diff --git a/docs/core.rst b/docs/core.rst index 2acf61183..599f403f2 100644 --- a/docs/core.rst +++ b/docs/core.rst @@ -51,6 +51,15 @@ Low-Rank Sinkhorn sinkhorn_lr.LRSinkhorn sinkhorn_lr.LRSinkhornOutput +Low-Rank Sinkhorn Initializers +------------------------------ +.. autosummary:: + :toctree: _autosummary + + initializers_lr.RandomInitializer + initializers_lr.Rank2Initializer + initializers_lr.KMeansInitializer + Barycenters (Entropic and LR) ----------------------------- .. autosummary:: diff --git a/docs/notebooks/LRSinkhorn.ipynb b/docs/notebooks/LRSinkhorn.ipynb index 72fef6205..dc304a367 100644 --- a/docs/notebooks/LRSinkhorn.ipynb +++ b/docs/notebooks/LRSinkhorn.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "id": "q9wY2bCeUIB0" }, @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "id": "PfiRNdhVW8hT" }, @@ -81,19 +81,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "id": "pN_f36ACALET" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" - ] - } - ], + "outputs": [], "source": [ "rng = jax.random.PRNGKey(0)\n", "n, m, d = 19, 35, 2\n", @@ -114,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "colab": { "height": 515 @@ -136,7 +128,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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cWnoLYUB8BLo54RsbtD18S94/DV0p1RDR/+fs4e5eSqklSqlvlFJ7FImVUmOVUvOVUvPz8/P3f7V7wfTp06koLWJMuz8KV0/MitL4uYo9JkrHtFNUlBYyffr0g74mgDPOOAOQJKmFP8d406oI8uXZpCA+AHFRMBKEuD2Ihr6ZquHQ8QihWxG6hYOBKDAduAO4DYnOGwK/AgtNEveYyU8VhTQNA93woZLjDif2mdCVUgnAJOBmrXXZ7+5eCDTQWncAngc+3dNzaK1f0Vp31Vp3rY6ZnqNHj+bam+5g4HuQU1EVjcfcLL/++usf3C85FQYD34Nrb7qD0aNHH/Q1AXz33Xc0aNCAgQMHVsvzHwso0VL52dAk9Bkh8CkI2qBFhQyzcAA1zePLkNJ/hUTsFqFb+LsIAd8gEfnDSNBwAjAfmAyENaSaWrkRFRdLXRsMc8JrQO3DtO5dsU9XqUopJ0Lm72itP/n9/bsSvNb6a6XUS0qpDK11wcFb6r4hVr4/8NVn+PF8g7eX6d2siT/NmMOAvj2AMsa0Uwx8D0ZdcXO1lP2vWbOGlStXMm7cOO6///5qKSQ4VvCyhtEmmYeBr0PSiCvkhDZRcbeEENLOpKqPS2ywhUXoFg4UlcAURHZYjlzxnQu8AHwHaC1WWZuWY21R0c2znDBYwQNIUHEkYK+ErpRSyAa0Smv91J8cUwvI1VprpVR3JPIvPKgr3Q/EyLnjs4+TkJy+m898V1J/Yk4R1950R7X1cLnqqquYM2cOp512GjfeeGO1vMaxAA0sAmLNRBcjNrCUSiiKh2gaNEEKipIxk6PIpbEL+RDbEH3TgoV9RRFC5EuBlUhgcAlSsn8XksdxaUjXkK+E0A1DovRaThiu5LgjqW3zvkTofYALgWVKqcXmbfcg3yu01uOBs4FrlFIR5Ls2SsfKIw8T7nvwYZq2aEX//v3/UAFar149fp45l+nTp1ebzAL8VlBkYXcUFRUxffp0iouLcTqdbK5dh879+qNc8nFcYIjHPDUKNYIQTJKioWyE0NORKB4kMrdxaKrwLBwb2A78AKxFHB5u4HLgJ+AmJDdj01LrYNPiaPFGocSA+maB23DzWNceX+HwYa+ErrWewV6+L1rrF5ArlCMKf0XWWVlZ1UrmFv6IefPm8dSzz/PZZ5+RUL8VeJJQhkF58Q68gVIC113DpWOv5PvEmigDKjxwRqVILUlIBO5BvMCxqEhhzRK1sG9YhyQ7tyJE7kIi1Xwk+RnTjRM01NSwXYms54lAhYJWDqhlg6HAVRyZrqojcU2/QWuNKD7HDw7zhU21wDAMbrrlNt54531c7YeRfuk47N6qzitxQCh3I89//i2PPfk0aRPfJaX7EIrjoG6KeM41khBVwGqqEqEKq0LUwp9DIxLefIS4lyG5lvOBNOB2JGK3AU4trSXcGjYrSNRQGgaXDdo5JDo/GbiII7fE/ogldKfTid/vx+s9khSq6off78fpPHbSe1prLrtiLJ9Pm0fKmGewe/bcE9FVszGumtdib3EiOy4eTf2n3sYxchhFSdAIKFFViaf1SHTlQgjdfUjeiYWjCQZSALQaCQgWIUR+FuK7vhnRzjFvj9dQT8MGBYU2qBWBzVFoaIdMBzRDHC/ncmTLe0fqRkNmZibbt2/H5/Mdk1Hr76G1xufzsX37djIzMw/3cg4aXn75FT6dOp2E0/71p2S+KzxZrakx4m623jqGRuu2UqFEPy9HCDwTaZsbQmQXJ/IFO/Y/IRb2BSHgW+B1xEv9CyKvnAo8iUguQ4AViITn0lBfQ2MNK5QQYnoQthjQxQ5ZDuhqPuY8jmwyhyM4Qk9Kknhsx44dhMPhvRx9bMDpdFKzZs3f3vvRDsMweOS//4fnhGuwuff9SsuT1Yr4lv2Je2Mczq7/oQ5SnVcbycRHkERVEkfwB9jCIUUlMBUoRlwZs5HcyhCEzCcCA8z7nEhSPUVDAw0bFWy3QU0DSkKwwwa9neBU0uukC9K75WjAEf19SEpKOmbI7XjEDz/8gM+wk1i35X4/NrHdUGa9fy/dnnyABLcbhdgU6yEuhHLE7eJFovMjPXKyUD0oQrziEaQwbSbiIz8JOA2YhyQxCxEidyAaeX2gtoafFSQqaBqBJWFIcUAPB7iUSCwdgYM39qb6cUQTuoWjG+P+9zqq1UkHlNh2pmdhT8+i4NtvKTn9dDKQy+k4pKgojFweJ5m/Wx704ws7EOuhG4nOZyDFPwOAEch4wjFIK2aXeVwUqKOFyHcqmGETT3ndEPxqQGcnJDmE+AcgkXm3Q/3G/iYsQrdQbdi4eTPOpu0P+PH2lLoEsrMpUFXDK3ZSRd6xKUVBjjw/sIXqwTqEvNOQqPxHIAXoB5yCyCx3IhKdHYnWw4hjJRNoouEzBakK6moIBmCWHUY4IWqXx/RDpJZ2h/i9HQxYhG6h2hAKhlD2v+HYsTlxBIMEkS9wAElwJSLRFshs0QDWB/lYhkaatS1AJDeAz5HPRF9EUqkJPIH0JzGoagcRARpqyNDSevlTm1gTm0ZhVkhaM5/lkP77NiQqPwFxtRyNsL4HFqoNqampFPh/38dtPxCqoFFKCsVI69wo0vkuw/w9ZlmswIrQj0UYSIfDNUBLhHA/RCo4eyKJygZI46ynqUp42pCrtmTTV95Kw0cKkpT8v0sYJkWgrlP85S4kkm9iPmf9Q/kmDzKOWNuihaMfpww5Cb1xT52W9w4jHMS/YT7dTzwREPKuj3TAq4F82Q1ENwWL0I8lhJBE5xsI0WL+vhHRtC8HrkT85acC/0E2+F19VM21yCtZwEQbZClxtWQF4MMoDHRBM4dc7WUhAcNpHN1kDhahW6hGXHnF5VSsnUX0AKJ03+oZNOzSBaNpE5IQO1o9JBoPUEXgbuSS3CL0ox+VyFi395C+4gYyr3M7IoVcgJTcJyMDJ8YipJ6AfA4iSMKziYbuGpYpmK5kaEorA3IDMN0G17lA2cUl1dF8vnOAWofurVYbLEK3UG2oUaMGp5wyAt/CL/brcToSpnz+JE688AIKkS+sn6oILB8hcBtyia2xqkWPZhQDHyBk3gGRS55ACsg6A2cAVyMVw/cjszo3UkXkBpLMbGU6WDoBz5lRuRs4JQyTA1DkhH84xWceBwxCrgbGIHr8sQCL0C1UK55+4jHUup+pXDVtn47X0QgFXz6Bjhq8ccONvHvnXSijalhJBInKvMhltg0h9GOnWcLxg53AW4hTpR9SW/Bf5O/bmaomWO3M44YimnocIsU4EAdLY7NAqJuGLQomKGiP9DHvHITnwlDPDRc4oEDJY8YgG8YlVDmojgVYSVEL1Yp69erx09QpDDhpMKUlOSR2PvVPq0bDxTsomjoelKLWxc+iwwFmfPwfQtnZXPrOW/htNmL9mTORalE7VbNFLRwd2ICU4NdEKjk/QSYENUMkkE5AdyRvMgt4hCprqgfZyIPIpt5Mg0NDTwX32aRMPwnoqWFaEN4HznKDYZfbVwLXIr1dLufY+9xYhG6h2tGuXTt+nDObPgOGsG3WR8S3HUh8y37YvSloI0K4eAcVS6YQyt1AYsfhJPcZhbLZwekm7cwHWfjpg6T94y46Pf5/xCNE3sj8V1E1ucjCkQuNdDqcR1UC8gPgK6AVQuStERuiQlrc/hPRz2MReSwRHkTklTDQScMcBQ8ocaisBK6OwmNBKHTAw07R0Xsg04huRMj8Mo5N8jsW35OFIxBvh6IE8nOpdeGT+NbMoPjnNzCCFSibA3tCGvFtBpA58l8ox+4xk83pJnHEXXz18rUMuO5aUho2ZAtVH1wb8sU+1iKtYwUG0ldlJaKPnwm8C3yESCkdkMi8P3K15UN08gVIstKLEHoUSYbXBGqYg5pPVXC7TTb3FsiV2/Aw3BsCpxvGO2TT6Ilo7lcjm8qlHLutIixCt1DtMIDPnxxHfNtBuGo0wFWjASl9L9jnx9vjkohvPYAPxo3n9Mf++1ukFnM2RDm2dNBjAWHgZ2TKVC+qiPwNJBrvjFgEByIkZADjEaJPRSJyL1VtHqJAZw0VWjaBLQquVaKF/wBcpGFmCJ6JQksPXGGHLxEtvRBxyKxFjj9WyRyspKiFQ4BP/QE2vTeB+A5DDvg53O2GMOd/r5FkVo7Gphj5kUvw+L98tIVDBR/iVnkXaI5IK1ORqDsBsR/WRYZEDEbI/DvETz4VicpdSFRuIFbGekAbLUnOixS8YoMflPRsmQ3cb8BbAZiqYZQHhtpFXslEPhdDEOnmSO9lfjBgEbqFascr383GkVILZ2qdA34OZ3oWjuRMts6d+1vEloQQe5CqAqNDhXnz5nH+mIvIqFkHd5wXjzeBzDr1GHvNdaxcufIQr+bwowSp4vwUiciHIRWcDyK9Vrohvu8LkHmcLqQC9HzgeeRvGWu2pqm68uqtwW5AOw2tFIxW0kkxHiHniyNwkw+2OOAZN0RtEtmXI5JOG/P306r9DBwZsCQXC9WK9RqCKwtxxP99p689IZW8wkLsCInXNm/3UzXNqLoxdepUbrztTrbn5OFsOwTPmQ/jjRMailYU8cny6bzb5wRat27FS88+RdeuXQ/Ryg4PcoDvEffJUCRCfwfYhGjXachmO5SqOoJi4AFEAqlN1dVWrPysAiFjpSGo4UoFdyuJsm9BdPEbgIUh0cvjPPCpA55FiHsKMIqqHj8nVtu7P/JgEbqFasU4HziLDel+9LehKDUMvEgEFyPxWFvd6sbLr7zCbf/4J96BV5M6tJs4cXaBzR2Ps88FxPc8l40rpzPgpCG89/ZERowYcQhWd2ixEbEe1gDORvqSv4q4U3oiyUsnQrCxq6cw8BwisTQwb491RIwgBBwP9NOQp0WeyVRwlulgyUI2j2c0PBSExQa08cKjNngckXE+Bq4311eXo6/97d+FRegWqg1+DTs3gNubhvaX/u3nM/yl2NLS8Jj/j5F4lOp3uXz44Yfcfvd9JJ/7771KR8ruJKHdIJzpWYwaczHffPEp/fr1q+YVVj80ok3PRZwlY5AI/SUk+dkbKbMHaWWbvMvjJgOvIbp2JnJVlYFo5HYkau+OfGYqNIxV8LiSQqKHkM2iF3BeFK4Nwk47XBAH/ZVINhcifvZ7kSEXbc2f4w171dCVUvWUUj8ppVYqpVYopW7awzFKKfWcUmq9UmqpUqpz9SzXwtGE16OQtBVqNutOqGAzkbKCA36uSFke4fxsUrp1+81zHiN2g+qtFC0oKODSK8aSeNo/9ysP4K7TgvghN3P6WecQCoWqcYXVC40U+LyGEPFlQFPgGeAppO9KH+RKaTAid8TIfBGik3+AuFoCiJYO8jcrQ1wt/TSUGNBaw3kKzlWiy18NvAzcAWSE4Tof5LvgeRfUV1JlOgz4GngU+AmJ7I9HMod9S4pGgNu01q2Rq6nrlFKtf3fMMMRO2gzpmTPuoK7SwlEHDczcCoSgQa1E6p92PuXLvjvg5/Mt/Y5uY8bgi48njd3L/aNUL6G/+uprxDXtjqtm4/1+bFyjzqjkOkyePLkaVla9iCASxwSEoK9AousnEDJvipTsVyI+8guo6omSg1RkPoBE7Qq5ikpGSN2G2AkHIF0QS82ofI0NRiq4FbkCmw38T8OHQXghCHHx8K0DZpkl/I2A+Ug16WTz+Y7WXuYHA3sldK31Tq31QvP3cmTGQN3fHXY68KYWzAZSlFK1sXDcYroBcRvkizygKQQuv56K5VMxwoH9fi4jHKBy+VSG3HAdQSSii1ClzVZnhB6NRnnmhZdwth16wM9hazOE/3v6uYO4quqFH/gCSW42RSJyD6JTv4BUdg5Ceq70ROSOzF0e+7D5mATE4eJDCKMCIel8JKrvqSHHgJZm4vNiBdOAF5Ghzh2Bewy4NgA/RUUv/9oG/2e+bjkS4f8LsUmeStUAjOMV+2VbVEo1RFot/L7JdV1ERothG38kfZRSY5VS85VS8/Pz8/dzqRaOJryZB85ySPJAZm0It2qDc+AQCr9+Gm1E9/4EJrQRpfKbp2h42ggatmz5G5GH2b2YqLoIfcaMGYRsbly1mx/wc3ib9WTNmjVs2rTpIK7s4KMUsR5+gpTKX4yc58cRnbwlIqnkIwU7F1PlNDKQBlrnIE2vGgN5SCIzjCTrfEh0Phiwaygxidxng4FKEqiDgCeRiLt+BC6thA02uCgOnrCJy+VKRAKqgVwFvGm+bs1qOzNHD/aZ0JVSCciEp5u11gc0hkZr/YrWuqvWumuNGjUO5CksHAXI0RBeJ9F5zwbwRTmgIeG513DGuyn46gmMcHCvz2OEA5R+9Rhtank58dVXfptEEys6iRUTVafkkp2djTO93gENuo5B2R14M+qSnZ2994MPA3KRaHwqUoRzAZKk/D8kGdka8Y7nIxH7peweCf+C6ObfI0RfgjiQUsznSUDcL12Athqyzaj8BgU3KYnI30Skk62IxPNlGB72QYVX/OUDlCRH/2WutQdC4m9xbLW//bvYJ0JXSjkRMn9Ha/3JHg7Zzu5/4yzzNgvHIZ4vB08BuOxwcjOYFAQ0uHHR7pWvoWkWOyZcR8msD4lWFv/h8dHKYspmfUDhG9fRu0Mjvv/2K+wu8bHE9FcbVRF6dUouPp8Pbf/7rb+U04PP5zsIKzp42IxIG3OBsxD74Xakhe0ExAt+CtLmti4io+yaRdiEJMyeNY/VyN+nARKdpyIN1DxIVB40o/LLFaTYoLeS53sAacR1MnCvhpsC8KkfvAnwtV0skZOQ4c8vIETeFxmEcQlW24ddsVfbopLQ5DVgldb6qT857HPgeqXU+8jmWaq13nnwlmnhaEEI2LRO5jm2rQMBJ2yzAQakFIM/xU3ai+8S//NCNn79AmVvXIcrqyW2OPFFxPlLqNi2hp5nn82pz3zDdZ06/WZLjMX0McHGg1jeNNXnv01OTkZF/H/7eYxgJcnJyXs/sJqhgRWIZtoQicYdiB3xS0STPgGJsFcgWvUVv3uOMuAx8zFdkc11BzKTMxf5uziQDWMgYGjYqqG1glOUROZzkd4tXwKvIInWUgMu8kOODdonwKs2mRXqAUYjss91SGJ1MrLBWE3Zdse+fA/6IHmPZUqpxeZt92CO39Naj0dcQ8OB9YhUdulBX6mFowLvBSFuu3zJR7SAGUFJYBo2yCiC/AYSUWfV6Uz2+NdRjz+FMWM6ocoilIJz09MY1r8/DVNSWI/IK6uoSq7FIc/nRD68MfmluiL0Nm3a4MteiceI/qGQaF9hBCqozMumWbPD57/QCIkuQ6Lpy8zblwLfIAnG/oilcDFC1Jeze++TCCLBfIPo6e2QAp7G5uPzEE19OWIbbAxka3nxixWUK4msmyE2xruRTWAi8GYE3q4UieUCB9yixKo4AJHW3jWPDwPfIgRjFdH8EXs9J1rrGeylp43WWiObp4XjHFM3QkIEGqRD7ST4sABsEYg4zWjKDzoefOYnz5GYQvTU07B5wKEgqKREvBESHYAQeg2qKgkjCJHbqX5Cb9u2LU0aN2LH+jl4m/c+oOeoWPYDffv1IyMj4yCvbu+IIM6RLUjhzuXm7YuQis1KxHpYCyH8NP5I5Boh0QmIjNIf2QjqIaS9DdHWcxGdfRgQ0LDRjMpPVfCokoTr/cgV1i2IXDJMw11hmOuHaAI8aZduilcj5f2rkB7q95vPP59ju/3t34XVnMvCQcOCKDg2y5dtSHOo1LBES08OR0SiL22AJww7suQxNm1+OZVEF7nIJf2u1LcOiRwrEQIPILrprgnS6vSh33nLjRgrDsxDr7VB+YLPmPbzNAYNGUZOTs5BXt2eEaDKetgYicjbIH3GH0OaaHVGpIytSIR9OVIqvytZrjQf+zZC/CDySkvkiqkS+VvFIv++SNl+iYYLlUwSGqRgBtJjZZq5rmeRhlsXB2B2EBIT4XO72B/vQhKgvyIb0QPmv8sQicgi8z+HRegWDhr+tx1cfkiPh/a1YGEUyg2J8FIqoNIJKIjzQ9BVReZ281NoU0LmGon0Yja0HCS6L0MI3I/oqH6qhh9UJ6GfdtZZhH15VK74ab8fWzb7Y2zeVDKvfoMlvlQ6devBli1bqmGVglKkn8kkJCK/GNHK5yBE/iXiNrkIiaxzEcLuw+5EWQDcBtyHbAT1ECmlMXKVlI3IJbF3MhSI17DBkNtvUzDF7Ix4KqKFX4OQ//+ARVG4rhI2KWibIP7y+Uiy7inE9aIRmWWJ+XrnYJH53mDJUBYOCooNqNggkXO/xtKL6zsfGFHQNkgugtJEwAYhBWiwh0E7wWmTiBLk39gIsvrmbUHz9liZeMyyGCN0g+r7IG8DnnC5Oe2Jb3hvbH9wuIhv0WefHlu24AvKF39DrTFPYHd5SOhzAZWeBE4cNJjlSxYSH3/wurjnIbZDF+IoSUYI8VckKg4hvVaaIoMnQEj99+ctiNgIY/1QeiFE3hyRY7aYz1GCyC59kY13s9mv/HwFqUoGWuQjycvvkaTbWHNt/w7D95Xgi4fRTnGvPIVE/A8iCdLWSOLuZ2TDPvUgnKPjARahWzgoeKEYPCUQ54SBjSCsYXoEXGHwuyDOB8FECEehKFlI2x2BgAPcSqoIQTTf2gihD9/l+TVC4LWpkl58VPXPPrB05Z8jpht/qUFvg9XNW9Pjkaks+tdwwtkrie88HGfaH2rnAAjlbqRs3mSCO9dR8/z/4EhM/+2++C6nU75jBW+99RZXX331317nZoSw04CRVG1wMxB/eBiJvpsjfU8iiG/791c0GpkWNAmRPYYjxTsGIqXkm49thZB9e2Ts204NmzQ0VnC6gm+URNWDEM39dkQGeA5wargyBGsDYCTBYzZxwdxuPt+JiGVyIGKX/AYJEAb87bN0/MAidAt/G1Fg1VpI0tClHjgdsMgQ/7LWoAwIO8DmAFsItB0iNvBEACXRvOlsJIJIBDupIqfYZXYAKVIpRKxsfoSYbBzcS/EKRBbI1eAvhZXFkJoPWfGdGD5yAW/nPs3Gd+/BmV4Pd6u+2L3JoDXRymIqV/5MpCyfhI5DST3pKuyeP47esLcbzhPPPM9VV111wAVLK5E+J/URHdyJnKvpSFQeQaLr1kiE7EOaZO3J5jcHsQTaEelkGWJZbIOc842Itv6LefzJyBXAVg0RLY20aiqJwBcg3Q8diNxzAjKYeaYBTwWg0ICkRHjNHERxNXKlkIKQ/rmI73my+d66HNDZOX5hEbqFv41PKsGdCw4bDG8ht80OQygMWkFcACqTwRaFSo/8G3aYCRybELpSQkghdp8+VEhV4UjM5WLIwwhw8BOiK4H3gVQDtvlhSy7EF0B8JTRfD5E6deg4+HGuavAwj3zTgYrVv2BzukHZsHkSSOo+krim3f/S4uhp0IGCaa8wb948unfvvs9r04jjYylCtjG3RxTpMjjX/L0HElVPRWSqs9lzv/hspKy/AJFjKpHNoI15/xakoKgWIn20QzaIjWbSs6EZlS9QcnXQBLlaeBaxPt6MRNcvReQz4ouDNk54UcmVxb2Ie2UzknQdi0T9H3D8tr/9u7AI3cLfxhfrIc6A5rUgNU6i8ukB0cjDWvRzXy1Izgdfijwm0Q/KDihJhsYKhKJIZWAsbl2DJOQUQvbx5nEgETocHEKPIs6PpUDLKHwehJIiUHlC5g2zocMyeH8kJFZCZSMP0WAl6affiTOl1n69llIKd83GbNiwYZ8IPYJE3puRSDlmK4xZEucjG1t3pNHSd+bxZ7DnWasVCOkuRaSOLkj03RQh7RIkudoDcaS0RJKbHswJVBrOVlBXCSl/hjhTBgJXIRbT58zXvjUMCysgkASj7CKvfItE4M8jzpcFiI2xNpIM7WOuxcL+wyJ0C38LK8NAtkTMp5jR+QYNaw2wByHogNRSCDSU/xsKIg5IDkkCzmYX/7kNIWoDGU0W6963GokQDUQusJu/e5AIPcDfJ/R8pHAlAPSIwIQQ2HxQnAN1CyHeBwOnwy+9wGVAyzWwdCzox4Iox4HVKmq7k2Dwr/vZBBC5pADxfg80b48gEfki5Jx1QwqBpiBR+XD2PJIvgkTCU5DeHOcgkbcf6WwYBjYgBL8E0eF7I1H5SlNeqa/gNAXZStbjMV9zHkLmQ5BIe4WG2wKwIwRGMvzHJlLNOOTK4HlEY9+JbAYJwOvm44/3jol/BxahW/hbeGkruEJQJxmamLm/uYbYFcNKJBflAG+hyC1KQ9gFzrDZl8UmpKCp6tGyErmEB4lKGyHe52SElGwIAQQQMvo75d+/IlKFG2gehtfDkG7ArFxouVXW1WkluIOQVwPsdiiqC3WbQmJyClFfCSQcQGsofxmpqal7vKsMibIDSHIx1tEwDPyARNYaIfEeCKF+iRT07OkZNbIBTESi5lORoq2fEXlFI/JKCkLs3yKk3tC8b7UZlZ+jIMtspjUOSa7eijhYtiOVnf2Bd6Lwjg/K7JCYBOOVSDf/RMj6PiSCNxAyVwiZn0nVRm7hwGARuoXdUFJSwoQ33mDCW+9RkJ9HJBIhKTmFQQNO4OYbrqNVq1a/HVtmQPEGiQYH7XKNvCgMYbMhlzMKoURIz4F1WWAzJMq1m/X7NiURtka+2A6kOjBmWSwx/61AtHQXVYRegOi+B0LoAaRysRKoryEahjci0MwBk7ZD1xXSh6aWD/rOhHfOhFQfpBfCkovgIgVFw4fx0aKZuDL3b/BFtLKEiuxV9O/ff7fb8xEidyL2vhTz9hASqa9AzlFXpMfK94h0MYzdC7F2xVpkGIUfkTLsCLm3R4q1yhEv+iBEcoq1AGiLFIVpLSR+qoISJS1uC5E8gxvpstgU8ZknAfdHYHo5BOOhrROeUbIJX4dIQD2RLo41kEg+iMgso9jzZmRh/2ARugUA8vLyuO3Ou5g0aRLxjbtgazkCR5eaOG12Kv1lfLx0Dm/36ke7tm144r+P0rdvX17KA3cFJHqgp8nAOzUsDoHTJxF6QiWUpkJiLhh2kVxqRSQxqtwitxi7GD0cCInvOl6uCNF0TRs7iqrE6YFE6BsR2QGgm4Z5IfjVgC4uGFcIfedBuRvqBODkKZDdAEJOCMdD1APOhiJFNL7hOib27EN8r1Eox74LP/7lUznjzDN/i9C3IiSbikSpXvO4IBJ9rzLfdxfkdX9ELIZDEDlqTyhCvN1bEHJuhTRcaojo7CBk3x4h+Y/N52+EkPoqDSFTK6+npEf6gwjZf4RUoH6BEPyFSM+Wf4VgnQ+CSXCuXXTxTUjV550I4T+FXBWMQq5E3kUifatj4sGBRegWWLduHSeeNJhA7U6kX/wi9oTfxUoptXDXbk5Cr1GsXz2DoSNO56XnnmFZwwtJBHo1lEgbYH4UNkVBRcWmmFoC29JhRy2JwsNO8Jp6rE2B21aV5ISqQQi7IoREr7uW+ccIoJJ919A14m0uNH8/VcNbQdH8+7jg0Qo44ReodEHDYkjyQaMcGHcWZOVDtAwWnQUtHHLF8HbzFrg6dKBswack9zhnn9YQKSsguORrbn/yW1YhXu96VFkPQa4epiIJYRsSkfdGEp3vIwnKrD95/hBiufwVIefzEN/4L+bzGEixVMyi+A7SO6UPQvqLzai8roLLFASU2A+XIwU/vRH3Sgjxm/cAvjHgf34pLtPJ8Iipl/+M9Ct/ArkKeAnxmg9Brq4+Rpw6sc3bwt+HRejHOXJycug3YBDRtqeR2HHYXx6r7E4S2gzAVbMJV910Gz2vTqJdz9MZsqvcEgF/EDwOSX6m+KCwHCoSRT+POsFeKYSu7OBVVYVBsYrPSvO5do2+yxAJwo9EsIm7HLMv3cpLEYklptGeq+HpgESyg1xwZxj6TZOripqlUFETRr8A89tKq4KCehC2Q2Jt2Vzu0dKSIPm1N9nRszs2VzyJnYb/2csDQublnz3IhXfcwsLOnWmNNKiK9d/wI5LLOvN8dEEqMWcgxDsQIco9QSMR8ySqIv1c87buiOxRiUTsJyGbwzcIkbdGEpVrzKj8LDMqn4lE2Y0QeWc9svF0QBpnJQNPR+GbCsmLJHjhOSVXARORxOqLSI5iCjKnsieit3+N1f62OmAR+nGOCy+9gkijviTshcx3hSujPimn3cPM5y/h9EEb8LokKVim4cdy6ediM5tyKSek5kFeJkTskKjAE4SIgqgdkpSUrbuQqM8wf6KIFBGrsYwNtqhACDwmueyLhr4IIZckZCPoYcB/gpJkHO6CqzX0/gVcPnltdzL0/x5caTCrDXReD9udMGU42DxQR8umsjIEbl2Xq8ZM5503BlOQvZyETsNxZ7XZrWAo6ivFv/x7fIu/ZOjtt3DVXXfRniprZiVC5BuoIvJ+SOHQm4jM8VfVkoupKgzqZ56zbxGppaN53tYhPvHeyObQzzyHTZCoXGmoraTNbUTBPxDCvwWJ0J9CCpDORaL+QuAfYVhSDpFEaO2USNyDzBT1Iv//HLFVXojYHzciVsvLOPjVvRYsQj+usXnzZmbOnEnGla/u92PdtZvhadSF4rlvwEm3AjAtBOsMSAkKWdsCkJ8AFQ5JhgY90MQGbh8EEwCHROhB5Msdk1tqIpHzaiThZlBlT6xESCMWlQf48wg9gkSsKQh59wd0FB4LgV3BWU4Yo6DnPMjYAQWJ0LkIKiPQrgC+awCZBbC2ORSkgZEi8y9bAa+HwFEI530EzUubsPOJRRR+PoEl371A0G3gqtEIbXeCv5TSLcvpePrp3PXYV5zZrdtv66tESHOL+d5jRL4AeAOJni/nz5GLkGYhokt3RRKbyUiVqEai4SASsb+NXKH0M49dichNMV95lhKZZ6x5jr9EiPliZDO8B9HfZ2kYF4TtAQgmw9l2uMn8292MVIeehUTpW5CmXHWRXMBC5KrkYFb2WqiCRejHMZ5/8SW8bQZgcx6YiuntMJTXXnmR+++6GZvNxmtFMsgi6gCikFwBJfEQcoAnBIYDgjZwRcSu6LBXWRZj2ngI0coLkKRdH0TiCCIRZQ7ikIgRwp9JLjuQ6LAzEsGOBtZE4e0QxCk40yUVlF3WQr3lsDUDBq+H1S3gjGlQEQfLmkLvxbCkJszpDilxUsn4agicJTB0CrRZDwtag8ubxJiaN3HOyhvpsGYmKzdtYlEwiCs1lbEnnkib9Kp+LuWIBJFNVbKzH3IVMQHRpX8/JWhX+JCIfBnS/XAIchXyMSJpVJrna635vNmILbAvQsy1gUVaTnymgtFKTuiTyBXBuUiE/iNiT+yOlOgnAxOjMNkn2rqRDA8qkXCyETvitUii9XlzHTchEtBiqiQbi8yrDxahH8eY8MZE4s548IAf767bkrIIzJ49m8yOvVkbgDiXuFnCdsgohrx0cAfEpmgHglH50IUAr11+j2nITiSSNhASz0V80wFEgtm1w2IMfnZ3SMQ814XIZJztyJT4HyLwXRhSFJzlEi9251xoMhM2ZUD/NbCuJXRZBTVt8H49aLwJVraFHfXAYwePC6aFIb4Emq2BITMhNx4KakLL1TD3LLijhmJejb6k9O3Lv9ndileGSCHbkffdCblqWIkQeUeEyP+M8KJIHuBbxN1yhnluPkIi8lijsk3IVc6ZyHi37uZPN6QAKKzBZ2rlWUo2ySsQd1FsHfchNQAXmq9TDjwYgVnlYPPKuRinRF+fg4yTe8hcw9PIJnuL+beaZf49zv6T92Xh4MEi9OMU0WiUkqICEv+kY+C+QCmFKz2Lbdu28UomhL2SBA0aopd7K6HSK6XzlV5INsBu+tADdkiyC4HH5oU6zX9DCAH4EMKKIJf+DqoklgiyQQSomvheifQB6YBE6CnAQA0fRWB5FNJtcLZTPN4dK6HJd7A1FZrugJJ6UCMfXLVh5SbY2goGzIMtrWFtF0ngLo5CajF4fDD2QyHGJR3AEZaCoy0DhJxPZ/eS+1JEWskx19wZiZzXIZFzG/44Jej3mImQbTwiaTRByL0RoovbkA1jq/n6XyJEPwi5ookH5mqZ7+lVMtdTKbENxtwrjyFXRqMRF83d5tpWaXgxDGvKJSpv7oD/IBvph4iD5nnzfT6HbDaXmn+vn5ANesRfvDcLBw8WoR+nCAQC2B1OlPp7M0603cXKQj/LNeABe5l0V7QriBpyjD0ClWlSNh4KS7tcQ0GyTUggDokAHVRNjo/1aSlgd9KONeaKReoBeVnWIMQyCIlgzwLSNIwPQbGGNAXnOsU21yEKjadAngcS/NAsAiuc4KkFNafBV02h7SpY3h5UQygNQSRBtP+oHW6ZBI4gLGoB3hDU2ga/jIFbnGIFjKEISXbmUhWR90Mi3wnIFcRl/PWUmc1IYVAIcaP0Bb5C9OgBSFStkerRroi+/wKyacV6oE8DUjWU79KDpRRxqqxGkpinIJbIj83XuAwh7M80fOiHojCEUuEsG1xvru1x8/w/jWxOH5qvHxtE8TUi0+xb93gLBwMWoR+n8Hq9GNEoOhpG2Q+8G4oK+ZgVSMbpALuWqUTKLwVF2+pAQgU4ohBxC/E4wpDsAGWTwRaVCKHFHA8OJPoOIaRQStVgi9gxit0JfRkSUcaaTF0ORDU8GZJjM2xwpkPkjfYa6k8DX6X0Zz9jGfzQEZoGoZYbdvigOAV6rIHNteCjdpILiARFSrpmHtReB9vcEPBC2AbrO0HtJjDADLELEY28wFxzJ4QktyHJzgZIYvCvXB6lSNS7maoofCOyEQykqoFZNhJ9X4zo3ZnIptYIudr51dTKnWZUblPSPuCf5jFfITJJLKl5CULuQcSS+HM52F0QSYJ7Tb3cbz6+A9L6dhZia+yFbCQaKURqgNX+9lDDIvTjFEopmrVqQ/GWpcQ1PrCvnREOUrp1Ff6mHaiTCL4K8XGHHZBWDKubQc088MeJPdFvgDcKDgfE2YXQIuZPTD93I7dvQyJEjcgniVRVioIQeRAphz/D/H8RQjBFGp4PgNcGdWxwil0i4zZAvaXg2AC5NeG86TC7O6QHIDQYkv8N37aCLkthXRv4tZm0/jUM8Hvg0u3Q+FuoCMOyrpAUlki9rAt0d8rGMwUoNt9DR4TIc5ACm5rseUrQrgiZx85A2h/EGm19hCQb+yBaehBxwwxFovWnzN8D5mt+D9TS0uY2FpUHECL+CXGeXIEUDF2DWAqvR64asoEXwrC4HNwJQuivIBtArvkcY5Crna+QhOwQJJmrgffM9976L96nherB37vetnBU446bb8BYMWXvB/4JfGtmkNKyC0mtG1HogSQ/2E03S1ohhDwyqSgQB03s4m5xAXl2qOmUCFMjJLYrodsQR0Qd83WKkcc5EXnFhkSFixCXxwrEcz0I2GZIwVCcDZrZ4HS7SBMtgHpbIWE2rK8JQ+fBqvbiua4cAI1+gh2GVLLWrIBfm0N2Y3BWynDr1n5o8BV4fbCyGbTYDn47rO4B8bXBp0RyKKdqan1LxCq4CBkuMZQ/J3ONbAbXIvJFP6RB2WzkqmOIeW4ciGMkGZE2XjMf29t8j5lIVO4w5PzeYJL5UqTny0pknVciEf2/zfN2N0Lm0zQ8GhB/OcnQyCVXBY0QF85diAPmBPO9LTbX0QPZaN40f7fI/PBgr4SulHpdKZWnlFr+J/efqJQqVUotNn/uO/jLtFAdOP/88/FvW0mkNHe/H6u1JrT8O5qdcyP10yDfkFmhtjDYo+AMgSMi0kvUKT1b7FGIj0K+DbIcVc24FLvLKS6qhhCDRJb2XY6Zi2wCPZFinL7msaui8EoQ4m3QyQ5D7BJFNgaySsA7FVbVgvabIJQCQScYTaGeC1J/gUVtodNiWNQM8pqCEYCAG9oEoUEetFsJuWZPl+x6sKMh6Bqw1S0J2HZIC9k2SLJxFmIBPIW/Ln5aiejZ3yJReKzPyasIcdY13/t2RHa5FHGyvIG4ddIQUp6NJD0LNQxV0lDLUNIM6xJEqvkMIf0rEf38MmTzcQOvGTC+AnJDcn5OtUvkn4hYQMcj/29g/p5tPm8LZDOegEgysb+bhUOPfYnQ32D3XM+e8IvWuqP589DfX5aFQwGv18vNN91E5ZRn0JHQfj3WN38y2BTdhw6nfjxE/eBzSR8QRwhyMsEVFKnCbhMZxBmV5JxfQZqzyuESa7gVa5/rRWyL6QjhB83b8xCJoD9CeOsRMkkCZkVgUlh6w/S3Q3+bEFhdxDbp/hY2p0BKOXTfJr1lIrXA6AwnfwmbPWALgjcMi7vCuprgDMCAECTEQd8voDgMizpBszzIT4S8WuCqJRHpVUhk/gEiaZyJuE3+yuGfB/wLSWI2NB/TAHgZ6Z7YA9GrNbI5dDNvexi5ImltvmYc4mBxGRIl36CgjhLCPQORRV5ChlHMRXIM9ZHWtycjmv+jUfi0VE54IBH+aSY/bYiWPwcZimE311tBVcFQAHHrnGb+38Lhw14JXWs9HZEnLRyDeOiB++jXsTnlX/4XI/j7tlh/hNaaygWf4V/2LYOf/YaG9e2sMCC9VLRww2YmROtJQjS3tgwQLtCSEK2l5Bivs8p66EQ+iA6qfOkViHyRgZDaSsTJ0hWRD2wIAWpgSVgIXQPDHdDVJsk5D+J7b/GTzLIM2+C66fBTR3FsFPSHR7ZD7jyY1Ro6LYHttWBDQwjaoX4IEmtB+nxosB5WNoE262BWe1jdFmomQMs4qTb9GJFMRiB+6z1NCorBj5DiPQgZn4xsPp8gcsspVFk1F5uPuQLpyzITIU43Eg3/CLg15GgYYkblSkkUNhLZIL5EfOiPIhLNcKR3eUOk7P8/IZhVAp4EMOJkRNwgZCO92/ybPIIQ/3hkE74BuSqpRCLz87B6mR8JOFgaei+l1BKl1DdKqTZ/dpBSaqxSar5San5+fv5BemkLfwc2m41JH7zHiF5tKX3vNsoXfb1HYtfawL9pIcVfPIpn03T6jf+VE5plURkHK6KQGJUeLiEnxFeA3ws18iAUBz2cklh0GEI2Tps4RlxUNdsyEBKMJUqDSITpQiLxWubPCoT86gEeDRtC0pe9HLEltlVCkMWIBn/SfMjPhaJEuGYqfNldukC6e8MTTlj8BcxKlT7nLgOmDYDNdaBlKSSngW8tZG2EXBuUJYMvA7bVh8wQ9KwtScL5iMZ9Ln/dBtZAWhHchDhg+piPWYlINKcjVxtOJJG6ApFfXEjRzinmOeiNXN0sNqPyIHCjGZUXIonXl5ENIxZNX4Rc1VyMSDZu4CMNL/lhXSXYUiHLKZ0aGyPPcxNyNXQNstG8iWywVyObZYl524VU9W63cHhxMFwuC4EGWusKpdRwpJ1Esz0dqLV+BUmY07VrV30QXtvCQYDT6WTi66/y448/cs+/n2fR+CuIa94LlZiOsjswAhUENswjUiOJXrfcSIvuF2ALxTOwHnyvoDwMcQ6wV4DDCa6w+LUTy8TK6HWACos33a8g0Sl6e4zQUxACSUMu32PbyTzE7ZKFHPsLoik3BGZo6aeiTBK70QUZCnpokR1OURBdAzkrYGcNOG06bM+SJGjjHtAsDV7aADWXwrqeMOBnKEmHhW2hVVDsfWlBSFwKkVJY3BGGLIBPLgBfTUgzYFEcnKGElPeG+YgskYzo5AMQl8rTyGZQjlgVneb77IJ4uv+LEPhAhERPRPzdTTQs13CukqZamLc/isgpk5AN7XOkGVdnxJlSFyH4lw2YUw5OOwRSZNjz1ciGuhrR3W9B8gJzkVa4jZGrD4VIYp9gtb890vC3CV1rXbbL718rpV5SSmVorQv+7nNbOHRQSjFw4CAujRvE0I+2803JZFYn5lJjewhvXBqOG25j3QW9sIUUM/Ph3hBs90CphqRSKHaZlkOjapzctoaSBN2qRD932iFkQLxbLtVTEfJ1m/8mItFmCULsuYg75Cfk+DYI4SkNnwXhQju8Y8C1Tlij4Cwt0X4vBTt3QoNZsCIdOq+ChiGYVgcyOsHaxvCehoGfws46UH8LuA147XToasB2NwQMaPoF7HBIcrf9ZljTF5Y1hLQiOKm5EOMpezmv2YgGHUYajXVFCHG8+X5GINF6GkLwach0n1fN930Bkgwdisgr5RpcZh7iRlNeqUSi8VkIwY5F/gb/QDaKEYgk4kKSyG9EYGEpZCZAvluOG2Su93vEBfMfxGY5BfH5d6Kqde92pPL1Mg7OgG4LBw9/m9CVUrWAXK21Vkp1R77LhX97ZRYOOXaWQY4fasfXpUmv6yluD52WQtQNuUMhS8NsO7SxQ1YWzDPgpyjULYc18TI/1BEWm1+cD/Izoa0fljnAExH3SWUYajhFEqmPWWxE1RDoMJIsTEAu799BIvK2SORoN+DlEDS2y+zSdJdEkCu16NH1FJSWQutfYEEi1C6CgWvghxMhVAfmdhSnSKMV0GQdfNcLTv4BdmRAtCFszYRICdTKhVBEzks4HupugXf7QkIU6iRCvFc2oD/TyssR0t6AuD6aIJH5G8gXZBQiqXgRQt+KkHYQuB2J+mNEXxcZNddMw9LfReXzkWlAXqS7YStEHrkPaI7IOL3N8zxVw9dBWOOD5GRpmvasuTaN6OtrkNs8SOXoNkR26W6+r41IL/VLsdrfHonYF9vie8jm30IptU0pdblS6mql1NXmIWcDy5VSS5BWDqO01pacchRibR5U+sHtlOZadXIkGi6tI33NAxEh3q5u+Mj0kW+LiIslrlJsgJ6guD9SSuQxdexQrCA+JM8bjUKmUwgvpjfHiKEEud2DkPtaRG6J6Xe5BswKwUAHLDMgw/QCrtBCiLUUNAlA/ZmwTMtGMeoXmNVFItmkPrBIQU0Ngz+FJU2g1WohtmmnQmZtqDRkfN51H0KpDbZlQb/58PMFEPVCjVIYWkvkkXbqj+cwghQG3YZsTj0Rcs5BiHIAQrTFyGbwI9L9cCyio/+ISCM+pH3BamS8m9OQSPxGk8xDSKLyGoRwP0bI/A1Eb++OeNp7Y04xMuD9CtgWBFsK1HGI86WJuc6HEY39MeS8vYpcXZxKFZmvQqyRF2OR+ZGKvUboWuvz93L/C0juxcJRDK1hbT7YKkF7hNDTzPryouYij1QGIMkBP6fAYAO2KXD6pQzeUwgRjzTi2pYlxO6JQooDtAFxIQgkSARRwymEECsYMhCyciAR6RbkUr4xQjKNgJ1RWBGG3g4YH4WmLrEKPqWFsM5SkBKBwDwoKIaKJLjyM8jJkqEahUPhHa9Ewc3nQnA7FHSD1ivAEQf5PSFkg9RKaLpVxugVpECnleBtCcvaQHwBxCWAO16uBjrvev4QnfkDRLvugMgYG5GeJ70Rd8l2hMDnIFH1FUjEeyciYaxGSNaH6ODNNSz8XVS+HrEcViK+8H7m8dch8tUIZDNwADuB16OwsAzS3FCRIE6Yq5C/QSlineyPFAhVIK6VENKkK2ZDXIh438/Han97JMOqFLUAQE4Z5AchIwIVHhlIYY9CKB5sCTLMwBOEpjZxfNgNKYTJKoRSJ0SVPMYVkKi+sAY0iYjWaw9DogEBmxnZOSQ6jfmcNhsGzu+/Z9WI01iakkaxw8mWxGQ+7z+ApZMnsy4QYU1EnDFzo/CiS8j0XC3yzBglo9OCyyG0GXYmwXnfQ3MXbGsMcwfB+5kSKWcEwTkZlreCdktF4599ObT1yCbi2gZpa2B9AyhLgTMXwuSLIOyHtFI4qZasO0VVRalrEYKdgnjDTzR/XkX05wuRK48khEDnIA6XM4EHEDI+DfEGn4dEwYVampyVUxWVG4ijYAzi8vkYIfP5SIFPFkLC58kpZg7wYgh+LYGMeMj1wu1KInc7stnchuj05yDn8n9IPuNyqsj8V+QK4ywsMj/SYfVysQBIdF6ioY+CBXHgDItfPJwh1rjCEDRQ4HJBspaosiwM3qBE9DGNLWJ+ooIJ0N4PK22yMaRoyFfiP8+2iT/6e8C/ahV5p51JxB8hse0Q6lz4HDZ3AkbYT2DTQqbfeh8/X3s9/d77AHvvvlzpgnIFjxsibzRRUlrfcz3kr4acJBi2CAaWwdsnwOa6MKOFOEmKIxD9HrLKweeBWoXQpg4821ESjSVl0CQXOiyBj0+HdptgxwWwJQm8O8ERDy0ShKR7IYmicQgRNkEItTdSEp+DbCBrqXLwfI/kDS40HzcJkVqWIgVD2xCtvLmG+WZUXstk0ByEfLcim8c55vl+CrE99kei53SEkD/QMM8PG4OQkgp+mxzb1HzcDERzv89c0wbEEeNGrhRizpUfEBIfvv8fKQuHARahW/hNbvEB9SPwjVui7IAHnAlQ6BPCa5AgEXc7Db8qSA3DDhd4y6VE3uuD0mRJjjptJknapYFVApCnIcEtSbtUgGXL2H7CQJJ7XUB825N2m8NpdziJb30i8a1PxL9xAdNPO5Xhn3zMnJMGMcWQSPdSJUOIu+yA/BVQ6IKWOXD6Jvi+HRSZvvKmCraGobIARk2Fhe2h6yJo7IHHR0ujsFwDWq8GXQZbGkCdfGitYdKJECmGGmXQq6mQJ8BUZCNphjhAhiJtav+NFPy0Rd5nAyTCdSCJ0CKkAvN8875yhPg/QZKeWkOJqupXrhEf8JPm8e8gBFyIJE/rIj1azkCi7hKkhH9pOWgbkCJDLO5BLJMaaZ41j6qy/gUIwaci0X+MFL4yb+t9QJ8qC4cDluRigdxyKAqIc8OogEpTS9hZSwjGH4BUByS4ZFhCsSF6c3wZlHrkXxBJxrBDMA5qhIVQ3U4wIhDnkb7ofq/oxKP8fuYOGUZKv0tJaHfybmT+e8Q17kLaqXfx5cizWL9tB4sQopkG1C4Rr3hFWIqbxs6G9Rmar/Pn8MK3Y1iVVZuvPXF8k5LIgvaNeP/Xf+DL3UytAGysLROJUjS02QQlIWiyBRa1h2b54B8B5RHZsGzxMCRBnCmfKiHO9kjk2oqq4RCXINF0BqJHT0e83JcjxT5fI4Seg1w1eJAK0NZaxsINUzDcJPNS4EbEE34eElHXRyL96xB551xECrEjm83zEZheDCluKEiQni6PImQeMZ9rE6LrJ5rPNQvZGC6mqif9x0guwCLzowtWhG7hN7mlYzqs98lUmqgd8mtAYoGUltfymM20tAyMcEXBWwj5qSLNOMLgDkJJMgTdcJKGNRFTZzbbz25zQEen+Js//eAD7Cn1iG91wj6t0VOvLXGNevDT44/T7emniVfQ1A+5i8FfBiEv3PYpfF8wjTt+vJFKfyEJ7YZQ48xHsHuTwYgSKctn0fIf+fU/nViW2ZOTX3uZRrb6+MsgulmSuAE3NN8Krh4wMxNChZBZCulNpdzdixT5dENklteRBOKlSDfCQqQEfgqy+V2DkOZNiHa9CjmPZyJReVsNES1OoFhUDlLifz+Sa3gZ2TwMpB9LCaLRn09V1P2VhtlBWOyDJsmw1SESTcxfXo7o9e2RqlEQ0s5F+uHEPOYacdt0RjYqC0cXrAj9OIfWYlcsB/p4YH4ioKA8EbBDJAKZLqkEjSoh91QF7rAQGYbo5toGKgL+eCmt720T6SAuKs+3PAlq+GCIQ4qG3nz6ORLaDNmvtSZ2PZ3I6xMYEAnji0DTJeDNh8J4uOxbmLThfa79eSTOLiOofdk4krqPxJlSC5srDpsnAVdmI1IGXk7Na18lu2FNnrmgB2rxMjKXQIWSRG+SH5rYYGlnaTjmKoXseKhMFH26thKNeR0ig/Q1f5YjxLgUcaGcgkghdyIkf4l5e8xJMgWRruZqiciHmWQeAB5E5JTeCLm2R5w/FyIbysmISyUZkcnGGTClEtYGIC0Fyh0SicfIfJv5fCOQKDyKOFlyEEtijMyjyFVALywyP1phRejHOfIqoCQArjgpcS8ys2GlSdJ61QskxknkFkGGVHgUuP0SiXsrpH+LIyqbQFkyJBmQYoMKu5T+R23QIgLbI7DWBRU7d+LbuJHkIZ3/YmV/hKtGA5QznoXz5nOytxc5O0XyGTobZq+bymPzryPjvIdw1Wj4l89jc3pI7HUOtuQafDZ4CH2emoOtTj3cQYmIE4YJCef7IbUMUpvIVUVLhAQfRHTzS5Cipm6IHfEHxGM+AinI2ISUz/+K9GBpixB0Vw1bNBT9LipfiTTD8iO+8JPM9X6A6NndEJkmRrZbgA+isKgMUp0QTJZ8wZ1U9VZZgBQ43WGurRIh7Yj5XC3M48Lm7cOwOiYezbAi9OMca/OgTEOrTPhsK0RsEHWAtguJ1/FAnBKtNw2x020GahbKJKLkEpFc3AEoThWfc/MwrI+A3wW2ENQMivSSHy/6fIOiIhxJqSjb/pen2LzJdFtRRNl22KCgwWbI2Brh8VkXk3rq7Xsl810R3/pEXC0GsPzlW6mIh3OngLsXPJ8Jm4KQVAwOL1yWKO6PD5Do+lrki1OAkN93SIXnJUji8gqEdIcjlZeXIOT7M9BJSx+aEbtE5REkuXsl4pR5ByHzILIhLELsiZdTRebTgbfD8EsJNPRCdoI830NUkfknSCT+X4TMC5Bq0AhSDRgj84B5++lYZH60w4rQj2PE3C2lwOgUeKIUUOJS0cjvdTxiy7Mh0bYGyqPQpBxyPRJ9u0LSkMtwQsQO3d3wtR/SbGD3yci5FU5oWinPWeB2Y0QiB7boaJgVhW5Ih4QQdF0I87Z/AakZeBq03++nS+p2OjteuZIO63Ow167FJ50ln5Dph4wyqN9EqkvnAZ2VRMmLEBnkZ0Sm6INE7A8gXvZ/IoQ7DNnIJgB9THml8HdR+VbEgbIN2SjON8/1cqQStBuS/ByKaO9hpA/NWj8sC0DzFFhvF40+FtEbSKVfAfAEshlvRpKvit27I1Ygla3nY3VMPBZgRejHMfIroMQPygNriyBsSJdBhamJK3CburkTKDEkwvaERKe1GyK3aCUFRREnoGQIdDgkWnuFgvI4qOWDSq8QY9s6dTB8ZUTK96/lj46EiBTvJJzajLATBn0v6/to23N4Ou6fHh+DzR1PXMu+2Me/widXy1i5lBA0KYRAPGxNEvnlVHlr1Eci52+R5Oe1SGLxasRx0gEhz8uQXuZzkaj8RzMqH7qLHfF9hFxtSIR8gfn7OOAZRJs/A9kYFFLQ9IIBM8sgOwLJKVBmFxdLjMx9SOWnE5GGPOY6vkGqcq+kiriLETK/CIvMjxVYhH4cI+Y9b5gBX22SCNympVRfqaopQh4txLDVkEiybjmUuaXvuTYTouUJkFdThkDHO2Q+px3pk5Jhh1VOiWpbAo44L3XOOY+K5VP3a72Vq38hOasV0foNOPVbSI1Cdo0ytm6eg7d5nwM+D/GtTmRh9sfkZIr+n1YJxeUQrQUPKUkYb1DSl+VbpB3BeYjT5GZkiPWtiMtlECKLvI4UCBUZ4j2/QUGmGZUXItbFlxDCfhVxzJQhyc4CJCF6OVXj3BYCEyIws0T08tJEqG+T5Gdz85hcRCvvjbhrFNKxcT6SC7mcqoKhPKSr4qX89TAOC0cXLEI/ThFzt5RoqK+gokJse1FDBiXHqapZn4lKyD6AjJLLygNfnAyCNmxSVbqumVgde4Wl70oFkBwBnw2y7FDDLwMU8jW8Z0DxjTdQvmwKRqBi39YbDVM2+2Oanf8vTpwODXwQ9MKs+oXYElNQjgNv5OpITMcfLCSsYWsUKIRUL/RIlCh6MEKAy5BN6TpkNudDCIEmIcnGixFv/HKgi5buhqfuEpWDJE5HIz1WnkQ0chdS2HM1kjjtjRBtHBLJT9IwJQi/lEKHBFjlFWfMA5gFWuba7kUi8BHm4yYjVwspSBQe01e3IVWhu1aEWjg2YBH6cYr8Sij2Q8QNc3bINKE6FaKJh12QYK8i9GQgPyrzOu1hKA9JVB6bH6oM8CVIMvVUJ/xgyPM4ApAYkh7g+U54MQ5ei4JLQdv27XCMGkXuF//Z6+g7HY1Q8MUTpDbuwAlJp9IiB1AwvSdsbWmgbH9elLRPUAqfNsiPQmJQrkB8tWCkTchvCkLkVyLe8kuQxmFjkKKckYgLZgLQUUvVaSFw/S5ReQUihTyA6OITzH8NpPf4h4jEcjoS5SvkyuAlDfMrYaEfWqXACpdE92OpIuhvECfLA0BHJOn5JuJXz6JqKAVIif8PyIZh9TI/9mAlRY9TrMsTH3lKPGzcLKX5njzQqRB1gsdWJbl4lcwELbZBegAKTJtiZbz8G18h+rnSMNsF7kqIt4M/KJH8ODdscEOKAZ0c0pZ1p4aE/z5NeclYcj64h5Reo4hr0m0354vWmuDWZZTNep+EtDoMuvIDTp5pw61gSTvY2RD8zdMxykvQRvSAXDMA0coSVHIqNaPgyJOxeR2TpPDGBjRUQqAPmsc/gpT+N0LK+ScjUXBnDV9rGKWgxi57zEIkmg8hCdBYgjMHGS7RASH3cxE3DYjP/QsDlpRCghO8yZKPeJAqd4pGmmltRKSXeMTy+CayEceeN4YViE/+IqwmW8cqLEI/DvGbu0WDowJ0FGo4pBsiNiF0uyFfeoVIAgAFCloWiEXO45P7og6pEo3YRJY5RcEzIcAOU2tCvF+kF4cDOjkhzxCtODMCJSE76f99Fd8X71H20tMU/vwq3ibdsDm9GNEg/q2LcbjdNB9yAw0GX8UFXznIULAmA+a3gBXdIC6Ygm7RGv+G+Xib9Tig8+FfO4O0EaeQG4GeJVDYSK5G8hFJ5COE0G9GbpuDkOI6xLt9soYftMgq1+8ir4QQO+KniJTyL6R1Lsjg5g8Qh0wbxJYI5iAKpFXw9DLoGg8LPHKFcBNiHQWRvx5D5J5HkC9yEeJztyP6foz4QfzoW5ENyCLzYxcWoR+HKKiEIh/47FBWJIVCRj74E8TlYriEFBRCTgVKIsdwFELFEKwFdbKhJFX08xXt5HiXkmTgBjtkaEgOCKkVKsiKwHYnbNdQQ0OeX2aOaqVwjxxNwqmjyc1ZiP+X6UQryiAhAdXpFurV7kftrYpzPhV9P98Jn/aCsr5QGZGCnoxLbiQ47gU4AEI3wgHKV/xIpwlP4CuFvDjISBb3ySdIMvNkpNDnO+T3OgjJ10EcLF/sISrfgJTu5yBa9QXIly2C2BptSJL1dKpIPgC8Y56bWQHomwyzHOJyuYyqL2uhuZ7eSOWpQsj6K/P30821xTAT2UTP3O+zY+Fog0XoxyHW5ot2q4FwBBITIWEtFKdIdajhAsOokly2athmA1tQyv+12bvFZshP1CHHRZWMSEuMikyT7YH0IDSshIUpkGqSeWEF2INQeyesagX1tsKmpmCr2xndZZfqUQNC2dBzsUT6AJ/0gm3dACcEKkUqemjkOVzx4C3E5W7EVbPxfp2LiqVTSereg7LajUjeDnVqwkM26ZqYg5BpAyTReQWSfPwJGKJhiplQ3jUqN5D2uRMR/fp5qoqB1iPtavsheYlzqNKxtwMfG7CpQoq3WqXAUpu4VQbv+rdDuiRejJToY65ptvnaF7C7BfEH8284bL/OioWjFVZS9DjEunwojkJ5GTg15HkgwwchswGXMitFjbJSXh7VjW/uv4tcNDXKIeAS4ihNgTg/lC2eRlmv+qhZsxmOeJsDUYnMa1RCsg3mmLPm2iooK4CglsHMJSkS4efWgrBHNgRDU9VcHUgrlkSsBhY1gfhukJ4um4Jhg9oG3J/iIeWh58j7/N9EyvLZV/g3L6Z8/sekPPEkrgpooqFmCtyppIDnfCXvpy8yOu5txM3SXsNkDacrOHkXMs9BHDATkCrR8VSR+ZtIxeZApPHVaKrI/FdgUgRmlYDXAbYkcQf9i93J/CfEn/4Pqsh8OuIzNxBbYsoux3+J2BUH7PMZsXC0wyL0w4RQKMT777/P4FNOo12X7rTr0p2hp57BpEmTCIfD1fa6hZXyUxqFUBiUGxIU1NYQMq2KTgf4K0opOacvvRyrsL3zImX33oU7T1MRLwOgDTsULZvG9jtP4e6GeThOO5nsubMJa3BGoLZPhkgUIFF/ooL8fKh0QZ3tQtDFqeDxQ0WySD02LY6ZGKHbNCSVQWUSlHohbxB0awwLAuIasRuwNR5UKRjnj6bZGXdQ8M5dBHes+ctzoLVBxfIfKZryFC0nTaJ94zYkhGBrTZnGdLOW1rJzkSTkFiRBOljDKkPcI9cryDCJXCNyx2XI+30E6XQYh/j8r0fcMl0ROSQmDEUQW+SCIHxfCr1NS2ITM/nZkqrnn4gQ9L+RZKxGrJM7qCJzzy7Hf4xcIcSI38LxAUtyOcQwDINH//0fnnz6WRzp9aDFAByt+oLW7CzJYd4/HmLsNddx1513cPttt/5ln/ADwdo8SYqWB4UwK+KgewS8HklsaoBAKXOu6svZiZt5ZbCNAp+m19svUZgDwUf+S4MtivxV08j7xyl8dqZmYCM3XWqHGTP8ZNwfTyXaqicL0iBiQJodEsIQqpTiotQiqLtdfOuJ5bC9LmBq9tGwEHsUQMuko3AcBJww51So2wZui4D2g8cN5fFAQKL7lHK4KuVWJp9fhwVv3YwvpSauLkPwtuiNskssHPWXU7n8ByqXT8GensKwH3/E37YDrk2wyQUnp8I5CsZr0bl/QIi0HdBGix981C5EDtI24THEvtgL6WoYS1wuQErvByMOlJFUfeGKgHc1FFXC4jAMSoFf7BLZX0JV9B5C/OoasTe6kPPzrvm7Eylyii1JY7W/PZ6htNZ7P6oa0LVrVz1//vzD8tqHC5FIhJHnjmLGknXEDbgGZ0a9PR4XytuE/4eXGNa/G29PnIDNdvAupN6YC6sLYGtAyDOuNlymoewn+KgBFBmlTP+/voyM38zLQ9RvG0p+pUHvd6Dg7GtpWXM4yx+LkXlVTPDV2jDnfmPH9c5UdN+e2G2QFZUuixEFKSXQaiUUp8HmhuKUya0vDhgdkIg1ahfZRSmJwFuvhFp+aNwU3o8DTwXY4sBwQIkNPAHp6TJoDtRZDQ4/ZG0K82vR5/y49Dm27JiJ0xlPlAgYUTJPHUmzq26g7kk9qR1RfBqG2jugRxokpkkHwvVIJ0QNjNbwuYZGyvSH70LmsxDCDiER8umYbROAZ5HEaAegC9LrJYblwE8GrCgFHJCSALlK+qnsKrGUINF+O6qshgFEvklChmictMvxUfO+E5Eo3sKxCaXUAq111z3eZxH6ocPlV17F5OkLSTz1HpTD9ZfHGqEAFZ89xEWnn8QzTz15UF6/sFIIfUUp+CMQSYAT6sPphfDrHPiwBfzyz66M8KzhtWHqD1cH+ZUGvd6DHUVhvjzHvhuZx/DV2jBnfaEw5i2jcd3G1K6A+fHSlbHxRnCHhMwjNsitDYYbvBHAB0EXBJ2ScDXsQug188ETDwU2mXhUQ8OmVGiwAbbWlSHPA6PQ91GY3wH6TYe4EGwx98rpfSMk5pQSdTpwDE/Eo22UJoNywvIIdMmGTW5oXhtetkmRzlogrGCohtlatPRdo/IAkuz8BmmcdSfS4wXEgXInUuDjQhKfsYhdI7LJzjB8WwY9vLDcI+6YK9k9ot6EROZnUtXXvASJvhPMY3f1mIeQq4lT2N3hYuHYw18R+l5DP6XU60qpPKXU8j+5XymlnlNKrVdKLVVK7V+T6+MEq1at4oOPJpEw/M69kjmAzeUhfsTdvPLq62zZsuWAX3fz5s2cfspQ1q1bx9p8CEVg+5r5zPm/ESToAoZlga8IpraV5lk1m/RnVo6iOCCP31FucO5HPtYXGdSItzH3AsXcSxwMbORgcU6U8yf5KPJLUKC15qutdtwNG6IyakAEVioh4uQyaLBFdHPDJn3To04pNkosFPkHhMTtUVNP15BXQ9wyYRskhSEnAWpvk7XGAz2SwP0DbGoILVdK18eSZKiZCz8OgqZbHDgT04nrkEy7JBshYJ4TbBHICkO4FDKT4DIlc1K/QUg01/TLX/87Ml+JuF1iJfxPU0XmPyAj405E9OsrqSLzSuBVDZv88EU5jEiCOXHQQom8syuZz0JGxF1PFZlvR2aBuhANflcyDyB20TOxyPx4x75cy7+BFLf9GYYhs3KbIfUX4/7+so49PPP8i3janYzN7d3nx9jjEvG2HsCL48Yf0Gtu3ryZAX174s3+mYH9evHLgnUsXjqf2Y8OpGPlD0y7oRfhUAFv1QBXCfhcUPbck5QPu5Q+78KKvCgDJvqIGDBwYiXriwzS4hRtM+0szoky9G0fvjCc/FYlRT6Da3/QvF9Sn/RJM3HFJVJsulM8AWi2TpwxhenSLqA8SRwZaQVgV+Jw8QSExO2GEHrYIVF7xAlJFULidTaJ06alhhqJsKwQes2GkniomSctDLw+qEiScXIRJ9iToF87+MEPC+LgIi0SUKRExu018sIqBWEt2vMTGm5TMGgXB0sE6Yh4C6JbP4YQuxNJSt6P+NQHIPLKmVR9uTYBEwzYVAazgjAkBb5zyhfnH8gkJJBz9RHSBuABqpKiK5GmYArR2HctGCpHXDXnIxKMheMbeyV0rfV0JIfzZzgdeFMLZgMpSqnaf3H8cYdAIMDbb7+Np93+t3h1tx/Ky6+8SmQ/+4dv27aNAX17cluHct4b6ea+7n7+b2xPvn1wIG8OjzDpbCfnZuVwcv9erM4v4Yeuokv32ahoc/sz5PU/hz4TfFza0cEn53n5V3/3b6QeI/MXh3v49Lw4Tmxgp/3LPt4qrkvtT2bSLJKCMyBEl1AB8ZWQ7oeiNEBDbk3R79NKhIArnOAMQUI5vzlcInZztJ1J9BUpkJQngzTSEiCuEeRpGPE1rGoGbZeDMyqSjVbw3cki1ySFgJ7wmpIWv7XiYGkU4gNQrxxKa4DLJmX3H2poCPRWopnHkI3ME/0AiZifR7RxkIKei5HHNUWep+0uf4efgO+jMK9E3k/9ZPGXX0bVhgCyYTyDlOf/G2nNC2JpXIS07B3F7hF4MWKlvAjxtVuwcDBcLnWRz3wM28zbdv7+QKXUWCSKp379+r+/+5jF9u3bcXjicSTV2O/HOtPqEolGKSoqIjMzc+8PMJGTk0NpWRmdagozXdnZQYIrSJpHM6Sp0Ejv2hGeW7STWluKyChLoSAelnWBupvyiPzyHXf08XBXXzl2bBeRiQZOrCQUhReHezirtdz3xGAPIR1kYpmB37CxIU6INdkvmnmNUihIFt950A2GVxKZWfmwMl2i4KQyicZtWhKj0VhbFiUDpuMCMoTa0xSMVKhhQNp2aLEUVjSH1GLxtNuiUJ4M27Jg9Ifw7XmQmgID/PCDE+oqaKVgeQkQgaR46XU+UUMPJZbBJPOlNVK2/zoi79yFROAxrv8I+BqJmhMR/Tp2XwjZAKJB+LIChifAL26pCr0Z0d5jKEc86vWQ8n6b+dpfI0RewR87I+aZa7uMqv4vFiwcUtui1voV4BWQpOihfO3DCZ/Ph9114I1Kwy4Pl1dWUgNz5iXy5Y7Z1hzmv+5dfldduzL240857bwz+fzMCH3qOzi/bVXzqs/XhBkzxcGIT37EW9KYubVheyZkuCCvbAeh4gJOrL/7BdzYLi4SXIokN4xoXtWrTynFkMZ2Xv1yO9GcAorbJeEJiB89Mw/CXshJBxWFinRwR2UWacXOQio/nohesgJfKAA1M4mefy7h/j3RJjU6wxCygd0JSY2hbaIQ4HRDovPlraD9QlOmiYoDZurJUri0pT50bSPSztc+aYnbOApb/FASgR41YRUiv7RAEpk/ITbAAkTHXoS4VG6hKmoOIeSejlgVu1DVkxykL/lHGoKVMD0Eo5JhkkPknLHsLo1sM1/nJGRjAbmyeQ/ZJMoRB82uX9RspAPkZVi+Ywu742D44bYjwUUMWeZtFkwkJycT8pUf0GO11ihfOXckJ3Mh0B+5vE9GvNtBxP2wHVgdifDyXXfyzYIFrAASBg+mz32PcOYnUbTWvLM0zBuLQ/jCmnMmhbh23P/4Z2ZXaldIMrHjEtgZD9EunXDe9jBD3/EzY+vuUs/ods7dyBzgy7VhRk0K4B06kmjjRsT5wRURkq27Dco9EPBAcQakKXBuzKP46ouYe0ZjIpO/weGLw23UwL6ugOiY86Bje2yff47S4obBBikuGJIoG9kMAzpvFD3dGQCvH+J9clyFXToxnv41LD8bcuywLSqJ2QQXrLNBch54DJiTCv9REnXvoKrV7UykN/laJLH5KFVkvgKRWHogg58vYHcynw98ZsDaUlhpwOBU+NIhSajb2Z3MFyDyykVUkXkIuSJIQqpVd+1jDmKp/BGLzC3sGQfjM/E5cL1S6n3kc16qtf6D3HI8IysriwRvHMGda3HXbr73B+yC4NZlOOrUZVpqKg2QS/beiJa6q6kwEolw8ehzicyZwtRXxjNx6k9UaM2zD9/Pa8NsvL4ozAPTgtgV+MLw/FA3d9x0HZ9/1Jk6zZqxMwmWtJDoPvLdNIynH+C+/i5GfuDnk/Pi6Ft/zx+VL9eGueyzAJPO8XDdz59S9N9/kHrzY5CkqJMLZUng84IrCK5UyN+0leCwE4ir15k6l43D7t1d/U3udS6BjQspGDsWvXUz+oYb8QJep4yv82nIjEK7L2F5S+g5S2QdVwiKUuHTU6HLIph/BlSkiq98kg8MjwzD7uGD6TZomyCRf3sbrNbiMpmOkOybSELyRkQXj+FlZCrR6chgiXN3+RsYSDOvQAR+KIX2XijywAoljb6G/O7v9TniirkHiYBAipTeRTaPOKpIPobl5o/V/tbCn2GvhK6Ueg9xYmUopbYhCX0ngNZ6PFUy4nqk0vnS6lrs0QqbzcbN11/Lkx9+u9+EXrbiW9TNNzJOKxpr6KDEkZFE1fCJ5pEID40+l8Jl3zN9jJ0pGyJcOOgEoobmjVM0+ZWaB6YF+fEiL3abYuDESu7s4+bx7n7+eWYvmLSURF8dmiyE5dFpbL35FD4/QypA4xyKkR/4yb094Q++dH9Yc/aHfiaeEceQpk5m1Tbo/e44yiMQf99juCsVJcmAAUW1IDXXj//UISS1PImkbmfs8f0qZSOuSVdqZ/yHnY/cTVzD+gTPPAM/kKBhh4a0VeAuhoyI9JNJrDDtkG5p9nXxe/DrXdBOCQFu8EOjZEhXsD0b7MnQOV2SpyA2wbaI4yQFqei8lCptugyZTNQWSYb2YPfCnVJkcLMrCJ9Wwugk+MIpTb0uZfckaRTZGLYjRUNmmxt2IqX8yYgNcldbIkjkv43dq0ItWPg9rMKiQ4SCggLqN2pCynn/xZWxbwnhUO4G8j6+n4TsraQnJRJGNLJ0oJ6Z3OsWiTB+9LlEl33PlyMhzilf909Xh7ngkwBDmtiZtyPKjxd5aZYuGvrGYoOBEyvpluVgamEaI+9dgstbk3AafHxtXe5sXsg/+7t3c7PEEqC/xysLQjwyPciPF8fTNM3G8rwo7cdX0uSDxdTO7EBRqkTOYQ/4P5xI9LkXqXnm/fv0/v0bF1Cw/CP0iqWkKoVTSxuBa56AVbWg+0xIrhDJxZcBb42ERD8EOkO4obhXcoOQUQZbUqCnH1ybwVcfkpLhBhskaSkE2qqqpgd132UNvyI9zc9BiPs8dk9OrgF+0lBQDksNGJUI79lFV7+C3SWWSqRYKBFxzcSiqU8XL+bjX3/FX1pKi7g4ejZuzLBhw3A65ZzPQBKjf+UdtnD84G8VFlk4OMjIyOCl55+j/LOHCRfvXZEKFWwl77NHqfHI/+iyPJECJCKvixDPag0/RODf337HtE8/5+3h+jcyBzijpZN3RnpYlmfsRuYAjVNtPDvUwycrw5x143fsbFeTiA1CTmjy8gf8d76DcfNCeyVzkERpzNI4OzvCqZMVWZfdQ1yLDgRdMp+0MgHcPgiOe47E9sP3+Zx5GnVCFZVimz2bqIZMAwYuFtmlTrZYD+Mrxd++viFsrwPNXVBZT7zfDQCbDwrc0MsGDbfDuiTonwg1zZ4xY5C5m3WQyDlG5jFynwychkTOF7N7A6xvgPlRmF8ilaz9k+FTuxDvrexO5jlI69yWiMtFh8O8/fbbNOvSndEnDeOrCV/x0xeLeOW9n7ns1nupWSeLf917Hx/s3EkEi8wt7BusvMohxCWXXIzP7+POe+7G0+0svG0GYnPvPnM9Gqigctn3+H+dzHkDnmLWqWdTUgxtV8OmlrBJQR0NrRWU26Bo2DAyr7iCwZPe5ZdRmhTP7qR+Rss/kvGSnCgXfxbk/P97noGB9nybC6tbQGYQ0tv1xXXbK9z+6BgmnuH+A5n/vDlCvFPRrW7VBjG2iwutNSe/7cdx4Q30PeNR1tvB7oKiFPGOVxZuxcjeTNzwfS8kVspGQosTKX33fYwevSgOQeuvYFEjOOFn8acbDhkWvbw1pJbDjydBpQ1Q8EkU0oLgTAOnDwr8kFAHCm0Skd+ixcv9oJKkaEwvz0Wi9pMQrbwPVTo3SGXmu4j1clI5nBwPazySRB3NH/Xy5chmcR7SirekpIRhI05n5c4S3B1Po8agbn8YnxfK38yLU74lMq4DUz7/FHr33ufzZuH4hUXohxjXXnMNnTt14tHHnuD7V68kvlkPIt4MlNY4fPlUrJtL2yan0PaaKdy6vAuzn4b/uxFStkKteHBmwU6bXIJnAZ1tiooXxzH7euj7/rvM+B2p/x5LcqIM+EDT+Y136THoPNbMhGIPZORCWRvIt0Ptlj0hPp5AeHeHy5drw4z6xg7a4N1hYU5t7vhNV/dFgDgvdUfdww5bGcGIHZsjnpAHEnMDlM//BWdCxn7P/XQkZmDP3UmShkZrITsZWqyRylF7RLTzhZ1hW00I1IVCt1iuioBmAcgwfZ0p62GVG/yJ8JYSO2BdquZ7plDl/f4AId9ixE2ya6OGbOBLDcoH7wRhbDK87agqKmr3u/V/h7TWvQk5xufz0W/ASWxz1iXlrNv+9Hy4ajTENfBq/A26MmT4CH6cOoVu3X6vrFuwsDssQj8M6NmzJ19M/pidO3fyySefkJubi1KK2rVPYOTId1nuzWTW67CsAvpvAu/j8NDt0GopbLFBx9qwzSF9uiuBWlrR7flxLAb6vf8us0dr4l1/JPWV+VEGfqDpMv51rj/vPBYthjrLYEsTyK4NO7yQa4fcAY3hm9lcM7gnEGJMBxdfrg1z/jd2wl/9gPvzHxj5+D2c0cLBR+fE8cycEPfOdlBj0goar4ny839bY/MmEvfBTJyVXsrGDiYyayZ2T/of1rQ36EgYXG7KIlB7g/Rp6bRA+r1EnbCjPrj9UFAb3qwHp2q5cokAJX7I9kINHwQDsKqRjMOLQ4qA0pCI+lOEbE9Hsv01EbdLP0SOqYv4/2cA6w3YXC5JzMtTYKJN+pxfCuxaNqaRkvw1iNQSe+fX3Hgz2TqF5AFX7lNr5LgmXdHG9QwbcRrZmzcSFxe33+fQwvEDi9API2rXrs111133h9sHADOHwa/p0PJt6JkDjz0Cd98NbebDtg7QtJ7o0xsR8mmAIr19Zza+9ya+sH2PhJ5XqQliY1XTptwOZDkMskJz8Pp7UbMAcltArUIo8YCzRRsS35vN2At6MWWjn8lb3bT68gdW7ixBj3+Ez0fF8Y/vg3R5pZItJQaOJk3Q4SAzH+7DHW1LyAuUMnFUL0hJY6B/BQ9dEccJbxRQvvALEjv/3pD35wjkr0MPGETLLbCtFpw1XVwtDkOKlTY1hHUtID5TyvvbanGvOKKQHoFGHuiyRmZzRpJljmcTBd20DGN+RUniMx9ogiQ+0xFiX4NsmgoZDO2Kwlw/ZHmgrgveVjKB6PdRfAAp4wcZVBHT3bcWF/P+Bx+QeclL+9Xn3tusB5Urv+PDDz/k4osv3ufHWTj+YCVFj0AoYEBjiG8GX50kU3w6lcIzD8OaplBrBZSug3AQEjU0VZD96v/Y8M9bmHGBjRrxe/6zntjQwTtDopSdPIDW8+ZRcOcY3n20D9/OuZ8NjaAyHjIrZaNosh6oWRNnahofrYxQ75HH2FRYgrr8TL4ZCcObOZlxaTxRDSOaOxji3ELhyLbc3KaEe/vYeW6g4uLkLXiXz+WVkzXta9qZcakXY+ZrVK74YZ/OQ9Rfhn/NrzguuoQa62QYR/1tQthBN8zvCu3XwprW0DVeers0USJHZVZCmgcIwK9+yKkpidDnlPRj8SK9zFMQb//zyGCJHxAyHoEkSrcDb2lYH4FPQ5AQByvc8J0p26xEyvZfRvqqvIcUJXkRHT5G5rnArRMm4G3aDXt8yj5/FmJQbQbzf08/t9+Ps3B8wbItHqHQwJNFsGgLjH4eOhUK0a9yw+13S+MpVwqE2kH07f+x5O6bmXG+2s3N8meYvCrERV9EaFPTzdunKgZ/AIy8lcCTD+IphwIv1FlUQO7lvbmszk5ObWQw/OMoNqX49hzbbkVGpQHNkLd9rMyPcmcfF//qX2Xq01pz23cBpm2JMvvyeJx2xYq8KP3eimA/8Vri2wzaw+qqUDT9DXxZTuJee5cWK6WL4m3PSK/0bU1gezpMOQ3Km8A1DnheQ22beNVT8qAiDTI3Sg8Yb1uoZYNMG2RpmFVayqbsrVT6fHRMTqZLkyZ0dDpxIf5zF3L1s1pDZSUsDMO1STDBLn1YhiGkvRGJ4rPNnyVIYVAiUumZgriTfMD0dp1wtj0XT4P2e/8A/A7aiFL4v8tZMn82TZo02e/HWzh28Fe2RUtyOUKhgN5pYMuHCTdA3YekIVXHIPzfo/CvOyDeD2njp/HF3dey9Er3H8h8SU6U/8yF8YNtvyVKDa35Ym2U9hkG351vI96lmDnGoNe7TxFKimBszUadMJycJ+/jsjo76ZoR4bPVBs8PVNz4bYD8yt013GSPYsoYL79sjfyhJUBUQ06FJsOrsJkKQ5tMO79cCH3efBGfOwFv0x78HlprSud/RkX2XDxvz8FZGUZpJ56QtNS122FJa6ibA5WNoJ0N2ihIMKC+gogf/JK7pZEPGtSWWaHPoHlqzlzefuZ55n/xOd6UGkSdbjb5y/ksGuKiq8Zy3tVX4c7KYgoQjMIcnzQK65gCjyixQtYA1lHlZElHIvBipIqznnlfCOkFsx5pz1Cel0tmaq0D+zzY7MSl1SI3N9cidAt/CovQj2D0AhY1gBrr4Z2r4erxsFND+wg89QTccwNsa9uRzHoteWrBZsadrH/TZpfkRDnxA43q2Zf+H8xi+nmaJDdc8XmA9UUG342J/01nr51o4+dzo3R+8TGaZdpZPPkdTmrqolMa3PZdkCZpNgr9Np482c2YyX7eBs5sVUXeyR71BzKPGJqLJvsp9Gs+PU8qVGPIqdCEQ2GiGxfgrtPyt/J/rQ38G+bj++lFtK8U+08zydhQTv6YdpT2GEXS9c9RlKZY005G2b1+mxQPtVEw1YCgDdYZUOCDeC/0yIEadhhWA970+Tj3/PP5ac4CvG2HUPuy8bu1HQjlb+ad76bwv2fb0v/++zj1plv5shLaxYm8MwdogzTYiiA2xxCil8+jqsXoN4gU40MIPoRE6g4gakRBHbjKqWz2/W6jbOH4giW5HOH4Bdi5DV4LQq+FcP07krirDSxzwn1jocBRQuHYfgyrsZmXTlYszTUY+IGmzssTaHbOOSy+/hriv36XOzuGufbrAJtvSiDdW0UsWms6vlxJ3UTFZ6O8LNgZ5bT3/CgFP17kpVGqjVPe9dE6w0acE16aF+adkXG7kfqu+D2Z71rw9MPGCKd9oQj/+ynsv8wk8Plk7KmZKLuTSEk+HnuEDikhBjVQPLs5HVXu477OPiYsd+DvfQlnd3kOmwM2t4Cvh4lTpbWCRRoCNjDCkF4E6UnQdA34akFJSoAVg04i6I8j/eTrfhsavce1l+aR99nD2C6/mFoP3k+RTRJNLsQdA/J/Zf5bav5eD2nHkIjo5tnm7XkAWiL04mYtyex7De5au3aI2TdorSmZeA0zv/+Gtm3b7v0BFo5ZWJWiRzH6AHPqQDMfrOgAP4+CmloiwtQwPPwK1K9MIfW1X/g6vyGjP5PIvO/4CVx07rl0Voq0F8ZROHw0jy1ykOBxcNakMIGIbORaa+7+IUihz2BLiaY8BD2zHPx4sZdfL4unTaadIr8mu9TAZYdXVjjh4is5+yM/hT5jj2t+eX6Y6VuiTP4TMg9+/DV67Fh4dSJszSb6+SdE3nsTpv2I1pqx7eDR/g4eaFnM8/383NHLyehWUYqnf8y8rlFcUVh1ojynz+zXkq8gqMHph4gbwiVyW04NmHPtdfgrHaQPufEvyRzAkZxJzZEPoV9+BccXn3EmMB6RTlYACxGv+jNIQ6/ewFlI/5UQUlz0nYZVGpYYUBQFvwHpUUgfcRqVa6YfwKcAQjvXEuew0apVq70fbOG4hRWhH8GIDUkIAs1KYdxOaBGBpnOh9+cSCaYA61zw2ChYnllC5S1nMey8q9hx47lkOaDMBrXXrGGLUix8+UXCgSCR3ByaLfuRr86Bh6YF+XpdhO8vjOO/M0N8sz7KrMvjSXILEedWGPR4tRJfWOOzxaGeeRn96D3c2biI+/ruWbGLJUq71rHx/DAPSil+2Bjh9C8U4U++xujbn4gd0kuhMFlcPDYgagOWLSNuUD/ePinMyJby/OMXRrlzbgKtnppNampD8hvA6pai0SukhD+iIDEKCUWAB+ptA5UCxSqHdc2bU/eK/2HzJOzzufet/RUj+wfunzcbjdgatyESSwnibnEiI/MAIlqklbCWoc9uoJMB4RAsD0OegrJNmyjv15msK1/F5ty//viV3z3H7ecN4h//uHO/Hmfh2IMVoR+FyENmWA5FfM4bkmGEDb73Qs5gUCeILhsAGoTg6veh87YUkif8QHmXc+mwEHIjkDx/Ea/07sZ3vbvT5uLL6fvieFq8/zHrmg5kyEcwfUuUnll20r02OtayU+jTVISqNvmSgKYsqCkOQPimfwqZNyn+UzKHqkTp/B0GN3wTYGd5lGEfBPG/+SGqZ3/UuOfxdm1PeXkeUDWhBwCPB+VwUBmGe38MMOTtSgr8gNuF2+ZmfVPwNAGXBpRIHLWADlHoGobaGhpWQM2QROe5r76Kt2Xf/SJzgLimPSjfuJmflizhJ8TOuAxYpmG+BqcByVHpL9NWwxAlE4/OUdA8CDXKJKH6qyHFYJUR6FTciJYpPamY98V+rSWUvwXf+jlcfvll+/U4C8cfrAj9CMQiREY4j6qClfFAcQh2roGAHTrVhwsfhy1LJFJPB7a64JnTYV4n6FAAXdcs4sF/nMArJ0UxtOaqn1xc9eN0aN+eucsjrL1pJC12/ERluY8Ut2JZnsHUC720ybRTEtA4bRDvUszeFmHQuyFITuEfbf3c12d3N03E0GwqNv7gstk1Ui9XLiZHm2GcfT7eJx/i7GaaifmZ+H6Zgy0jUyLtijI8LRvzf1187CyL8unqCJ1r29haqulW382bG2rgW7OB1Hg7+ebHthZQocAbBU8F2MJgrwDDBal1YH7TZmT0vQZ3nRbsL0pmvkN819q0ePJJHECBhmIlrW37KnHUlANbDVgcgooAbLPJ0GuHDbYp6Y/eZQFcPx7CdlgRv5Un3+9B3AmjiW87cK9rCBfvpOyT+3jpqf/jwgvH7Pd7sHDswYrQjxJopLtfMTIUwYXILu8iem1NF1yYATluiO6A6/4BNRqL7BBAhjzc+jkMnA9z8xdx3+0n8L+TopzT2sF5bZy8PCDEKwP741m8lDazS/BtXkHX9DBfj/Yya1uU01o4aJNpZ1uZQbf/VdJ3QiVFfk2PunbaZ4AqLuLC1rtXOMYSoG1eqmTyqvBu9yV7FBe0czJ5dYTxJynaFK4k/tG7mTNa8dJgBzfVy8fbvwdGXh52gPh46NyF5+dFmbw6wvRLvbx5ZhyNU228uTiIrXtv/PF2CgCUnK9SG1RoKNXgD0DAL424cjPFEx7Ny8GZVveA/h6O5NqQvZ0uCuoo6GKHZ21ypRQPFATh5zKYVAZLwlDmhFpOyHWIVbHFCnjrarjjGfC7YWc9qJtZn3+M+R5jzgeU/fwakbL8Pb62EQ5SsewHSj+8m8ceutcicwv7BMu2eISgEiHugUgJOshE+a+RgQuZiEabWguaroDJbrgtAD/dC63uAn+uzMUsD8LAtxYxcc4JvDY0ytmt/7+98w6Pqkz78P1OL0kgJITee++9hSqgNFEBBRHsu7rq6rq6+rn21XVdy+qqiCIiRVFEqtJ7R+lg6CWUBEifPuf9/ngmhC6rrLB47uuaayYzJzPvOZP8znOeWvQVD6pnRxPi/i7tcTmKMbJqJi91ttPpEx/lEhQTNofx2GF6WpR7mto5VmDQYUwBLctZ2X7cIFi7Pq0/2syqO71USbSckc2ycLiHG7/wA0UpjR//EOLV5UEWDvfw8Q8RjmSFWTVSfhfg5Y5WWJLJ26mt0ItXE0ouCTXqotcsYOlID8mxTJyP+7kYNi3EtMM/QkE+1d1xZCs52VXWsD8KpcOQpcGpQReDWnYIadhgGD87VVBZLFSPRjikxPLpriWL5mgQVkVgdywnPsku7XV3IJ9Z6yA8OApK7wa/lirV/GTwKHDGw50j63HXM2v56/MvMv6zh/FUaoiu0ASLMw4jEoQT+/BvXUCzZs14/uvJpKam/qz1m/z2MAX9KmAf0pVvCOIT1sgQYD8y07LQkVED+D8F91eCY4fg6yPgrQ2tnoEST8CRPIgL53LHkg681cvg5ljr28N5BqW8CqtFobVG+/LoUznAK12ddPrEh8OqWHu3h17jfXz4fZjbG9p4rK04e2yWIOM3hSnnhYPbN3N7MxupYwuYf7uXJ+cHyA0WpSbOvk3eAyAroHlmYZAFwz3M3R3ltRVBFg4vEvNCTol6x5bYevSm3DfjWDnSdUrMASxKMa6vg2Gzd/JN91SyZy3iREIcbouIeK4Cqx+KFUBUSaMxnyGBUoqXIJp/AovT8x9/L5H8kxyqnkzXMCSF4LsQHLdKoLmYC1pYYJUBm5CrpNqZ0P87aLAIjALwWeFoOQjHg1NBQjnoMQQSiwOUYdR77/LP115l/PjxzFu0hJNZu/F43NTpWIN7x71qFhCZ/MeYPvQrzCKk1esARBRygC+QTn+1Y9tEgRnI0AUXchZesBvWK3jCBlsrwP07Iev/wAhont40nO2RqXw7DNYfjtJ7go/+tW30rm7l/plBhja0M2d3hNrJFgpCMP1WN3dPD3DCp8n0adJOGPStYWXCTR6UUoxaH+TROUHmDfPQqryNd9eEeHROgHgH3NPMzktdi6pH318b4tG5AeLsiqUjPRR3KUr/I5/vhnroXu3C9sNTC4J8s9Ng0TDnGWJ+OuGoptr7ITIe/ivRJ57EDoSjcgIskyF918MOCJWNVWoqCDzwINbNmSS2H/YffS9aa46Nf5jm77xP5c7dOGCBclZoZ4PFFtgXht0aXBZomAMd10KddZCwUXrN5HvgeCmZZWpxQMk60O0GKXgyMfklXMyHbgr6FSICTEJ8481iz21AAqKDkD4hINb7d0jvkETgS+TS/q9heHY7ZDjgsUqw2QWDv4f0lyAuavCnTXewquBLjhYE+KSfi6cXBtmTZfDdUA+ty9t4ZVmQvy8PsvJOL88uDpLl10wd7MEfhi6fFrDzhEGfGla6VbPzyHcB5sbEvJBlByJUSFBcP8HPTXVtPJvqYv6eCP2nKywPPkborX8woafBgDp2Plwf4oXTxtT9HKKGZsgsmOmqi2/2fPB4sSFtdEv4oeQxyHdAfimw2iFFyQzS9K07ONCpPeXvGo2yXTwH/XQCh7aSs3QUnbak0clhoaEdlhjwTUhiHIYVOgSg1Q+QcghSloHjGNiccNwNeSWlva+1GJRvDKmp4HD8xIeamFwCZi+Xq4zjwGTEN14K8Y1/hXT3GxHbJoIMDXYisymXIHnQNyN9uU/Y4bZS8GwurNwPZWrC1qZQ9wHY/7aFgWVH8MW6iUwZ5MZjhwM5RWIO8ER7J1YFjT8ooFMlK1MHe3DZFC4bLLjdS6dPCpi+M8qMXdFTlvnpFDboWjDcQ5exPtJz/Uz4EdSseSS174i/f1+GdkvlM8Lc3UyUrMvYgp8l6oViPttVl7hp8/F5vVg0OCNSHZqQB7YQOOKhpQviorAzBOkhiHPXxla/ETlrvqR42yGX9Hk6EiJvxWfc/chDpHotLI3CSwFI0+C1Qi0NfbZBwl5IOAglVojvPhoPJxziL4/awV4WqjWCtq3E125i8t/GzHL5ldmETLC5ExHzQ0i+eSrQMbbNTuBjpAqxcez10kjmSxzS/nUB0DAFeoRghg2qnRTR93WGpU0Wc9v3fZgyyEHXqjZKxVlw2xRbM86s7PxTOyczb/WcEvNCjuQbHPdpHmhhZ/Ed3nPE/HQcVoXTBpO2RwnMmIu1fUdOKMhp1BTHlEUMnWvj6+1hhje2UyzeSZsxfnadPH+F6fk43TLPnz2f4wkyss8JOBQkRiAxAHigWwLk5cO2XMgIQql02FsOSv39M/J2LiZn/bSf/DwjHCB31j+o0bA6oft/zxs++DwMB6zQyQF90uH2eZC8ExLXQMklUMwJfg/keCAnBYw4cFaHhm2gfRtTzE1+PUyXy6+EBqYj/UC6xZ6bB+QC/ZHAZwhJWyyOCPx0JHWxN+deSs0CGgG2Anhin3QA7FULnlm2mM39r+ervpquVYt+K+1ElC5jfTyX6uTOpmde++/JMhjzQ4j/6+Rk10mDbp/6+Ht3J0MbOjjuM3hteYinOjpPVY8Wkh3QdBhTwJ4c8M+ajys1lWSkKjI+Dyo5IXfz96T3SKVesShba7XBaN+DuDefY+Wtlp+01AvFfJarLsbs+fi1prgnjjwLJEbBYoGmJzSe3QVsqhyHxwLHveAOQSgKKgh+O9isUHz9Pnbe2wOHJ4W4Rr1xVWx4xpAJIxzAt30p/o3TKd22JcnvfcIRjxOPFWpaoUoOlNsAyTlADiQthZIh0A5Jm/R5perVmgT2MtC8GdSuedHdMzH5WfxiH7pSqifwFqI7o7XWr5z1+h3Aa8g8AIB3tNajL/aevyVB9yEpiZ2QTJU8pKS/LdJbG6SUfBkSHD2ETCHqi6Qrno8QMkxhOPDJXnhPQxMLTO1aifsqHOXZ1HNLywtF/YXOTkY0EVHfk2XQZWwBxV2KFK9i87Eor/VwnRLzbp/6UApcNqn+LBT17IAm9ZMC0vJtBKZ/R/FOqcQBhxV4QzLAOTc+1mnw+++xjvmIYFwxnGM+xAj46FfFYNKAizuVl+yP0HlcECNtJ0lLl5B11524PhqDd+gwahuwNapRD/+RvLEfkDB1AeFOrWlzGNKcEJcLOU7IToaK6ZBZHGruzGf3krEcH/MvlC+Ip3QNsDkJhwrw791ASvOWeO97kGDv3qTYFI1s4ApD6S3gPQDFNOjdUGkTpDghwwCbghPxcLIYOMqDPRHat4aKFS7pT8PE5D/mFwm6UsqK9BzqjmjNWmCI1nrbadvcATTXWj9wqYv6rQj6AaSl6hCkG98mYB0S+PQiBUGF/vNGyHzL2kjr3J8aUvY+EjQ1IpC5DdId0DljDa/268q4nlH61Dq3pe1NX/hx22HiQM8pMX+8nZO7mtoZ8LmfFQcjrL9Herl0+9RHr+o2Xurq5IFZAX44Kn54Q0OPcQU0K2Nl5UknO7rcRPC9j1BWsbjLZoEnHo44wKXBHTXIvWMo3umTyA9qysRZWHZanvnF+MuSKG/vcGP3FzC6O9w1T+F/830sQ4Zif+iPlJ42mmdaRrl3oZVyH8/F1rA1+U6I2iA3DqofFGs9OwWcBlRzwQdezaa1KxmVlsbOAh+JnmLkN2uLv3oVytrgZqsEnmvthbztUDwsPvri30PNTMAKmRFItMH+BMhNBmcFcHohtQOklPypvTIx+fn8UkFvAzyrtb4u9vOTAFrrv522zR2Ygn4OS5HBBzciKYdTENdIKiLWGxBx7x+7z4o9/qkxwLuQoGomMm6tJeA4DpNOQqUoVDq8kBeu78akAc5Toh4xNMO+9p/KZjmcp0+J+e9aiKUcimr6T/KzOj1CmTgLfWraeLmrE6UUhtY8MCvAusNRooamfUU7b/Z0kheCjl/A7s4DyR/1EUlhC1pLxklxBflRAzViKLVXTqdzqRAz06QC9FLEvJC/zA/w5bYwy0Z6ySzQtJ+k8bXqQOXNy1lxKyR5LMzaGebmmVZKjJlLfrfWlM6G4/HSDiApBJVzoXkZyE6A2m5YGIUdQQiGIeiACla41QphC6Qfh4obIT1XrpAiJ6HORqgcgqNhKR4q6YAtCRCsAPYUiPNCl45QrNhP7Y2JyS/jl5b+l0PaOxdS2Mv/bAYqpTYppb5USp33glMpdY9Sap1Sal1m5vlLnq8FIoiLxQnchEyI/xDJLe+MuGDGImLcGbHQayBW/MXEfB/wKmKZpyApjxWRnuChJGgfge2EGP3YI/So4eTOaQGm/xg+R8xdNsVry4NULm7h/uZFVrzDqpg62E3nyjZuOE3MQYp7/tHDxcFczeYMzV1N7SilSHAqltwC1RZ+RbH77sEXgSy7BCxzogaWEUOpExPzObv/czEHeLmriyH17XQe66OkV7FssOKGI8tOiTlAt6o26iYanJzzGZ4AFLig9BHotgNqn4BgKVifIP700QWwLF8Kkiq64Tkn3GqHlBBkr4WSS0XMi2twpkPXdVAuCPtC0qeltBO+LwXh2uAoBSUS4bpuppibXHkuV9ridGCi1jqolLoX0atzOg9prUcBo0As9Mv02VcVJ5HCoH5IZsr82HN3Iwd7NeIvvz72Wn7stYtJ3EHkBHEMqInko1dFKkkNpE93QwXBKpC4Ncr23HxqV7DyRCs3fSb6GdnEzsy0CPsejj+VzfJKNxedxxZw9/QAH/ZxnRJuh1Xx5S3nVr/4w5p+k3x0qWKld3UrPT7zMXeYh/opVuIcULeEZv+2rSRrOGaFoKGxjhhKrZXTSY2J+fzbLyzmWmsCEc7on346z3WWic+dx/pYONzDV/2L3icU1Vz/Neyo3YGEZ//J7RtgRbK4Xexu+L4KNHHC+qAUYxlOaG6HYTZp7BWvoeIumLcdSkQkrdSroN6PUHO/dK3MikAZO2CHZVXBWwaUDUqnQMf24Lj0FHcTk/8al2IqpSMDWQopT1HwEwCt9QmtdTD242iKamV+U2xBcsdHICX8HyOpiTcjvVo+RlwtdZDKz+uQ9rgX+hLSkUjzP5F0xVqIf70kIvJlYs9nAkcBjxt6pbipOnEl4w6V4pvdFiYOdPH6ihDXVbNx0xc+fGE5j6bnGRzMNfh8a5i7pwe4mOvNH9b0neQjxav4tL+b2xo6+WcPJ93H+dh0LMLw2ZpvLbXwfj6XdA/U0BBXUICePYtbqkZ4a1WIV7u5LijmUUMz4psANf6Vf9GUxic7OAlGNJ9uLGoCFjFEzNdVaEu3F6dRL9fB9yVgdzmolQPbKoobZWEUjrigkgc+cMAtdmnx0jETsubDvM1QPAJHrJDkhP4roOo+2BOEgihUd0LYA0sag7e8iHmVStC5kynmJlcPlyLoa4EaSqkqSikHMBg4I6FXKVXmtB/7Atsv3xKvfjQi0IeAO5A88s+BW5A5lEuRA9YZCYrakR7nJS7wfkeB14G/x7atjWTDFEPcN2UQt84mJIukSeyzLUB8OejuKkmzcSuZvC+Ru2cEGdHEzoSBLioUs3DDBB/rDkfpPNZHKAJTB7lZfyTKH74NnH/ftGbA50ViXjgbdEgDB//s4aTDGD8LAxXRMxbhT46ju0XWFI2Lo+KSFTz3g4vft3QwfKqftenRc94/amjunBbgQI7Bn9s56TK24LyiHohIK94W5aw83LooOyY3CGsOhqnaehDK7iAxC76vCsWCsCMFtidCgQf8LnjKDn+wQZ4FuvghcTVMWwb786Qyd28p6OqDAdMhkA27glJIVN0FR8vAspaQWAyUgnq1YwVDZiWHyVXET7pctNYRpdQDSAW6FfhYa71VKfU8sE5rPQ34g1KqL6IzJxFd+03gR9wh7ZEuiV8iQj0SCXJ+hBQHFUcCn7dT1OP8bDKBccBeZLp83dh7HgZOAMnAHuRsqZGsGTsyDMOJWPylrbCrPNz+2WKOHD7IwDp23r/BhUUpPu7rYuS0AK1HF+C1w5xhHqqVkCBmouvCOTUeu6IgJBOCTq+RGdLAgUZxz6JD2Pbuw9WiPvlaTjw5Co7VrYtt/nJGdW3HPQ183DDRx4whHlqUk3c5Xcxn3OrBY5dK1Y5jClgyoqiitFDMk92KcQPc2E4bOF3CrVhxm5VObzyA4bQTGDwUZwDSi0FeIrjtUNkiJ9ZkK3SMwrFdMGcHZERjU4biIa8sPDwd1D44GIYCAyo4IMEK25rB9ng5/gAtmkKtGhc8XCYmVwyzsOgXcAixzIcggc5vgD5ICuICxNJuAKxCerFcKDX5BCLkaRQFOSvEfj8xts0xxAK3IBZ6NtK0qwNQGXHpTAV6axg5+Uvm3z6IW+paGdVHxLyQqKEp/888HmnjZGQTO90+9dGzuo2/nRYAPZtQVHPzZD8K+OJmNw7rmdvdNjXElJJtic6ZjxuJH3ypoIKGHAN8G7ahe7bjruo+JmwOM2OIh6ZlLOeIOcA76yI8tsxCnDXKqtuslE9Qp8T885tFzENRzfw9EXpWt51a89aMKB0makKvf0SxfoOpFYVNxaGNBXxheM8JnkxYvQly8mLH0wZ7akJqNnT8HPJ9cDAkAd3yDnC6YVV3OJQnA0SsVmjXGiqWv8AXaWLyK2A25/ovsAIR9BsRl0pm7PFJRFibIrnMZZDo8PmkMgv4DAmSlkGCqKVj75WMiHQOIuKF80NPxLZJpaiBVxQYr+GfBuRN/pLMEYMYVMfKB2eJeSGrDkW4YYKPRLdiYB37RcW8kAuJ+j9WR3l+Szwpy1azu2JF7IhfPw+oGY1l7ZwA58qZrBrYh3H9XTzyXYDW5a3kBfUZYn4wx6Dim/nYFy0humMriU89Rk1viPQ8zY77XbjtFgmAToVFu4I80srBq50USimO+wxajYfw0Mdpfff/cbAcHLJJvn+tHKi6C/Yfln05BFjKwd4a8NAMSFwJGWHJLS9pgxQ72CvB7PZQkC7H3eGI5ZgnY2JyRTEnFl1GokiXRIVkqnyKWG83I6X8CxE3yTYkmNCVc8U8F3gXeAIR7yZIoLRwtqYV2I+IebHYTQNVkIBrb0TM12oYHIV2Yfg8CrVmz+L4iEEMqGm5oJgDtC5vo1IxCykedY6Y+8KSn/77mf4zAqUOq2LSQDdr0qN8uF6Ckv9YHeW5zfGUXbqaEhUq4gLqqtjQZg07FewzoPiPe9j+uxG81cvNrQ3sfNTXRZyDM8QcoEIxCy92cWAffCNGrz5kjXyADceinHAnMmC6oiAkYr6yYls8m/by/uFS/Hmx5rjPoM1EKBhwLz37Pc3GShCMwBggfT/s2yxibgB7EyC/LVhLw1NvQ9wK2BuErChUcYqYe3vAVx3BHxNzrwd6djXF3OTqx7TQ/wOykXL7vogoL0cswDwkb7M+kkLYNvb4bPIQ18pG5CRQCbFmA4g//CSSxhiHZMkUPt85tj3ACQ0va1gdlRNFMys0VuBeuYLf9+pG36ph1h8xLpoi+MbKIP9cFSQ7AH9u5+DpjtImwBfW9BjnI8uvSXBCkzJW3u0tKY1RQ3PrFD+ZBWJV//t7g+c2x1NqyWrKVq7IOqQMvhUSK8jWclUR3bkHR2prXmlWwAPNLy1L9qWlAV7e6MYIh5nQU/O3tZptJy3YwwGCZSvieuoN8m7pg+vYSWzXtYEjh7Dc8QDqhdfwuhX1g1AtAHV/gIO5kFkJamTD7spQtTKUXwZtpoAvBOlh8FqgnB0cCeAdBh+fAM8xqeRNLC4FQ+6fqvYyMfmVMNvnXga2AyuRoOa3iNCOQJpk+RG/dwYSDD37oBYgrpV1iDA3RsTOQCxvH+J+KRZ73UB86a1j7xUBRhkw2RChrGmBITZorWINuoCtyclYHU5Sq0Apb5Sun/rOK+pvrgryrzUhlo2IY0tGlMFfydi4P7Zx0mOcj+yAZuN9HjYcNej6WYCgivBBTxtDZsLcox7aljX41/ooL25NIHHBamyVK7JLQQklfuk1OjbfFCixew/Znf8zMQf4czsnM9PySPMZrDzkoFNp2HIwgK9UHdylmpD/1yfRTz1E/MjfYZ+8mPwVi6je/VYy7QoXcCwbSh2DQ7mQYoEqFni/IzwTBsdHUHsTHAsX5ZaXsIGrLtgHwagfoHi2uIrKlIKO7cBupiWa/I9gWug/QeE4OAPxi09FXC0GIuyVkJzwGxA/+OkEECFfgQh+FUToVOyWRSzVMLa9BwlyVoz9vFTDWwbsNyDJAp0UtFTQVongBJEmXhsRF01w61Y+S23P6x2CbM+IMmvXmcU8b60K8tbqEAuHe6lUXJ6bmRZm8Fd+SrgV8Q7Fhns97M7StJ2oMf76NyJjR5N0MA1/7SZYvp6J75YBGGlp2GauxlW3IsoKtwFzlFxd5CCl8UZeAbpWFV5oXMAfW51fzMdsjuK1am6pe+brj88NsGBvhPQ8jdcuvVhOOMuRPPwdlNVGJDeDSO5xctd9hYpz4JgynYSEYrxwGF6MA4cF4iLQaieUaASZSTB0D3y5Be6cA0diGZoVHeCyQ7F+4G8JHy+Bkj7JGKpaGVq1MNMSTa4+zKDozySApCS2RqoHDyMZHLORboc+pHKzHWf6yYOx31uCWN3VECvagviXTyAibkcEvjwS5HQDRzS8ZMB6Q960rQXaKUhVclLwI4UBhSKegrQNKIkUIq3fupXRqe158yxRH78pfI6YFzJpS4jbvw7w+UAndVNstJ+o8bzyFhl33oUzO4fcf/+LlD/+kYDLQ3wkgj4RIFwijkoOaIhUVa5ATmwBoJYhw5kdN/WjRdpiZt3IGf3WAd5dH+HP672ocIR3OwS5vWGRqK84GKHnZz6+usVDdkAzbGqQErf/C0fJSgQObSVr8tN4K9Qmru8zZC0eQ9RaQNmpcyiX5SC+ALJKS6dHXzw8ArSZDcZsyPbA3BowcK1Y5vaSkDQSjnrhs6UyaNoB1K8DjRpIvrmJydWGGRT9GRxGgmo9kHL9eKA50tMgiAjzICT/vPD/PoT4yO9FLOeGiNgqROgOI3nPhYHOTsg0om6ISyU1AtdHJYf7Xht8boPXLdBDyWi614GnYuupGFtbMlJgNBfpyz3QXY85zy7j0QVO6pa00ru6jcbvF1xQzE/6NS+vtVP+5psZOs9B6/EGoVffIjzyLoIaKiYWo9TTTxNye/ACIWyU9caRYpNOknk70xg38g4O5eTwLCKIuwxwWCyEx09hfelm1HgvQEZBUbHQu+sjPL7eS2jBKkLfLeP+ZU4+3RQ59fqifVFSvIq92VEKwpo4uyZ/47cEDm0l/+u/8s3Ndto6dpM/7XkSO41An8yj9Ouj2ZcI0QoQXwqCbqgQgXlbIH8+hIKgT0CrY7CrIXhbQKknYbcVxi2CsjExb9UcGjc0xdzkfxPTQj8Pq5ECnoaI5dkfyV7xI8LcGbHMCwkj3Q+/RQKalREfeQQR/wByQnBSlMZYAlig4c0oHNBQOibcPWJFMHmIz/4HxDddEenfEkXS7tKBpCjU2wONtkDZzWDZhjjsgXEnp3Dn6ps5/piHydsidK9mo2Kxc8/fD88NMcZfC2PJcuJ2pOE/sJ/y/fuTjrhOKlggS0GJqGaX30+qz0OoOByxw/GdaYQ6tKFZgp+0+OrUWbiU5QnFqGKA3YC9m3dAz7Y09eTx4wmDzfd5mLzD4PH1XkpOWonjq0nsTW1CZOsPuJ97mveud3JTXQeN3s8no0AT71SEoxA2NP4w2BwOpt5ko1tVG+GoZsBXUVaEqmFrejP+NROosWkbRpwiZECnPVB9McxoCs48eHAy1ImC0wVr74UqtSG0E+b8IFdIDqtMF6pwvrZzJiZXEabL5RKJIp0PyyAuFjcipAsR90g5pPdKYbVkBKkMnXHath5E4H2x7exIoLMB0uY2PeZS+d6Qzn1dLHB9zDeej2TO/IBkvFSNrSWAWMM5Uai3GxpvhBpbwLMt9uJZbM/bTrc17Xj5uiDDG108GHnCZ9B+fJS9x4O0eflVDj/0GAeVWKie2H7Y0UQee5jIRx/jmbUY1aYpZXemsadjG95sHeCuxlbumaOZEqxCZN5SPMWKkbtpB1zXln+1DXBHIxt3zQgxdXuYYEIifLsG28TRlBz3Nhl+TTSqqeQKciTPoGychf05Bk+0tzOonoO8EBwvMFiVHqVTJRudKhftz0vLIvxtnRN39z+QtXQMqZM+w9e+AxVXQZYNGqZByUMws5f4xl9YDdVvA1sK/OUA6A1QNwAuB3TuCCWTLnSUTEyuHswsl0sgB8kvb4VUdnZBslJ2I5Z1f4pKv6NIb/OpyGV6VYqE/ChipbsQge8EpGh4U8OfotLdr5kF/s8GXZRY8EuAFxCrvErs/ZKAAxEI7Ifm66HHRii5Ayyhi+/H9rztdFvbjr9dF+T2C4j5h+tDfLs7wrgBbpI8FpbdBs0/1Kx76nHsObmE776Xsp+N4+juvYQtClvaNsrt/oEnUiM80CeVpNFj2Xv/XbzZOsDdTeQzRvUA5uzl824dyH13NPTryb/aBhjZWFJERt/gAIuFLwIplLAk449LIKRhal/QWvHgIjfB7AKygwpLqZq8svoATcsYtChrZfjUIPuzDVqWK9qfV5ZHeGWdE0fZOmRNeR536aocXbqKVroDroNQMw8Wt4BmcfDIHHhvKLzQBv6pYe0q8ByEPVWhcSb0bAcJCT/9N2JicrVjWuhIyf0SxFWSifi9lyIWdhugRWw7A2my9TlyJixP0dQhH2KlJyE9WDohLpV3ohIsrKjgRgv0UZI5sxAJbAaRMv84ICsEOYeh/kZosQIqpIGryLV8QY674fsKsERt5/0P2/F6lwtb5h+sC/HysiAty1k56ddMH+Jh2YEot03x06emjcnbwhRoO96G3bEVL09090pK529n5QgXSR4LX2wJMXxakJe7unik1Zn5fFpr7p2jGb2mgNF9XKfEvBBDa275JsqclNbw5F+J9rmOBYMteOyK1p9F8MeXJD6xCsV7P0ro6C5yv3qKsq4Qg+vb6V3DRr9Jfj4d4GZnluIvyx3Yy9ahSv4Gvh5oocf4ICdLN+aRgas5VEmREoAEOyxtBBUrwi0p8GIUTh6HLiuhThDcyXC0I9xvP7NHjYnJ1YzpcrkAmlgwESkUqo5MA8pArOv+iKVd2E1xPBLgLI9Y5gHEWnchVnV7wK7hRQN+MMCt4AYLDFbgUtL/fFPss0sDtiDkpYNnD7ReBvU3QEL04uJiALuTYEMFOJwISkMxPyRu2cX901ryynVBhl1AzN9cFeT1lSEWDfdSubhixDcBth+Psj9bM2WQm/YVbTy7KMDrK0M4Wt6KCuWTtH8uK263nhokAdB3YgG7szTLR3opflZTL601O08a1Ew6dy+2ZUZpN1FT5p/vkT9zKnU2f0u630ZeyKBFySizDzmJ6/ZH3FWb4du5Cv+Ml/hDSzsvdpFq1pUHI/Sb5OeWeg7GbjGomWRj0VAb8U7FSb+m7achjDb38Hirt8hPUXhTIJQKO7yQF4aU72FGOXBrePwg3NgSjtpkluuQixxzE5OrCVPQz0MQqfpMRrJPqiPBUA+SmlgZEfJvkWyXKCLkVkTILUjjrEZAWw0fapgehYCC9hYYriBFyQljG+JLTwyA5TBED0C1DSLipYNi2V8oqcJvg01lYXM58DnluYonoNlBKJ8NygXUheW25fT7e3dmDbbQsty5YnrSr6n7bx/tKigm3+zGEqv+fGZhkN41bLSrKCeBbZlRWn5YgMNhp2Sc7RwxBxHtXuN9HMw9v6ifj0Ix973yb/rWHU56NI8tf+6GatQCW3wKwTmfY6vTAQ6cxFW1GQVfPsmIxjbe6uk6ozXByoMReo73USfZytxhHuKdRa+d9Gsaj4mQfNvf6H/vQ7SrCbstkOODxQfgoAc67YdNdcGTCP+2yPe8HqkC7vqTe2FicuUxBf0sjiA+8ARETDMRq7wtkkII0pflAyQVsQIi7iHE1VINscYPGPC+Ie9XS8EIC1SPWeJpgN0P3gxw7QfPbmi2BuqlQ6K+cPDimFfcJ7tKgVbgiEC9w9AoHRKCiI+nHtJboD5yaRDT7xkzZjBy6CBm3KzOEPWTfk2HSeC6fggnli+jAzsZ2//cXi/bMqN0HFPAa91d9Kttx2GFOMe5Yp0d0LQfH2FPYmWq5B5i+WAuKurbMqO0n6AJvvhvXEOG0zENDns1e7K3klKjPhENGcWg9oydrLujOTYjwO31NAv2RVk03EO5hDNPKPuyDVK86ow+MACTtoa5Z56dMnOXUal5A6oC7bJg5l6IRsFqhyNV4eZ4WKYk8Pxe7PuchVyVna9lg4nJ1YQp6KexFrHIDIpyuCsiOeUJwCLgHcQnXja2XTj2WjOgioYPDNikpdx9iILmFvHB7/aD9QTEHYYSP0KZPdBkJ1TNER0+W/IMZAjDhgqQGQvKJeVDkwNQKxNsRuyDC8W7HmJSxvRNIyejjUhmzGEgbcYMVg4dxHcxUT/p13SfBGUGDOfjf7zNsewC+rdpxJAy6bzYxXlqLXlBTeW38nixs5P7WxQ9fzbZAU2HsUF2u8tQ6sc9ZAwfStMfZrB00PkF3R/WlH8nTPX7/8G2p39PGQM6a820Zx/l6JtvUOmpl6g54i8sLwP95sHqBzqSmbOJexuGSXZGGP1D5LyifjaTtoR5YI6dR+YtYVPLRhwGMoNgz4AKWVA1H0rUgPiSEh9piKR/7kdEvRbSaK0nMmXKxORqxRR0RDy/RnK6rcgldj4yxLkRIshvIL70lNj2ChH7VhpWGDBXy/PXWSQL5ocC2JcDOgsS90DZvVA1HZrugdKhcwdZFNhhQznYUg4CDrAYUDUTmhyCsrmxjUpQJN4NED+PknVtQU5G+5CceBC9r46cbGoCRGHSNzP488hBjL8B/rTEirX3cJJefJsTNoVlzhx23T2AWQM55WYBCVjeMdVPep4ESs+2fiEm5uOipCe3InvfBlxLVqK6t+XddpKaeD601twzR/NlsArZ85byjDuBL26/Df/SqUwbYKHPFGja+S8seu8vNNkOSSs3883/dcBpi3JfI4Ntx8JsOBplzV0XFvVJW8LctcBO3flLGNG4ETcCr2bCjIgMi3ZoaOKCAV5JRw0gJ0EP8h1tRq7GaiPzE4dz8WHdJiZXkt+8oOch1lcQ8WXvRTJXBiKpia9SNBHIQNIUm2pAw1RDeq40VNArG/ZmwcEARLOh3F6oeATq7425RHRR6a0GDifA+gqwP1ncJ+5QkfvEWzgWsyQi4A2AehAoAz8qWdePyEkHJPBaGRHuqlEIRaTB1O6IVGbu13BMQyhWkJk7bwZrh/en4533c+8rb1Pcrhi9ZA7zhwxg2o3QPibmBSGNx86pjoojvgmQnmecI+q+sKbVJ1EOl+4AkSAF25fgSizOG20DjPiJXPdCUZ8QrIy1QjWMb7/Ba1fMvs1DilfRfqwm48G/0KLnX0jK0yx99QYKFs/CarOiHXHgLs6dlY/xVs9zZz1prUl6I0i7Z/5O98ceYoqGvDyomAaNjsLqOnCiAuQ45FC3R/rvZMX+DjIQl9oaRNSrIllMd2GWUZtcnfymBX0X8g+qkFxzN3A3crn9ImKxF0P+eVOAmhqWRmGvhgoFkHpYJtykR0EHoPo+qH4IWuyUlqyFzomwBbbH3CdZcZJ9UioXGh+EGpmniUMZoD5E68P++rA6RVruZiEngcICproGlA9DOAx7ThPtI6eJttsCZRWUV5BihTgbRO0QsYHNAsePHkWVKkVIKazAO62b00Nv5b1eIozbMqO0/yzC9ZWijB0ggdKj+QY1/pXP+Bvd9K1VlHaY5dfUHRUm312Wyjqd/SeDvNXLxYjGFxqodyZaa+6eGeLzzUEm3OgmquG+GQFmxUS91QRN7bZPcsS/B8f2ySwYBEOnBFiyP0ytknZm3+rk3TUh/tDKcUaQVmvNn+YEeH+LlVaff03XKj1ZkwvbK4PFCnd6oYUL3lVynIOIJd4VOdFnIINIaiKVue8jGUirkZmwJiZXG79ZQS8cOKERF8sAJDPlGcTf7EUuuatpOBmF3bkQlwv1joElR5o22YNQbxc03gkt90KpoLhscpywoTxsKwMhO1gNqH4Mmh6ElIKiNegKkN0A1jeFtbVl1qWBCHySAZXCUCYMeVHYHYV9MdEOxkTbaZFsmRQFJazgsYK2y6zMkEVcMUHAiH2NCpn9mRd73qUlxz0OCB08yJudWvHnOrn0rgadJmnsTVoRXLGIG+vYeLGzk27jfPSvZeP5zudOMdqfbdB6dD7BKDyX6uTBVuf62rdnRnl1eZA3e7rPm9J42xQ/OUGYeauHqTvCp0S9pAcafxikfHEbS4baiXfCPdMDbDxmMPNWN0O+8nM0X+OwwrxhHpI8FrTWPL0gyBfbImT5DQIWJwmjppLUqSet86FJFZhsl3jIEMSbNUaJZe5BrPXyyNXZD0hR13rg38iJvwBpmmZicjVxTQl6IBAgPz+fhIQEHI7zW4chxBeahgheHaRA6HmkhN4NJEYg+QQczYVoEFKywGFAjhuK50CTHdBmGzRLl7zlA8XhhwpwMEncJ3EBaJAODQ8XFf8ELbCzPnzfAn6sA7kVIOwCdxhKRyA+IuX7BzQc1eCLibbdAomxnuIJFvDYwGGHqA0CVmm6FUbE2hJ7HACcWnrExAHFlFxhJCPvk4Tczk4+WbN/P31bNyOUn49q2574DStYNMTCsK8DbDoW5Q8tHecV80L2Zxs0G5VPuQQLi4Z7SXQXbbc9M0r7MQU0Li3j5eYMOzOlcWtGlO7jfPyjh4tbG4j1Xyjq7Sta2XXSYPEdXoq5FL+b6WdzhsFXt4iYl0+w8HFfF0/ODzLtxzBf3eJhwuYw09MiLBjuYfmBKHdO8xNUTpqNmoV9UBcCFmhgkUKvKVqu0G6OHbuvYlku5ZB4iA/xqycirq5/IXGKGohFb2JytfA/L+iBQIDJkyfz9zfeZvvmjdhdbsJBP+06pPKnR/5Ar169sFolTS8DeA0pwXchHQnfA/YYkHQSKqWDLwh+A7RNtgkrKHcM2m2CzluhUi5sLwMby0OuR9wnZbLFfVLthFjYB4vBrhTY3gBOVIGTZSGQDF4r2KMiwoWirQFlgQQlwuu1iIWt7CLYuZYisdZIzvvpYl1CifAkA0lK7hOB0+OWQeTklRO75cZup10sYBgGY+8eyYJPPqV0jWoE0/eS64vy7+tdDKpnZ96eCP1ry+DliKGxKM47xm5fVpTmHxacIeqFYv5CZyf3N3fw8LdBVh6KnBL184l5IVN3hHl/XZjPb3JTLHYC6DvRh9umOe7nlJhbLQqtNQ9/F+CD9WFqlLCwcHhRv/dvdoS5bYqfZ154lVv/9Dizo9Lq+LAVkhU0V7BEi0XeD8lwWaDkeLdErPXtFLVweA1xhV2P+N9NTK4G/qcFfd++faR27UG+rRiW+r1wV2uOslgxwkF8O5ZibJlNrQqlmD3jG3YUL86biIg1D8PcIBwIQ9U9kHACMkuAzy1T3V1BqHkArlstaYIHUuDH0hC2gT0CtY5Cw0PiG09LEfHOSISssnCiLISSIJIAAYuIdgTAAh4FLov4t60xwY5YxQrXsgkeJIAahwh0JSRVrlCsiwE+VSTKOafdF7ZyUcj7FT52xH4v4ax7D0XpkuPGjePh++9k9Ugn140roFFpK4+3c9D2Ix/bf++lVrKcFAuzWUp6YNYg6zm9zAG+3Bpi2NQA9UpaeLuXi74T/fij8P3dbmol20R4Y6L+xnUu+kz00bmyla8GeS/p7yMQ0fSbWMC245puVa181Nd96uSiteazTWF61bCdMZFpRlqYYdMMlqxcS4MGDQAIa1hjwOQorLaCoaCBkh49mUiLhs3AFiXzQ9shRkFG7Hj/FbHeRyAnfxOTK80vFnSlVE/gLcR9PFpr/cpZrzuRRJJmiAE0SGu972LveSmCfuzYMRo3b0mkdk+8zfqedxttRClY+CFejtP6w4XEGS42JMFxG5Q+DEEn5MfJ5JmSWdBoJ3TYANoqAg2Q4IfKGYABO8rBkRKQ5YXjJSAvGfwJEHWB4RRxttjAqSTopq2grGCzynMeBSW0CGpJJX7Z0shJxCYfQW5MrPNiP5+NBfHvF4pyoUDHIdZkASIyvvM8DuiiIc0RLYJWeO/PL+CzG7rQNrqDD3toIgZ0GFNAuwo23uwpbpbsgKbDGB/7QwlYbHaaxGcz+1bnGaK+Jj3KDRN8fNTXyfy9Uf61JsyoG1x8sEmRdizA6jvPFPV31oZ4p7eTd9eEuamujWdTL00ad52M0mK0H48NulWxMKa/+4KDr2ekhRkyzaDbvMW0aNWKbsis1dOdchkaZkVhgoajViit5LidQLJE1wMnVdHAkL2xY/po7NjfyYUrek1Mfi1+kaArpayIO7o7khyyFhiitd522ja/Axpqre9TSg0GBmitB13sfS9F0O+8+16mbs4gPvWui26ntcGJr17APWwY/O5B3AUQcoFNQ7kMaTlb4yC4oxC0ihUescDJBDheTO7zvGBYIOoUvzdu0B6w2sS14bJAvA2KKwmuFfqpE2JCrRAhVepc67nQKncgWSz22HNRLa6ZHA15WsQ4ouX58HkE2YilUqrYPbHndOx3Cn/PruTk4ordF96soSCfDulF9az1fN5H449Ag/cKuK6aDIPOCULq56CvG0ylvgMI7d3Lro8/oGrBbqbdZMFlU6eJuYs1R618+H2YsX0sXFfdTtqJKHXeLSDObSftfiel4iRwuS9bUyXRQkaBQZexvksS9UO5Bp0naLo+8DhL5n3HwdWrGFDfzafXn9vW4NtdEYZMjfLt/MU0admKzUhAfG/sONdACoZqx74bQ8MmA0YbsMQCFiWH0x/7TrfHvsdaiKXuQ2bFNkXqFkxMriS/VNDbAM9qra+L/fwkgNb6b6dt811sm5VKKRvigiypL/LmPyXoubm5lClXgcTb38YWn3zB7QoJ7N9E5pqxJKzZTslsRa09kHIScuLEzRJyQHYc+FygLSKK7hA4DbDZwRIPRpJ06CsZC1Imxi7D7RpCSlwr/phwKiBqFAmqJSawUS2DIc62kHXsVijCUS0nAo+SJl5xMQE+W4SdCmxKeqcX3hfeLLGbVcVOJApQEFXiBy68FQ7ZWDh2LF///m423e1kwFRF3WT4QxODFh8W8MVNbv62zoa71xC6vvM+AaXIA06Ew6wedCPV0xbzbMsovSf66VHDSY1kG+8fScH1u4cIP/ckn18Pd8wIk6USKWfPY/0I2xl9Vgo5lm9Q6518xvRzM6DOhacvN/3EIKPzcKr+7R20DnLw6T/gnjOBrSPVOVb6SyuivL6nFM3nrqFiySSqWqG5DZrZ5ES6FmnHcAQ5qTYEeiPpnjlaevC8Cxy3yPEyYsJ/DDlhu5CrqQGI66XDT/41mpj89/il/dDLIR1gCzmEtA0/7zZa64hSKgcxdo6ftZB7gHsAKlasyMWYPn06nor1L0nMAZwVG2CZX4B75UaCDRqzsRZYo+D1Q2IWlMiCKrvBaQUjEXIrwskKgCVmYWvpNa6DcFLDidPEOKpFTO0WubecJa6FQmpYYjeKnkNJVkzhvSZ2r6SRVy6y/WlGNwZFrhgLRVb+z73ZYreE226jwoyp1Pz3dBqOGMHKzZuYPPZ7Gtw8kEFfTqH9fXfT7933cSp1at6pw2Gn3xdT+OCWG+k6fjaPjhnHly88w+Yj8MXilcSnpPBN8eJcN+IO4hLiKGfPYeXt9vOKudaa5xcHqVPSSpcqF//T+79WUe6Y8ineFiM4snQSzsUTWDr0/EHav7SxcMKfwRc921Lq8xWsL5bEQhv47BLHKGGDclZoY4W6NnHHvQ1kaRH81lbp5XJMw98NWBRLB41DXC7ZQLySfvnHAHtmJt+88Sajx3zCyYxjFE8uyZ0j7uDRRx6mVCmzcYDJleNXHXChtR4FjAKx0C+27dGjR9Hxl/7PoZTCUbwslVceoc6xxhTLlcySzHKQUQ4ON4S05CKL1sppgnzaYyunWcSIxWxH3Bg2ROScsZsDsd4Kf3afdu84bVtb7N5+1u8W3mzE1nPW48vur7XZiEyczJw5c+jVqxc+n49Vq1bRtWtX0tLSqFGjxvnTFe12Bk3+mv3791OtWjWe6tcPgLi4OABaDxtG/qrlzPtyDMuHO8/pzggi5g/MCvD9UYNvb/Ocyma5EGK9h7n9T20pV8zG8qGWMwKgB3IMyieIta6U4vUuoBccZsJNban3yUocJUqQaJHYSdQiIwU3WSBoB8MOcXYoaYNEK2yxwUK7XLklWuEfwElDgkYHkJPrSeTk+N2B/cxt14H40g1w9v4L5UuUJ5x1mI/nz2Lsp81YtXwpVapU+RlfjonJL+dSBD0dqY4upHzsufNtcyjmcimGxJp+Ng6HA2VcwnSH0/D6I7x43EmXDsB1yHWDGcU6A5vNRu/evQHwer107SpNY2vWrHmxX8Nms1GtWjWgSMhBhPrJxx9l2YwJLL/j8oh5IX1q2XDNCHB3A0Wyp+hPdUZamIFfhhjcyM2Y3pwS9QebakaN2ktk93o8JbtjBIEI6KjENxxWEXhtFbfUQQvssovIR23gsIDLDsvsYLdDDatY7z8C31vFWle3DiO+ZjfiWw48tR5HckUcXe+jYP1UbhpyG+tXrbik/TMxudxcSruKtUANpVQVpZQDGIwM7jmdaUhPI5C40YKL+c8vhUaNGhFJ38Klvo0R9JGXtYu6z9WVCFisqZXJf5d/v/suX376PvMHc14xB3jo2wArDkUvKObpuQafbQqd813bLIplIzz8fVmIj36QhM0ZaWFGzrYy+9s57LPX5q5ZGkNr9mQZdJmgee2111kyvDvTa8Gk+vCPuvBoLRhRFa4rD41ToHIClHZDkgWSQ5DoB68PyAN/FuRlwtEMWJcJszPhxxzw5EKJtdtQ27aTcIGMK0+TPqTt3M3GjRt/2UE1MfmZ/KSFHvOJPwB8h3gCPtZab1VKPQ+s01pPAz4CximldiFXp4N/6cI6dOhAMbeDwIHNuCo1/MntfVvn07VbN0qXLv1LP9rkP6BTaiovPutg3p4Qg+qfK+i5Qc3XOyJ47BYihubss+yhXIPOY33kBDTbMg1e6nJmlWqtZCuPtXPy0LdBNhzVfJ5mZ/q382jVqhWt5i6id/dUbp26nVWH4fG//o3fPfAgxD4lwQIJLqh9kYSaUBTyg5AThKMh2B+Cg2E4HIZjAclyyVNQYIWjS1bjqtwEZT1/MFdZrLirNGX16tU0atToPz2UJia/mEvyoWutZyFxo9Ofe+a0xwGkqvqyoZTiqT8/xp+efxVH6ZexOD0X3DaSk0Fw3RSenP715VyCySVQv3595ixcSo/OHYAQg+oXiV1uUNNjYpQe/W8lKTmZ1M8+YNFQ2ylLvlDMb6hhZfx2GxP3xGO3F/BsB8spUR+/OcIbP7iY9NVkXn7+GaZ/+x6tWklM3uv1MmvuIm69qT9P3Nuf+373+/94/Q4rlPDIrQrSIuJsAhHIC8JYr5VXoj/hBoyGsdnM2esmV4ar+i/vrrvuZNWatUyZ8lc8PR/Bnlj2nG2C6TvI//Z1nn/madq1a3cFVmnSoEGDc0Q9N6jp9QU06X4L737wESrm50797H0WDbXhj3BKzCf+aOe9j8bSoWNHOrdvBUszebaDhQlbovxpiYO5i5ZRr149brjhhnM+2+v18s3suf/V/XPZ5Da4bxee+fODeEJ+LI5zO6YboQD5u9fRtetH/9X1mJhciKu+9F9rzcuvvMqrf38NR+nq6EotsTi9RH3ZqF1LsQZzeOO1v3Pbbbf+Cqs2uRibN2+mR+cOvNQuxEdbbDTsMpB3P/gIi0Uscq01T/zpj8wc/z7ZBUG6VrHy3QEH7344loE3SclORkYGndu3opb9KKsyXKfE/Gqh/02DWLYvH2/X+1DqzDa+BQtH0aqMjZnfTLmCKzS51vmf7uVSiN/v54svvmDmd/PIy8snKSmRQQMH0Lt371ONuUyuPJs3b6ZLp/bcNPDGM8S8EK01Lz77f+z4MY3JX01h4sRJp8S8kIyMDB647x7++sJLV5WYgxS8derag/0nfVjr9cSeVJ7wycMYW7+lfIKdxQvmUrx48Su9TJNrmGtC0E3+d8jPz8fr9V6wBe/p252eAvm/QjgcZsqUKbzzwWjS0w9TpkxpHrj3LgYOHHjBls4mJpcLU9BNTExMrhEuJujm2EQTExOTawRT0E1MTEyuEUxBNzExMblGMAXdxMTE5BrhigVFlVKZyFjHK00yZ7X5vcYw9+9/m2t5/67lfYP/3v5V0lqfd8ztFRP0qwWl1LoLRYyvBcz9+9/mWt6/a3nf4Mrsn+lyMTExMblGMAXdxMTE5BrBFPTYBKVrGHP//re5lvfvWt43uAL795v3oZuYmJhcK5gWuomJick1ginoJiYmJtcIpqADSqmblVJblVKGUuqaSKNSSvVUSv2olNqllHriSq/ncqOU+lgplaGU2nKl13K5UUpVUEotVEpti/1dPnSl13Q5UUq5lFJrlFIbY/v33JVe038DpZRVKfWDUmrGr/WZpqALW4AbgSVXeiGXA6WUFXgX6AXUBYYopepe2VVddj5BxoFfi0SAR7XWdYHWwO+vse8vCHTRWjcCGgM9lVKtr+yS/is8BGz/NT/QFHRAa71da/3jlV7HZaQlsEtrvUdrHQImAf2u8JouK1rrJchA8msOrfURrfX3scd5iCiUu7KrunxoIT/2oz12u6ayM5RS5YHrgdG/5ueagn5tUg44eNrPh7iGBOG3hFKqMtAEWH2Fl3JZibkjNgAZwFyt9TW1f8CbwOOA8Wt+6G9G0JVS85RSW85zu6YsV5NrB6VUHPAV8LDWOvdKr+dyorWOaq0bA+WBlkqp+ld4SZcNpdQNQIbWev2v/dm2X/sDrxRa625Xeg2/IulAhdN+Lh97zuR/BKWUHRHz8Vrra3bqtNY6Wym1EImHXCsB7nZAX6VUb8AFJCilPtNaD/1vf/BvxkL/jbEWqKGUqqKUcgCDgWlXeE0ml4iSYawfAdu11v+80uu53CilSiqlisceu4HuwI4ruqjLiNb6Sa11ea11ZeR/b8GvIeZgCjoASqkBSqlDQBtgplLquyu9pl+C1joCPAB8hwTUvtBab72yq7q8KKUmAiuBWkqpQ0qpO6/0mi4j7YBhQBel1IbYrfeVXtRlpAywUCm1CTE+5mqtf7XUvmsZs/TfxMTE5BrBtNBNTExMrhFMQTcxMTG5RjAF3cTExOQawRR0ExMTk2sEU9BNTExMrhFMQTcxMTG5RjAF3cTExOQa4f8BFdAMlzKyEswAAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -184,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "colab": { "height": 515 @@ -206,7 +198,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -249,18 +241,18 @@ }, "source": [ "## Play with larger scales\n", - "One of the interesting features of the low-rank approach lies in its ability to scale, since its iterations are of complexity $O( (n+m) r)$ rather than $O(nm)$. We consider this by sampling two points clouds of size 1 million in $d=7$. " + "One of the interesting features of the low-rank approach lies in its ability to scale, since its iterations are of complexity $O( (n+m) r)$ rather than $O(nm)$. We consider this by sampling two points clouds of size 100 000 in $d=7$. " ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "id": "CRTAJb8ae9Je" }, "outputs": [], "source": [ - "n, m, d = 10 ^ 6, 10 ^ 6 + 1, 7\n", + "n, m, d = 10**5, 10**5 + 1, 7\n", "x, y, a, b = create_points(rng, n=n, m=m, d=d)" ] }, @@ -275,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "id": "GPWnpdoZfGWc" }, @@ -284,11 +276,11 @@ "geom = ott.geometry.pointcloud.PointCloud(x, y, epsilon=0.1)\n", "ot_prob = ott.core.linear_problems.LinearProblem(geom, a, b)\n", "costs = []\n", - "ranks = [1, 5, 10, 15, 20, 35, 50, 100, 500, 1000]\n", + "ranks = [15, 20, 35, 50, 100]\n", "for rank in ranks:\n", - " solver = ott.core.sinkhorn_lr.LRSinkhorn(rank=rank)\n", + " solver = ott.core.sinkhorn_lr.LRSinkhorn(rank=rank, initializer=\"k-means\")\n", " ot_lr = solver(ot_prob)\n", - " costs.append(ot_lr.compute_reg_ot_cost(ot_prob))" + " costs.append(ot_lr.reg_ot_cost)" ] }, { @@ -304,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "colab": { "height": 319 @@ -326,7 +318,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/docs/references.bib b/docs/references.bib index 1d021ce4a..7afad766f 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -564,3 +564,15 @@ @article{crane:13 keywords = {heat kernel, discrete differential geometry, geodesic distance, Digital geometry processing, distance transform} } + +@misc{scetbon:22b, + doi = {10.48550/ARXIV.2205.12365}, + url = {https://arxiv.org/abs/2205.12365}, + author = {Scetbon, Meyer and Cuturi, Marco}, + keywords = {Machine Learning (stat.ML), Machine Learning (cs.LG), FOS: Computer and information sciences, + FOS: Computer and information sciences}, + title = {Low-rank Optimal Transport: Approximation, Statistics and Debiasing}, + publisher = {arXiv}, + year = {2022}, + copyright = {Creative Commons Attribution 4.0 International} +} diff --git a/ott/core/initializers.py b/ott/core/initializers.py index 05e700a3e..66f17118b 100644 --- a/ott/core/initializers.py +++ b/ott/core/initializers.py @@ -21,6 +21,8 @@ from ott.core import linear_problems from ott.geometry import pointcloud +__all__ = ["DefaultInitializer", "GaussianInitializer", "SortingInitializer"] + @jax.tree_util.register_pytree_node_class class SinkhornInitializer(ABC): diff --git a/ott/core/initializers_lr.py b/ott/core/initializers_lr.py new file mode 100644 index 000000000..29ab7e402 --- /dev/null +++ b/ott/core/initializers_lr.py @@ -0,0 +1,345 @@ +import functools +from abc import ABC, abstractmethod +from typing import Any, Dict, Mapping, Optional, Sequence, Tuple, Union + +import jax +from jax import numpy as jnp +from typing_extensions import Literal + +from ott.core import linear_problems +from ott.geometry import low_rank, pointcloud + +__all__ = ["RandomInitializer", "Rank2Initializer", "KMeansInitializer"] + + +@jax.tree_util.register_pytree_node_class +class LRSinkhornInitializer(ABC): + """Low-rank Sinkhorn initializer. + + Args: + rank: Rank of the factorization. + """ + + def __init__(self, rank: int): + self._rank = rank + + @abstractmethod + def init_q( + self, + ot_prob: linear_problems.LinearProblem, + key: jnp.ndarray, + **kwargs: Any, + ) -> jnp.ndarray: + """Initialize