiEAA9fD6wLUc7eYDWajr+EPqa6gHgbAOJ2f+cKkFwEEfAWEmtQNWhnhBKLfNT1GtkaHdjs/aAI4Hn77sA/R36KU/ruq+t9Xg+Q94biD4ERKcNEECDCw43N/w7GHYwA5RFFKmaAxNkEYAr5LIqWI5QNWQHyeoCC0+AB4Ki5bTB1Db0jBezor+3a7GXp5J2me44lUBy1Cdp2CGyHuCEwnwLEC7v6Q8L1gitUSZkAMQUo2ARNj9PSo2JRVQ9QUAjMjLmkTilATUM5/W8gLrjgAvT09ODaa6/Fnj17sHz5cjz44IPCxLxt2zaPUnPKKafg3nvvxTXXXIMvfelLWLJkCe6//34cddRRYp0vfvGLyGQyuOyyyzAwMIDTTjsNDz74IOJxOsh3dXXhwQcfxL/8y7/g3e9+N0qlEo488kj86le/wjHHHDOeh1sXONlpT0TQJkJgjQlR5EcGAABZJMFphhnlndHrGDB6NwJP/gdN51z+f9yGqgCtrDvCzOzzjqePmkbDYDufoz6gmUeGKkBEN6BpBs2IaJQPKEs7wRejnWKRhQiAXNPbQGSGB7DKeQLQgJhV34AnG9cNWCCEiAaWXNXJkBhSWsFbB4jNNrkCVJaOewDCKWShA8iSGJJRE1qiHRgBDDtHww9mtNomJj342KJplODwDCe5HQZXg/xZYAANBy2d04qXdgzi+e0DmM+6xIchX7KFotRhuudYAoVRm6A5+XDrAHlN0FqDslr5/vEQGABEeD+wQAWI7k9Jc8+jqgqQFRACMyKusVsRoKZhXAkQAFx++eVCwfHjscceK1t2/vnn4/zzzw/dnqZp+MpXvoKvfOUroescf/zx+MMf/lD3vk4kBiWJuq3BHqBChvod8ro7cBl8wIDtuYFWxNBO+mgXqKIz/VD3td436WPrXG+GxrRDKAHqZZlgAQRo+74sVt/2F6zVY4ja2YYRID1Pa4LYcZcAOZoJkAa3gXActyptjRha93PMZrNZzanvpiDPcnlj16jpJUBZxJFCIbAZ6ghPBZ8CBKiYH0YctBP89JgBIyF5+fKDQMv0pu3bRIGPLemYCUPXhAlabog6VCEEBlAV6KUdg9iwZwg4Zk7Fz9vHaulEDA1JqXVFHKVRh8B4hpWrAPkI0DgqQNwIXUkBKmkxgE0m6zNBs+vTiLnHpEJgTcMBlwU2WeBmabgeoEr9wAgh+NFTW7Bua3VjIidABYkAcQUoCqv2vlgjktm853Xvaz1v0MeuQ73Lu5gPqO8toJgBMszvIhGgdVv70ZcpIuewwbdBqfDRwgAAwElICpBGP8NuVBXkwjBw55nAXWfXVcI+8dI94rlW56Atr2/ChiV52bgJmoe5NNv9LjVOgMBCYOPVBmU/QiFLbyZ5xBA3DaQTcakW0EDzdmwCMShKbFAFoiVGb+68rk/BsoV/RW6GKoPXD+oeqn7d8PDXtFQMmnQzT2ijV4D4/oWZoHWjMVmtwgMkK0B6BRM0m6x5FKCqJmjpO+QhazMqhcAUAWoWFAFqAvIlW1x4bcmIWwixggL06q4hXPfAq/iX/3256vZzw5QkWRF30IhE3c7opVr7Yg1LZQP2vuF9rWcDfZx+mHc5N0L3bXRrAMXaPDWA+Ay1AC4RN4YAxVgjVJFeCqYAASCNqgT9yFeBPS8B29bWXr+j+zW073tB/FuvAmT6CFDJcn8/WQEC4JHbORnK8NemgAm6mKc3k4IWh65raE2YU64f2KCUAQYASV8aPK8CDQAtIQrQzFZ6znQPVb82+0bcPmDyzTyO4qgVIK6+pEPS4BsWAiuWEyBThMACJopssibGLgCDYdYF4QGSrjs+ETOiLqlTIbCmQRGgJoCnqOoalalryQLjilEtRun8CCVATtSV/02WdUVvoDUqQMNSwUq/AsRDYH4FiKfC920K9f/wY8iDzZAaRIBEJ/gWlwDZLFukIX2wtj/rZr4BIuW6Ktb/mO4LoTPLemV7uf1FBJa3PgkbXLnPR5dmm0IBIvQ1bQooQKUcLTVQ0inhT8cjohbQVFGA5BIbAMpM0Nyvk4oanro3Mma20u+vFgLE+2l1tcQ84ZwERp8Fxkl+WgsuhMhDYP6kgHoRmAVWKQ2ejVXFmhSgCnWAjJhbCLGoCFCzoAhQEzAgzdB0XXM9QBUKIRZYT51CDeTFYkXftLhLgLgHqJ4QmDW4y/2nigKUK9q49lev4Hc7meE21w/sep4+DyFAvD5NowhQyqYz/EhLl1jmKkBjvPlbReCB/wte4h9AbU0MS3ngpZ8CAB51jgNQfwjMgGSC1hzvwMzIUZbQwVaTCRBLnx9hCog2BRQgiylAJZ2eh63xiNsQNTfQpL2aWPDrixdYTQkTNB1DOAEKU38A2qMQAPYM1qkAeUJgReSKo8sCK9p0X1tCFCDR2oclBYwWQgGKyiGwSiZoen0ViUuAskVbjM8e8BBYUCVoFQLbL6AIUBPgztDoRdTGZmr5khM6Y+LppIUaZlROjoZmDNYGg/7DO6NbKNY4K8vv2ymek75NrpRbyrnqThclQP/5xNv48dqt+MYj24A21kbkLVavKYwAOWyAaIQHyLHRQuhAEm11ja6OxhSgsRqA/3ILVcGSPNUf1ONUDW/8Bsj1Y6/WhUed5QDqD4EZZSZo2QPEssACFCD+fJgpQHI9oQMVVoH+JpZBb+BTMQQmV4EGgBTzAAkFSFSBDvb/ALSFBkDN0tWKGXIP0PSWmOdmnkBh1M1Qi0wBSvE6QCEeoAis4Ho9NSLIA1Q5DZ7uT96XPxSozAeZoFUIbL+CIkBNgD9Gz7M15Nf84MQoX4MCpDFvSiTZ7i70dEavTQnQR9wQmOaUqLEZYBlehPp6Ul3oGyngjsdp3Z++kYLrA9rxV/roqwFUHgJrQB2g/CAM0O8m0SYRIL0BClDPm8DjN9LnZ3/D9RjVQoDW0xpKP7fPRJGw3wCjJ0AmLO/A7HhN0IbjEiCDDbzcA6Q5DfJB7cdwCqwSNKszlZYVoCkSAvOPL/4ssEpFEDla46YgBdXCYH3cBO1TgOJjqATNz3GRVRaiAEVgBys1NYAQElIIMSQE5tgikUD2AAEh/s1AEzQPgUVVFth+AEWAmgBe74cPUJqmVU2F56Ev2yGwqoSwjBKdUSRapeajhnvB1tQZnRBEWcXiHsIUD+4DEv6fwwBNw3cf3SQMlpmiDbvzYLYNtp9hHiARAmuAPyfbBwAYIkmkk26RRUcPGITqxV9upgPXIe8Fjj4PiLAbaqkKARrpATY/DgIN/1/xDFjsctPq9C0YfhO0NDBzUsNN0HJrAE6GMgGvHahwGCm1Tar6tManngIkl9gA3BRvvwcoqAYQh6ZpmNXGwmBVCJBbdTrqUTMSY6gEXbId6HCQALtufR4gXtneHAMBKliOSOSsyQQthepzxKueBfqAgkzQMgHitY1UCKxpUASoCfAPUACkfmDB5EQeSCr5gEq2g6hFL6hk2k0HlxuDloo1kIFcP0wWMvmLcyRdxn0/wv9zKLb1ZXHPM1s9b82mF3u35SNAQyILjIfAxq4A2SO0COI+kvbMbAnzADn2GNSPIeaFOuojtLpclN1Qq5mgWWFGK9aBnZgOG3SQ1eskQKakGPlDYHxGykmO6RRoer7jiLYnwywNXG9kG5T9FYwAEaYAtSZkE/TUIkB8UiVM0EWeBcY8QhVCYAAwI12bEZr3GGuJmR5VNDGGLLCi5bjhLyBUATI1G3atWa0+yOqUhwCJOkC+7UoTtbzjC4EFKkAVTNBmTAqBTZ0q5fsbFAFqAvwxesA1LIaFwGTSU4kA9Y4U0KrRG3OyVSJAuvtZVi1VkVkG2D7SgledRXTZXq4AMQLUdRi+9ccNKNkEZxw6HR28nlHS1/aCe4IY3BBY4xSg/CBVq/qR9ngbCAuBaWPxAHGzMx+wIknv8irvK/Gy+TonQPWRMVMiLv4Zr8aIXYa3uwDoICspXiJDbAooQIST6YirAA1iapmgXYWZnnepkCywSiEwAEIBqkaAuPqbipllWWCjrgMkN0LVTddPw2CYrgcoMF29BmTZvkUNXag+gFsJusxbVHL3J+94s+eCFSAe4peLk4aEwMZg5FYYPRQBagLkRqgc1YohehWg8EGlZ7iANBs4dCkLDLoOiykQtREgWgOom3RgI5nHNs4ywXpoCGyzNg8PvLgLmgb809mHoSNFj6cnKik+8TYg0e7ZtKgDRBrnASqwRqgDSCNquqc1YYPQmBqB8lltNOV9rOYB8tUM6WihN+V6U3cNWQHSbJTk35+RKdHuAqBSvTTo5jSuAB34HiCdkU5NEKCIKIRIpogCNOD3AEV9HqAKbTBkuLWAKo8XXFlKRQ1vHSBt9L3ASrbUByzaQpVXCZox9hBYTnSC994GQ03QPARmJkShRo5A5T4wBCaZoLmq5ViNawekUBcUAWoCghQg/jysGKKcTVGoMKjsHSogDaZMxNs8r/HO6FaxBjWEKUA9pB1vOowA9W2is6C+TQCA216hp8/q5XNx5Jw2dDBC161Nc+Xfdq8aVLLdRo1CAWpAFlhpmIabMob3mAnLAhtTFWQ+q+Xen1oVIEbseIp6Vxsd8OoNgUV8/dssKa2W1wjymDKtoiBANtGACCVHYy0aNxmgcTIdo79VayIiFCAnW72K+oGAIV+IXWSBFf0KUOUQGCdA1TxAmRAFKI7imCpBiz5gPv8PANGXcCwmaD6p5L3SOMywNHg+qTBjZaVEas4CC0qDB5QPqElQBKgJCPQAMfIQVgtIJj2VUkt7Rgpo1ZgyEWv1vFZPWwheA6ibdGAXptFUascCNj0COCWQSBK/3koH1s+8k5qeeQisP+cA05gROsT/A8ghsLETIDtDCVDW9BEgIUOPhwJULQTGurHb9Huf0U7fV28WmOkjLlbJPRZugjYiUeSFopZ3C7YhgmiUZYhNAQ+QwQiQEaWkL2bqyOr0e58KChAhREywyjxABVozx+0EXyUExhWgKrWAMrIHSDZBj6UQou14FSA/WEjfxOg9QNmAGkCAnAXmV4AYITPjZZPQwOQVkQUW1Aw1SvsJ8mNTxRCbAkWAmgB/pVYANWSB1agADebdmVPcS4BsbgiuIQSW20cJUK/eia6WODaRufSF134FAMi3HYSCRQfRJTPoRcwVoP5sUSJAwSnwgGSCboT8m6FZYPlIu3c5Gyi1sdz8OdHxE6BqWWDsuIYt+r3PbKPvqzcEZvoUIFvK4uPHZZpR6ft0m6IWEEE0RmeifiJ1IMJg7T8M5tfSNA12tJ2+GJYGv2Md8OjXgPzkN6Nmi64iIrLAGAFyCK0nVkshRECqBj0cfn3ajptKnvKboLUxmKBtWQEKIEAGT4MfvQcoqAYQIIfAQhSgSLxMAQr2AFVphgpIxRAVAWoGFAFqAtwsDTds0VbNBF2qzQQ9ONQPQ2MXrl8BYjdI26oeAisO0CKIxfgMHNSVcsNgG34PAOiOUmKzbF6b6CzPPUD9mSJw9PlA6zzg8Pd7909WgEZTCfqpW4FbT/S26QCg5SkBKkY7PMuFAjRaD5DjuESHD1aRGrPAmGlykBGg2R0sBFaPAkQIopp3ffn34yn1RiTq7a1mcf9RBLEYV4AOfAIUsel3bsZT7kIWCjYKQ+Vm01fvB+4+B3j8m8DrD0zQXo4f+I04Ymjixp6UbvCZoiVMy61VCZDrAQqrtszDagCQ1C1PqDk+BhN0yZaywIJCYFwB0sbiAaL77leAItXS4CUFiPuHKnqAZMVdboYKSJlgKgTWDCgC1AT46wABqNoPzOMBqhACGxncB4C1gIgkPK/xthB2qQYywAiG3TILi7tSeJMrQEyq3WDPAQAcPbe97Bj6syVg6QeBq14FFp3q2WygAlSPB+iVX9AstLcf8yw22OzeinsJEJ8pjroPluzz4env0Vo9QLxqbATJqIH2Fu7Fqf2mQAKKF9oBHqBoJIoir05rFcSss0AiiMWZAgTrgM82iRD6nZtxSTVIUAKkEds7037mTuC+T7mzchZGncwYzLqTKz4x0XXNUwvIDYFVSYNnClDRckKVae7/MXUNMcd7PdA0+FG2wrActPA+YEEhMOEBsoJ7dtWAUAWIh8AqeoDoezlJDBy3gyZfciVoQBVDbDIqTwEUGg7bIRhiErTXA1QlBCYrQBUGFd4I1Yq0IOrLnLB1E7BrawwazXYDAIzWOVjUlcJangnG8FyGVlteNs/13PAQWFgtI8CnAAnFoo4sMJ6KOrjdszhSYA1gE52e5TxbZOwESANYbRlhhq6aBUb3NY8oFnQmoZt0oDXqUIDsUqHsInVkAsRUnUg0SrPqNNAbOitCWUAU8ZhEhO2SO/s8ABF1KAGKJlwFKBZvQYGYiGkW8B9HAgtOppmJL/2MrpDooL3rDoAwhMgwTXjPmlTMRLZoY6Rg1VQIEQBipoHOVBT7MkXsGcoLhVcG9/+kYiY03008gQLyY6gE3VKLAgR71K0weJ+yMg8QV4D8xKpU7gGakY5ha182eNyuZIJWIbD9AkoBmmDIJmBZAeLhsDATtFcBCidAhZEBAADxhb8AwOZ9saqFwBwHiSKdDSenzcHirqQbAmN4op8SjSAC1F+BAA0FeoDqqAPECcngDs/ieJESIJ23qeDgdYBG2waCD+rRFDUtAnUrQAUSxfzOJEzTHbRrhWx45ll8soLHw1qRkBBYESYSCV+NoAMYcaYAxRLuTbM1GcEd9rkomi206NzGP7jk593XAMf/LX1+ANyEhuSqzBI42ckWbYzUWAcIcIshhmWCZeSUel8YJ6GNPguMpsFXUoB4HSB79HWAeAgs4g+BhZmguQLkeoB4z7TgEFiACVrOAgNcciefe0/dCqz9Xh1HojBaKAVogsFj9C0xU8SageoKUN7jAQoeVAghsHMDgOHtBM/h1EqAcvuEYbZ1+jws6kphDzoxjATSyIFoJjbZM9CZimJuu6sudMghsBB4QmDcA1RPJWihAEkEyLYQs+kAoqe6vOubXAEa5Y2fqzzc9yM/r6oAUYKURxTzO5LQTTqAGrBBCBEhCnoMJdptfvEZwPKLxGJLIocFLQaTWHAkSZ2n1EejMS+hFApQBPH4FCFAjo0o6HcTT7kEKB2L4CbrozBOvhqfOyIPbP0LsOM56k87+jzgyZvpigcAAfJngHHwENhwvoSRYm1p8AAthvjGnmHsrUKAklFDuh40AARxFGE5BCXb8Yx1taBgOegIaYQKQITAxlIHKB8SAuN9GUtlITDXA8TrAHGCOJSnTVn5e+mGeB2gkGaoQHkIbGgX8Md/oc+PPg9omTGKI1OoFYoATTCC/D/y/8NBFxK8pCcsrj6Us5CwM4ABGHIjVAbeGLQqAWL+n17SilmdrVg0LQVAw0ZnLo7TN2EoOR9WzsTRc9s8N3Fhgq4hBGbomtQMtQ4FqBigAOUHoIMOVpEWfwiMNSAdtQLkS4GXn9dYCJGGwBIwTbqPBhu0+UwTALDreeDFe4Htz3gIkM1qNllEh61HAOLNAuMhsFhU9gDlXQJEokjGpCq6B3JDVEmRiyfcm2YrCwcNFggwZzn9kyFm4ZM/C0yU2PCNL7wa9N6hgrCB1aIA8VT4PYPB16i3CjQjkMlpQLaX9fEiyJfsuglQSc4Cq5gG3wAPUJgJOrQQYkyo8NPT7rU1lCt5w4Q8zOUxQUtp8EB5CGzXC+66e14GDnlP7QekUDdUCGyCMRBQAwjwEqKhAENdLQpQz0geaSYbG74iiIDbGJTUSID2kg7MaYsjHjEwpy0uwmDbDPooh7/kYxrMlULj8nyAnpmOSSGbGhUgQrwhMD6Ss0aoAySFlmTc8xbuARp1GwhBgKRBuM5CiDkSxYJpSRgmn7U65d8PHyR92+SG5xJMt7O9XR4Ci8ZiUmVttxBiARG0xCMoEHazO4AVIMLIsUM0JFMSAYrzyUXIOcDDxQeCAsSur1YfAeIhMB7KihgaYmb14X9GlWKIPAvMEwJjqoWpOYjAHpURumRJdYACCyHS44tqNuwqzaHDUK0OUHkaPNufiFsJuiVm0grYCEiFDzJB+wmQPwts9wvuut2v1H4wCqOCIkATjMGAGkAAnXXwQSqopkShBg+Qtwp0eQiM1EiAcvuoutJN2jGbhbgWdaXwB+cEWEYCD1rHAwCOnuslQNwDREgwiQNcAjS7PVF/N3iLzigBUMmY13XJ0sw32gjV+71qLAQ26jYQgQpQbWnwxGeCNpgHyPA3NAXc9GHfd8HVHguGyOLzhMCYnygWi/k8QC4BSsdNlHDgE6Bint5EcoiK2jeAq3QM5ULOgSAfxiRFUJFVwA2B8b5e6XjEG4INAVeAwkNg3ARtuGGc1HTx+miLIVZXgNzf1x7lOS0qQcshsJ3rcfLuH0GHU+4tEibomBiPo4buFrH1K98VK0Gz10QILEgBUgRovKEI0ATDlajLMyraKnSEz9eQBdYzUkBa4407AzxAnABVqYmT6aMEqN/oFKRsUVcKf3KOxTeWP4TbB04EACyb1+55X8TQkWbr7wsJg/Hjn9UWrz8N3q+48DAYV4DQUlbbpHEhMNkDVFshxFLB9QDN65BN0AEKEO9W7yNAvG9bCaZLgKSq1ibzAMXjscBCiEWYSMVkAnTgVoPOZzgBinnaG3A1ZChUAZq8BKhoObjvue144MVdeGPPEHqH6e8uFGWrADi2uI53s6rO1TLAOHgxxGom6FRUUoASHYBGSUVslO0winIl6AqFEIEay3oEIFABuv+zOH3r9/AefX35NTpEi8OiZaaYhMYiengNN67yOCVaTwzwVoIGgKjv3Nv9ovv+JilA/Zki/s9/Po3/Wb+j+sqTHMoDNMEQJkXfDA2gs7adA7lABaiWZqiV+oABbmd0UmXGVNhHiyDmY64B76AuetP/3St7YTsE09MxMTh6jiEVwXDBCk2FH2Sz8DltcbxVbwjMb5Ye3AHMOloQoH0kjZk+BUhnCtCoiwDKWWAcNSpApXwWUQBahIYRCTNuUgXIN7hyguYrCsnVOgumO+uVfj+DVYmOR2MYCfIAIYquKUKACjl6E8kj5vHQ8RAYLz9RhknsAfr9K7vxhV+8VLa8PRmh5+d3jwOmHYLktBsBAHsGuQJUKwGq3BA1sA9YrIWGiYvDo64GXbSIWwixggcI8Cqi9cBthsoI0NBuoOd1AMBB2m70+a/R/i30sWORIEBRwwiv4SaRNDglQI95m6EC3hDY8B5gRCrw2vsmJbBm+Tg7nnhiUy/+sqkP+ZKDDx83r/obJjGUAjTBcOt0lBOgtgoNUeWwV1gIjCpAwW0wAAB6QFZCAAjrBG+lZopl1AgN7GID6DKfAZpDpMJnggclHhqb3Zao3wQdRIAAQYD6SbpsYNdFCGyMdYDkQZg/t+nsOgw8BEYMehPhapSpObBs3/t4CIzYrhoEyQOkmSCil5ukALEQWDwWlzxAUisMwkNgbJA/gENghSy9ARc07w2DnxPDIWHZyawA9TDFJx03Pef+0tlt9IY9vBvY/ixaYv4QWG0EaFYbPXf7MoXysC2AEakOkDtZSIsirAkUBNGoB55K0IFp8BIBGmWfv5xohsqujc1/Fq8t0PaWm6AHttLH9oXCAxQz9fAMXkM6D/kYF5YGXxxxw1/TDwfi7XRS1PPGaA5tTOjPsCKqFQruHihQBGiCMRjQCZ4jbCbhOERccEAFE/SwpAAFhMBqbQxqZPYCAPTW2WLZoq6UZ52j55UrTED1WkCcAM1pj9efBl8WAqPFEB3WB2wfgggQ89000AN0x9rd5a8H7i8dwIkZ5zsjXrL8sr0copNUIE6AbBjCBC2rOLyoYjIheYDsgqcVRkssghIxPds7EFHMMQKke43wbggsTAFi10op6yGfE4kd/Vm8vrt+BYrXozn7yFl46bqzsPbqd2Pt1e/GYbPSLqGzC2iJ0skKL1HREqueAg8AnckoIoYGQoC9w+UTlawwQRtuCCzWIhGgIvIV6paFHpflIKrxthEBCohuwGG3L2eUpJ4TM5EGL1WXX6B1e9Pg7RIwSJVxjwJk6m4NtzICFPG+H5BCYAGFELkBevZyqmwDTfEB8bG7OIrfbbJBEaAJRlgWGIDQC8nfeC8sq2LvcB6t3AMUpADxC7JKRlQiTwlQrGOuWLagMwk5M9+fAcbh1gIqH5Rsh2CYSeYeBcgpVVRSBEIUIGuEFm3sDzBBG+YYG4H66gB1D+Vxw0ObYRP2ZVTKBGOhPcIrSHuMm77fQL7xSooYYWTVhqsAyb8f9wAlJA8QKeVFtW+aBeaGwKxiHSUHJhksZoK2tDACVEUBAprWlfuiHzyND972F+zL1HczL0peFE3TMLstgdlt7HyTjiVteq+van3AOHRdw4w0D4OV+4BGgkJg0RZxvSS00StAMVQgQKCJAQDglEZJgOQ0eEJ8BMinAA3uoOqsEQNaZqLITdCyAuQvYqsbwgsllJ+yEJiUgcj9P3OWAzOPos+b4APi959KBXcPFCgCNMFw6wCVm6DDLiR/DD00BDZcQFp0UA4iQDW0hXAcpC2qqLRMdwlQ1NQxt8MtenjU3GAC1C4UoPLPkDPDZrfFXcUCqK0haogJ2h6h+zustyLqS+3VG5YFRmdqr+4aBKAhi7j39QBo7JicBihAlmaKLD7heXBs6KzxbTLumsrtUh52kZ4HVAGSCNAobxaTAaU8/S1KhrcHHlcFi5YT7EcxowD/jZoQBnMcgu37cihaDt7sru/zZS9K+YvutlpN7+9eawgMcHuCdQ+WX6NeEzT7vGhKKEBxjNIDZDuIMn+bIAs+8KSAakkdYfD0AuvZQMOFGh0/5mq9cCzpmhThrwWArotJaczUhZofZF1wM8F4CCysGeqwGwKbfQwwixGgPS+P6tjGAk7ClQKk0HC8+/AZ+ODyOVg4LVn2Gs/M4IMKh5/wFEIGlL3DBbRq7IYcYIIOLM3uR7YXBhw4REPnDK8BbnEXvVhnt8XFrNCPSv3AeGgvFTXQmoi4WUtAbZlgXAHiBkhGgAjzABWi7WVv4QTIxOgGSX8I7JWdNEyRAxvYKihAOiMyWqQGBUgmpRIB4iZoWzOFiZ0PorKZPZmIo8g8QE4xB4d9n0USQSpqCA8Qzyo7EGEX6G9l+QhQS9QEt6uFlWdopg9IzpLa1leltpQPRSkUUwaZAOne67GlDgI0qzVcAcp4PEDsWol5PUCjM0E7oqp3mAJkj5UAyVlgXP1ZdDpsPQpTc9Ba6nZXlgzQgJuJ61WAamiI6q8EzUNguX5geBcADZi1zKsATXADY67eKwVIoeG4/N1LcMuFx+KI2eUKDY9FZ32ScS0KUMGyMZAtVfQAuQpQuBpCWKpnH1oxp9O7jcWMtPnr/8joTLEQWIAJmhOgtkSEFmHTTZQIm7nWowB1HkQfh3cDdglajhKgYrSj7C1mhGeBjdLQ5yNAVAECsoQNyhUywXTbLZxGF7izdLvk+w2COkbDNTxbcBUgHi4rSSpSIhZHkbU6sa2CS4C0KExDl/qIHbgEyGG/lW16ybmua1jMTPy/fXl32fsANJUAyROerfuqVBf3oTIBcntzpXTv9VhLGwyOmaIYYvm5wwsheuoARb0eoNGkwdtWCabGxjkzeLJli7pYYwuBJWUCdPC7kU3Sid+04k535X6mAHUsBABJATIk60JQPzBfNWh+nfPlIvzKSE7XEqoKTT+chs9y/W76/QSBh8CUAqQwoUjFwgiQTwEKMEH3jhQBELd4WIAHiGchVeqLNdTDiyB2iIGP4/zj5+PwWWl86pRFoe/nIbCgOkCDUpVaTdOQihpSR/g6CFD7AkrmiAMM74aZp41QrXhn2Vt0qQGpM5qeQb40+Fd3+RQgXwdsz2czAqRzAqRpsMDL7HtvSINZ6fhlBYhJ546kABGmFsnhrGg0CktjhShLeRBGgGyW+ccb4R7QBIjVXXLMcnX1b09bDAC48/G3gwf2ZhIg6XrfWqcCxMeCwKrO0rGUE6DaFaCZFRWggErQsglaK4yqErRHpQ4JgdnMX0NGmwXG0+B1B9jyJF140DuRbZkPAJhWkshyiAIUq6oA+TJveSjM9ClAHLOPoY+RONB1KH0+wT4gVwFSWWAKE4hElHds9ofAqitAPcMFpJCHwTwhQQpQLW0hhnt4EcRpZbPKo+a24cHPn4FTDukKeiuA2kJgPN0/FTOlVPjaQ2CWmQRamT+pfwsiJUpKSKKcAJlRSlQisFAaTddokQafwmC2hB39dB+y1UJghMBgg50hFVF0WCjKL9sPDEvbkQZ/vp6tm0JO19gyq+iuF4lEYbNZpUyAHLbM0g58BYgXphSmcwnnrZiHGekYdg/mcf/zO8teJ5wA5QfHdReDICtA2/aNLgQWSIAkE3RK8/7utRZCBIBZbcwDFGiC5iqKPw2envPxUSpAkLMVQ0Jg3AM0mtpWtkPEOJrue4l+V4kOYNYy5FsWAACmywRISoEH4PUAJSt4gOQQGCHlITDDBOTzdfZy97nwAZXXeRpPcAWoZJPRTRonERQB2o/AS7L7sybKFKCAGdXeobwb/tJNN+wiQROd0cMHDN4GIxubHrpOJbRX6Ag/6OtTlIwarhG6Jg8QPb4HXutHLslS9JlJ0CEa9IAGsAY75shomyZKIbBXd7s3x6ohMLsEHfR30mOukmaFmJEdO9gELbLAtIjrIXJ4CMxtlGoYOghTe4iVF8oRV4CcgBpCBxy4RyxSrgDFIwYuPZ2GTr//57dElV9CCP7td6/j8a3sxtQEBWhEDoHV6wGya/MAJeA931rrCYGlw/uBeRUg9nmeNPjReYD4NUCgebxzMriqORoPkLxPyR1M/Vl8JqDrKDAFaIYlK0A8BLYIlu1Wco+auqjqP5Argfj9OrIJ2rEhQl2yqiVnIcqNerkPaAJT4YuW4zkf/RnIBxoUAdqPwAty+UNgfgUoHyBNeoogxlqBgCKFoi9WBQ+QPciKICZmhK5TCZ0pVwHyDwZBCpBbvK92BWjEiWEPmArFUkcHkEJLvNwrYEaYAqQF9N+SseM5YMOD5cslX8Nru9w6La4JOsSzIVW31qUbss2yTIiv3oynOrcnDZ4udzTTNX/zEJioEm1A0zSh9qBUoH8AHIOHwHgbjQM3C0xjBFmLlhMgAPjYSQvQlohgc28Gv39lNwgh+PKvX8Odj7+NPot9d00gQLLiO5grBSsJIZBDMeUvuuHZuDZ6E/TMNt4PzNenziFC3UlFdW+4mCtAo6wEzVVQYkQDxzJgbFlg8hhrbmUFEA96JwCg2EoVoBk2q8pcGAGytNQGOhZ6SIFsgrYd4iEPALyJJ7b0/XkIkBQG4/V/5OcTGALzZyAf6EZoRYD2IyRCCBBXgHgdnmAFqHIjVMD1w1Sqiqxn2EXfOqvm/ZbBQ2Alu3wwGPIRII8CVEs7DHaDyyGKPWAK1W4qDwdVgQYgGpCasMrbT3AQAtz7UeCnFwEje72vcYUnkhT+H0AKgYUpQIysOURDVFKAeAjMr8SEKkAOV4DkEBhdl6tINg+rGdJsk/mP+DKbp9DXWnV7EkJn55Amty2RkIqZWHPqIgDAbX96C//2u9fxw6e2AABGCFNMm6IAea/3eozQtSpAceKdYIzGAzRSsDzXtEzcUqbjlnLwm6BHUQdIc+h5Sozg8BcAURh0LArQtEgJ2o6/0oWMAFmMAM22mQLEw1/xdiDe5vGQRQ0d8Yghvv/QatB2yetrksN63AfUebA3e5cToL63KhdcbSD8+3+g+4AUAdqPkIoFe4D4xcpDR4EeoJGCWwQxKAMMgM4uRqNCCCySpxlV0bbREaBE1BCzUf/FVKYARSUPUB1p8HnEsMVmGV+9GwAA/SgvgggAGhtoorDCFaBcP22nQZzyjAs5BMYywFpiphsCC/MASZ3g4xEp/V0LJkBhWWBcGaIKEN2ORrwmaEvzEiDNKkCz+A0kzt7PCdCBqwAZjADpsWACBACfOmURklEDr+8ewg+e2AyAFvUcRvMIUNY3UagnDFZrHSDDynlUonqywFpiJlJsciZ3hecp8IauIeZI+ywRoDgKo6oErVlcAQonQEGFQWsFV65OjbxBiVv7QqCTGuWttkUAgDQydGzwG6DZ8Ri6BtOg32l7tYaoVkHyNfnCeny8lsNfANAyA0jNAECAva/XfYyjQb+vEOeBngmmCNB+hLA0eH7BtQkCVM7KqzVCBQA9Ur0oIC/e15oOJlG1IKwdBq/EKxSgmIk8b4dRRwgsR6LYmG+ny1jTzzAFiA80JuxwD9DANvd5fsB97thCmcrrCbzVQ8nQcQs7qhdCZMeTRwSJqHuZuSZofwjMHTh5EUO6Hbqeo0ekLD66TE6RBwAi+Q00JrfzZY5QgA5cD5DBVC8zHhwCA2iW4sffsVD8/9UPHomPv2MhRgh7T5M9QEB9RuiKafByVetSzmN8bomZwEs/B35zVdWmvgDQ2cKyO6UbpKgCHTWg8fCXmaDGXlEJenQKkM4zVc1o6DpjMUHzMfYcrKULDlnpfnYsib2knf7Tv6U8BT7AeB7eD0wyQctVoOWwHh+veQaYjAkuiOj3bqoQmMKEgXuACpZrsgMkBSheWQHyeIACYNTQGd1k0nMsET6LroaOVHA16HIFyHCLIdaRBp9DDC+PpD0v7QsjQIZrgg419PGmqgCd8XFI5GbDPvqbTEtFsXhasnoWGCdriLndpgE4XAEqI0BSA1SZALEbgaObEgFiYTHRJ4wdN/d42a4CBEGAGEk6gBWgiEO/N1P2VATgM2cejPcdPQvfPG8ZPnHyIkxPxyQFaOI7wmf8IbC+2sMdhUpZYAUvAUrG3POwJWYCj34NeO6/gKdvq/o5nSl6HvVJBCg0BR4QtXsSo6wErfFwUQ0hsNEQoFzRRhJ5vNNhBGjZBeI1U9ewjTAPZP+WAAXIbYPBwY3QZQqQbIIWVaB9x3TK5cCyC4HlHy/f0QluieHP3lUKkMKEIRl1b+By6qhfASpaTpnBuEfOAgvxALkEKHzAiBJ644zEwmfR1SD6gfnkVD8BSkbNuuoAEckD9IqPAPUjLUKEHjDSENXs8g7sYsdCCBAnN5qOV7rp/i2d04pE1ESOcAUohABxBYhEETfLCZDjG7SJJONbRbkOEKv6rLkKEDexW8IgzS5jdtPR7II0g6bLeBFFcgB3g4869HuLxCuT945UFN/7Pytw/vE022d6S6ypHqBs0cKZ+otYk/oLgPpCYMWAm7GAZIJGKUvbVYASFkPXXLL35C3ASE/Fz5mWKleA3CKIZlm9LKEAjSILjBACw/ES+MD1dEldqRP5ko1z9GeRQIF6b+afKF4zdd1LgHwp8G7Y0f3O28L6gcl1gPwp8BwLTwE+fAeQmla+oywsN1HFEP2TVkWAxojbbrsNixYtQjwex0knnYRnn3224vr33XcfDj/8cMTjcRx99NH43e9+53mdEIJrr70Ws2fPRiKRwMqVK7Fx48ay7fz2t7/FSSedhEQigY6ODqxevbqRhzUuiEd0oYzKPiA+gLRJN3i/CtSXKSKtVQuBVW8MGmHkqFIYoRrCQmD+NPhUTFKAavAAEaaM5EkMOcRhx93Kz/2kJUQBcr+zkr//ltix7e7zIAUo2oJXd9Mb45Fz2pCMGpICFDJbL/FeXFHEozIBCjFuSgqQIzd9Zes5esQtY8B+P7skGaQBQXZ0uwidye26LwR2ICtAnABFk+kqa3rR1RLDCFOASJNCYDdHbsN19m2Yi576QmA1mqBRygmPoQiF8fO7OAw8fmPFz+kMIkCeNhhSDSDA9QBp9dcBshyCSJU+YADc1jAVsloBqti8vnvIM2nMFm18xHic/nPMRZ6QVMSQFKB9mz0p8HR7bgNaDu4BqjkEVisijFD6G0GPE/wKkAqBjQE/+9nPcNVVV+G6667D+vXrccwxx2DVqlXYu3dv4PpPPfUULrroIlxyySV4/vnnsXr1aqxevRqvvOLKfzfeeCO+853v4Pbbb8czzzyDVCqFVatWIZ93b6C//OUv8YlPfAJr1qzBiy++iL/85S/42Mc+Np6H2hBomiZqAWUL5QqQrHDImWCW7aBgOZXbYEBuCxE+YMTAQmCx8jpCtSKsFhBP7/UoQMIDVP0Cd1ivpxxTjbKJ2eK1fUgH1zbR3WVW2M3fowANuM+lWS3PADtqbquXAFVTgBBBXLo5CQXI8v0GkgJkSwoQL3pIdBOaQQd87uGypU7xAKAxAmQ4BRhMAdIiXAHiM9ED1wMUB/3eYlUUID+mtUQFAbJzE18IMV8ooEOj59oifQ/2DOVrVk1C0+AtX9p1KSsIUDpulmclPXcXzTYKQTABktpg+ENgY1CASrbbB0yLVAqBectChOHffvs6zrnlCTy2wVW5tMHtOMV4jf5zzAWe9U1DxzaHK0CbXQWIEaBigAIkiiGWmaClVhj+Rqi1gF2/NVkEGgD/pFUpQGPATTfdhEsvvRRr1qzB0qVLcfvttyOZTOKuu+4KXP+WW27B2WefjS984Qs44ogj8NWvfhXHHXccbr31VgBU/bn55ptxzTXX4IMf/CCWLVuGH//4x9i1axfuv/9+AIBlWbjyyivxzW9+E5/+9Kdx6KGHYunSpfjoRz86nofaMLjVoCUCxAaQlpjhpsJLRugse114gMJCYBHeGNTyeIwECEGM0AugWhihEuRaQByOQzDMBsy2IAWohvRsInmAAGAgMlO8FmqClmZbdjHkM6p4gEgkiTd2UwJ05Jw2JKIGcrVmgZFooAeI+AZtIoXnHLlas8MJUAS6qOTNjNGMAPFtcoXPcIqCJGm+TvQHcgiMn7uxVH0KUMTQobFidCQ/8R4gK+cqNYdE+kAIsKO/NhVIrkjsfcHXoqWURQvzAKXjpte8f9A7qYryyJdDPyeIAI3IneDlPmCAJw2+3lYYJYsgBt/5GwCuAGlVSP3bvfRYX9/j/razt/4KAPBGfDltrSPB4wHa+Ty7xjWgjfYIc03Q7nXdHlYBX64DFBYCqwReQ6xC0+VGotwErdLgR4VisYh169Zh5UrJXa/rWLlyJdauXRv4nrVr13rWB4BVq1aJ9Tdv3ow9e/Z41mlra8NJJ50k1lm/fj127twJXddx7LHHYvbs2TjnnHM8KlIQCoUChoaGPH/NADdC50rlIbB4xBAXnSxNcrWorUoavCgKGJISTuyiaKUxFhO06AcmDZbDeUs0NW5N0IHL4wGqReJl6/AU9G7drVZNTdDhHiDANQ2XIZQA0e+zoCdQsBy0xEws7Ez6FKCQXmBSGnxCCoER0cAxXAGSQ2BicDci0EQdJ64AuZ3igeCbhR5lCpBxYCtAJctCkrV7SNQZAgOAaIqGjfUKvd3GC3LYbWmCnn+1+oCKYWnwfjN3KSc8hi3xiEuAjCiw6gZA04HXfkULggaAEyDZBM3D9IEmaJEFVqg7BFawbUQ1pgBV8ABxdVeroGjT/aSf3zfC9p0QLN75AABgfcc5ZetHDB3bCJtc8Uy61rnCj1QIyLxrCwuBCRN00VXcKhi7y8Cv6VrKhDQAygTdIPT29sK2bcycOdOzfObMmdizZ0/ge/bs2VNxff5YaZ23334bAHD99dfjmmuuwW9+8xt0dHTgne98J/bt2xe6vzfccAPa2trE3/z58+s42sYhqBo0v+DiEUPEnWVmzs2IbXo1BcitiROUEVXIu4NuLD76EFhHQEool4bjEV2QOG8z1BoK9LFZEH/Pdsvt/UXrAAUoQJoGi6WeW0F9sKwCMCKdjwEhsBGHfm9HzE5D1zUkIqbUDLVaCCzYBO1Pg5fbkxCPAuSmweuGlwC5ChA9biNaToD4by5CgQeoApTNuopGok4FCADiKeonM6wMa1kwcXAks/JBJq04XDcB8itAhSAFSAqBSfWtMHMpsJxZBP709cDPcU3Q7rk54vEADbvbA6Q6QPVngZVsgqhQgCqZoGvzAPGxtHeE7fuOv6I9tw1ZEsOb095Vtr6ha+hBG/JEmlB1uKUTKqXB9/sr4MsmaH7tVQmB/ecTb+OOP79FtyMUoInxAHEFyGShBuUBmmRwWMPLf/mXf8FHPvIRrFixAnfffTc0TcN9990X+r6rr74ag4OD4m/79u2h644ngqpB8wEkZuriopNlZR6Lr1UBMmGjFHBi56WbSLwhafDuzdafAQbQOkBuK4zqF7hmuanlALCp0C5ey5ptiBjBpzP3yATWwPFnV8h1gNhNot+ix3PkHKoSJKNGnYUQ3f0iIgvMrwC5vzeR4v0im0uPQPeVMXCEQZoRIDMGh7hmTovoMPlgW0MfuMmMfMZVPKLxymnwQUi2uob6UFVvnKBJCtAcQv2RtRihbYfAYqHsshCY38wt1QFqjQeErN7xWfq44znA388KrgLUn3HPHz7uJGOGRKh8JmjWDLWsR1YFlCwHMd67rKIJmmdFVj6nuVIlCNAL9wIAfu+cACNeTpYjhgYCKRMMEP4foHIa/F+39OOgL/0OR177IN71rcfQk2XjrF2qKQQ2mCvha799HTf8/g08sbFX8gBNrAl6RpqOb0oBGiW6urpgGAa6u7s9y7u7uzFrVnCV4VmzZlVcnz9WWmf2bGqMXbp0qXg9FovhoIMOwrZt2xCGWCyG1tZWz18zIEJgAQpQLCQExrMx0uAKUEgWGAuhRLTgthDFPB3E8iSCiGmUvV4r3I7w5QqQTIBSdTZD1SVVBQBezdLfyCYaSCz4mAG3E7oTpADJ4S8gMA1+0KL7vGQmvVkkokbthRBJxOMBCpu1eoiJRIB40UOim1IrE/p7OyJFnm4/GjFQhKuCFRARiqE2hpThyYBcht7Q84gAev3DWkdrGgXCvrsJzgQzSi7h6mQdyGupBeRpyVCDB+h9R8/GiYs68aFj57nnLVcYOg8GwFLjM71lnzVN1AFyryFRByhaIQSGAgghdTXVLNqOUIAqpcGjRg8QHx/7RorUHP7q/wAAfmmfIcZbGby6s4cAtVdWgI6e24a57SyTkACZoo3NvRm8tY/tm2yCrkCAeobda//fH3wDjsGU+AlQgAghYszm/d+UB2iUiEajWLFiBR555BGxzHEcPPLIIzj55JMD33PyySd71geAhx56SKy/ePFizJo1y7PO0NAQnnnmGbHOihUrEIvFsGHDBrFOqVTCli1bsHDhQuzv4HH6TEAafMzUhZpQkGRlEYsHG9RCFCBPUcAAZl/I04usiMoSbTXwEJjsAQpUgKJm7YUQ7ZKY6XV1tAMAnsvMQCnajpfIwWhJhO+zLTqhB4R/OAHiA5zHA8RCYEzt4VlmiYghNUMNnqkTqW2HhwCFVK/V5LCLVR4CgxH1FLIkhJSFwGKm7n6foARIeENMXkTxwAyBFZiROI9w02wldKXdTLCJJkC6RIASxX2Io4CtNShAFQkQ9wDxGlGlHJbOacXPP30yTlzc6Q2BAVRpYCZf7Hu77LM6UvT8yZccMd5kikFp8F4TtKHRcFa+WAcBstwssIqGYV4YtIoHKCcrQDufA/KDGDHasdZZ6rk2OXj4Z3uoAlRugm5LRvDkP70Lr31lFf76LytxAasxVWDhd08z1IoEyL0+X901hIfeYr9jKRuozDUSwwVLKIoz05wAKQVo1Ljqqqvwgx/8AD/60Y/w+uuv4zOf+QwymQzWrFkDALj44otx9dVXi/WvvPJKPPjgg/j2t7+NN954A9dffz2ee+45XH755QBomvjnP/95fO1rX8MDDzyAl19+GRdffDHmzJkj6vy0trbi05/+NK677jr88Y9/xIYNG/CZz3wGAHD++eeP5+E2BEEKEA93hZmg6UBEkCSVPUD8wgvzAJWYB6igjY0AcRN0rmQL8haoAMWM2gshSjOg1tY2pGMmRpDEj078NT5avLZibyOXAAXMFDkB4o0HS1mXgLCbxDAjQDyE4AmBWflAz4hddP1K3hAYz8byKUAkWAHiITDNcBUgEzZsh4BYboYYQG+CBYm8FhB1a5X4MsgONJRy9AZc0OowmEroalIxREIITMtLduZpPdixLxecqSmBz851zb1puy8yQpJiiQJ+r5q/cCEAdB5EHwMIUEvMFGnf3EzsrQTNvrOYlwABtB9YPUboku0gqnEFqAKh1XlYN/ycdhwismT3ZYpw3noMALAheSwI9EAFKBKkAAV4gPykU9M0JKMmpqdjmMZahxS5qujxAIWfoyJMx/CdPzMrBnHGXb0dyLg+TZ6oUgsB2jOYh1PlXN1fMa4E6IILLsC3vvUtXHvttVi+fDleeOEFPPjgg8LEvG3bNuzevVusf8opp+Dee+/FnXfeiWOOOQa/+MUvcP/99+Ooo44S63zxi1/EFVdcgcsuuwwnnHACRkZG8OCDDyIedy+Ub37zm7jwwgvxiU98AieccAK2bt2KRx99FB0dUpx/P0WwCbrcA+RJgy9YSCEPHexkDVWA3BtoUBZYkRGg4hgJUGucVZqFGwbjfcBa/QoQ8wCRaiEwqbu6GYljbgcdYF/ssVGCSX0NIeAEKLAIIC+COPNId7bMjdCMAA3ZdMBKCQJkIiMrDQEqkF0IToMnOk+D94fA3N9Tk+q38Nkt0aNSZ3sblkNEKj33AMVM3fVUASiQiLhpaQa/WRyYCpBT5Ofu6AjQ9LRbDBETmApfsBwkiDe8sdjoQdF2sGeo8jUhZyNpmp8AMULSwhJG/OeoVORTQBCg8npAmqaVpcKPeDxAvkKIRkSEqOpth0FN0DXUzBF1scKJQd6yhXDiEMBmBOiV6HK6bwEKkKFr0DRgK88EA7whsLDSAxLERFUmQFZ1X1MfI0DvPGw6ulpieHOfNE6Pcyo892x2JKNi/6t5gB5/swfvuOER3PiHDRXX218RftdoEC6//HKh4Pjx2GOPlS07//zzKyo1mqbhK1/5Cr7yla+ErhOJRPCtb30L3/rWt+re32YjESmvA+RRgEQWmFcBEkUQddMz+/KAKQVhafA2KzQ42psIh6Zp6EhG0DtSRH+2iFlt8aoKkFPKoaLrSGqDkYiamN9p4I09w3iNdWgPLILIYFfqhC5CYAuAeDuQ20fDYOmZEgGKiv0FqAeogAgcokHXCJ1dx7xmSodVrS5qUY85O5QASTK+JoXAeNhPMyMuAdJsFG3HrSbNPED+EFgRpusBilSfLU9mcMJZ0kdJgKRq0BPZDyxTsNACLwE6KjmIh4aoD4j7SoIgqkAHmf8FAWIqht9D4g+BARUVIIAaofcM5bEv61WAaAgsYHuRJFAYqjsVnobAeOi3wu/paw4cBLnPWhJ5mLvXAQCeN2nj0USAAgRQRW2Lw7yqkZRLJOHaDwKrbzPw6y7vyApQ9RBYL1PXFnQm8Z7DZ+Bff/UKbOgw4Ix7MUROgNqTUXFs1RSgF7cPAABe3TXxBUQbgQMuC2yyww2BuRc1V3vipptCLleCzhYsbyNU/2yQg4fANBvFgAGpxG7alj42BQhww2C8H1gQAUpEXBO0IytAW/5C/zw75xZBTEQNzGMK0GZW5CwwBZ6Be2QCs8A4AWqbByTa6XPuA2KD+gAzQcshMECr2A6DKxK24ZPwuQfIN2jLoSldUoB4yjv0iPAARWDBsonbJ0wKgRXLPED0EjdECOzANEHz2kmlMYTAhlkIzMlPXAgsW7TRonnJyaGxPgDAtiqp8G4oJuAmzhUZfuO2coAj3cyCCMu0g+ljCAHiYZ19gSEwnwka8BVDrC8EJrLAKoSLuLFfr+ABkq0EJ+pvULLUvhBbWXgrSAECaD+wt8kcDJz8JeAD3/EY6ws1KUCMABG2fcsNgWUdA2f9x59x15Oby97HQ2BdLTFceOICLJyWQo5Xyx9nBYir9R3JiNj/agrQ3mG6v0P+CtiTBIoA7WfgHZszAQoQzQILqgNkV22ECsDXF6s8I4p3IbfGqAABUkNUdlEFESBN00SrBjFDzfUD//0h4J7zvGZg3l2dUFPx/A6aZcJDz7UQoLIqyIRIBGg+kOhw9wFwCZAvBBYxdEQMrWItIH5DdnwzWJEF5mvMKg/icpjK4ITFcNPgTdiwbEf4iIgIgRk+E3QUMTbA8yKKlRrhTmZw07k1SgVIboeRHxkY3U5YBWDjQ+G1oQIwwsLXAMT5t1CnLRuqGaErd4JnKhb3AAFeBcFvWgZcBajv7Yqp8DwEJkzQnkrQkhIqCFCdCpDtIMY9QDWYoI0KpF5OJjlVZ8VwDzpTEKNQBcigk8i+4z4HHH2e5zU++ayoADFSmnckEzRToPdmCd7sHsF963aUvU8mQBFDxyWnLZaKxU6MAtThUYAq/257WdbagCJACo0A7wUWlAYfj+jCT+KpBF200FqlBhAADwGyAtQQHkaw6qlUGgJ/Q9ShAAIEADBZ6ii/uHeso1JxKRuYkp5jpmKuAHFUMkGHNgLN9bvqTesclwDxWkDstazPBA3QmWPFWkCCAPkUIBYC8/cvkgmQEaAA6UZUeCoioCEwN62WLo/6s8AkD5DOG6XuDwoQIZ7mr42AUxwbAYoYOkoGVUOyw/1V1g7Bs3dS4v7kTTW/JVu0XAVoJvU6zrBpmY9aFaBgAsQVIMnIK4fB+DkrK0A806kwCGTLi8bya5pXg/b2AvOZoAGRCh/X6lOAPFlglRQgo7oClPUQoFfpk8VnCkIWZIIGXCN0kBHdDT2GB+2FAhQQAuOlKroDPF49TF3rYmrbvI5EfdXyxwA+WW1PRmr2APUwBaisB9okgSJA+xmSoheYFAITafCGVAhRUoAKsgIUXg9Hnk1ZAX2xhGoxypuIjOmskNYdj7+Fp9/uczvB+4gKr14sjL87/uq+KFdl5goQYkhEDMzr8Harr6gA6bz9hO8i5Qbo1HT8dWcOuwqMrPgUoCxiMHTNc6NJRk2pHUZAzRY223b8WSx6sG9BI+7vqUsKkBjczYjHxG7ZxA2jMY9TmQlaqgOkm9VvFhMCxwHuOAO488zGzmjHqAABgB2lk4dCZpR+hs2su3hP7YbQkYLteoBmHgkAaCvQxJBqJujQKtCAS0jibVI7Bek8DfTsJGjLByAwDCZXg3YcInyKqagRrChJClA9/cBKNdYBqo0A0X3sxBCW6qyp6WJXAQpKgwfcrLogr6RoQBup7gHKyQoQG39KcMuE+IlhLyMU01rocU9Lxar3HWTYtHcEl/74OeHLqRcDgQpQbSGwwVxpUmaCKQK0nyGwEjT3AEXkLDCvAuTxAIVBd0mCHRAC42GEMt/KKPC3py3GnLY4tu/L4cI7n8brrJloW9JLgPQoHSQ1Ls/veNZ9Ua7KLLXBSEQMzOusQwHSeK0hnwLEwl+kbR4u+/FzeHgL+07KCFAcqajhybRJRqVaQEEEiH2XxPSZWHlTUl/qvCEN4qbj/jaGwxWgiCBPJjOxkwAFyO8BinEPEG+EO1EEyAkZOHP7gD0vAd2vAC/9rGEfx6tnj+ncZUZ2azQd4QkBYX207OHgVj9ByMghsBm0eGukNIRWZKqGH+Ts0DJwQhJLu0kRsoIQlAYPVMwE62xxQ2ByaKnFdFwy7jdBg3qAZEW7GuRu8LWYoI0K5zQ3QZ+svwYA2B07CGiZLhSgMA8QV4CsoIKxlcznDFxBydlsHakZapG4n7l3yL3WCSEiBDadEaCudIwW94Q7PofhgRd34aHXunHLIxsrrhcGrwJU3QNECBEEiBCIZteTCYoA7Wdwm6HSC9R2iKjaHDMN4ekIzQKrpABpmph9VCJApAEhsIOnt+AP/+8MXHQi7bTMj8EfAjOidJDU7QK9ae5Y574YpAARaoJujUc826qkABFBOvwKECVAVstc9GdL6Cds9uojQBkS94S/AFYNusLMTBC6SHAIzK8A6ZICZMgKkNwTyeCVvG36fYoiiVwBMnxZYBExkzPYTHpCPED73gZuXAw8+rXy1/ISuVh7WzhRqhchnqt6YCRYZfHcKLLA+jdDy9Gw0UhP7W10MgULKT55aZkpPDvztZ6qqklNClAsHdxRPEgBAipmgk2TPEB8gmboGmKOtN0ABSiuFcUkrhYUbYJYLSEwURg0fNtcST9VfxkA8FL0WACuxYAr7n7wMh5WwPkpFPlKChD7TbI8BCaZoOVrdPegS2oyRVuM611pemzTUlEx0cpmK7doGcnTY/3Lpl5PBKFWBCtA4d/tUM5bUHfQ3wh2EkARoP0MbgiMnnjyCehRgORK0AULae4BqmSCBmCzlGm7VJ4SzptwOn7VYpRIxyO44cNH478vORFz2xOY2RoT5mUOM0Y/SycWsPc16j/gkG+WkgeIk8D5kgpUmQBxBSg4BJZN0HTXwTAChLgwQHNQBSi8HYZuMwLk+y65bA/frFUexE1SEsTAJG4aPFePaB0gR5AoTQ/3APGZqB7x9hEbV2x6hKp3Gx8qf03+TXs3AJsebshHcsJZ5rmqA5EkmzyMJg1eIu7JQm/NVXszBcttYRNrEfVm5ml7q/pmhBJRyQQdDVOA6idAnawdxr5MUdQASkUNaFxNMhNCjQTg7QdWhwJUtKRCiBVM0LWEwLhR+zSD+n+e0Y5CyXZExePQLDCDh8BGqwAxAiQrQJwAEfc7ksOcPPyVjBriPhCPGCIpJTNSOTsxV6LfQ8Fy8OTG8nYm1SBM0ClJAarQwmTvsDdEOxl9QIoA7WcQhRDZACOnu8seoFAFqFIIDG5NnMC2EFy1qNR/ZxQ4fcl0PPHFd+HxL76rLOsiEpMI0ZYnvG/0hMC8HiAAmNfuvrdSHSBBgPxFAJkCNBChqcKDhN0MfIUQcyRWRoASsgcoQAHiqexa1B8CC1aADPgGcfZ+Tox0I1peyFLKEAPogOz3AAkFiDfCnQgC1LeJ7UAAkfAvW/vdhnykZod4rupALEUJkD6