the low-rank factor :math:`Q`. + + Args: + ot_prob: Linear OT problem. + key: Random key for seeding. + kwargs: Additional keyword arguments. + + Returns: + Array of shape ``[n, rank]``. + """ + + @abstractmethod + def init_r( + self, + ot_prob: linear_problems.LinearProblem, + key: jnp.ndarray, + **kwargs: Any, + ) -> jnp.ndarray: + """Initialize the low-rank factor :math:`R`. + + Args: + ot_prob: Linear OT problem. + key: Random key for seeding. + kwargs: Additional keyword arguments. + + Returns: + Array of shape ``[m, rank]``. + """ + + @abstractmethod + def init_g( + self, + ot_prob: linear_problems.LinearProblem, + key: jnp.ndarray, + **kwargs: Any, + ) -> jnp.ndarray: + """Initialize the low-rank factor :math:`g`. + + Args: + ot_prob: Linear OT problem. + key: Random key for seeding. + kwargs: Additional keyword arguments. + + Returns: + Array of shape ``[rank,]``. + """ + + def __call__( + self, + ot_prob: Optional[linear_problems.LinearProblem], + q: Optional[jnp.ndarray] = None, + r: Optional[jnp.ndarray] = None, + g: Optional[jnp.ndarray] = None, + *, + key: Optional[jnp.ndarray] = None, + **kwargs: Any + ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: + """Initialize the factors :math:`Q`, :math:`R` and :math:`g`. + + Args: + ot_prob: Linear OT problem. + q: Factor of shape ``[n, rank]``. If not `None`, :meth:`init_q` will be + used to initialize the factor. + r: Array of shape ``[m, rank]``. If not `None`, :meth:`init_r` will be + used to initialize the factor. + g: Array of shape ``[rank,]``. If not `None`, :meth:`init_g` will be + used to initialize the factor. + key: Random key for seeding. + kwargs: Additional keyword arguments for :meth:`init_q`, :meth:`init_r` + and :meth:`init_g`. + + Returns: + The factors :math:`Q`, :math:`R` and :math:`g`, respectively. + """ + if key is None: + key = jax.random.PRNGKey(0) + key1, key2, key3 = jax.random.split(key, 3) + + if g is None: + g = self.init_g(ot_prob, key1, **kwargs) + if q is None: + q = self.init_q(ot_prob, key2, init_g=g, **kwargs) + if r is None: + r = self.init_r(ot_prob, key3, init_g=g, **kwargs) + + assert g.shape == (self.rank,) + assert q.shape == (ot_prob.a.shape[0], self.rank) + assert r.shape == (ot_prob.b.shape[0], self.rank) + + return q, r, g + + @property + def rank(self) -> int: + """Rank of the transport matrix factorization.""" + return self._rank + + def tree_flatten(self) -> Tuple[Sequence[Any], Dict[str, Any]]: + return [self.rank], {} + + @classmethod + def tree_unflatten( + cls, aux_data: Dict[str, Any], children: Sequence[Any] + ) -> "LRSinkhornInitializer": + return cls(*children, **aux_data) + + +@jax.tree_util.register_pytree_node_class +class RandomInitializer(LRSinkhornInitializer): + """Low-rank Sinkhorn factorization using random factors. + + Args: + rank: Rank of the factorization. + """ + + def init_q( + self, + ot_prob: linear_problems.LinearProblem, + key: jnp.ndarray, + **kwargs: Any, + ) -> jnp.ndarray: + del kwargs + a = ot_prob.a + init_q = jnp.abs(jax.random.normal(key, (a.shape[0], self.rank))) + return a[:, None] * (init_q / jnp.sum(init_q, axis=1, keepdims=True)) + + def init_r( + self, + ot_prob: linear_problems.LinearProblem, + key: jnp.ndarray, + **kwargs: Any, + ) -> jnp.ndarray: + del kwargs + b = ot_prob.b + init_r = jnp.abs(jax.random.normal(key, (b.shape[0], self.rank))) + return b[:, None] * (init_r / jnp.sum(init_r, axis=1, keepdims=True)) + + def init_g( + self, + ot_prob: linear_problems.LinearProblem, + key: jnp.ndarray, + **kwargs: Any, + ) -> jnp.ndarray: + del kwargs + init_g = jnp.abs(jax.random.uniform(key, (self.rank,))) + 1. + return init_g / jnp.sum(init_g) + + +@jax.tree_util.register_pytree_node_class +class Rank2Initializer(LRSinkhornInitializer): + """Low-rank Sinkhorn factorization using rank-2 factors :cite:`scetbon:21`. + + Args: + rank: Rank of the factorization. + """ + + def _compute_factor( + self, + ot_prob: linear_problems.LinearProblem, + init_g: jnp.ndarray, + *, + which: Literal["q", "r"], + ) -> jnp.ndarray: + a, b = ot_prob.a, ot_prob.b + marginal = a if which == "q" else b + n, r = marginal.shape[0], self.rank + + lambda_1 = jnp.min( + jnp.array([jnp.min(a), jnp.min(init_g), + jnp.min(b)]) + ) * .5 + + # normalization to 1 can overflow in i32 (e.g., n=128k) + # using the formula: r * (r + 1) / 2 will raise: + # OverflowError: Python int 16384128000 too large to convert to int32 + # normalizing by `jnp.sum()` overflows silently + g1 = 2. * jnp.arange(1, r + 1) / (r ** 2 + r) + g2 = (init_g - lambda_1 * g1) / (1. - lambda_1) + x = 2. * jnp.arange(1, n + 1) / (n ** 2 + n) + y = (marginal - lambda_1 * x) / (1. - lambda_1) + + return ((lambda_1 * x[:, None] @ g1.reshape(1, -1)) + + ((1 - lambda_1) * y[:, None] @ g2.reshape(1, -1))) + + def init_q( + self, + ot_prob: linear_problems.LinearProblem, + key: jnp.ndarray, + *, + init_g: jnp.ndarray, + **kwargs: Any, + ) -> jnp.ndarray: + del key, kwargs + return self._compute_factor(ot_prob, init_g, which="q") + + def init_r( + self, + ot_prob: linear_problems.LinearProblem, + key: jnp.ndarray, + *, + init_g: jnp.ndarray, + **kwargs: Any, + ) -> jnp.ndarray: + del key, kwargs + return self._compute_factor(ot_prob, init_g, which="r") + + def init_g( + self, + ot_prob: linear_problems.LinearProblem, + key: jnp.ndarray, + **kwargs: Any, + ) -> jnp.ndarray: + del key, kwargs + return jnp.ones((self.rank,)) / self.rank + + +@jax.tree_util.register_pytree_node_class +class KMeansInitializer(LRSinkhornInitializer): + """K-means initializer for low-rank Sinkhorn :cite:`scetbon:22b`. + + Args: + rank: Rank of the factorization. + sinkhorn_kwargs: Keyword arguments for :class:`~ott.core.sinkhorn.Sinkhorn`. + kwargs: Keyword arguments for :func:`~ott.tools.k_means.k_means`. + """ + + def __init__( + self, + rank: int, + sinkhorn_kwargs: Optional[Mapping[str, Any]] = None, + **kwargs: Any + ): + super().__init__(rank) + self._sinkhorn_kwargs = {} if sinkhorn_kwargs is None else sinkhorn_kwargs + self._k_means_kwargs = kwargs + + @staticmethod + def _extract_array( + geom: Union[pointcloud.PointCloud, low_rank.LRCGeometry], *, first: bool + ) -> jnp.ndarray: + if isinstance(geom, pointcloud.PointCloud): + return geom.x if first else geom.y + if isinstance(geom, low_rank.LRCGeometry): + return geom.cost_1 if first else geom.cost_2 + raise TypeError( + f"k-means initializer not implemented for `{type(geom).__name__}`." + ) + + def _compute_factor( + self, + ot_prob: linear_problems.LinearProblem, + key: jnp.ndarray, + *, + init_g: jnp.ndarray, + which: Literal["q", "r"], + **kwargs: Any, + ) -> jnp.ndarray: + from ott.core import sinkhorn + from ott.tools import k_means + + del kwargs + jit = self._sinkhorn_kwargs.get("jit", True) + fn = functools.partial(k_means.k_means, **self._k_means_kwargs) + fn = jax.jit(fn, static_argnames="k") if jit else fn + + arr = self._extract_array(ot_prob.geom, first=which == "q") + marginals = ot_prob.a if which == "q" else ot_prob.b + + centroids = fn(arr, self.rank, key=key).centroids + geom = pointcloud.PointCloud( + arr, centroids, epsilon=0.1, scale_cost="max_cost" + ) + + prob = linear_problems.LinearProblem(geom, marginals, init_g) + solver = sinkhorn.Sinkhorn(**self._sinkhorn_kwargs) + return solver(prob).matrix + + def init_q( + self, + ot_prob: linear_problems.LinearProblem, + key: jnp.ndarray, + *, + init_g: jnp.ndarray, + **kwargs: Any, + ) -> jnp.ndarray: + return self._compute_factor( + ot_prob, key, init_g=init_g, which="q", **kwargs + ) + + def init_r( + self, + ot_prob: linear_problems.LinearProblem, + key: jnp.ndarray, + *, + init_g: jnp.ndarray, + **kwargs: Any, + ) -> jnp.ndarray: + return self._compute_factor( + ot_prob, key, init_g=init_g, which="r", **kwargs + ) + + def init_g( + self, + ot_prob: linear_problems.LinearProblem, + key: jnp.ndarray, + **kwargs: Any, + ) -> jnp.ndarray: + del key, kwargs + return jnp.ones((self.rank,)) / self.rank + + def tree_flatten(self) -> Tuple[Sequence[Any], Dict[str, Any]]: + children, aux_data = super().tree_flatten() + aux_data["sinkhorn_kwargs"] = self._sinkhorn_kwargs + return children, {**aux_data, **self._k_means_kwargs} diff --git a/ott/core/quad_problems.py b/ott/core/quad_problems.py index 96033f246..c2a3568f9 100644 --- a/ott/core/quad_problems.py +++ b/ott/core/quad_problems.py @@ -416,7 +416,7 @@ def init_linearization( ) def init_lr_linearization( - self, rank: int = 10, **kwargs: Any + self, rank: int, **kwargs: Any ) -> linear_problems.LinearProblem: """Linearizes a Quad problem with a predefined initializer.""" x_ = self.geom_xx.apply_square_cost(self.a) diff --git a/ott/core/sinkhorn.py b/ott/core/sinkhorn.py index 704b5be1e..a19404ba6 100644 --- a/ott/core/sinkhorn.py +++ b/ott/core/sinkhorn.py @@ -70,7 +70,7 @@ def solution_error( g_v: jnp.ndarray, potential or scaling ot_prob: linear OT problem norm_error: int, p-norm used to compute error. - lse_mode: True if log-sum-exp operations, False if kernel vector producs. + lse_mode: True if log-sum-exp operations, False if kernel vector products. Returns: a positive number quantifying how far from optimality current solution is. @@ -390,13 +390,13 @@ def __init__( @property def norm_error(self) -> Tuple[int, ...]: + """Powers used to compute the p-norm between marginal/target.""" # To change momentum adaptively, one needs errors in ||.