aZqg7nxNPIyh5DfwVkCnaSGnsuou2iIrD87Weqv4LUQgxsA5QUAhMVoBCQmAVUuF5FlhfpuitARSUAg8I8k89QKMMgVUYiwyzeggsV7QwT+vBAm0vSsTA4/lDPfaCeDT4FhjRK4TApN6MYeCvuQSoJIXAJAI0KBEgKQNMBj+nM5nKBEg+rkde31tx3SDwRrftyag00Q4/B7kBmmMgN/mKrCoCtJ9BECA2YHDpOGJoMHQtNAtMeICqKkDhjUF5t3UyhptIGHRd8/TO4YjGJIKw2UeAAkJgeRJ1CVBHjQoQV13KTNCUAPUYNFNmAJwA9dN1GQnJICAEFtHdEFhA2rNh0/3VfUUpNdEM1Z8G77tBMDLKQ1a66XqAaCFL4ipAPAQWKe8Fxgcyk3uA/ERrPNDLPAj5ci8NYb9pjzGLNnHd/Diw+6UxfyRvlDuWczeRplmAEat+AsT9P+L/Gn1AHg9QLC0UoPk1KEBBPancF6WsLBECC1KAfKRFpMIHeIAYARrOW6JmTGAfMA5PHaA6TNCeQojVFaCyyYOETMHGKSz9/UVyMLZlDKFGGboWquJwBSgwBFaJeDLw6y5j8ywwtxlqXvIAeRQgQYB8x8yIZC5TuUGuhwC9sbduUzIPgXVKIbDKCpD3HqIUIIUxw2+CFlWgTbfaL+CrA1SoXwEKKgrIwwhaWCXpcUAyHnUl4a1P0seuQ+ljYAgshgSbtc3vdBWgSiZoYf4O8QDtJtMAAANyCEwKa2URF1WgxX5HTakhqm9gIkSksvsJEA8RaD7CU0ZM2GzRAFOAzKh4r6sAeQlQ1AhohioIUMz9nHFuqughQL7PyrEU85eKs5E55Fy6cO1tY/5IHnIkxujP3ZZWSoCiduUbTRmsgiBxQ6yY4uDe2nxA2XxRSmBI16UAFS0HB2m7cO32S4CXf+HdH36uyx6gYg0eoI7F9DE/UJYK356IgLcc295Pt+UhQH4FiJug660E7VGAwgmtblQ39meLljBA/8U5EkXbEcpFImKUtxBhMCuZoCuVH2CICgLEti+lwYvUeHhT4d0UeJ/qxVTkQq4yMZfDjL0jBby4Y6Di+jKKliPChbW2wvCHwAaUB0hhrOCx36LlwHaIm+nBDHeiwFbJqwC11ugBcip0Rhdhm3FQgMKQjErGXU54Dnkv+3/AXbEo1QEy61SAeNNEWQGyigCbpW+1OgEAA5AIECNctmaiBDMgBGa4/cD8CpBdEn3ZjJhfAWK+BR8ZM+AbaBgZ5YO7bkalLDCaBq/7PEB+BahIXAUowszYOkhg89aGoZRzyws4Vll4sJilv/EwEvjr7I/Rha/8AhjaNaaPFdWz/abzOtDaRs+DhFOu6FXEnlegOUX0kTRedGgIqb9GAlTKS2Qr2iIpQD0oWk7FWXzRcnCa/jLmFDcDL9zjviA3c4221GeCjiaB9Bz6fJ+3UrGua6IW0PZ9lLS1xAw3BBaiAMXrrARd9DRDraAAmTUoQEUbR2lbAACvG4fTfWfkLawIIgBEKpmgK2XfMQgFyGLrSCZoURwRwO7Bcg9QV9pLgAz2G1UjQNz4zNXqh1/vrri+DK7+6BodS2tJg5cz2AClACk0AHJhrmzRcqtAcwUo4lWAeD2O1loVIJ0rQOUhMD6L1v2+lXFEKmZ6OpijfSHQdQh97gmB0ePLkhji7DtaOI0O7C0x09NvqwzcmCn7boZ3ASCAEcP2At3OEPcA5QfFTaSk0+8i7TdBRwypPodPMbDcUIMR85q+uVrjV4DKBnFGULkyRBUgHgKjCpAm9QkDgJhhhHuAYtJ37K+I3Uj0vQVAumn7wmB2hipAwySJP4/MBxaeSn+Xv/7nmD7W5KbzMRCg9g6qBCZJDo5dB0lk/p8XnYPRDUqicn3lVX6DYLPGqw50ShjaadbkPK0HAKkYgihYtpstNbhTeoERoEiKes78JmjHCQ+BAZVT4VkYjJOIZLSWEFid3eAt4rbCqJDV53qAwrdt5bM4SKPkem9yCQBgG6uwHWaABtwssEATdKXsOwY+Xmes8krQOckD1C0RoL4MI0ApL+kz43QMKRUqE3MeNTjrSOpprMcH5KbAR5ldoXoaPA+BRVi4UBEghTEjZupCZs4W7bKUS785jXuF3CywCmnwqFAVGW4F4gkNgUUN5Il0wc8/kTYlBQJN0HnJBH3w9BZc/q5D8K/nHlH5Q4I6oUs9wPaOsHYd3AMEIhSJAiNAQVlg2bBWGKzAn0M0obxwaKx6rIcAEQKTKUAl0TsoDzg2VWzgVYB0jaBkWYAwSFfyANHt8RAY3bFxHKh63/T+7ydAOa4AJfHSjgHgmIvoCz4PTb3gxSO1MWQwdnRSAqRrBANDddQCYsU7X3AOwV7SDgAoDdSmaDlMPbEiLbSHX9t8EGhIaEV0YaiiclK0HJcADe10w43+qsx+E7SVgyCpfgUIADpZGKyCEXo7IxGhfcAAKQRWZxaYbSNWQyFEtzVMuALUOvIWDI0gH2mHlqbZnjv6qxMgUQeoggco0HvFwMfrEqTwO1N1ZQWoe7ggqk33DrMQmE8Bisbpb+RUIUCcZL7/6NkwdA1v7BkWv1M1uI1Q3b6CQOU0eB5KXDSN7h9XkSYTFAHaz6BpmicVPm/5PUBeEzTPFqvVA8QzoojfEAy3/ow/bDOeSEVNz00b8050SZzHA8SzwFwTtKZp+MdVh+GCExZU/AwtqGu0RIB6RniJ+ggKGiMsQ3RGnWf/15UFZnGyFkXc9z6Ne4DkfZGei7CaVfAoNUbE9QDRtxRF9gtXgKKGvxBiVMzOojIBCvjtGwaeAcaR92Z9Efb/EEni1V1DsNoX0RcGa1NMwsCLR45FvYzEkiiBnlsD++pII2bk7QVyMMBustpIjeEHlhVnm4yImFForBozNUKHz8Bpujj7LYsjboadXAMIKDdBC8KulZVpAFAxE4w3ROUqSipmAEX2eRUUoJE6iuSVLCIVQgwPgekm97WF36RnZKkfbbjtMEEsePiuUgisYhp8TQoQU1DksY0XVrXdz7Udgj42/oRlgcUS9NzgvRrDwBWgWW1xHL+Q+tkeqTEMJtcAovtfnmzjB/cALZlJf3elACk0BK4R2ipXgHwhMGpcI95MkgoQRQEDFCB+EzEmMASWjBlurxsAmHe81JV9wF3OTdCsGWpdEAqQdIFyn0rbfE8654jOvj92Q+YEyJ8FloxKITB/HSCmAOUREcSVw+1gLQ3aEiFxCVDes9yQKkEDgFUqCg+Qweuh6Boszf0ubT0qTJ7RiOGqS+MZAuMGaA5/JljeVYAKloO3iqz/2uCOMRVFjDgNUC81DTmNkoWB/r7a3pPpA/qpV+YF52DMmbcIABDL1RZ+0Jh64nh6cslG6PCbe0HOlgLcMJhcBRooD4Hx1yNJT5dzgRoywbxZYBnv53HwXmAohhKgdVv78XaP19tSsuyaWmGIEBhskBBj/5w8PYZMxxGivcT2GhQgs5Y0+EomaIMTIGncYN971vF+LvcBhRGgRJISDM2qogBJxR3fu5SGwR6uMQy2L+N2ggdQmweIjZtLZtDfXZmgFRoCUQ26BgUoU7AQQwmmxk5UvwztgwiBBdwEI4TFdH2+lfGERwEyE8CsowNDYI5UCLHSwBUEUTBNVl1YiIukZ6N3xP0uhjX2/TEClEWYAmTUpgD59lVnKo4uzVrlCtUZIilA0v5SBcglQI5VgiYbpBlsXXou+Scihi7k+KAq4A1DX2UCpDO1YJjQc2xdfxzQdJohk+kZ9cc2QgECgLxO92tkqMaGqDtpAcS3nNkY0Vpw8EHUv9Zi1Uag9BK9KRK5i7qnGGJlBUiEwAChWgoFiCsynFxxr1qYAZqjUjHEpFeRSUWlEJh/e1wB0goYzpcToN6RAi64Yy0+efeznuWWXYKuMeJRAwGKwApsWgoA80v0GIrTjhDEYkc/vT7DGqECrq/FrpQGX4EAaRr10ZRkAsS+p5zt/VyeCt/ra4TKkUix37FC7zxCiDBBJ6MG3nMEJUDPbO7DUL46MXFDYFwBcj1AQeQyX7LFb6oUIIWGwhMCK7l9wACpyzBbni3arvoDuHJ3CHhGVFAYJMpuImZ04giQxwM051h6k+dd2Ysjoms4KfJeYLHQ4mWhCFKAmLqUNds8g6dIhWcEiBOSlrI0eEOQo3IFSKpZ5NvXoBCYXXL3K+tRgOigZBMNkYhbCRqgITCuIvEQGOBtBUEkMhQ1dRHesQKqgDcEhLgKUNdh9FH2cQHQiywEBnqOvbgzC6Rn0xfHEAbj5H2s6mXJpL9/pmYC5Ia/Fk1LYTZTgLqcfSjV0P7BZKREk9WTGhWgol2FAPFweJkCVIUA8VT43L6ygo6dPoNu5TpAkgIUQID2DOZhOQQ7+3OemyyRCXoFEzQn/hFYorKzB4RgkUXVOXvmkZjOiAVXcOIVQ2DMw+NTgAghlStwS4iZOhzotN4VIL6nEVYckWeudg/lkS/ZQiXze4BaWui5EXHyoZ6wguWAfwWJqIHFXSnMbU+gZBO8uadyAUVADoF5FSAguBYQzwCLmbqo7q8IkEJDkJRqAfkNd/GIV5rMFC0keSXZSFJUGg4FD4EFEKAIy7yIxCcuDT4VM90Q2Lzj6aPsY2IKAim6qkqlAmRB0EW2iHTMbLu8bgtHP88EYzeTERbmCmyFEVYIkd1oClLKvrsvrCWFpACVGAFyiOZuUwqBlWDSrBRNg81JjFUSHiBD8gbJfdyIRwHSxGy0NF4K0PAeoDgCoul4PMO6ivsUoAhTgKwIHdRf3DEAtM2nLw5uG/VHRwlL4x0jebci9CaeHxlwF+5+MbDYJQDX/+McgoNntGDaLOpHi2kl7OmuXgzRtDgBksiDUIAq9wPztIwA3BBYNRN0GGHhiLUALdTL5FeBOn3hGZoG7/s8DpYAkEARwwEqBFcmHOILtVjShK6iAsRN0HawAjS0C20YgUV06NMPEyEwjsohsOA0eHk/K4XAALcatKhEz4zn3APEzcO7B/MiDB819bKM0wTzACW0IvoywZMX2WSeZJ/LDc21NCmVs8AA77EFZYJx/8+M1pj4HEWAFBqCZIAHKO6rAySywAo2WrgCFDagSXCrIpdfSPwmEomFzAzHAcmogaecI6n35cgP0YWGSXsYAa6CwAZvx0yEFi8LgyYUIGkgYIbRfofeMPkF32ezGyhTI0Yc+t6yEFhENkH70+C5Byg8BCZngVnMj1WCVBNJMkGXYIrS/LySN7GKogeSIYXAZAVIfh7RXTneKo4TAWLhr+H4XGwYZiTaT4AserM8aB5Vfd7sHobVOo++OFB7E1E/og1SgPg1VMyw/X7ll8AdZwAPXVu+LiEiBPaCcwgOmdECLZLAkEbP3Z5dWyt+lGU7opGomZAUIKaAprVcFQ+Q7aaLAwEKUJgJuooCBEhhMG8toGlBCtAI85mwRq4CUjPUTLGcpMhhMY9HiJ33jmZUnNAZEa4A2cEKUDetAP0WmYNksqXMW1MpBGaGpMHLakgtChAAOFLoGnBT4xewMh7dg3lBbKa3xMrGNy3qKmncMO0HzwaOGrpQr/iYlamBAPlN0PIkM8gHxAnb9JaYaEqdLdoV0+b3RygCtB+Cz0yCFCC5EjQhhCpAggDVQFw4GfARIMdxa29EExNHgFIxE/9lvw/HFP8LZM6x7gt+I7Ro01H/Da6SAtRr0Rv14i56zL2cADHCNWTT7yvIBB2eBu+GwMIIkNy/iIekbA8BygsPkAUDBvMk8EKWjlUUtYMMKQQmqz7y7FnXNVjj7QFiKfBb9bkYYh4fT+8vQhC1qfrQNX0GZrbG4BCgR2c3zjGEwGJCvRzbuauxQqIW7wj/4s/o46aAxq59bwH5ARQRwRtkAZbMoORpyGSVxat0hc8U3D5gRkIqX8F+t1iVFhLlJmj2/fkVnnpDYAAwjRGgnjc8iwNDYCNM6eKqEQdPgwc93/xGaJkAZQuSJ443Zda9n+UHP+9pYdDy78nZQwnQ62QBklFDZLBxVFSAQipBy72xqinRggD5jmOYFUdcxAjQ7sG8KILo30cAnlBibwgByjH/T1zqUM+VpKDwox9cAeIhME3T3HYYgQoQ3Y8Z6TjS8Qg4Z5tsKpAiQPshPCZovwLELlqHAJZDkC1YaBGl9KsrQCKt1BcCK0iGylh8Ak3Q7CK1HM070xCp8AMAAJ0rQFU8TkHgHhlP12iWjr23SG82B02nNwPREZ5h0K4hBGblvBlMQgGKeAYkwK3ZoxN3fYuFwCyZAEndo6kCxEyZnADZJVEATlaAiER6iC984CpA4+QB6qUp8C/lpmOI11TytTMR+5xow7J57QAgZYKNUgGyLZEKPVYFyEhQAkQKQ1RJefsx+kL/FiDjS43f/jQA4FUcjBJMHMIIUD5OCV22bycqIVO00MI6wRtxSQGqsYJy0ZJaRgBuNW1OOssUIH8IrAIBmsvC0VvXehaXKUARAxhmqdbpmd5tSL3AgHICNCTdLL0KEL25kgop8IDkAdJs2AEEyN79MgDgDWcBUjGzTAGqlE0aCekFJvt/qinRfNLq6H4FiI4JC1kIrHsoH5oBBkC0A4lrRU/Choxcke4X948C7phVSwkCvwkaAGJGeCaYHAIzdE2QrcFJ1hBVEaD9EMlYuQmaEx85Nltg/VuSbIZVSwhMpFL7iuHlCkXEmJ9gIgmQPAvzSLVyJpjjuG06RkGARL0QDwGiN+Y9Bfra3PYEoqbuNkRlGHbo6y1RLwGKRyQFCPBmgomijZWywGQFiB6bBbebu1PKi35tJRhiRuoqQC4BMiPSACuRHs1HgGyN+4fGNwT2cmGmqwDJBIiRTovoiMbTWD6/na4/wsjuaENgUuXt6BjP3WiS7pNWGAY2PSJuxgDKizVuowToaYtWGD54Or3+nBQlAvZg5WKImYJLgDzXLrvhxbRSxTTkQlAWGCFSYUJugg4LgVUYLxadTh93/NWTfdTuywJLayPudxSiACW1AgBS5gPyKEBF9zlXp50KBmgAnqxIK6i3YferAIDXyULETB2tcdOj2lQOgfFCiL4QGFfka/Ah8pIljhamALkeoNBGqIBHSQtTgOQMMA43BFbdjM9T2DtS7nfK9z9QARriChD9jfh5oRQghTGDm9iyJUsMgHHTmwUGAIWSjWxRUoBqIUBcDfERoILUk8icwDR4Q9c8IT8BOQQm3eAwihm+7leArKLY5q48fW16OobWuOlmgTFwklPeDJXWL3IImwXKBIgpQDmpajWHW77fHVRsiytAbkkAu5gTRKVETFGa39HdEBivgMu9EICP9Ph6ull82+OVBcYywN52ZossLyITIKZMjCCBZNzEsnmU+KztY7/paBUg6QYdGWMRz3i6HQCgl4bxxK9/CAAgYL/xTh8B2k7Tt9c5h2Jue0LccMx22ktLr1IMMVN0Q2CeGjp8xl+LAiSboEtZmrVVZoL2NUOtKQR2MCU0dkFUugaYSVfqu9dqsSyxeFt5GxKpJlMMpbJQjEyIMtK1r3EFqEoITK6L5fjrmpVyMPqpIrktshiapkHTNE+IqWIvMK4A2X4TtLcuWyXwsdr2KUB5NsnhrXxyJRtv99LfJFABivDzoYS+EAWIe4DkY+KZq5liZQXojT1D2JcpImJomNPu/mZRoQCVn4NyCAyA8AFNtlpAigA1G1axrACcMEEXyhUgOTabtxxkCnZdHiA9KCUcQD4v3cAnsBkq4JILz4UqFKBBd+YKwBhFoTuRecUJkORL2Z6lg/n0dAzpeERqh0GRQRwx0zUWciSjJgh05HgGm5wKzwshkkiAAsTImFS8zWbhSEczRF80u5QXRMWCIQZkrgCVSkU37CM3jJR+O39TWx4+GxcPUCkHDNAsrrfJHFHnh8jFLKUq0KmoiWVz2wEA6wZ4uGzA28izRhBGPgskglgkvCluLUilaTiuAyM4JvcMAOB/7FPpixIRQHYf0LsBALDOWYKDZ7jEOdFJTd3xfOUidB4FSCZA0g2vkgJE0+B9N8ShXRUKIdZBgDQNWHQafb7lSc9LchispcTCgn71B/BUmU6gvBaQ/L+s/mo1hsBkBajkJ/U9b0AjDvpIGplIl1gsE4zR9AITVaBrUYBYCIxfd2JfWSi6LRERGVSv7Bws2z8BSUnr9XVg53CLIMoEiGWBVfEA/XId9Y69+/AZaI3LClB4R3hhgm7lCtDkzARTBKiZKGaB7ywH/nu1Z3FCqgPkmqDdn8rtB8YUIFEFuhYFKCAjCkAxRwdHC4an5cJEgMetPVKt7AFiA3eeRBCLRlAvDMNHgLgqEW1B9whdNr0ljnTcLPMAZUm8zAANUE+WpiGwGKLNbjAFRMs8QEbEbWjKs2K4B0g2QTvFvFCGSjCFJM8reZeKRRis+KUpK0CRSgQovBHumLHvbQAEI0ihF63CA+RRgKQq0MmogbZkBIu7UsgggVJ09GEwS5RIiFTsz1QLdGaCfm/8dbRqWYyYHbjLOoe+uHO9O1lhZKgvtgD9aBUGaABon0EJULuzr6L/IlMIUW9NyQNUYfZe9JugARoG8xdCDKsEXW3CxAnQ1r94FstG6ESeESC//weg4wi79hIolqVjDxckBUh6Tef+xAop8HRFyfzvV4CYAfoNZwFS0k29q0YFyAzpBSbG4xqKsfKJqqVJ+6kZtPEt6Dg+q5Veo5v2suSAdLgHCACGRoI7wnP1POHxALGJZYVz0LId/O/zNFT7kePmefe/ogfIzQIDgFalACnUjb5NdMDa/LjHlCxM0CVLMkG7F5xcDTpTtN06QDWEwIQC4XgHjFLevWlPNOS0fwE5BCb6gJWHlGqBEWX1QvwEKN7mzmTSMaTjJgaIXwGKlRmgAarEJSLBtYDsAi/aWO4Bksv3c38BV3oczRCtLJxSDg5TakowRFouEQpQARF28zPlLLBIEhbRadsLn1pm61wBGodBioW/NjqzAWjI6ax8v6zoFFwCxL/TY1gYbCDCbqCjyASz8u73XS01uSqYamKwCs3mEe/HG2QBsiRGlUPe7JX5f141aSPeQwIUoJnor9iMMlO0pBY20rXLSKyukXJlQ0LBsl0TdIJ2ocfgjoBCiJIJmhCXrNdKgLY/6wkzdqZibP+ACG/50RJAgABfNWjveTeUC1GAWEHWSn3A6A7osNktzPYToG43A0weM6bVqADxpAN/Flh9ClAAATLcQoOapmEmI0DcahTsAXKv4+HhYIWUZ4ElI7ICVD0N/omNvegdKaAzFcU7D5vheS0sC8yyHdG5fgZXgBJKAVKoF1meVULcWhoIK4To/lRyMcRsQRpEayBAGlOAdOI9UUsFSjKKWpVZ1zgg0Kwnm6ClNhh19wGDt2s0IUQQICeWFumf09MxpGMRDMD7HeZIPJAAAb5q0FItIFsq2ugvlsYJqCkRIIdVu7Y1U7SyIKW88DVYMKALDxB9vxUSAtOjSfxj6dP4h9KnYUSDQ2DjogAxA/TbZDaWzGhBtIWGknS74N48pRAYP8ePmksJ0E7wVPj6iyGWOOEkjSNAHPGjP4DOdBIvEZYWzsNgzP/zVIE2DpUJEFdDZmgD2NbnqxElYaRQ2QMEVO4AXrQcxHgzVF63Z2hXeCFEEFpfqpYQGABMO4QSG7vg8T/xEFgqZrpNX8MIkKRm1e4BYmSmmgIEplgDImFAgBugnYUe/54nBDaKStD1eYBY0gFkAkQ/n3s6Z7d5r9HpQSEwIyKKKWYzlRUgTwgsXj0L7Bfr6YTjA8fMKbt25JIrMvoyRRBCCfC0lAqBKYwWGalf0PBu8TS4FYasALkhsIzcCqMWDxAbVHRfCKzEZtElrf4Q01jBL1o5LdbTEZ4rQGR0CpDJjlmUzGceICtCZ8imrqE9EUE6biKDOBxImWmIlbXB4EhEDeQCagE5jABZenlRM8NkdYDgCIMln73aMGHrdHvEyovlngFUUnFEF2zJCxEzddzvnIYHnFPLBjRHhMDGwQTNFKC3nNk4flEnjFgLbG4Q54pbvlwBmtdBb5A7HVo7Z1QhMEYSCogK78aoIVchj6SAxWfiiNmteMGhPb6w8zmq1rICiA9laNuIQ6ZLBIj5YeJaCd17w43Q2YKFNLgaIxEgIyqM13ZYBWr4QmC8g7scAvOnwQN0MlGtEjRHiA+oIyXVxhpmNYDSAR4gwJPSX8kDJKu/Blena/AiupXRpXOaEGAPS4EnCzyp4Z4QWA1p8P5eYHUpQBGuAEktbNgEh4fQuALE4a9WzUHYd5HPDQdWvXZDYOVZYGEEaDBbwkOv0fPzvBXzyl4Pa4jKM8C6WmLienNN0CoNXqFWCAUIPgIk1wEK8gBJIbCCVV8ITChA3ovCKnICNPEKEC9CeOMf3sDGbjZ4e0Jgboij0qwtDDxLihZMcxWgvEG/r2ktUei6hnQ8AkBDznRvgllUUIAiJg2NAB4TtMMIm2OUD+C8c7upSSEwyw2BOQZXgAqCqPD0dQCilYltuQqQ3CNMHpj9fhiuHo0rASJzcMKiDiTjEQzDlwpfkEzQMdd8DgBbLCmEUycEAdKqhExqgazELFkJROI4YnYaz3MCtOM5YM9LgJWDFWvH22Q2ZqRjghQAACJx5Ax6Dg33hB9PJl8KDoFpGixGhJ0KDTALch0g3r+rbxPAq4zz8UDy4lACVKMCBAQSIK4AJaMGIBSgMALEUri1QnkdII8J2lUZOAHSzOq/Z4lNDoisAA3tAvIDcDQDG8ncUAUoGQ2+rgGpErTjV4Bq6wMGuGO23BCVKzmxAAXIYBOxIPBq0DFSDCQZuVKQCbpyCOw3L+9C0XJw2Mw0jpzTWvZ6LIQA9Yy4NYA42hMqDV6hXsiF1fhMCi6LzxSlNHhZAZJDYEW7LhO0CAf5ssCsgqtaTDSueu+hWDq7Fb0jRVz0g2eoIdATAqP7lkVsVCEwM0KPKapZtJAZC8VkNXoD4Ddhnt6bNdybYKZCCMzbEd4lQLxvmR1Qx4Q3Q6UKEAuBWW4ITFSNtfKiX5vHQ8AG0DACJJszyxQg9t6gPnBjwsA2kL2vA6AZYCcs6kRLPFJWC8jOsf5rSCLFzvHpLfQG8GZ+9MUQecixIeFbmQAdfi4AYOnsVpcA7X0N2PQoAGBH6igQ6DhhUWfZZgoJ6qfI7QsnQIVCFiYzsvtDb1wJ5GqiH45DYDnErQPEQ2A9G9yV5AmRbISuhwAtZARIqgfUGagAVfEAoVjWldwTAmM3acchomK7v45VEPjkwJEr27NzcSCxEEVEkIjIClBtHiC3EnSICbomAsSyqCQFl6fE83FspkSApqWiItTthyaVRggqhsgVNI8JOsoVoOBSCjz76yMr5gYWdQz0ABGCQ/58BW6J3OoJ1wkTtCJACjVDVoB4FVd4FaBChRBYvmT7FKBaQmDBHiA+02wGAWpPRnHP352EI2a3onekgIt+8DS25RgRkE3QJFaWVVULeKFAUTJfCsUAbtydE6ARzb1x5BArK4LIkQwJgRFWB8gxAlL2GVkxYaPEBlc+eBPNEMXfiFWAI7XIENuWFKBIQAjMqwB5vytOnsoyZsYCxwHu/yw0K4d1zhIMtxyMeR0JpGNmmQJkZwcAAMMkKWbfXWn6O2+xOAGqXwHipvNSIxSgZCcl39E0sOS9AIDDZ7ViLzqwm0wDiAM8918AgL/ahwIAjl/UUbYZ0sKLIe4ue43DykmG1oj32uXkmZSCCRCtSExE8VIRAuMlHqJpQJd+f9kIXQ8B6loCpGbQ2lYs7Hfi4k50pqJ41+EzalCAOAEqeDxABcv2KAu8BEbJcYs76pEaCBDcwqACrIP9SIQSU1kBkusAxaPhY4lZzQQ9SgWIh6GDFKDAFHgO3g5DC+4HFuQB4uNZkAL0ds8I1m8bgK4Bq5fPDdn/gDT4whAW7P4DPmg8hXlJd7nyACnUjxAFqJoJWg6BeRSgGkJgIhxELFGHBnDNlkGqxUSgI0VJ0OGz0ugZLuDK+1kX6sKQ8CzkER2VB0iTPEAl2/UADTKFgitAvAbGkESAsiFZYAAzQZNyBYgTNhLkYdBdMmaLEBib/WomHLavmlWAw5QaW/ZlcbJjFaFrxLNNwHuelCtAbiPVhuHp7wFbnkBRT+Cq0mdw/OJOaJqGlpiJIZ5Rx7K/HKYAZbWk2Ldk1EQqamAnYSbo4d1lbVqqgStApUaQdzMGrPk9cMkfRVPSg6anEDV0rHcOdvcRwO8GaOf3IAUowoohGtluz3Xm2W/Wb6xkJL1kBW74NIwAlfUB61jkXaGsM7usANXoAQICfUDzO5NYd81KfP6MeS7hClWA3Bu37Pnx+4F4CKxkE3FcdSlA8jktQub0/V4PUH0KUFgIrJZyC6KSMnHXtbgHiJ3/syQPUGAKPAfLDEyggJ4AAhRUB4iPW7lSea+0B16kE+4zDp2OGa3BXqtAD5D0Pc9qcVUj7gEaVGnwCjUjW9kEnQsxQXMVJF+yfc1Q6yBA8HZndn0rzSFAAJXW7/m7kwAAr/WzU5M4IkMuh9ioPECcIES46sIUiX02vSn4Q2C8FlBJi8KCWcEEbQYqQJpViQDRbdE0eDqw8JAU0Q0QfhO3C4KoeAqpsWPRLMkbogeHvcoVIOYvshtEgLpfAx75MgDgJ22XYSuZJchAKmaKatD8+yYs9Fg0veGe6ekYetFKw3/Ecbua1wh+7jbMvzZzKf1jiBg6lsxswfPOErGMaCbWFhYiHTNxxOxy/0Sik86qpzn7sGswxMfDzMqWWa7E8D5uJCRjj3aCl242sTRVauT/ZXCFqV4FCJAI0BNikaZprvpjJrzmcc/nSgpQIZwA8RBOSfI16f7K0gHg14bHAyR68XECJHuAojhr6Uycc9SswPpeYrdDKkHXpwCxiaocAtO8IbC2RERcp4Ep8GKH5I7wQSEwZoKOyARISuYoesNgW/voeHXyQdMq7H9ACEwaO2YmXQIkK0BhhH9/hCJAzUQVBahoO2LQCFKA6MkGpOpohhphqdERWMI4B7gzzSDj7kRiWksMbYkICoi6ZIyFB3MB3dVrgpR6XpI8QL0W3b4bAqPr7XPojaGg08E71AMU0QMLIQpyEtS5XoTAHJFiS0Ql6IggTbpdEETFkQkQ8xDFId0Y5RBYRQLEG602YJZmFYD/uQywiyBLVuGmfacAAFYspKpJS9ws7wfGHksR73lKCaiGXHI2XVBnGIyfu+MZvj1C9gEB6E0fhjxiOG5hR2DmmdFKj2W6NoAP3voX3PPM1rKbKWE9u+xI+XVL2HWoWcEKUFkjVCMKtM5x//dPhrgClB90b2I1EyCpL5hMyDgBSs8EwhqDij5WRY/nx18TiPtUiraDKEvt12vKAnObAwvwshms5YRMgDRNw50XH4/vf3xFxWamYb3ARBp8HSEwjwLkC4FpmibCYIEp8BySlyqoH5irAEl+QNOtIO8Pg/FQFScuQXAVIIk8Sb3xpifKFSDLIWVka3+GIkDNhCcLzPUAySpHoAmanZj9GTqQpUQz1OoDWiTCOyhbnngtn2kGqhYTDG6ytHh1YEYOc6M0QXOCINLg2Y24mzVCnc762fC6GX02HbQLGu8DFhYCM5Eh7PuSssB0mxIgLahvmc5N0K4CxwdvRzdF7RPNLohZrS2ZnDVGYhKaNAv0hMDKC2YKcKLUCAXo2R8A3S8DiU7sPvNGjBRsRAwNh82iykM6ZpZ1hNeLrPxA1KsWcAVuMMoIUJ2p8KQ4/gTo8FlpvEIWCz/WyzotgHji4vLwFwCRFr4wMoTekQL+5X9fwdm3PIHnt/WLVfQSVYCcAOWWRDgBClaAinIjVCNGCUiblMpcpgCxc1GedEVqJEBdS2ihRStPTeAcfNIW5v+RPtefBRamAHmOq4YssEAFiCdNkMrXbyWYDVGAmFJP3M/nKfHyOMZT4St6gKSO8IEKUIkVQvQp5C0hqfB87G8LyTqj+x/gAZKIZpc0vCUihvAf1uoD+sOre/DrF3cFepomCooANQu2Jcx6AOhNgoVRooZeNqv0KEAsBLYvW4QOh3VbhreWSAhcP4ztLVvOfStNVoAAoIPNSkSohJHD0XqAeApwRLNRLNnCk7I7T5f7Q2B7WWgsx4ochknl3jpAAQQoSMIPMEGLwVszxEBHFSCuDMkKEJtByj2g5DT4Ch6ghipALNMGJ/09NoxQwri4K4UIGwRTsXIFSC/SGz6J+EJgbODvM3gxREaASnng/s8Bf/gXr/LgB1Pc7HE8d5fObkUBUbyuUxXot8PUDxTk/wEgCNBRrTlc9zdL0ZGMYNPeEXz+Zy+IVfRKXdmZeqjbFTxAvAgi98q0SmbWMgLEfotMD/vwSE0EAwAlVzOPpM+7JQIkK0BhkOoAyWnvvOaXP1W7ZEvephrC8U5QFpggQOUKUK0QIbCxpMGzsargyATI6wECgHceNgMxU8cJYWQakEJgwR3hg+oAAeG1gHgqfWsFAhTkAZJDstPi7nejaZrUDqO2Cda//e51XPH/PY8tFYqFjjcUAWoWcvvYE80t+DVCZ1SapnlKmgNsxrD1KWBwh2Dm/Zmi6/8BapO0dVcNkQmQCNvUEHcfb3AFKMfT0YeoP2q0hRBlgmBbRXFD3p5zO8EDEgEq0cGGV3kOrwNkoIe0s310fSsGk4n1oMatXAHSCGybDlqEFaUkekR8/4ZEgGQTtMYIUJwRIAe6x0BbyQQtasGEKEBr3+rDSf/2MP746p7A1z3gIb94O95ktZuWzHBvui1xXxaY48Bk7SWcWJtnU/z73635CNDTtwEv/ARYeyvw3x/2Thg8+xJedqBR4D6f/5u7FDvO/DZ+mVmGqKGLjvZlYARIG9mDNacswiP/8E6YuoatfVns6KffnWH5mpZK4GnPWohh3VMEkf+ubZUIkE8BqjX8xTGDeaJGqwChgKLliHAKV4BmsfBPRg6B1aEAiRIRdvlkLuNwAlS/AmTowZWgi/WYoDmBIO51WGIKovz+z7zzYLzy5VVYPr89fGNSg9zeTEAdoAATNBBeC2iQtSGprACVe4AyOZeQd8S8301QJthPn92GR98ILgbKf/PEGBsYjwUTQoBuu+02LFq0CPF4HCeddBKeffbZiuvfd999OPzwwxGPx3H00Ufjd7/7ned1QgiuvfZazJ49G4lEAitXrsTGjRsDt1UoFLB8+XJomoYXXnihUYc0dvCBKNnpxu6HJCO0z3gbG3wbuPsc4L5PiRNzX6aIJA9/SeGTipDCQQM590LiHZgnuhN8EDqSdODLsH5SPFSYQxSJCqmroZB6ClmlovAA7S2x2LsvC+w5ZwlIrA1/1ZcD8JoJZSSiBt4mLGzT655/JidAsWT5myTDssWyvyCZoDVRqbsoiAqRQ2Ds90vAbZMho5IHSHwPvirgHE9s7EH3UAGPvdkT+LoHgjAn8GY3vZEvmekqGWmPCXoIKA5DAxsw48EhsG1OF10wsB0Y3Ak8/i36vx4Btj4J/NcqoH9r6L6Mp3+tIxXFrNY43iZzcOu+EwFoWDavLTwky0mBlQfyg+hMRXEka/vx1y108mOyzEE9HqDcciLsBBuoaSd4ThTYcdejANWSASZjZgABEinwM8rXF5/LFSC6rzwVntcE4llQuRINCZcsgqhWjwLEQmByXTPmmxqx6bWSGo0CpFcOgdXjAcpJChCvCeQv5xGpVllaKijZO1xbGjwgtxlyr3lCCEZyBcxCX00ESPYAZbOu19Fjwkd5Jtgbe4bwz//zMv7xvpcCt8/7l4WNrxOBcSdAP/vZz3DVVVfhuuuuw/r163HMMcdg1apV2Lt3b+D6Tz31FC666CJccskleP7557F69WqsXr0ar7zyiljnxhtvxHe+8x3cfvvteOaZZ5BKpbBq1Srk8+WDxRe/+EXMmTOnbHnTwf0/yS4gzW6iAZlgAA2J6SPstX2bxaC7L1uUukmnwo2IMng4yKcA6VaFsM0EgytAw/DOUnOIja7bt2QStkt5T0XiRMQQA2TM1BExNGwnM7H771/Dd3EhgPAQWDJqYjNhN7rcPiC7D7BL0Fl9nkAFSN4XXgHadhUgnvliOsEmaEGAWNjTkyGGagoQyyBzglUFbor3D/qB4ApQJIFNe6kCdOhM96ZbFgJjpLNATETjXmLICdDbxXa6YHA78PB19DPmnQhc9icgPQfo3QD850oP2QRc9XK8DfyHz6bHd/8LVO2rHLKIu8U82XV9Elv/2c374DgEMYcpQQEESGf+MdMO9wCVKSUyAfITnCgnQGNVgF53l1VrgwGIG3faoMfBQzF+BQigPiCvAlQDARKlHcoVoGFGgJKj8gA1wgRNx5W8445ZvCZQLd3kvTvE0+CL6MsUyjKtcgHd4AF37JI9V/mSgwvxIJ6OX4Fpb99fYf+ZAiSNBzn5HusLS/sboj61iWY592eLZfvrOATZUrlxe6Ix7gTopptuwqWXXoo1a9Zg6dKluP3225FMJnHXXXcFrn/LLbfg7LPPxhe+8AUcccQR+OpXv4rjjjsOt956KwDKXm+++WZcc801+OAHP4hly5bhxz/+MXbt2oX777/fs63f//73+OMf/4hvfetb432Y9YMPRKkudwCRq0H7Kz9zKTzXjxiLT/dnSnWlwAOQCJDtidW6vpUA1WKCwdsKDBDvvuTIaNPgpVL0uQGaag1gCClMT7v9ujRNE5lgw0UiZk2V6gDlEEcv9670bhSDLwAYgQqQFI7jsj1/1A3Rq00Dgc5mso5eHgLjCpDjI0DRgGxB96MZAQrxAPGSC37ZPxA8a9CMY+NeqgAdKilALX4TdF6qAu2b8fFq0G/k2umCfW8DL98HQAPedyMw62jg0kdQnHYEkNmLgT/f5nk/J0DjbeDnYTDenubEMP8PxzSWNbbrBQCuX+jZzfuQK9lIgX6HZqI8hZwTYd0JJkAFyy5XSmoKgXEFqF4CRE3fGN5NiT5QvQgiIMalNp0eB78R80e5n1S2SH1xMR7aq4UA8fM/wAM0PAYFSJig/b3A7No9QFGhAElJLcSbBVYzpDT4fMnxZFpZtiP2y2+dCAqBDeZKOEZ/i27v1Z9V3f9Cyf0O8lIIzB9Kb/NVg376bUqACHGvGbEdywbnRKPxaDUK40qAisUi1q1bh5UrV7ofqOtYuXIl1q5dG/ietWvXetYHgFWrVon1N2/ejD179njWaWtrw0knneTZZnd3N/7/9t48Xq6qTBt99ljDmc9Jck5CEggQTCBhSiQe4BNpIonGIS0i8IsyiNDapBvk+wBBhtsoHaVFaZBrpPu2fnaDKF7k++Qq3nSYLm0MEMBuRkHRBMLJwMkZa9573z/WsNfetWuuOrVP1Xp+v/OrOlW7qnbt2nutZz3v877vZZddhn/9139FPF56Uk+n05iYmPD8NRSsBlC8v4AC5MvmYStBx0InbaA4lc6hs4I+YAB4GrWh+BQgqgqoQZlLM4x+GgIbtX0EqFoTtKLwlZdKlTdbMZCGkVd7g/mAJlJZXp22mAkaAN5R6cTz3htuOMZRYBbJAgMAi2Zu8JCUakAVOrirNETiCYHpXhO0rfhCpWV4gFQ7mAAxGT1TgQL0XlpDIkMywA4fcCfVLn8aPFXdxCrQDKwa9KvTXaQJKCWoOPlCYMFJ5H73AjzduQ4AsPttb5o8I+8zRYAAIraefHh+BWgPjjyD3P7xCQDA+2nF6D8cmMbu0QTvAxZIgKhiYzqZwOaXJFvK1zW9az5Am6gWDoFR5b1SAhTpAnpJ4UceBivVBgPgTY17FHK+TPpCYF1RnROUqXTO+70qCIF5wrqUAI3nyHPVLJoMlgbv7wYf0JuxECIBBCjj5GeBlbdDNCFDI8dGzJxKCOVM8k3QrK2Su814MotuOocof/oPgJZjyN9/txwLQyotEKCcN+LSI3iAbNvBzrdG+XPTQrNbwB1rgOIFKRuNhhKggwcPwrIsDA56L5DBwUGMjAQbLUdGRopuz26LbeM4Di6++GJ88YtfxOrVq8va1y1btqCnp4f/LVq0qKzXVY3p4iEw8USOGqpHbuyCe8LGK+gED8AbAhPMajqdRDSzsZNIOWAK0MGcl0AkUaUJGm47CTVJiGfa6ASg8PALAyNABybTYPNOMQUIAP6sUAJ08A1ODNIwEA2SdhX3kmPl+3lvLlWHJoTNNEqAeH8wACr9/QqHwPLLJfDXFmiDwsBk9GyuHAJEBsLdk+QgHTmn0+Nj6BAUIEcIgU0KfcAYBjrIb5C0Nd5CAtEe4KybPdvtpaZ1PTPpeVybIQJ07HyXVCwb6i7qnwAAHPkhcvvHJwDHQW/cxPtomPDx1/eji4avlQATtE7JcwQZbx0WinQuQCnRDFdNLqQAsZpMlXqAAG8YzMq6YfxiChAlQN2cAGU9t91RnV9fiTRRgLiyVYYJmi8OAkzQrgJUfRp8ngm6AgWI+XySlqAAcRN0dQpQt06OjZgJxq5bVcl/36AssPFkFt0Kzbyys5yg+xGkAKVTIgEqoAAlsnh1ZMJjhk74+pGx/+OmVrD/2UygJbPA7r77bkxOTuL6668v+zXXX389xsfH+d+ePZU3ZawIieIhsA5PQSvVIzd22u4EUEkjVAAFPUC6Qy4oPQwKUAe5kJhJmSGFKgshws0W0SgBSlGDdR4BipDt3hWq9/plZf44ncjfAiWw773JG0aSfQ24vBSFG5cty6cAaTpMQ0earhJZ1pSj5CtA5YTA/IO0Qn97pYAJOslDYOUToLcmyAQhGqABsvJkCpCSS/LQy6QTyyOUpq7y0gfJXtJfCx+6gVwbAvYkyDam5V2xqjNk4D9ioINPMKcE9P/Kw6I1JJ19agQ48Bp5HfUBPfHaAR4CC7p2Wfg0ioxnAmLwdIIXTP68Karv2MEf2q5UAQJcArTvZSGdXgfihasJMwLUCTLh+j1AXVHDM0lnRQ9QOQpQUINfGjpOgXatr8Jky3uB+UJg7LcwtXKywGhLI8EEnbarDYGRcblLYwTInQ8SQhFEf3HHLnZsUz4CBNfMjDf+38CPZHV90sJ4kE4Lqo9VyAOUwW//OOp5zq8Asf+bGf4CGkyA5syZA03TsG+fNw1u3759GBoKXjUMDQ0V3Z7dFtvmsccew44dOxCJRKDrOo4+msTiV69ejYsuuijwcyORCLq7uz1/DUUJBSjuUYA0jwLUYbkEKF5pCIyumAxYGBeywAzqNQj0rcwwWBbYu2nvhJZ0IpUPHBQsVKSnCAGaourEvC7vZzAFaGScDKIdRVYoLH3zDUvIBBMG30JkzV+7RBFCYKau8tL5Wo4pQK7SoOneNHinSAisoAJUIATGFaCyPEBkAH3zEHmNaIAmn60hI7Z4oNWdJ9ARaEplRPSVVV8Dzvs3YM1f5W3z1hRdPfsIkMb9a40l77qmYgXN5Bo+qsikz18QAQ4nFbLxh8cBuMbpXbsPoaPItcs8QBEli1SAApTJ2YjwOkDCOfyR24H13wCWfMj7Av+xMau4zkUFiC3WOubl9THzgBKgDpucy34PUHfMDYElMjlvj7MyPEB8cWDnK0ApmNBUxdMguFy43eB9ITCr8hBYwnLHjzStCl2tCbpDJd/z0LRIgFgn+Pz3DMoCIwqQSIC2AQHtK1i9ubQQYsumBdLjM0GLIbAdf3jP81yiQAismQZooMEEyDRNrFq1Ctu3b+eP2baN7du3Y3h4OPA1w8PDnu0BYNu2bXz7JUuWYGhoyLPNxMQEdu7cybe566678Lvf/Q4vvvgiXnzxRZ5G/5Of/AS33XZbXb9j1WAeoI45AC2bj4l3+YkY8xMgQQHqsMfd+2wVWbEJOodDggJkOOT99UjzFSAWEtmX8e6LrUerlkuZAmSkSC2ZUYsMKEvmeFfCzATNFKBiVWQZSX09R8n86B95QcSUYxQkQCwcx5qgssFb0XSYmkuADFYnRsgcYyGwqFK5AsQIkFYoBJatxANEzrs3Rsm2S+fln38dERMTDv0NaW2fSSc/BAa4BOgdzAWWfzwvo3E8kcVImn53y1s4TWdG4QYTIAD45jkr8c1zVmLdcUXCPiLEMBhc47RlO4J6G5AGzyr/FlCAMoWIwtAK4ANf4l4/jjwFqIoQ2GAAASrm/wE4AYo4KejICQoQ8wAZfBKczli0GWqAslUAfHEQ4AFKOibipla05UUhMAXIr4ZW1QtMqATNUuKrDYHF6XU/KiSwFKoBhKn9WJT6PbnrJ0AQrqHJvcC+l+AHI47ieJDJiCEwvwJEvZvTWTzz1nue95j2h8BCogA1nH5dffXVuOiii7B69WqccsopuPPOOzE9PY1LLrkEAHDhhRfisMMOw5YtWwAAV155Jc444wzccccd2LBhAx544AE899xzuPfeewGQTJ2rrroKX//617F06VIsWbIEN910ExYsWICNGzcCABYvXuzZh85OcrEfddRRWLhwIUIBrgANCDVDkiRGH+v1maC9HqBobhIAeU0lbTAA8EFFV2xMTLtypuFkAAUwo1VI43VGV1SHpiquiZbCCeqtVSYYUTDS5MLcnyWTxtG+idtVgMixKdYwkf1Gf8z0wOmIkVDPgdcBlFKAdMARZHubDg6qgYihIk2le50qQKJxWuUhMPK726p3/4q1wlB5jaHiClDJNHjb5gbIVw/mAESwdDB/Eu+I6JjIdaAbSWBsNwBgEjEsDFj1sWrQBwJqnADAnkMJXlgxbgcToEYrQABw9LwuHD0vgLAUwlFnAttAOqnnMhjqiWJxfxy7RxM8mSGQAAkVlIMUIE8IrJz6X/UIgQ0sJediehzY+zx5rJj/B/A0Se1CgpufSVVoB0c/cxM2pjqxAx/kKoVL7EqHNJkHSAnwACURqcr/A7h1efwG9IrS4Fk3eKEZapIqQNWaoNl171WA8huhAgDu/ww+uvcFLFK+g+mMq1hOTifRwboHLFoD7NlJwmBDK337TwmcQMBzGeH69IXAWCXo10cmYDtk7Dx6Xide3DOWpwBNpwuQthlGwz1A5513Hr71rW/h5ptvxoknnogXX3wRjz76KDcx7969G+++64Z+Tj31VNx///249957ccIJJ+BnP/sZHn74YaxYsYJvc+211+Jv/uZvcPnll+P9738/pqam8OijjyIabb6Bt2yIHiAzzldKbGUl1nMgCpBIgAQFqIJGqAA8akIilYTjOLBsBxGqABkhUIBUVUFf3MC44x2knRomOFZN2UwTBei9XAyKkq8AdTMCNFFaAYrSi9dyVDjMezHyXwCAdJGMNTcERpUfunrlChBNlTUpAXI0MQRGTdA8BObdv7hJevKYupr3+apBQ2tOKQ9QiRCY0KDzUJZ83hED+SGVzoiOSUZiKQGacDoCC58xBehAgb5Au0cT/L1iSBISRsEIUBgyGPMw7zgS5s5OA+88B8D1ARULgbHJP4JsXgoxENALrBTyQmBVECDdJCQIAP7wGLktpQBpOm/R060kMJXKwXEcTKayOErZi75X/g0bD/0QAAnTeEN75ZigCytAKZhV+X8AMQ3e8dSwqUQBYupHVlCAWE2gahUgk173onof2AYjPQnsfQEAcISyz+MBykwLFdVXfJrc/j7fBxSkAHkIkM8EzSpBM854ypJ+Tor8ClAyW7zEyExhRj598+bN2Lx5c+BzTzzxRN5j5557Ls4999yC76coCm699VbceuutZX3+EUcckVeIqamwbbeWRpyaFbvmE/Vnci8wb5knTEAUIPdki2TFEFiFHiCzE46iQnFsxKwpJDIWFAV80DFjzVeAAOID2jvl3ZdaahQxpSSSIcd9EnEs6ovnrcRYCGwfJ0CFB1DRHJ3rOwrm/pc5ASpoggZggxEgSnw4ASIeILZi5KEqMQvM7wHyKUBRQ8M9m06GquQP0hoPgdVogs66ymEKJt43t4N7JkR0RoVq0LRVyCRigXF/ToAKKEC7RxOYBO2PBQfITPJFg+EUaT3SbKgqCYO99DMSBjv8VJxyRD9+tuvt8kJgSsbjwWDIWFZFLSPqEgIDSBjswKt8ci2pAAHkd8pMohsJTKaIzydrOehTiJcx4qRgIovptIWOCCoyQTN1lBf3dBxO0JOOicEqFQZD8DXlbIf3BktX0ApDpf6jjGCCZhlhFRd0peeDaecrQMmgRqgjbkirD1PYK4TAspQAZbQOmO9bD/zqGuDtZ8icFHdrWwV5gKxsYQXInxX5gSP78fyfxwDke4DcNhgtrgBJBCA1Bjj0pGIZFNwITRSgPBO0cLKZGZcAuY1QyzVBq6SzM4ABZRKHEhkkMxafUCMhMEEDJBV+GlFvnZtaCBDrwpwhF/+EE88LfwFuCIypIJ2RwqnOuqbyVVKq5yjy4L6XAZDBt5QJ2qEeIIUSElXTERFM0ByiB4gSIFUh++cnQADw4WMHcdby/JW5ZpAJRS/gAUpkLBjIlfYAUQN0To3AgRoY/gJoMUSm4lGSNxlQCBEojwClYSBDQwhI00QAx4FJ1Us1BOplIJgPiBqhT1nSDwM5V+kIUm8N1wOUCihLUNAEXQj1UIAAtyAiq9VUrA0GA2190q1MYyqd42GwPtUNZXYjIVSCrsAEzQkQayuT4fuWQqRqk62mub4h0QhdiQIEkMVrVtAZWEZYocVRQdCxjyWreD1AZJ88PbXe/R2/26NMeRQYKzEGAMgatLbT3OXkmDFVjyJIAfIQIL8JOo8ADXAFTqxDBLiEqNkKkCRAzQDz/0R63NWbLxMs5k+DFxQgIzPG73dyE3T5A5pCU2T7lEmMJbJI5WxOgMISRiDFEBXeET7jaDAj1Te7ZApQNDsGgEzEwQTIexF3lpDQmeyc6DqCPFBBFphFvTgqN0GbiOhaPgESCyGq3ucctTBB84MRIA35CpBlOzjdfg4vRT6Pv0hvz3veAxpiyFCv0jEBxxFg1aC959OEEw/0Zcwp5QEaTQBQuA/IYfVshGJsejWZTTMBRoDe2QWkxnH4QByHdwqkxgxSgFwPUJAClA5qhloMdSNAx3n/L9YGg4HVAkICk6kszwAbNNxQarcyjelMjvQCq8AEzZo7cwKUdbObUjCrqgINuCZoAMgK4dZKPEAAUVEyAgFK2lUqQPT30yxyzIKywLwKkNt/qxdT3l5gSXLt2MyfdczZ5NaXDs8VoJzNIyhiN3g/ATI0lR/vroiO4xb08Gs9kUeA2sQDJBEA7v8RUml9tYCKKUB6WlCAKg2BAVx16sckxpNZJNM5xBRWVTYcBIgVQ0zRjvApRCpfNQmwFZr9RptLTjhxHD23sALEUGqFwn6nyc4lnseLhcB46jpXgGghM10nafCOj9SIIQ5fdk+QAlQIrgKUT4BSWQur1N8jouRwXO7VvOc9oJNMghKgggpQVFCAKCYRDxz0yvEAAeA+oCxdxYqtR8KQwRiI3kWkLYZjAX96GoqiYHgR+b45NZKfsQW4HiAlW1ABqsgE7Sc8tSpADOWGwEA8QJOpHCZogbx5uktWupHAdNpCJpdDhBdCLMMEzfvb0WNBw7O2oiELrao+YIC3OWmQAlQ2AdI1jwl62iKvi1SsANHwr52BAhujYggsiEwIClCvMo2pTI6TGCU9BgCwI9R3upQSoDf/3U3IABChtY4cx+2JZou+n4A+db20hMkpS/qhqQpXgBK+bvSSALUzxBpADLwj/F4AXkNbxFcJWqMnMAC3FUa5JmiAx3n7lQmMJbJIi+XNyxlMZwCsGOIULVhYdRsMCj9RmEQcRxUJgTEUywID3N9pPHa45/FU0RAYeU/b9obAmAcoXwEygu8DHnWoFHRGgAIUoETG4sbqQiEyjizrtk0VoMFiCpBXlZlw4oGkkmWBjU5n8jxIOcvGO4dodV+qKGWmxuiT5PzPOSrMEFQxLwhfOvwX3k+ufSWoEzzAQ2CRAgqQxwRdVhaYXwGq0gPUezhgCOSplAkaEBSgaUymclwBmqsJBEiZxnQ6551gKzBB+xWgnBoBoBQsYloKmqrwSgysGGLOsrnBt6IQmGCCTljVZoG5v18UGUykcvw6Ya0w+JyRTfHCmwDQq0zCcVzSoWVIVXaFJd4sWkPOh8R7nteJJI0RP0UkPT4TNOCGwT5wJFlkdwglDkQwRaql6wBJFICYAcbgU4C8laC9dYDUlOvir7gVBsCJVz8mMZbMIJMUimKFxEjKiiGyjvBJJ1J1FWjAW0wQINlIwQqQd7uyFSAlDnS6k0GqWBYY62BtsRAYVYA0M9ADxKo/A/D4gciLKiFAtJVEAAFKZS2eYqs7wd3iOZgC5JgwNRWL+4NDT51iR3iKacQDV899cZM3xXxvyvv5746nkLMdmJqKKUqostPeEFgKZtmTUlNw5Jnk9rVfArk0D4FpQQZoQKgDFKwApXM2TKUCs3A90uAB4iEUVaCOcjxArgI0lXYJ0IDPAzSdycEWDPblmaCpJ46pmvR8yKq0cGANHhN/P7C08DuUG8IyfR6gaW6CrvBcFZT5DqrWs0r+eQrQ/lc8WXF9tObPdJqoQKyVjNZBq5lrBjB0PLn/rhs6EwtIpnM2KY/hqbjt7QUGAB8/YQEOH4hjw/HzPfvkN0GzfS6WZDITCPGI0cKYZo1QxRBYMRO0VwFC8hAAclEWTaUtBBYCowpQJk0mNBtKeXH3GUA/DYGxVPh6K0BavIdXLhXRXWkIjBoPkxnLTREGSYMvRNh4CIxmgbHBm4fA/ARIyALLU4D8hKgIDKoqGAEhsETG4v3FjDIVoCRMHFkgAwzwdYSnyJndgYXpVFXhjWn9PqA9NPy1sD+GpErezw2BzRICdPRZJFw08Tbw/I/cBpSFlFu9uAIU2AusGDTDS5ZrSCjgBCg+UF4GmqAAiSboXsWt6N2tkBCYLZpsyxmLNJ8JmvnTFHJMagmx8FR4SoAyAgEqWwEyNI8HiDVDrZgAqSonhHOjZD8OUSO06wGin8P8P7TvYL9Gq3Cnc0hlbXQ45LgbcaGdC6sBRLNYyUcqPPstk7Mxlc7BUISxw8pfKH3pQ0fhyWvOxIJeQtjcStQ+BYhXr5YKUPshUAGiBGhqBLBtbwjMpwApjoVelaZpV+MBop/br0xiLJFBJkUmmAzMvAq8zQLzAI1a5EIiHqBaCJCXKAwMzAncrmoTdMYC5hzNH88orqKRty8sBMYJEJWmdcNTB4hBEUmO3y9SiQk64hbBhK/HUTLrhsCMkgqQm2bsb4EhIkgBsoucp64PyLuyZP6fxf1xJGlI1EoyBcg1nVfbJmVGYMSAM64h95+83e3KLhQKzNsegKlYyGTzCWnGqjAEBnhJT7UhMAAYpEbocvw/gEcBsmwH+ycIyekRmjp3YxqJTA4OVRUsRS/eYoOCXRuqLwSWoZ6/WggQu36ZCZopQLqqFLy2/YjoXhM0U4OqGsvoAmZejBAy5gPKK4TIVJzDVgEA+hRXARL7gBkdve57z6cKkGCeBoR+YDkLk6mcS7qBPBN0EAopQGyfqzWp1wshHjFaGEEeoM55ABQiXSYOFleAAMzVEwAclwBV5AFyTdBjiSxyVAHKKuFQfwCWBQYctMignXTMwF43ZcOnlAzOC/YuRA3VkwFSbggsmcl5FCCriHzvqNRcaPsUIO4B8v0OemEFSAky0BaAabgeGce3ektm3BCY4WRh+yrgekAnmSQieYUkRXRGdZ61BQAJJ4JopLBPp1A1aJEApTXyfjYjQFQBSjtGuBUgADjpQuKhmd4PPH0neawQEREMwOz6FJHJWZXVywF8BKiGel9HnUVCMkedWd72lAD10Il37xghrZ1OvgLExjlLLXMsYgSIkXZ2PoApQDWEwHz9wCpNgQcYAXKv2QwMqIo3y6z8HSK/3zymAFEClBcCYwboJWcAAHpAQl5TjABRQqTEet335grQf3r6grFq0JmcjYlUFgaKK0B+FPIAtUUvMIkCCFKANAPomEvuT77LQytAvgIEAPP0JEzkYCj0xKrIA8RCYJMYSwoESA2HARpwQ2D7M2SfknVWgBYNBXsXFEXxGKFLESCvAuQSoJxaxEvFGziSwYSlpau6EZgGrxX1AJWvAOmmO6lkMl6VJZnN8f5ippLzpP7mQay0W4SU+hWgCcSLVuZlCtBBnwdIJEAZnRAGh9UBogpQGmZVTS9nFLoJnPlVcv8gaZlSKgQGALbYf4nC2zS0TLIg+vtqCYHNPQb4ym5gXZl9FSkB6qWm572s0bA9wTfpBkmDZ8Zau8yxSPF7gCg5Z53ga/GY+PuBsRT4SgmQDfIHABnoiOjV9Sdjv98AJUCjCZ8CZGokrE5rkTHjfZczBQU2ptOWtxM8M0EDpBaQapBivLRqOyAqQDYmUzmXdAOBHiA/CmWBuSZoqQC1H4I8QIDHCB3zK0A+AjRHm3IboQJVeYD6aAjMogNsLkQEiIXAXrNIdtxuZ15tVUMF4jDhxHDkYE/BTcUwWKkssLhIgAbcEJhdJCzBFSBfCEzXC2SBiaTH52WqSAESsqSyGb8CZHMFKIJM8XYYvNJupGjYiVSCdon5ZIEaQAyFiiEyD9Ci/jiytC4UUnTy5B4go/IO283Ayk+TyYah0HWrqryBby6AAJFCiKx0RZnZb4z0GB1lhZeKolzSBbgESPEqQPGcQICUBM0Coz3uyvW28RAYXQjSSTlZRwWI9QNLV5gCT7Yl52ROJ8d+2olWX86DGqEHTK8CxLLA4qYOvPcGuT7NTh4C02CjEylMpbPeTvAiAdJNYO4ycl/wAYm1gCZTOa8CFJAF5kepOkDSBN2OSBQgQEIqvKmr3IAW1bW8ENiAmnAb2ukxQK3gROIhsAmMTYsEKDwhsA7a0+rf7ZNxdd/d+EbugprqAIntJAoVQWTwKEAlBlA2wCazFtB7OM82c7TCkxIjQEwB0rkJmmaB+TxAqkimNH8IrBITtAHLoavaPAXI9QCZyCEbkHnEIZigi5EOvwI0iVhRRa2cEFjOIARIpam8DlOjnFmgAAHkOj3rJvf/QllgACy6IAlSgDzd4MsOgVEFqJbwVzWgE20XD4GRzvCm5c0Cy1oOH4vKVYBYZXTNpwAlndo9QG4/MK8HqFIFCACePuY6jKy+FiMYqLwIIgP9/fpM8l1Hp1kWmKCmMP/P0ErSY5KS3h5lClNcAaLHPepbBAb4gEQP0EQy60YcgMA6QH508ErQheoAyRBYe8FxgkNgQF4qPFM8IqICRE2Tfeq0mwJfif8H4ATIVCxkkxN8gLXU8NRRURQFfR0GAAX/Mb2gaHPRsiAoJdPowFB34e8qEqBSChALyyUyOUDTkexcDACwixSU9DdwVEFN0AY1QRdLg8/zAFVgglYVbsLMZf0KkBsCiyBbvB+YEGYoRjo6I14P0ITTUXRCmhOgAE2ksrzx46L+OCyTESASAstlhH0JuweI4X0f5avzvEWQAIuSaCcbHAKr3ATdXALUSQlQMmuhB9OeTZgvJZUiY5pTtgJEiI4bAiPHKmGT19cywbohMK8HqBICwxSUlwc+grdXfAlAFW0wGBgBMsh3PRQUAmP+H5bWHiOZXn20GnRBBUh8jaAAsWsqk7Mxmcq65xxQVggsVlABkiGw9kR60iUzcR8BYjU1aIYIu3ijhqAAUZLUp0xV1QaDbB/nE7SWfA82HTSKGXebAVYLaD+dEGsxQYtEIWt0FY3BiyGwUhKtJwQG4M2ll+L/s1bgjdiJhV/Ewli0eq0OFgIzoapKnhKniunAml8dKp8AAUCOEaCMd/WWFOoAmUq2eD8w7gGKFK1o2xnVYUHDlEMm8knEiofAmAIkVINm4a+BDhOdER22SRYArJaJlZ4lWWAiFAU45/8CTrsSOOlzBTez6e/uqY1DUXElaMANgdWSAVYNor0AgJiThEbPdTEFHgB66KTMSnI4ZY5F7LrWfARo2qmHB8hngqbXRCVKIyNL6ZxdUSPVQFAC1K17CZDHBM3Um/knkFtKgHqVqbwssHwCRI3QQi0gdk1VHwJzTdRsUWXbjlSA2hZM/THiRKIUwVZolOyctXweDuuNkVRjRpposb1eTFXeCFUEVZ+67AmkaSFEu0jYphkYoHVhWFJCLSZoT02RaIHUY4quSPkmaKYWse7xby74JD6XvQEWHfQDwU3QFmzb4ZMC69Zu+7LxNEPMAvN7gCojQFn62dmsjwBlbE8ILFfMAySkwRcbzBnZYdWgJ53yTNCiAiT6fwDAoSEjI0cJUKY8NSp06F8CfPhWoHNuwU3Y9agErLQzlg2T1WQpt3YXG29mWgESUv1ZGCxfAaIEiClAZYbjeRq8jwBNWnVQgDRfGny2OhM0QEJIKfr6ittg8B0i50O3RojvIV8afFwXQmDzvQpQL6Ywmcphejrhzht5BGgFuZ14m3SGh08BSufcsCtQVghMPP5sP1M5Vw2SHqB2AzdAB9Sh4QSIDAK3/eVKPH3dmaS8uE8B6oFggq6CAClCMcTpabIac0LSBoOBKUAMtRAgkSjo8d6i2zJSo6tKSVWBlXx/5q1RjCez/OKOFnud4AHK2ragAJHP9Stxil5EAarABA24CpDlI0CJTJYPjOWGwJKIFJ0MNFVB3NR4/64JlGeCnkrnuEQu+n8AQKHk1cyRc5Z5RrIKUc9aCTYzNweEwEgrjCpN0DPdNFbT+RjFiE6fQrP46DjIVQm60Ct3LGIZkrqvEvSURc6z2jxAwQpQZSZoV0FhClC0agWI/G4dGvUAJTKwbYf4DwF0JN8G0uOEEDNDMyVAPbTVSGba7SKQV4Mq2gP0HUHuUyVJVLAm/WnwZShAopeVXdNiUcSqj0WdIAnQTCOoESoDu+gFyZuHahjbpgpQF6aEIoiVr+gUoRhiOklWY8WMu80AS4VnqMUDJJKIWFd/0W1ZCKwjopdMVz1qbieOGexE1nKw/dV9XI4uStZ4CCyHrOW4BIgqPbaworcdBYYukAbVHwKrzLjuEiBvcb2c0A/ORHkhsGQZYSexH9hECQWoM6Jzf8TBSTK4+gmQSpU100oAjsP9a1klXOS9HmBhoCAFKJ2zqk+Dn2kFCPBUgwaAXqYA0Qk3ggz/A1C2sVuh1worJcHI+bRNQ2C1ZIGprBI0U4CqMEHTcSCdtd1O8lV7gGh7D+rVOzSd9aopozT9fd6x7kKJe4AmMZ3JIUcrqGf0zuDEGZ8PSFSAJlK+StBleIAAVwVixEf0/zR70SIJ0EwjqAgig+5VgDxgbJsqQF32pNsGo1ITNOCmwmMSKVoJGka4CJBfAaqJAAnKSVdvYeMp4CpApQzQDOtXkCrej740wld5RfeVl++3kM3ZAQRISFeHxr0I4msZ1ApDYDy12ucrsdJuSCKilMoCo2GnEmnwADmGrJ3JBDqKTkiKonAV6B2aKr17lNwyAqTFyKpVgwVkEy4BClEJh7qBjQe+UINtO8haThWFEBkBmmEPEMDVBqYAcQ9Q7yIAZBLsQtIN65WpAKkFPECsDlAtvkE3C8yvAFVggvaEwCpXkDygClBMJb87K2zIYI7+ntxhoSxA8ABNYyptwU6OAQAss4ANwNcTTNz/iWTWWwfIsTzd4wuhw1cNmoXCvqFtBe77DLD/1ZLv0ShIAjTTKJQBBrgXfVCJcTYI0pYZnfZEdW0wGCgBG1Am3RVmuVL6DCFPATKrP11FBai7JAFiClB5A9364wgpffL3B3gjz2KZHoqoAAkhMCbniwbQLHRomrBKqlEBsmgfMsuXBeavNpzLFFndCQpQqdVwZ1THv1pn4zHrRPy7dXLJkMSRc8i5/Ff/+hx+8uxu7H6PEDPmATJincg59DNTEzxDKkw1rOoGuiBRfQuijGVDgQ2TpSSXe92yQqudZTQwrTe4AkRDYKwRanwO9+R1K9N8glXKPK9ZhiTz0XnOTU2tKTOQV4L2eYCqDoFxD1BtJuiIneKtON45RL5v1FB5ZqQns1AwQU+lsqTQIQC7UAuW+YUVIFII0ddHsJx2GJFgBegUvAS88Wsgk1/pfKbQXAt2O4IrQEEhMDqQ5XwxfysHOHRFTkNgHaICVBUBImGgPkziEMjrlZB0gmfo8xGgqrMnAHTHXd+DVsID1BMjg6q/L1ghLJ/fhcMH4vjzewn8+mVSwqCcEJjieENgUPMJUA4a70pNdt5fJbqyS9iiCpDt8wA5vkHIKosARUr+Jp0RHU/aJ+BJm2SllDKV3/LxY/G3D7yAl96ZwHX/t5uOu3iA/H7xiI4pxEgIJe0SIKsFCRC7HjWfAuSpAg2UHwI7+SKiIhy7sU57WAF4PzBCfObSqtCI9ZHnUuPoRkII65VbB4iGwHwKUBKR2lrnQOgFVpMHKCgLrLZCiEouib64iYNTaa6Uxk2dZBgD3vmAjvM9mMJ02oKapi1kCiVpsEywg78Hssk8D1AeAbLSAIp7yvwKECNCMb6An2FPmgCpAM00WBHEIAWIhaD8rFocAGkIrMOexJoFdOCrJqYvmKCjNO6uhowA9ftDYDUMaP3dwjEqtPqh+G/HzMG64wbxhdOXlPXeiqJg/Qryu7ABqRgBYuE4xc55QmCMGOUpQGo9FSDqAfIZGO2MXwHKN966OyVmgRUfQvyEp6QCNLcTD//1afjqR5dzFc3QFF63STRVIzXB/XK5kPnX6gGFKUCWTwESawAB5YfAYr3AKZcVzTxrGHwK0BzaoZwTIPgVoMoIEL+GWGsUx6y50WZeGnw1hRCNoBBYbQoQsin0xck48DZVgGKGBmToMRXnA08ILAedFhBVYwUq4XfNJ6qcYwH7X+HXdyYoDR4osyGqtx8YC4FFHfraWtqy1AhJgGYaRT1ABbI+xJOMKkCKncMpc1hxxOrT4PuVST6Yqma4CBAphOiiXq0w8tI/feiOGvj+51bjIyvnl/32H1nh3ba4AkSfcyzkLIt0Zxf3URcJkMazKMhr3b5CgK9PWBnI0WKX2ZQ3DZn5ehj8CpF3W6EOUInJoMtHgEopQADJvrnsg0di25fPwMYTF+DLHz6Gk8C4KRRXTI/zCc8OWQ2reoAtSDTb+1t4OsFDye8PF0b4FCDWoZwQoF7yHBKIUA+QUmZYj9XB0uFXgEweeqkWhq8S9ESKmXfLf18eAhNM0NUXQmTzQ4Kr464CJBIgYT4QTNDvTacRt8k2erwv+DMUxVMPSPQATacy7ljFUAYB6vD1A0tkclAhZDE2w5NGIQnQTKOoB6iQAkRPFEUlpfPZYD/+NrmtKgTmmqBZBWDVDNcq2u8Bql8doOIEqBqcsLAH83vc41fUA8RN0DlkxGwsRowEM3rW0Xk6LoOtuANwxQSI9iTKpSa9T/hId1DxPQaHp8EXrwMEEA+QiErSkhf1x3Hn+Sfhrz/k9liLmxomQYl6aoInDISthlU9wBYkuo8ApbOWtwhiNY01Zxo+BaiHmaDj/QI5SriLMaPCNHifB6hUo95ywK47FgJ7+xDZ98P6yl8o1rcQIiX+uRRXx5kHiBAgekwDFKAeZRqprM0JqN7RW/hzBB8QU7umMxZy4qKIegnL6QgfpADxLgaADIG1Fc78KrDhDpdli+AEyDf5MEKk0cGOxnUxvofc1mSCdkNgWhNPxCCIWWCmpnpDQZVCLCDYAAKkKArWUTM0UFytUnnxNssbiqLhLXH1m4PGS/IzWAIBUsstgsdeq5PBMZf0EiDF5zuzihAgiP23ysgCE1FLWjJAwqC8v1h6ghuEw1bFvB7QKBE2nQxPxQaoAqRUWAW62RBIDgB0O3SyFkNgcENg5RMgsp0Oi1RMFcKztRIglgZvUQVoD81IXFQBATKDCiFW7QFyIwR+BShWUAEic0UvpgA4nIAqxQq1DtIssgOv8319byrt9f+wHnYVKEDJjKsAsarzgNLU5BtJgGYaSz8MvP8LQM/C/Od4Fphv8mEsm/k9KKvH1D5yW4MHqEdJ8GwyPRIuAhQ1ND6I1dQIFfAqQCU8QNXiIytcAlSeCdry9uRijxteD5CueQmQI5K5CgshOnRwtNNeAqT6CJBTaGCzLSjUk1ZOFlieB6jGyq+eEFhqQshgDFf4th7Q6PUYQYarB0CVjVCbDV8doE6bnn8eD1CCEyCtzJIcHgXUynpM0OWWsSgEXgmaKkB7DnmrkpcD0UPDCyFWnQVGPzebRD+1B+wVTdCBClAvAEBXbHQiWbgPmAhGjjJT/Pp+byrj9f9UQIBihlcBmk5bQheDjqYqmJIAhQm+StAcogIEuASIoRoFKNYLRyE//5BCyp4bIfMAAa4KVGtGRyUeoGqx+oh+zOksvb+serNq55ATQ2BMGRLM6Hl1gOANgflN0SVBB0cn7fUA+Y22BT1AQqgsp0VLqnJdvhBYrZMSMUGT4+OkJ/h+2yEr4VAPMEU2qmS5egBU2Qes2RBIjgYLMbuQAkQbBJepAHmSAOws94SlYJblNysGTTBBT6ayGBOa8pYLMQRWswLETdBJPi7yRqiFTNBGjC8OepWpwp3gPZ/jRiLY/h+cSrvnnGq4510FHeGZByiZtRBnClATDdCAJEDhAhvE7RxJfWdgJ5legABVY4JWNViRXgDAAuU9+vHhUoAA1wdUkwEacAmQFmlYwUdNVfB/fOI4fHTlED64tHCmDc8CcyxYOYEAUUKqCpNaDnpeCMxLgCob5FV6rqhstcj2PU8BKhACEwhQOQ0rRcKjKLWXvo+bGqaoB8hKjrvErQUJEPMARX0KUFWd4JsNOuH2KAlvH7Bor1cBoqE9tdxWGEawApRyaidAogmaZVv1xY2KSLybBVYPBYgRoERAjTTBA8TUGf6k2w+sLAVICLV5FCCx9xy79qvIAptOCyGwJtsuZB2gMEEcxHMpQKPEhvlEWBiHypocVZa2d+IDQGoUnbSekBlCAsRi3TUZoAFXKSnRCLVWfOz4BfjY8QuK7wpTgJwc9wBlocOgUrBumMg5KnTFRqZkCKwyBUiNksFRzblZX1nLRsTxDmQFTdDMAO2YMI3Sny1OQnGj9tL3cVPnafBWYtytkROyEg51ASUBEQQoQMwDNMtCYD1K0q0CHekhIVzBIJ0A+T5qmYsUXVCKHCvDQ6J1CYGprgna35S3XLhZYFbtdYCECIG/RlrcUIMVIIAQoMm96FWmC3eCFyEk47B9nUznMI8TIFEBKqMjPFOAhErQNTXyriOkAhQmiKsekVnnKUC+XlZVnkSqLxMtjCGwflrvomYCxKrgBnmvZhi8gzUsHgKz4H4/U1eRBm1Z4QSEwNTqFSA9Rs4VLeuuwpNZCzHFN5AVWtlV0AcM8KbB15qWDBCVLaGSAd5OjfMMKSVkbVzqAhq68HuAPIUQK6wD1TTQCbcL09SQC3chJ6TIV6ps6ZrGK4PbQmZjPbLAuAJk2dhziBmgKyVAASGwqrvBuyEwf420TsMmkQMgnwDFXSN0j1JOCIwRraTH4+cpUlnIrxqAoF5gvItBk0NgUgEKE1SNKBVCLBtAgAJUBw8QANXXkFUNWRYY4CpANYfA5i0Dzr8fmHNMHfaqNrgeIAs2XUHZivv9IpQAdSBNssDyFCBBealQATLjZOAzLFcBSmYsROEjPAUJUHmd4BnENPhaC9MxZPROwAGc5AQ0mwykapMH0oaAkrqokvEoQOmcJRCFWUL8qLG2A0kMKKQYHx/HBAVoGvT7lKlsaZpCrhHYsJJjfBmRglk3E3TOdhWghf2VLRLZIiFnO1wBqb4bvECAfApQjyosYAy/AtQLoAIPEA+BpWAKJTi4CVoz3bmojI7wQb3ABkISApMKUNjA2XcxBagOHiC4HeHdzw7fYDrQUScTNAAs2wDMWVr7+9QIlbavUGHxnlxiajtRgMj3zkD3tsKALwRWoQIUiZMQmGl7CVAMvoGskLRNV3ypMqpAA74QWI0p8AxZGhpWUoegOYQYhK2NS11AJ6IoMryKMOAzQVdYBqFpEELPH+gTDNCAzwNUmbKlqwqydB1vJ0mbhywM2FBr9gDxStC2zWsALaxUARLUnklaSLH6bvD0HLez6It536NbZXNEND8zlB7nQeUQoix0Wo4CZKUR0d3Fl+ecq8AE7e8FNp3OuSEwP1mbYUgCFDawE0ssTMezwAIUIEWtfhXo70cWwtXk4QPkAhnsDt++VQtvHSAyKNr+EJhDQ2DQvM1QAS/pqTALLNJBBr6I7Z5fiYwl1OWgKEMBKqegmxgCK7e5bCnkTELitMQB/lgrE6AIsryKMDBL6wBpBp/sLj2Ons95CpAQAitTAdJVFTl67VhJoiylFfLa2gkQC4E5VdUAAuBRUFjn9ppbYQDoUDKe9+5Ui/SFpMd5kbIfAOBAKV4KRJgHokLLFYM13xUJUBkhsCAFKAx9wAAZAgsfgqpB8zpA9KSLCx4gs6v6Ogr+dhwhJEAfWTGEf75wNVYfUaB0+ywEC4FpsJAOUoA01wOUDcgC84bAKruE451ksulAkoRSdI16gLyERym0squgEzzgDYHVSwGyzC4gARjpUf6YFrIq5nWB4WaB+RWgWZcFBhCik50GDv2J/M/GMUqAokoWnQ4l5hUoQIwAOSkvAeqskXCLlaCrqQHE3kNXFeRsBxOUAFVd00wYn5VcCn0dBvZNkOu0U6yr4wf1jC6mBChndOapyoU+Jyp4A11yaghZYNVWgpZp8BJBCKoGXawOUJUZYADyFaAQrqJ1TcXaYwfRG58lUn8ZcBs42shkAjxAhiYQIA2GrxWGaQqTXoUKECNAcaS4JJ/K5ofAlEIhMCHNuJwQWMzQwPhbvRQg2ySrV8UhpCDtGDCNFlzL6a4HSFSA0rOxECLghsEYAWLjmNlFVAkAcxTarbzcXmCqgiwnQOS1Kaqe1lp1nJmg90+meL2dw3orHyPZdWI77P8qrwNFEYzQCU+lfJbJW0wBYgTIMkvUQdN0rjJHHHccMIJM0FXUAUpkcjILTKIAOAESQmCFKkEDtRGgjvCHwFoRmtDAMUsVIDGzKyIqQI4Of+Z4Z0z4naqsA9SBNF+RiiGwNPPXWAWkbRoCK6cRKkBahLBQRL0UIMdX5yQFo/rU4jBDDIEV8gDNNgUIAMb+TG7ZOKaqUCg56mVZShUQO4sFMmh18wT1z9UrBPbWQbJPg92RqrJRI77X1HSuejrCuwTIDSkFKUDkOM+h5nOnnEKwlGiJynC1Jmh23SeyFhzH8fYCkyZoCQ+MgBBYMQWoSgM0gFnhAWpFsBCYrtjI5pgC5DNB01WsrehQ/CFOrfoQGBsgI0oWEwkazhLS4NMGVVesbPDrKwyBAa4PqF5ZYFqkC7bjHpMUTJg1FlgMJQzXBO3xAM3mEBjgqtviOOaflCtI72cmaBYCS9p1IkBUeWX9tipNgWfwE56aSnoUKIYYB10wFyFADGqsDALE+9C5BXlNkQAV6lsZAKYAOQ5ZbMkQmERhCFU4OXgWGD3hjbhLhmqREAUClIUBFIsLS9QNmuABytEQmKP4TNB0FSt6gzjEsFfFrTDc8yUxSUMGQhp8xiCDo1rQA8QKIZZnggZcH1A96gABQCxiYAouWS+nKeusBF2FR5UsUhlvGvysK4QI5JOcYgSoIgWInoeUAE07jADVpw6QQ0NXlfp/GPwEqC4KEPUAMURpOYjABbHoGQWgxXtLfw4990whOcJTCZrNRWUUQhTT/t+bItvHinmWZhAtOGrMcnB3vagAsTpA9DlFEeLntRAg1wSdU2fRQDrLwQoh6rB5JWjR2BzRVWToqjaQAImqT4UhMOgmIbsAklOEAJHuzLQiNVWAVLs+hRABdyVeLwUobmpuQ1QQBag1Q2BCU9y0W7Zg1itADGJBV3938goUoBy9RpQ0VYAoAapXJWiGSjPAGPwLharT4AGPB0gshsizOot4gBjKIkBcAXLHgWATdGkPkKoqvCjlgSmyvVSAJIIRJC366wABAgGqgUGbcTis2mysBQvJhRWqqwBlaSVoO08BckNg+a+vvhAiAKRU8punpumEkbX5iixLDcZqiRBYqsxCiIA7EdXLAxQT2mGQfWlRBUhISrCE1iQZy/ZW5Z0taLACpGTI+ZyCCVWpvXiqvwDpwioVIP+5WbUJGnA9M5lpTzsMlwCVDoEpfrIZBDoPGQVN0EwBKk2AAPfaPzBJs9ZY4cZ28ADdc889OOKIIxCNRrFmzRo888wzRbd/8MEHsWzZMkSjUaxcuRK//OUvPc87joObb74Z8+fPRywWw9q1a/HGG2/w5//0pz/h0ksvxZIlSxCLxXDUUUfhlltu4Rk3oUYgAQooesZO6lo8QHCLIZbbe0eiDlDJAOhVgHxp8HQVm1MCCI5IeipVgABkNTLoZKaJApQUssByZi/5iFIKkGOWPZAfM0hMy0fPq0/GB1GAXHLQsgRIM3h9KEtQgNKzsRAiUIIA9Xqfq8CPyFRSNU0KLCZhosMM8M5ViHwFqPYQmKEp0Grph8eOYWrc4wEyrAJ9wADAiMEWCWU5JmhKvg27gAk6qFxLEbBwJFOAOtqlEOJPfvITXH311bjlllvw/PPP44QTTsC6deuwf//+wO1/85vf4IILLsCll16KF154ARs3bsTGjRvx0ksv8W1uv/123HXXXdi6dSt27tyJjo4OrFu3DqkUIQ2vvfYabNvG97//fbz88sv4zne+g61bt+KGG25o9NetHUYAAcoVU4BqnFRYfFivTt6VqAJUwdEgdIP3maBfco4AALylHVHw9eRNKleAOAGivZOSmRz3AFkRMjhqToHFQhUm6Os/sgxPXvMhfPCYuRXvaxDipuZRgNJOi2aBwQ1N24InMJ0TCyHOooVLHgHqLfxcBSEwRoC4AuREajZAA/kK0KIK22AwiCGvqttg8DfoJbepcU8WmGEVCYEBUMWEl7KywMh5p1lpTtg8JugKQmCAqwAdZApQu3iAvv3tb+Oyyy7DJZdcgmOPPRZbt25FPB7Hv/zLvwRu/4//+I9Yv349rrnmGixfvhxf+9rXcPLJJ+O73/0uAKL+3HnnnbjxxhvxyU9+Escffzx+9KMfYe/evXj44YcBAOvXr8cPfvADnH322TjyyCPxiU98Av/jf/wPPPTQQ43+urVD6MPCwWRGcbJjKezFKnqWA+YDkgrQzIGqNrpAgEQFKKJr+FfrbJyQuhdPm6fmv74WDxAAS6fd1JOEAKUzaZi0yqtNB1jdLkSAxErQ5Q0fuqbyit71QNzU28MDBMCiCo+dEUJgsz0NHiAFXMXxrA4hMC1DzucUTMTrUHPKEAiQpioYqrIavaiU1uT/AVzSmBzjCpChKVBZc+NCC+Ji4cYgsAVxLskrTnfotBRDhSZowPX/MQUoLJWgGzpqZDIZ7Nq1C2vXrnU/UFWxdu1a7NixI/A1O3bs8GwPAOvWrePbv/XWWxgZGfFs09PTgzVr1hR8TwAYHx9Hf39/wefT6TQmJiY8f01BYCFEnwkaAFZdDCz7GLDy07V9HlsZzKaV5GyHSICoB0gkMkxZGUdnngxPtq0hCwyATQdJiypAuZSrLrAiaZpdygNk1j6YVwmiAPlCYFoLpsEDsFRyXTpCXbCMpxDiLA2BxfsKPwdUpGzyEFjWDYHVaoAGvCGwBb1RnhZfKURyXpP/BxBCYGNYPBBH3NRw1NxOIFMkBAZUToCEcizsOu/UhFYYlSpAEa8HiLfeaeUQ2MGDB2FZFgYHBz2PDw4OYmRkJPA1IyMjRbdnt5W855tvvom7774bf/VXf1VwX7ds2YKenh7+t2jRouJfrlEIbIUREAI7bBVw/n3AwFG1fR5riCoJ0MyBeoA0xYZtMQXImwXG4G+DQV5It1XU6koX0EHHSZNB06aDpwOFV+stHAJz0+DNKieEWhHzZYGlHaNpZKzRYN4NJyOGwGZhN3jAO/H6GzoLz+UUs6L2Phb1yemUAKUcs+Yq0IA3BFat/wfwEaBaz1MWAkuOoTtq4IlrPoSffelUIEMbzBYkQL3Ce1SgAGUDFCBd7AVWpgeIKkAHqQIUcdpAAQoD3nnnHaxfvx7nnnsuLrvssoLbXX/99RgfH+d/e/bsmcG9FBBUCZorQA1Y7TEPUAjbYLQsBAXIZh4gLV8BAhC86mRqURXqDwAo1Div0AnDpgbbnBaFYuRnf3jgUYCao7oEK0CtOZTZAU0nMx4P0GxSgHrd+0UIkGpUFtbzZ0omYdbHAyQsLmojQFrg/arAiAxt+zGvK0rULk6ACoTAxFpAFSlAKU7aYpoYAiu/FQYgeICm0lAhhHBbWQGaM2cONE3Dvn37PI/v27cPQ0NDga8ZGhoquj27Lec99+7dizPPPBOnnnoq7r333qL7GolE0N3d7flrCoIqQQcpQPXCnGPIbc/C+r+3RDCENHjbzg+Bla0AVeH/AQA1SrKylAxTgAgBsrUYNDr56E7pStDN8t0E1gFqUQXI0fJD4p40+NlaCLEoAapM1bJ810EKkZoboQI+BahKAzTgVX1qvma4CXrM+3i9Q2ABClBcq94EzbPAJtNuGwygtRUg0zSxatUqbN++nT9m2za2b9+O4eHhwNcMDw97tgeAbdu28e2XLFmCoaEhzzYTExPYuXOn5z3feecdfOhDH8KqVavwgx/8AOpsqXIcVAm6kQrQso8BF/8/wNq/q/97SwSDK0C2W+IgwAME5GeikG0pAaq0DQaFFiWrRC1HB00a1rL1GJ98ChMgsRJ0swhQQB2gFlWAWJ0uxa8AzUYTtJiwUYQAVUrq/ApQyqmPAiR2TK+2CjTgJT1Vd4JnEEzQHjACVKgsSsUEyFUeWZuZmFq9CZopQKms7RZBVNSmh3Ab3kL56quvxkUXXYTVq1fjlFNOwZ133onp6WlccsklAIALL7wQhx12GLZs2QIAuPLKK3HGGWfgjjvuwIYNG/DAAw/gueee4wqOoii46qqr8PWvfx1Lly7FkiVLcNNNN2HBggXYuHEjAJf8HH744fjWt76FAwcO8P0ppDyFBkGx1UYqQKoGHHF6/d9XojCYBwgWFNsCNEARQ2BaKQWothCYESMTkZ6jtWVouNUxYtBoKNQsJwTWVAVICIG1aisMgF/zqkCA0rM1C0w3SeXfbMJbBRrwTsoVhvWcgBDYnDqnwS8MSwhMMEF7UCoExgiQopIMvFIwXAWIXedRpbpeYIC3Cnycda43OiryejUCDSdA5513Hg4cOICbb74ZIyMjOPHEE/Hoo49yE/Pu3bs96sypp56K+++/HzfeeCNuuOEGLF26FA8//DBWrFjBt7n22msxPT2Nyy+/HGNjYzj99NPx6KOPIholP8q2bdvw5ptv4s0338TChd7QjsMau4QVPP0woA7QbJK7JQqDhrAMWNBBMysEMqNrKlQFsJ38YmyebasMgZlxMgBG7ARyls1JjaPHoJkRum+lQmDl9wKrN2K+OkCkG3xrZoGxiUhsTeJVgGaRCRogE3g20VgFCGZdqo4b9QqB6Q0IgSXHSJMyRSG3JUNglHBGustLnBAIDltcRFQ6VumVdYMHvH0AuQLU5PAXMAMECAA2b96MzZs3Bz73xBNP5D127rnn4txzzy34foqi4NZbb8Wtt94a+PzFF1+Miy++uJpdbT4CTI9cZpxNhkeJwhA8QDqtv6P4wlkRXUMyawWHwJgHqIoiiAAQ6SCTTRwpTKVz0KgCpBgxaCZTgAoQoJxbCbpZqou/DlAapmeyaiUwU7qa8xIgtxnqLBsTIt3A5Lv5BMjsJOqEY1c8ztmqXwGK1NwIFXDDNp0RHXM7q198egoh1po4wEJgdpYsRsw4WSDbVJ0pRIBYuRMxG6wYAhSgiEcBqtQE7X7vWEj6gAEzRIAkKoAhFaCWh+AB4gqQj8yYukoIUFAIrEYFSKceoE4lhYlkDmouCaiAYsahmTQLLEgBsnKcjFdSCLHeiBleE3RWjdTc9iCsUOh4oNn+ENgs7AUGAL2LgYOvA32Hex9XVUKOUmMVj3OO3wTt1KcO0ILeGG7csBwL+2I1nV/eEFiN14zZCSga4FjkWJlxV/0BCmdVLToFOOECYMkHy/scoRwLIy8uAYpUrgB5QmDhqAINSAIUPrABLSsVoJYFHbBVxeH9ddQAAgQUSINnalGVChDzAMSRwkQqC9VKcQKk0ywwEzk4juMd+IXSDMQD1Jywk6YqyOju4GlpsywMVAGYKV3zh8D0WegBAoBP3AW8+zvg8NPyn4v2kEm9wu9k+frlpeqUBg8AX/hvR9b8HnWtA6Qo5DglR0kYrHuB6//Ro4UTIzQD+Mut5X8OX4gncfFpS6BrKgamFfe9KvYABYTApAIkkYfAStBSAWopqC5xiNImpIpvFcuM0IGhnRoVILby6kAKu6fSvA+YGonDiNAmiIqFXC4H3RAmF+r/saEgDaOpxmPH6AJoUoqttu51oVGfBGtN4jgOSYNnBGi2jQndC8hfEJgPqEIC5PiSAepVCbpeqGsvMICEsZKjrhGa+3/q02wYgKcl0/BRAxg+agD4JyHsyn4jOwvYdklfkdiahKfBh0ABatHUiVmMwErQTAGaZYOdRDAE4hKloSbFp+6xQVMLGlhqrAPECZCSwv6JFO8Er5sd0E1XTclmfKs7mgKfckwASlP7b5mmiWmHXA/WbCMBFcD1ZKWRtWykczb1jrGqvC303RkBqjEElqxTM9R6QWzTUpd6VUJDVAClDdDVIKggrxiJEL1nZaTCexSgEIXAJAEKGwIrQTMFSIbAWgJi0UOFDB6qVkABCvQA1RgCi7ghsJHxNGJ0QFLNOAzTzXbJ5BEgNwUeqNNgXiXEYoi2Xn2GTtihUUUuiizSORt/ODDlpsADrUmAKg315xVCND2ek2bDUweoXgoQ4NYCok1g66oAGa4CxGEFKEBAWWEw0ZQeJhO0JEBhg78StOM0tg6QxMxDkOyZAqTqXjLDBk0tiABRAlP1CkoIge2bSHIFCEYMhmHAcshnWumk93VUAUqA+oSaWHwwbmr4qXUGXrSPxJ+No5u2H40GU4CiSgaprIVH/vNd1wANzL4QWDEwZaPSNHg13wMU1hBYfRWgMXLbEAXI9QBxiFaMChWgeJAHqF3S4CUqgOgBchyXdQNSAWoVqCpsKFDhcA9QRSboIz8EfPBaYOnZ1X0+XSlqioOx8QnuAYIZh6KqyMBADBlYhRQghylAzVtlx00d3859Bt/GZ3B8CAbSRkGlZtQIspQA7XUVIEWtuhp4KFEHBch2iD8tTCGwuhZCBNzjxBWgBhCgogqQQczYWoQszstohxEYAmtyHzBAEqDwwSMtponJLOg5iVkNW9GgOjlElWACxAbKQBO0HgH+4qvVf7ggPU9OHEJMyXgeZwQol/ErQG4fMLKPzVWAGFq1DQYAno0TRQbP/mkUe0aTONpg/p8Wy36bs5Tc9h5efDsfRBM0Cc8q4VKA6tkKAxAaoo6RW5YFVqgNRjXgClBARwK2ENcpASpDAYp56gCFoxM8IAlQ+CD6GXIpwLbc/1tJ7m5z2NAA5HhFX1X3eYCKhcBqhaoio8VhWglMTY57QmAAkKFpxblssAk6iQgUpUCbjhmCOKC2bBsMgC96okoGP9v1NgDgQ0d1A39C6ynCJ18IDB0PzD++stcJi4ckTKhKnYhGneBthloPBaiX3OaZoBvgAQo0QdN5iNcCKu0BMnUVpqYiY9nokCZoiYLQDAB0YsmlXNat6uWVMJeYFbAVMhCyEJjmk/3dNPjG/OaWTtWexLiblkoVoCzIhJIfAiP/pxzSB6yZxQdFBaiZSlTDobshsN/84T0AwF8s7aXPtdiCSNWAhasqNvf7FaCOiB6qwph1LYQI5Jug06wPWAM8QFbGXYSLITAgOGO5CFgqvDveSAIk4YeieKtByxpALQmHEqAI8wD5CRDzADVIZbFoIcGYk+JhOHbeZRWyL1bGN7AJClCze2+JpsqWVoDoSjyKDBwH6IroWHUYHR9ajQBVCUUgTPWqAl1PeENg9VSAxshtQ0zQAVle/rmowo7wzAfUqdLtQxACa+GRYxZD7Agvq0C3JFgDxwjt6aT5QmCRBhMghw6WnUoqLy2VK0B5ITDXA9Rs0uENgYUn5bnuoKtsRpQ/fNwgIhBaEkh46gCFLQUeqHMzVCDABF2iE3w1MAQrRpZaMRzWtofORZowT5UBds12+DyHzYQkQGEEr8KZlApQi4IRoEIhsKEecg7M7W6Q0ZUaJuNI5XmAstQDZBfyADnN6wPG0NE2ITCqANFJ4+PHL3BX5K1mgq4SiscDFAmfAmTUuRAiN0E30AOkam65jlzSm43Mxip2WyYBYtdshxoeD1C4zhQJAjG2ytomSAWopeDkeYC8vocvnnEUTlzUi9OXzmnI56t0sOxQ8kNgORoCc/wDm1AIsfkKULuEwFwPUG/cwGlHzwH+IFVhDwQPUNKpXx+wesGrAM2SEBhAzr10lihAYtd3ngVG56myO8KT3yVMvcBaeOSYxRCrQUsFqCVhq14CpPiMnx0RHWctH2yY10ajHeE7kB8Cs+iE4hQMgYXBA9RmChAyWH/cECF7ckzwQNHzTdBhgq4qYJHsuqbBZxOkG3umASZowFuTTlSAGOHUKlSA/CboEChALTxyzGKI1aBlFeiWhENDYJrikAeq7etVJfRYN4DgEBhTgGx/eqvQC6zZpCPeNmnwLASWxaWnHUEey8kxwQNfFljYQmCKomCgMwJVAXrjdVDtIt3u/dRYYzxAgDAPCck4quFmI7Pzr0wTNFOA3MKrzSdA4TpTJAj81aCB1qv50eZgITAOtbLU31qhR0k7jS4lKVRmJQpQTqUhsGxwCCwUJmjRV9HShRDJWKDBxtK5LDVZEiARogJEGqGGywQNAPd+bhUOJTKY01mH30zVgEgPkB4nRuhGhcBYKnw26ZIccR7iJujSdYAAVwGK2N6yG82EJEBhBBvYxBCEHOxaCv4O1jPe0oCaoPsw6T5GFSDWW8nxx/Y9afBNNkELq/xmtuRoOMTCqNkkqcEimyN7oPjrAJnhm9ZOWtxX3zeMUQKUGncJUD0rQQNCNrIQAhN9Z/z58hUgDRYM1solBApQCy+dZjF4GfKUe3LJwa61kKcAzfCgTQefOcq4+xg97yyVEfDCJuhme4Bi7dIKo1g9FpkFBgBQhEk5GUIPUEMgGqEbFgITFaAA0s1DYOV5gPrihmuABqQCJFEAIvPmWWBSAWol5ClAMxwCY4PlHGUCADE+a1SFstQC5kY6AYchDb5tPECKQohOLsUJqBsCk4siwFcIMYQeoIZArAbdiErQgM8EHRQCY+NEeQrQZ1YvgjI1AjwP0sg3BHNaC48csxhiJeigE09i1iOfAM20AsQIEFGAbM0NtdiMABUMgYXABG0IIbBWJkBAfssBrgo3fwIJA1RPJegIOkPoAao7WDHEqRG3QGEj0uABWo8uYB7SK/MAzeuO4opT55N/zE5C7puMFh85ZinEStAyBNaaCIkHaABEAbIFr4lDJ1bFn90hpsE3udlk2zRDBYQFEVWAZCFED1TD2wy1rUJgE3vdx+rdW6uUAlRhCAwAkKV+pRCEvwBJgMIJsRK0zPhoTajh8ADFaBFEW5hMSylAKcdsuu9GzPRpeQLkT4qQ7XE8UIRJOYx1gBoCFgIbf5vc6rH6L6I8kYgAE7RWmQkaAJAhY0gY+oABkgCFE6LkLRWg1kRIPEAcworMoYOcWqASNFGAmhtmiHo6bLd4yENMihBvpQIEwBsCS4awGWpDwBWgd8htIzKq+EI8VdwE6QXH/AAAIkFJREFUXWYIjLwXU4CanwEGSAIUToiVoKUC1JLI9wDN8CSeR4ACQmB2oRBY8z1AqqrwWkAtrwCJBekAuSjyQdW9ClDYmqE2BMwDND4DBChXqA5QZd3gAUgFSKIMiJWgZc2PloTiJzzaTCtA3gFTERUgeq6peR6g8ITAADcTrNlkrOEQQ+KAXBT5IHqAUiFshtoQxGhdoal95LbeKfCAOw9lC5Rj8ZvzywEdQ6QHSKIwgsxncrBrLfhDXjMdAot0eT/eFENg5FxTRQ+QlQXsHACqADXZBA0AA51kMO6Nz/Cxm2nofgWIhcDkmAAAqjAph7EZakPAQmCgnQIaogAJ5vtAE3Q1ClCDUvarRBucKbMQoulRNj5sTTQ9Dd47AKkRYUVGzzVVDIGxlRvIKjsMvptvnnM8Xnl3Au8b7Cq98WwG+60So+RWpsF7oPlCYO2hAPV6/693FWjAqwAFGe8rbIUBQAiBhYMANX8ZJ5EPPaAOkMz4aCko/oyNmU6D1yO85QUAaGJMnioOmocAkUHOhooM9FD4bk5a3IdNaw6HEoJ6Ig3FwtXk9g+PkVsZAvNA11TkHHI+ZtTmF+mcEXAFiKIpCpBQrqVcyBCYREmI7nqpALUmmq0AAXCEQUgRCJBKzz8tQAFKKxEASntMMmHB+z5Kbt96klT9ld3gPdA1BQmQY2EZna1PiAHXBM3QSA9QLl2iDlAlIbAGNW6tEm2gFc5CeOovyLL3rYg8BWimPUAA1EgXaagIeLLA2LnmDYERAy4hQG2Qeh4mzDkG6D8SGP0jUYHkosgDTVVxS/ZizFfew1TnULN3Z2bgD4E1UgEqVAlakwqQRCMQWAlaDnatBCUECpAi+gaEuhwKXfnpTgABoivtMITA2gaK4qpAr/9KhsV90FUFP7f/G/5Pa2N7GKABkjUq1tJpCAESu8EXMUFXQoBkGrxESchK0C2PfAWoCYqKOGgKCpBKzz/dzrrPp8YAAAmFbCdDYDOMY9aT2zd+7YYRZCFEACQExtA2BAjwqkBmAxIBPN3gA7KR2flXSSsMlgUmCyFKFISsBN3yEDtYW4renMaAom9A9AMZ5FzzKEDJQwCAQ+gmL5UEaGax+APE+Jp4Dzj0FnlMqsIAiALE0BaNUBlEI3RDCyGKCpAQqq+wGzwANwQWEg+QHMXCCFkJuuUhlu+3lSYN2h4CJChA9L7uCApQ4j0AwCGQlaZUgGYYmgEsPdv7mBwTABAPEEOH2UYKkGiEbgSh4ApQHZuhyhCYREkYUgFqdYghMFtp0qAdKUSAyMBmBBEgh7xGmqCbgPd9xPu/JEAA/ApQGxEgTwiswa0wgjoSVGWClr3AJErBIz1KBagVoYaBAHk8QO6KTKdkyEAGcGilWUqA3rMJAZIhsCbg6LO82YJyUQSgjT1AnhBYI9LgRQWILoZqrQPUjgrQPffcgyOOOALRaBRr1qzBM888U3T7Bx98EMuWLUM0GsXKlSvxy1/+0vO84zi4+eabMX/+fMRiMaxduxZvvPGGZ5vR0VFs2rQJ3d3d6O3txaWXXoqpqam6f7eGgBEgO+eeMDLe31IQQ2BO0whQIQWInH8qHN7+glUhPmgzBUgSoBlHtAc44jT3f2mCBgBoggIUbycPkKgANaISdKmWTGIIjC2USqHd0uB/8pOf4Oqrr8Ytt9yC559/HieccALWrVuH/fv3B27/m9/8BhdccAEuvfRSvPDCC9i4cSM2btyIl156iW9z++2346677sLWrVuxc+dOdHR0YN26dUil3JLcmzZtwssvv4xt27bhkUcewVNPPYXLL7+80V+3PhAHtvQEfUyu9loJnhBYMzLAgIImaN0UBjm2uqMK0H6LEqAQ9AJrS7B0eECOCRS64AHqlB6g+oEvxLNuI94gEzTgKkSlELJeYA0fxb797W/jsssuwyWXXIJjjz0WW7duRTwex7/8y78Ebv+P//iPWL9+Pa655hosX74cX/va13DyySfju9/9LgCi/tx555248cYb8clPfhLHH388fvSjH2Hv3r14+OGHAQCvvvoqHn30UfzzP/8z1qxZg9NPPx133303HnjgAezdu7fRX7l2eAjQJLmVClBLQQuDAlTAA6SZwvnHCRBRgLgHSGujlXaYwNLhFc0tVNfmEBUgGQKrI4yAhXhQCAwovx9YO/UCy2Qy2LVrF9auXet+oKpi7dq12LFjR+BrduzY4dkeANatW8e3f+uttzAyMuLZpqenB2vWrOHb7NixA729vVi9ejXfZu3atVBVFTt37gz83HQ6jYmJCc9f06CqwolGpUW52mspKLpAgJpQBBFAQQ+QaRjIOpTgWF4FaNShWWBSAWoO+g4HPnkP8PE7vRNUG8PQpAm6oZWgASDFCJBAesT75bTDsLJETQLaIwR28OBBWJaFwcFBz+ODg4MYGRkJfM3IyEjR7dltqW3mzZvneV7XdfT39xf83C1btqCnp4f/LVq0qMxv2SD44/tSAWopaEIIrHkEKFgB0jUFaVCClkuR+D7PAiMEyNQkAWoaTvoscPKFzd6L0EAqQGgMARIX4inaMkcMgamqa8ovxwjNCngC7aEAzSZcf/31GB8f53979uxp7g75s75kFlhLQQkxATI00vEdACnDkE1wJWgUXTA0BaraBg0nJWYFRA9QR7uaoBsRAgNcFYj1DCw0LxUKgY2+BUxRvy8zQCtaaDIYG0qA5syZA03TsG/fPs/j+/btw9BQcNO6oaGhotuz21Lb+E3WuVwOo6OjBT83Eomgu7vb89dU+OP7ITlhJOoEkfSELQSmqcgwBchKc/XH0SJIIiJrAEmECiIXb0sFSI81rpUOC7NyL6qvaTObl4JCYIlR4HunAf/0F8RELfp/mlH5PgANJUCmaWLVqlXYvn07f8y2bWzfvh3Dw8OBrxkeHvZsDwDbtm3j2y9ZsgRDQ0OebSYmJrBz506+zfDwMMbGxrBr1y6+zWOPPQbbtrFmzZq6fb+GQipArQ0PAZr5TvAABBO04jm/DE1F2hGkbUqActE+AIpMgZcIFRRF4T6gtqoEPWcpsPzjwPBfN+4zmBXDscmt34ohtm3yY/SPpPDh+B7gdw8IfcDC4f8BgIafLVdffTUuuugirF69GqeccgruvPNOTE9P45JLLgEAXHjhhTjssMOwZcsWAMCVV16JM844A3fccQc2bNiABx54AM899xzuvfdeAORkv+qqq/D1r38dS5cuxZIlS3DTTTdhwYIF2LhxIwBg+fLlWL9+PS677DJs3boV2WwWmzdvxvnnn48FCxY0+ivXB36Do1SAWgseAtQkRSVCVc5Il2dFZmgKJqkC5ORSUGgGSC7SD0AWQZQIH+KmjvFkFn0dTVpMNAOqBpz3b439jDwvqm8e0osoQJOC3/Y3dwOfuIvcD4n/B5gBAnTeeefhwIEDuPnmmzEyMoITTzwRjz76KDcx7969G6oQwz311FNx//3348Ybb8QNN9yApUuX4uGHH8aKFSv4Ntdeey2mp6dx+eWXY2xsDKeffjoeffRRRKPuj3Xfffdh8+bNOOuss6CqKs455xzcddddjf669YN44mlmaCRDiTohDApQ/5HAqX8D9C3xPGzorgcol0nDSI8BALKRPgCyCKJE+PD3f7kS+yZSmN8jSwPUFXkLcX8IrIgHaEqwqYz+Afivn5H7IakCDcwAAQKAzZs3Y/PmzYHPPfHEE3mPnXvuuTj33HMLvp+iKLj11ltx6623Ftymv78f999/f8X7Ghp4CJAMf7UcwuABUhTg7K/nPWxqKs8CszJJGDQEljFJ4TXpAZIIGzYcP7/Zu9Ca8HtR86wZRTrCM/OzapD09xeoWhWSPmCAzAILLzwEqI1k3XaBQHqUkP2+hmCCtrIp7gFKGUQBkiEwCYk2QSkrBpungjrCMwXo5M+R17EaQCFSgORIFlYE9VyRaB0IBCgeC1dBO01VBAVIJEBMAZLDhoREW6BUNnKxjvBMARpaCZxwvvt4iEzQciQLK4S6LNIA3YIQCJBhhEsBAoAsV4DSQJK0wUjovQBkFWgJibZBSQWIhcCKKECdg8Dw37iPh8gELUeysEIqQK0NMfOrWSboIsgpZJ9sIQSW0IkCJKtAS0i0CcpVgIqFwDoHgbnHuI18xSauTUYbFU2YZZAm6NZGGEzQRZBTyEBHCBBRgKY0aYKWkGgrFDI9+5/3m6AdRyBAtC3VR74JdMwB3n9Z/fezSoRv5JUgEAmQbITaehBJT8hM0AAlQA5gZ91CiJMKqRskTdASEm0Co4QCpBdQgFJjbm2gDkqAehcDn7i77rtYC+RIFlZIBai14WkqGD5FxVKFZqhUAZrUCAGSJmgJiTZBqUKI7H9/HSBmgI725PuIQgQ5koUVhlSAWhph9wCphHQryUN8dTcOGgKTJmgJifaAqACpRn5B3kIhMB7+Cu69GRbIkSyskApQayPkHiCmAOnTtJy9HsW0Q4i4qYVPsZKQkGgA/B0JCj3vD4ExBYj5f0IKSYDCCukBam2E3ANkURO0kaAEKD6ATI40RJQKkIREm0BUgILmIa1AJWgxAyzEkCNZWCEVoNZG2BUgOrCZCTqQxfuRzlkApAdIQqJtUFIBKtALjDVClQRIoip4FCBJgFoOHg9Q+AiQo1IClDpIHogPIE0VIJkFJiHRJii1ENcKdIOXITCJmmCUYN4SsxthD4HRwU6BQx6I9bshMFkHSEKiPWCU6EnJCJK/ErQMgUnUBKkAtTbUcKfBMwWIIz4gQ2ASEu0GvURLJtbWInnI+7hUgCRqgkh6pALUevB4gMKnADl+uVuGwCQk2g+lyrHMOYbcHnjN+zhTgLpkGrxENRCZt1SAWg9h9wD5BzsxC0wSIAmJ9kApBWjecnI78Q6QHCP3rSyvHi9DYBLVwaMASQLUcgi5ByjvnIv3cwVIeoAkJNoERgkTdKwX6D6M3N//KrmdPgDAARQNiPU3eg9rgiRAYUWp+gsSsxueEFj4CIUdSICkB0hCoq2glzBBA8C8Y8nt/lfIrdgEVQ33WBHuvWtnSAWotRFyD5AiQ2ASEhKl6gABbhiME6DZYYAGJAEKL2Ql6NZG2ENgft+ZYIKWlaAlJNoE5UQiBo8jtywENktS4AFJgMILWQm6taGqgEIvvxCaoPO6QMf6kc7SLDDZC0xCoj1QlgJEQ2D7XgYcxxsCCzkkAQorZB2g1gcjPiEkQIp4zukxwIwjY0kFSEKirVDOQnzOMcTwnBojLTB4CCzcKfCAJEDhhWa4CoGsA9SaCDUBEga++AAAIJ2VJmgJibaCqrrEp1Co3ogCA0eR+/tfliEwiTpAUVz2LRWg1gQjPiH0AKniORcnqayyEKKERBuCpcIXW4hzI/SrwKQMgUnUA2wSkgpQa4Klv4dQAVJNLwGybAc5m/QFk3WAJCTaCOUsxOdRI/S+V6QCJFEnsCqcUgFqTYQ5BCZmfwgp8IBUgCQk2gqMABVTqsVUeJkGL1EX9C8BoAA9i5q9JxKNQIhDYJqY8io0QgWkB0hCoq3AFkPFspFZKvy+l4HsNLk/CxSg8C09JVxc8GNg6gDQKwlQS4IVQAyhAmToGtKOjoiS8yhAqgLoqtLkvZOQkJgxlKMA9R1BIha5JPnf6AAinQ3ftVohl3JhRrQHmHN0s/dColGY+z7i7+o7otl7kgdDU5EGHfB8neAVRRIgCYm2AVeAinhRVY2MZwxd4Vd/AKkASUg0D+ffD6QngI45zd6TPBiaggwMAEkg1if0AZMGaAmJtkK52cjzjgXefZHcnwXhL0ASIAmJ5kE3AT185AcAzAIKkPT/SEi0GaLd5NbsKL7d4LHu/VlggAYkAZKQkAiAoan4T/tI9OkJxAdXID0qawBJSLQlTv8y0LUAWLah+HbzRAIkFSAJCYlZCkNXcVn2Spw0J4qHOudiz5vvAADmdcmSDBISbYUFJ5G/Upg3+xQguZyTkJDIg6EpcKBiyibGx/96exwAsOKwnmbuloSERFjRNQTE+sj9WaIASQIkISGRB0MjQ0PWItWfX9orCZCEhEQRKAqweJjcn7u8uftSJmQITEJCIg+MAGVyNmzbwcvvTAAAViyQBEhCQqIANn4PGP0jcNjJzd6TsiAVIAkJiTwYGqn1k7Vs7B5NYDKdg6mrWDoY/uJmEhISTUKsd9aQH0ASIAkJiQCYPARm8/DX8qEurgxJSEhIzHY0bDQbHR3Fpk2b0N3djd7eXlx66aWYmpoq+ppUKoUrrrgCAwMD6OzsxDnnnIN9+/Z5ttm9ezc2bNiAeDyOefPm4ZprrkEul+PPP/TQQ/jwhz+MuXPnoru7G8PDw/j1r3/dkO8oIdGqYEQnZzl4iYa/jpP+HwkJiRZCwwjQpk2b8PLLL2Pbtm145JFH8NRTT+Hyyy8v+povf/nL+MUvfoEHH3wQTz75JPbu3YtPfepT/HnLsrBhwwZkMhn85je/wf/8n/8TP/zhD3HzzTfzbZ566il8+MMfxi9/+Uvs2rULZ555Jj7+8Y/jhRdeaNRXlZBoORi03k/GsvEyM0BL/4+EhEQLQXEcx6n3m7766qs49thj8eyzz2L16tUAgEcffRQf/ehH8fbbb2PBggV5rxkfH8fcuXNx//3349Of/jQA4LXXXsPy5cuxY8cOfOADH8CvfvUrfOxjH8PevXsxOEjS7LZu3YrrrrsOBw4cgGkG9yo57rjjcN5553mIUilMTEygp6cH4+Pj6O7urvQQSEjMauyfTOGU27ZDUYDemIFDiSx+sfl0rFwoSZCEhES4Ue783RAFaMeOHejt7eXkBwDWrl0LVVWxc+fOwNfs2rUL2WwWa9eu5Y8tW7YMixcvxo4dO/j7rly5kpMfAFi3bh0mJibw8ssvB76vbduYnJxEf39/0X1Op9OYmJjw/ElItCuYB8hxgEOJLAxNwTFD0gAtISHROmgIARoZGcG8ed5KkLquo7+/HyMjIwVfY5oment7PY8PDg7y14yMjHjID3uePReEb33rW5iamsJnPvOZovu8ZcsW9PT08L9FixYV3V5CopWh+8zOxwx2yUaoEhISLYWKCNBXvvIVKIpS9O+1115r1L5WjPvvvx9/93d/h5/+9Kd5hMyP66+/HuPj4/xvz549M7SXEhLhA0uDZ5D+HwkJiVZDRYUQ//t//++4+OKLi25z5JFHYmhoCPv37/c8nsvlMDo6iqGhocDXDQ0NIZPJYGxszKMC7du3j79maGgIzzzzjOd1LEvM/74PPPAAvvCFL+DBBx/0hNUKIRKJIBKRfY4kJADAUL1roxWHSR+chIREa6EiAjR37lzMnTu35HbDw8MYGxvDrl27sGrVKgDAY489Btu2sWbNmsDXrFq1CoZhYPv27TjnnHMAAK+//jp2796N4eFh/r633XYb9u/fzxWdbdu2obu7G8ce6zZi+/GPf4zPf/7zeOCBB7BhQ4kOthISEnlQVQW6qiBnkxwJ2QJDQkKi1dAQD9Dy5cuxfv16XHbZZXjmmWfwH//xH9i8eTPOP/98ngH2zjvvYNmyZVzR6enpwaWXXoqrr74ajz/+OHbt2oVLLrkEw8PD+MAHPgAAOPvss3Hsscfic5/7HH73u9/h17/+NW688UZcccUVXL25//77ceGFF+KOO+7AmjVrMDIygpGREYyPjzfiq0pItCxYLSBNVbB8vlSAJCQkWgsNqwN03333YdmyZTjrrLPw0Y9+FKeffjruvfde/nw2m8Xrr7+ORCLBH/vOd76Dj33sYzjnnHPwwQ9+EENDQ3jooYf485qm4ZFHHoGmaRgeHsZnP/tZXHjhhbj11lv5Nvfeey9yuRyuuOIKzJ8/n/9deeWVjfqqEhItCeYDOnpuJ6KGNEBLSEi0FhpSB6gVIOsASbQ7Vn99Gw5OZXDOyQtxx2dOaPbuSEhISJSFptYBkpCQmP1gITBpgJaQkGhFSAIkISERiJ6YAQA4cVFvc3dEQkJCogGoKAtMQkKifXD7p4/H7/dN4aTFfc3eFQkJCYm6QxIgCQmJQBy/sBfHL+xt9m5ISEhINAQyBCYhISEhISHRdpAESEJCQkJCQqLtIAmQhISEhISERNtBEiAJCQkJCQmJtoMkQBISEhISEhJtB0mAJCQkJCQkJNoOkgBJSEhISEhItB0kAZKQkJCQkJBoO0gCJCEhISEhIdF2kARIQkJCQkJCou0gCZCEhISEhIRE20ESIAkJCQkJCYm2gyRAEhISEhISEm0H2Q2+ABzHAQBMTEw0eU8kJCQkJCQkygWbt9k8XgiSABXA5OQkAGDRokVN3hMJCQkJCQmJSjE5OYmenp6CzytOKYrUprBtG3v37kVXVxcURanb+05MTGDRokXYs2cPuru76/a+rQR5jEpDHqPikMenNOQxKg15jEojjMfIcRxMTk5iwYIFUNXCTh+pABWAqqpYuHBhw96/u7s7NCdLWCGPUWnIY1Qc8viUhjxGpSGPUWmE7RgVU34YpAlaQkJCQkJCou0gCZCEhISEhIRE20ESoBlGJBLBLbfcgkgk0uxdCS3kMSoNeYyKQx6f0pDHqDTkMSqN2XyMpAlaQkJCQkJCou0gFSAJCQkJCQmJtoMkQBISEhISEhJtB0mAJCQkJCQkJNoOkgBJSEhISEhItB0kAZKQkJCQkJBoO0gCNMO45557cMQRRyAajWLNmjV45plnmr1LTcGWLVvw/ve/H11dXZg3bx42btyI119/3bNNKpXCFVdcgYGBAXR2duKcc87Bvn37mrTHzcc3vvENKIqCq666ij8mjxHwzjvv4LOf/SwGBgYQi8WwcuVKPPfcc/x5x3Fw8803Y/78+YjFYli7di3eeOONJu7xzMGyLNx0001YsmQJYrEYjjrqKHzta1/zNIlst+Pz1FNP4eMf/zgWLFgARVHw8MMPe54v53iMjo5i06ZN6O7uRm9vLy699FJMTU3N4LdoLIodo2w2i+uuuw4rV65ER0cHFixYgAsvvBB79+71vMdsOEaSAM0gfvKTn+Dqq6/GLbfcgueffx4nnHAC1q1bh/379zd712YcTz75JK644gr89re/xbZt25DNZnH22Wdjenqab/PlL38Zv/jFL/Dggw/iySefxN69e/GpT32qiXvdPDz77LP4/ve/j+OPP97zeLsfo0OHDuG0006DYRj41a9+hVdeeQV33HEH+vr6+Da333477rrrLmzduhU7d+5ER0cH1q1bh1Qq1cQ9nxl885vfxPe+9z1897vfxauvvopvfvObuP3223H33Xfzbdrt+ExPT+OEE07APffcE/h8Ocdj06ZNePnll7Ft2zY88sgjeOqpp3D55ZfP1FdoOIodo0Qigeeffx433XQTnn/+eTz00EN4/fXX8YlPfMKz3aw4Ro7EjOGUU05xrrjiCv6/ZVnOggULnC1btjRxr8KB/fv3OwCcJ5980nEcxxkbG3MMw3AefPBBvs2rr77qAHB27NjRrN1sCiYnJ52lS5c627Ztc8444wznyiuvdBxHHiPHcZzrrrvOOf300ws+b9u2MzQ05PzDP/wDf2xsbMyJRCLOj3/845nYxaZiw4YNzuc//3nPY5/61KecTZs2OY4jjw8A5+c//zn/v5zj8corrzgAnGeffZZv86tf/cpRFMV55513ZmzfZwr+YxSEZ555xgHg/PnPf3YcZ/YcI6kAzRAymQx27dqFtWvX8sdUVcXatWuxY8eOJu5ZODA+Pg4A6O/vBwDs2rUL2WzWc7yWLVuGxYsXt93xuuKKK7BhwwbPsQDkMQKA//2//zdWr16Nc889F/PmzcNJJ52Ef/qnf+LPv/XWWxgZGfEco56eHqxZs6YtjtGpp56K7du34/e//z0A4He/+x2efvppfOQjHwEgj48f5RyPHTt2oLe3F6tXr+bbrF27FqqqYufOnTO+z2HA+Pg4FEVBb28vgNlzjGQ3+BnCwYMHYVkWBgcHPY8PDg7itddea9JehQO2beOqq67CaaedhhUrVgAARkZGYJomv6AYBgcHMTIy0oS9bA4eeOABPP/883j22WfznpPHCPjjH/+I733ve7j66qtxww034Nlnn8Xf/u3fwjRNXHTRRfw4BF137XCMvvKVr2BiYgLLli2DpmmwLAu33XYbNm3aBABtf3z8KOd4jIyMYN68eZ7ndV1Hf39/Wx6zVCqF6667DhdccAHvBj9bjpEkQBJNxxVXXIGXXnoJTz/9dLN3JVTYs2cPrrzySmzbtg3RaLTZuxNK2LaN1atX4+///u8BACeddBJeeuklbN26FRdddFGT9675+OlPf4r77rsP999/P4477ji8+OKLuOqqq7BgwQJ5fCRqRjabxWc+8xk4joPvfe97zd6diiFDYDOEOXPmQNO0vAydffv2YWhoqEl71Xxs3rwZjzzyCB5//HEsXLiQPz40NIRMJoOxsTHP9u10vHbt2oX9+/fj5JNPhq7r0HUdTz75JO666y7ouo7BwcG2P0bz58/Hscce63ls+fLl2L17NwDw49Cu190111yDr3zlKzj//POxcuVKfO5zn8OXv/xlbNmyBYA8Pn6UczyGhobyEldyuRxGR0fb6pgx8vPnP/8Z27Zt4+oPMHuOkSRAMwTTNLFq1Sps376dP2bbNrZv347h4eEm7llz4DgONm/ejJ///Od47LHHsGTJEs/zq1atgmEYnuP1+uuvY/fu3W1zvM466yz813/9F1588UX+t3r1amzatInfb/djdNppp+WVT/j973+Pww8/HACwZMkSDA0NeY7RxMQEdu7c2RbHKJFIQFW9w7ymabBtG4A8Pn6UczyGh4cxNjaGXbt28W0ee+wx2LaNNWvWzPg+NwOM/Lzxxhv493//dwwMDHienzXHqNku7HbCAw884EQiEeeHP/yh88orrziXX36509vb64yMjDR712YcX/rSl5yenh7niSeecN59913+l0gk+DZf/OIXncWLFzuPPfaY89xzzznDw8PO8PBwE/e6+RCzwBxHHqNnnnnG0XXdue2225w33njDue+++5x4PO7827/9G9/mG9/4htPb2+v8r//1v5z//M//dD75yU86S5YscZLJZBP3fGZw0UUXOYcddpjzyCOPOG+99Zbz0EMPOXPmzHGuvfZavk27HZ/JyUnnhRdecF544QUHgPPtb3/beeGFF3gGUznHY/369c5JJ53k7Ny503n66aedpUuXOhdccEGzvlLdUewYZTIZ5xOf+ISzcOFC58UXX/SM3+l0mr/HbDhGkgDNMO6++25n8eLFjmmazimnnOL89re/bfYuNQUAAv9+8IMf8G2SyaTz13/9105fX58Tj8edv/zLv3Tefffd5u10COAnQPIYOc4vfvELZ8WKFU4kEnGWLVvm3HvvvZ7nbdt2brrpJmdwcNCJRCLOWWed5bz++utN2tuZxcTEhHPllVc6ixcvdqLRqHPkkUc6X/3qVz0TVbsdn8cffzxw7LnoooscxynveLz33nvOBRdc4HR2djrd3d3OJZdc4kxOTjbh2zQGxY7RW2+9VXD8fvzxx/l7zIZjpDiOUBJUQkJCQkJCQqINID1AEhISEhISEm0HSYAkJCQkJCQk2g6SAElISEhISEi0HSQBkpCQkJCQkGg7SAIkISEhISEh0XaQBEhCQkJCQkKi7SAJkISEhISEhETbQRIgCQkJCQkJibaDJEASEhISEhISbQdJgCQkJCQkJCTaDpIASUhISEhISLQd/n+f+BmiX/sIjAAAAABJRU5ErkJggg==",
      "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": 1,
   "id": "bae5870a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T20:33:32.369676Z",
     "start_time": "2023-10-11T20:33:32.368206Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6e588f40",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T20:33:35.179414Z",
     "start_time": "2023-10-11T20:33:32.596532Z"
    }
   },
   "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_25 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": 3,
   "id": "51949a6b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T20:33:55.486203Z",
     "start_time": "2023-10-11T20:33:35.181463Z"
    }
   },
   "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": 4,
   "id": "45b7a038",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T20:33:55.494957Z",
     "start_time": "2023-10-11T20:33:55.489068Z"
    }
   },
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8kqkyBaGqrg.webm\n",
    "metas[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f61fb73",
   "metadata": {},
   "source": [
    "## let's download all the first once and for all  -- TODO: stratify the cluster inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "081022c5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T20:34:03.453467Z",
     "start_time": "2023-10-11T20:34:03.451690Z"
    }
   },
   "outputs": [],
   "source": [
    "chunksize = 250\n",
    "tot_steps = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c306e1b7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T20:34:03.663475Z",
     "start_time": "2023-10-11T20:34:03.660317Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "463759"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(metas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4589a1ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T20:34:03.830881Z",
     "start_time": "2023-10-11T20:34:03.828478Z"
    }
   },
   "outputs": [],
   "source": [
    "chosen_metas = metas[len(metas) - tot_steps * chunksize : len(metas)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7eb3d481",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T20:35:23.463460Z",
     "start_time": "2023-10-11T20:35:23.460389Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(chosen_metas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c002d52f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T20:38:53.791558Z",
     "start_time": "2023-10-11T20:38:53.768770Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('pop', 921), ('rap', 737), ('positive', 584), ('uplifting', 538), ('piano', 534), ('rock', 529), ('inspirational', 506), ('motivational', 492), ('', 492), ('happy', 457), ('energetic', 457), ('romantic', 443), ('background', 429), ('corporate', 424), ('optimistic', 396), ('bright', 393), ('hopeful', 377), ('cool', 362), ('light', 354), ('dreamy', 335), ('calm', 324), ('fun', 317), ('drums', 312), ('commercial', 310), ('powerful', 309), ('joyful', 305), ('electronic', 302), ('confident', 302), ('advertising', 288), ('travel', 280)]\n"
     ]
    }
   ],
   "source": [
    "# A BIT MORE EDA ON THE METAS\n",
    "from collections import Counter\n",
    "c = Counter()\n",
    "for meta in chosen_metas:\n",
    "    for t in meta.get(\"tags\", {}):\n",
    "        c[t.lower()] += 1\n",
    "print(c.most_common(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "756c81a7",
   "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": "50a81c41",
   "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": "9f806c40",
   "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": 17,
   "id": "e0c74dd8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T15:11:05.854472Z",
     "start_time": "2023-10-10T15:11:05.849634Z"
    }
   },
   "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": 18,
   "id": "df651f23",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T15:11:06.524388Z",
     "start_time": "2023-10-10T15:11:06.523005Z"
    }
   },
   "outputs": [],
   "source": [
    "train_data = []\n",
    "val_data = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dff3fd2f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T15:11:08.344822Z",
     "start_time": "2023-10-10T15:11:08.334150Z"
    }
   },
   "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": 20,
   "id": "eb78c40a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T15:36:42.217757Z",
     "start_time": "2023-10-10T15:12:16.248894Z"
    },
    "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 [01:08<21:38, 68.33s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1/20: 0.0s downloading, 4.8s loading, 61.9s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|███████████▍                                                                                                      | 2/20 [02:16<20:33, 68.53s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 2/20: 0.0s downloading, 4.7s loading, 62.4s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 15%|█████████████████                                                                                                 | 3/20 [03:34<20:33, 72.54s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 3/20: 0.0s downloading, 6.2s loading, 69.4s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██████████████████████▊                                                                                           | 4/20 [04:49<19:39, 73.70s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 4/20: 0.0s downloading, 6.1s loading, 67.6s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|████████████████████████████▌                                                                                     | 5/20 [06:05<18:38, 74.56s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 5/20: 0.0s downloading, 6.2s loading, 68.2s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|██████████████████████████████████▏                                                                               | 6/20 [07:13<16:49, 72.12s/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, 60.9s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 35%|███████████████████████████████████████▉                                                                          | 7/20 [08:27<15:45, 72.71s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 7/20: 0.0s downloading, 4.9s loading, 67.2s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|█████████████████████████████████████████████▌                                                                    | 8/20 [09:39<14:31, 72.63s/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, 65.9s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 45%|███████████████████████████████████████████████████▎                                                              | 9/20 [10:50<13:13, 72.11s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9/20: 0.0s downloading, 4.6s loading, 64.7s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|████████████████████████████████████████████████████████▌                                                        | 10/20 [12:07<12:15, 73.54s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 10/20: 0.0s downloading, 4.9s loading, 70.3s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 55%|██████████████████████████████████████████████████████████████▏                                                  | 11/20 [13:15<10:47, 72.00s/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, 62.3s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|███████████████████████████████████████████████████████████████████▊                                             | 12/20 [14:27<09:34, 71.81s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 12/20: 0.0s downloading, 4.9s loading, 64.9s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 65%|█████████████████████████████████████████████████████████████████████████▍                                       | 13/20 [15:36<08:18, 71.15s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 13/20: 0.0s downloading, 4.8s loading, 63.3s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████████████████████████████████████████████████████████████████████████████                                  | 14/20 [16:51<07:13, 72.24s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 14/20: 0.0s downloading, 8.2s loading, 65.0s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 75%|████████████████████████████████████████████████████████████████████████████████████▊                            | 15/20 [18:06<06:05, 73.10s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 15/20: 0.0s downloading, 6.5s loading, 66.9s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|██████████████████████████████████████████████████████████████████████████████████████████▍                      | 16/20 [19:19<04:51, 72.87s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 16/20: 0.0s downloading, 5.0s loading, 65.7s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 85%|████████████████████████████████████████████████████████████████████████████████████████████████                 | 17/20 [20:33<03:39, 73.24s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 17/20: 0.0s downloading, 4.9s loading, 67.5s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████████████████████████████████████████████████████████████████████████████████████████████████▋           | 18/20 [21:50<02:29, 74.62s/it]audio needs conversion, will be slow without using joblib\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 18/20: 0.0s downloading, 5.1s loading, 70.9s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▎     | 19/20 [23:06<01:14, 74.74s/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, 68.3s encoding -- 2.1h processed\n",
      "  loading audio...\n",
      "  embedding audio...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [24:25<00:00, 73.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 20/20: 0.0s downloading, 6.2s loading, 72.1s encoding -- 2.1h 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": 21,
   "id": "cb95ea16",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T15:38:59.453945Z",
     "start_time": "2023-10-10T15:38:51.141586Z"
    }
   },
   "outputs": [],
   "source": [
    "np.save(\"/home/tony/Data/MERT/mert_95M_25hz_norm_val\", np.concatenate(val_data, axis=0).astype(np.float32))\n",
    "np.save(\"/home/tony/Data/MERT/mert_95M_25hz_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": 28,
   "id": "aedeebae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T14:48:34.708961Z",
     "start_time": "2023-10-11T14:48:33.513987Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1144573, 768)\n",
      "(12298, 768)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "import numpy as np\n",
    "import faiss\n",
    "from sklearn.metrics.pairwise import paired_distances\n",
    "\n",
    "model_name = \"95M_25hz\"\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": 15,
   "id": "e8492b0f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T13:23:01.307114Z",
     "start_time": "2023-10-11T13:22:59.268772Z"
    }
   },
   "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": 16,
   "id": "2ddc9906",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T13:23:01.312779Z",
     "start_time": "2023-10-11T13:23:01.309128Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.18719457, 0.14210634, 0.11559282, 0.15465523, 0.16351742,\n",
       "       0.30876616, 0.14416328, 1.0065194 , 0.46821287, 0.23377253],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_std[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "694889ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T13:23:01.360310Z",
     "start_time": "2023-10-11T13:23:01.314945Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.18896   , 0.14390066, 0.11504049, 0.15495309, 0.16608714,\n",
       "       0.31336072, 0.14451231, 0.9961263 , 0.4713227 , 0.23656246],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_std[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fa8d9a17",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T14:47:42.198673Z",
     "start_time": "2023-10-11T14:47:42.196569Z"
    }
   },
   "outputs": [],
   "source": [
    "DO_NORM = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a21d1807",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T14:48:06.582160Z",
     "start_time": "2023-10-11T14:47:42.682855Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculating codebook 0...\n",
      "train codebook 0...\n",
      "\n",
      "Clustering 1144573 points in 768D to 4196 clusters, redo 3 times, 50 iterations\n",
      "  Preprocessing in 0.47 s\n",
      "Outer iteration 0 / 3\n",
      "  Iteration 21 (20.84 s, search 19.65 s): objective=4.8403e+07 imbalance=1.109 nsplit=0        \r"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1084751/3178925830.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0mgpu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m     )\n\u001b[1;32m     38\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"train codebook {n_codebook}...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m     \u001b[0mfaiss_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_arr_resid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"finish train codebook {n_codebook}...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;31m# score preds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0my_cluster_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfaiss_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_arr_resid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/suno_env/lib/python3.10/site-packages/faiss/extra_wrappers.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, x, weights, init_centroids)\u001b[0m\n\u001b[1;32m    453\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIndexFlatL2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgpu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfaiss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_cpu_to_all_gpus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mngpu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgpu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m             \u001b[0mclus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m             \u001b[0;31m# not supported for progressive dim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    460\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0mweights\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/suno_env/lib/python3.10/site-packages/faiss/class_wrappers.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, x, index, weights)\u001b[0m\n\u001b[1;32m     81\u001b[0m             \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mascontiguousarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'float32'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_c\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswig_ptr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswig_ptr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_c\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswig_ptr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/suno_env/lib/python3.10/site-packages/faiss/swigfaiss_avx2.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, n, x, index, x_weights)\u001b[0m\n\u001b[1;32m   2675\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0mparam\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m      \u001b[0mindex\u001b[0m \u001b[0mused\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0massignment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2676\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0mtype\u001b[0m \u001b[0mx_weights\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0mparam\u001b[0m \u001b[0mx_weights\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0mweight\u001b[0m \u001b[0massociated\u001b[0m \u001b[0mto\u001b[0m \u001b[0meach\u001b[0m \u001b[0mvector\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mNULL\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0msize\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2678\u001b[0m         \"\"\"\n\u001b[0;32m-> 2679\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_swigfaiss_avx2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mClustering_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# https://github.com/facebookresearch/faiss/blob/edcf7438bb1862d544db53ed20c60795bdb45e3d/faiss/Clustering.h\n",
    "n_codebooks = 1\n",
    "n_clusters = 4196  # 1_000\n",
    "\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",
    "# input_X_arr = (\n",
    "#     X_arr.copy() if not DO_NORM else ((X_arr - x_mean) / (x_std + 0.0001)).copy()\n",
    "# )\n",
    "# input_X_arr = np.hstack([X_arr, input_X_arr])\n",
    "# input_y_arr = (\n",
    "#     y_arr.copy() if not DO_NORM else ((y_arr - x_mean) / (x_std + 0.0001)).copy()\n",
    "# )\n",
    "# input_y_arr = np.hstack([y_arr, input_y_arr])\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",
    "        min_points_per_centroid=128,\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",
    "    print(\"val score:\", round(np.mean(paired_distances(input_y_arr, y_preds_prev)), 3))\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",
    "    print(\n",
    "        \"train score:\",\n",
    "        round(np.mean(paired_distances(input_X_arr[::100], X_preds_prev[::100])), 3),\n",
    "    )\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": "markdown",
   "id": "e1a7fc17",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T14:53:40.801166Z",
     "start_time": "2023-10-10T14:53:40.798758Z"
    }
   },
   "source": [
    "## 95M 25HZ default with norm\n",
    "- val score: 24.94\n",
    "- train score: 24.8\n",
    "- val score: 24.493\n",
    "- train score: 24.338\n",
    "\n",
    "4196\n",
    "- val score: 24.384\n",
    "- train score: 24.139\n",
    "- val score: 23.537\n",
    "- train score: 23.196\n",
    "\n",
    "NOTED\n",
    "- More clusters (like 4196 vs 1000) definitely help -- if the data is large enough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8a9fd591",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-11T14:47:35.404719Z",
     "start_time": "2023-10-11T14:47:35.258464Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do norm True\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": 8,
   "id": "e3837120",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-10T16:30:24.913010Z",
     "start_time": "2023-10-10T16:30:24.906972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroid_saved = np.load(f\"/home/tony/Data/MERT/cluster_centers/normalized_2.npy\")\n",
    "np.array_equal(centroid_saved, codebooked_centroids)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e7e2f6d",
   "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"
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   },
   "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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