||_1 norm. # In that case, we add this exponent to the list of errors to compute, # notably if that was not the error requested by the user. if self.momentum and self.momentum.start > 0 and self._norm_error != 1: - return (self._norm_error, 1) - else: - return (self._norm_error,) + return self._norm_error, 1 + return self._norm_error, def __call__( self, diff --git a/ott/core/sinkhorn_lr.py b/ott/core/sinkhorn_lr.py index 9430e135c..54bbfc82c 100644 --- a/ott/core/sinkhorn_lr.py +++ b/ott/core/sinkhorn_lr.py @@ -14,27 +14,35 @@ # Lint as: python3 """A Jax implementation of the Low-Rank Sinkhorn algorithm.""" -from typing import Any, Mapping, NamedTuple, Optional, Tuple +from typing import Any, Mapping, NamedTuple, Optional, Tuple, Union import jax import jax.numpy as jnp +import jax.scipy as jsp from typing_extensions import Literal -from ott.core import fixed_point_loop, linear_problems, sinkhorn +from ott.core import fixed_point_loop +from ott.core import initializers_lr as init_lib +from ott.core import linear_problems, sinkhorn from ott.geometry import geometry class LRSinkhornState(NamedTuple): """State of the Low Rank Sinkhorn algorithm.""" - q: Optional[jnp.ndarray] = None - r: Optional[jnp.ndarray] = None - g: Optional[jnp.ndarray] = None - costs: Optional[jnp.ndarray] = None + q: jnp.ndarray + r: jnp.ndarray + g: jnp.ndarray + gamma: float + costs: jnp.ndarray + criterions: jnp.ndarray + crossed_threshold: bool - def set(self, **kwargs: Any) -> 'LRSinkhornState': - """Return a copy of self, with potential overwrites.""" - return self._replace(**kwargs) + def compute_criterion(self, previous_state: "LRSinkhornState") -> float: + err_1 = kl(self.q, previous_state.q) + kl(previous_state.q, self.q) + err_2 = kl(self.r, previous_state.r) + kl(previous_state.r, self.r) + err_3 = kl(self.g, previous_state.g) + kl(previous_state.g, self.g) + return ((1. / self.gamma) ** 2) * (err_1 + err_2 + err_3) def reg_ot_cost( self, @@ -44,11 +52,15 @@ def reg_ot_cost( return compute_reg_ot_cost(self.q, self.r, self.g, ot_prob, use_danskin) def solution_error( - self, ot_prob: linear_problems.LinearProblem, norm_error: jnp.ndarray, + self, ot_prob: linear_problems.LinearProblem, norm_error: Tuple[int, ...], lse_mode: bool ) -> jnp.ndarray: return solution_error(self.q, self.r, ot_prob, norm_error, lse_mode) + def set(self, **kwargs: Any) -> 'LRSinkhornState': + """Return a copy of self, with potential overwrites.""" + return self._replace(**kwargs) + def compute_reg_ot_cost( q: jnp.ndarray, @@ -60,12 +72,12 @@ def compute_reg_ot_cost( q = jax.lax.stop_gradient(q) if use_danskin else q r = jax.lax.stop_gradient(r) if use_danskin else r g = jax.lax.stop_gradient(g) if use_danskin else g - return jnp.sum(ot_prob.geom.apply_cost(r, axis=1) * q * (1.0 / g)[None, :]) + return jnp.sum(ot_prob.geom.apply_cost(r, axis=1) * q * (1. / g)[None, :]) def solution_error( q: jnp.ndarray, r: jnp.ndarray, ot_prob: linear_problems.LinearProblem, - norm_error: jnp.ndarray, lse_mode: bool + norm_error: Tuple[int, ...], lse_mode: bool ) -> jnp.ndarray: """Compute solution error. @@ -85,16 +97,13 @@ def solution_error( norm_error = jnp.array(norm_error) # Update the error err = jnp.sum( - jnp.abs(jnp.sum(q, axis=1) - ot_prob.a) ** norm_error[:, jnp.newaxis], - axis=1 + jnp.abs(jnp.sum(q, axis=1) - ot_prob.a) ** norm_error[:, None], axis=1 ) ** (1.0 / norm_error) err += jnp.sum( - jnp.abs(jnp.sum(r, axis=1) - ot_prob.b) ** norm_error[:, jnp.newaxis], - axis=1 + jnp.abs(jnp.sum(r, axis=1) - ot_prob.b) ** norm_error[:, None], axis=1 ) ** (1.0 / norm_error) err += jnp.sum( - jnp.abs(jnp.sum(q, axis=0) - - jnp.sum(r, axis=0)) ** norm_error[:, jnp.newaxis], + jnp.abs(jnp.sum(q, axis=0) - jnp.sum(r, axis=0)) ** norm_error[:, None], axis=1 ) ** (1.0 / norm_error) @@ -104,12 +113,14 @@ def solution_error( class LRSinkhornOutput(NamedTuple): """Implement the problems.Transport interface, for a LR Sinkhorn solution.""" - q: Optional[jnp.ndarray] = None - r: Optional[jnp.ndarray] = None - g: Optional[jnp.ndarray] = None - costs: Optional[jnp.ndarray] = None + q: jnp.ndarray + r: jnp.ndarray + g: jnp.ndarray + costs: jnp.ndarray + criterions: jnp.ndarray + ot_prob: linear_problems.LinearProblem + # TODO(michalk8): Optional is an artifact of the current impl., refactor reg_ot_cost: Optional[float] = None - ot_prob: Optional[linear_problems.LinearProblem] = None def set(self, **kwargs: Any) -> 'LRSinkhornOutput': """Return a copy of self, with potential overwrites.""" @@ -153,8 +164,6 @@ def linear_output(self) -> bool: @property def converged(self) -> bool: - if self.costs is None: - return False return jnp.logical_and( jnp.sum(self.costs == -1) > 0, jnp.sum(jnp.isnan(self.costs)) == 0 @@ -163,14 +172,13 @@ def converged(self) -> bool: @property def matrix(self) -> jnp.ndarray: """Transport matrix if it can be instantiated.""" - return jnp.matmul(self.q * (1 / self.g)[None, :], self.r.T) + return (self.q * self._inv_g) @ self.r.T def apply(self, inputs: jnp.ndarray, axis: int = 0) -> jnp.ndarray: - """Apply the transport to a ndarray; axis=1 for its transpose.""" + """Apply the transport to a array; axis=1 for its transpose.""" q, r = (self.q, self.r) if axis == 1 else (self.r, self.q) - if inputs.ndim == 1: - inputs = inputs.reshape((1, -1)) - return jnp.dot(q, jnp.dot(inputs, r).T / self.g.reshape(-1, 1)).T.squeeze() + # for `axis=0`: (batch, m), (m, r), (r,), (r, n) + return ((inputs @ r) * self._inv_g) @ q.T def marginal(self, axis: int) -> jnp.ndarray: length = self.q.shape[0] if axis == 0 else self.r.shape[0] @@ -178,14 +186,17 @@ def marginal(self, axis: int) -> jnp.ndarray: def cost_at_geom(self, other_geom: geometry.Geometry) -> float: """Return OT cost for matrix, evaluated at other cost matrix.""" - return jnp.sum( - self.q * other_geom.apply_cost(self.r, axis=1) / self.g[None, :] - ) + return jnp.sum(self.q * other_geom.apply_cost(self.r, axis=1) * self._inv_g) + # TODO(michalk8): when refactoring the API, use a property def transport_mass(self) -> float: """Sum of transport matrix.""" return self.marginal(0).sum() + @property + def _inv_g(self) -> jnp.ndarray: + return 1. / self.g + @jax.tree_util.register_pytree_node_class class LRSinkhorn(sinkhorn.Sinkhorn): @@ -195,166 +206,146 @@ class LRSinkhorn(sinkhorn.Sinkhorn): contained here is adapted from `LOT `_. The algorithm minimizes a non-convex problem. It therefore requires special - care to initialization and convergence. Initialization is random by default, - and convergence evaluated on successive evaluations of the objective. The - algorithm is only provided for the balanced case. + care to initialization and convergence. Convergence is evaluated on successive + evaluations of the objective. The algorithm is only provided for the balanced + case. Args: rank: the rank constraint on the coupling to minimize the linear OT problem - gamma: the (inverse of) gradient stepsize used by mirror descent. + gamma: the (inverse of) gradient step size used by mirror descent. + gamma_rescale: Whether to rescale :math:`\gamma` every iteration as + described in :cite:`scetbon:22b`. epsilon: entropic regularization added on top of low-rank problem. - init_type: TODO. - lse_mode: whether to run computations in lse or kernel mode. At this moment, - only ``lse_mode=True`` is implemented. - threshold: convergence threshold, used to quantify whether two successive - evaluations of the objective are (relatively) close enough to terminate. - norm_error: norm used to quantify feasibility (deviation to marginals). + initializer: How to initialize the :math:`Q`, :math:`R` and :math:`g` + factors. Valid options are: + + - `'k-means'` - :class:`~ott.core.initializers_lr.KMeansInitializer`. + - `'rank2'` - :class:`~ott.core.initializers_lr.Rank2Initializer`. + - `'random'` - :class:`~ott.core.initializers_lr.RandomInitializer`. + + lse_mode: whether to run computations in lse or kernel mode. At the moment, + only ``lse_mode = True`` is implemented. inner_iterations: number of inner iterations used by the algorithm before - reevaluating progress. - min_iterations: min number of iterations before evaluating objective. - max_iterations: max number of iterations allowed. + re-evaluating progress. use_danskin: use Danskin theorem to evaluate gradient of objective w.r.t. - input parameters. Only ``True`` handled at this moment. - implicit_diff: whether to use implicit differentiation. Not implemented - at this moment. - jit: jit by default iterations loop. - rng_key: seed of random number generator to initialize the LR factors. - kwargs_dys: keyword arguments passed onto :meth:`dysktra_update`. + input parameters. Only `True` handled at this moment. + implicit_diff: Whether to use implicit differentiation. Currently, only + ``implicit_diff = False`` is implemented. + kwargs_dys: keyword arguments passed to :meth:`dysktra_update`. + kwargs_init: keyword arguments for + :class:`~ott.core.initializers_lr.LRSinkhornInitializer`. + kwargs: Keyword arguments for :class:`~ott.core.sinkhorn.Sinkhorn`. """ def __init__( self, - rank: int = 10, - gamma: float = 1.0, - epsilon: float = 1e-4, - init_type: Literal['random', 'rank_2'] = 'random', + rank: int, + gamma: float = 10., + gamma_rescale: bool = True, + epsilon: float = 0., + initializer: Union[Literal["random", "rank2", "k-means"], + init_lib.LRSinkhornInitializer] = "k-means", lse_mode: bool = True, - threshold: float = 1e-3, - norm_error: int = 1, - inner_iterations: int = 1, - min_iterations: int = 0, - max_iterations: int = 2000, + inner_iterations: int = 10, use_danskin: bool = True, implicit_diff: bool = False, - jit: bool = True, - rng_key: int = 0, - kwargs_dys: Optional[Mapping[str, Any]] = None + kwargs_dys: Optional[Mapping[str, Any]] = None, + kwargs_init: Optional[Mapping[str, Any]] = None, + **kwargs: Any, ): - # TODO(michalk8): this should call super + assert lse_mode, "Kernel mode not yet implemented for LRSinkhorn." + assert not implicit_diff, "Implicit diff. not yet implemented for LRSink." + super().__init__( + lse_mode=lse_mode, + inner_iterations=inner_iterations, + use_danskin=use_danskin, + implicit_diff=implicit_diff, + **kwargs + ) self.rank = rank self.gamma = gamma + self.gamma_rescale = gamma_rescale self.epsilon = epsilon - self.init_type = init_type - self.lse_mode = lse_mode - assert lse_mode, "Kernel mode not yet implemented for LRSinkhorn." - self.threshold = threshold - self.inner_iterations = inner_iterations - self.min_iterations = min_iterations - self.max_iterations = max_iterations - self._norm_error = norm_error - self.jit = jit - self.use_danskin = use_danskin - self.implicit_diff = implicit_diff - assert not implicit_diff, "Implicit diff. not yet implemented for LRSink." - self.rng_key = rng_key + self._initializer = initializer + # can be `None` self.kwargs_dys = {} if kwargs_dys is None else kwargs_dys + self.kwargs_init = {} if kwargs_init is None else kwargs_init def __call__( self, ot_prob: linear_problems.LinearProblem, - init: Optional[Tuple[Optional[jnp.ndarray], Optional[jnp.ndarray], - Optional[jnp.ndarray]]] = None + init: Tuple[Optional[jnp.ndarray], Optional[jnp.ndarray], + Optional[jnp.ndarray]] = (None, None, None), + key: Optional[jnp.ndarray] = None, + **kwargs: Any, ) -> LRSinkhornOutput: - """Main interface to run LR sinkhorn.""" # noqa: D401 - init_q, init_r, init_g = (init if init is not None else (None, None, None)) - # Random initialization for q, r, g using rng_key - rng = jax.random.split(jax.random.PRNGKey(self.rng_key), 3) - a, b = ot_prob.a, ot_prob.b - if self.init_type == 'random': - if init_g is None: - init_g = jnp.abs(jax.random.uniform(rng[0], (self.rank,))) + 1 - init_g = init_g / jnp.sum(init_g) - if init_q is None: - init_q = jnp.abs(jax.random.normal(rng[1], (a.shape[0], self.rank))) - init_q = init_q * (a / jnp.sum(init_q, axis=1))[:, None] - if init_r is None: - init_r = jnp.abs(jax.random.normal(rng[2], (b.shape[0], self.rank))) - init_r = init_r * (b / jnp.sum(init_r, axis=1))[:, None] - elif self.init_type == 'rank_2': - if init_g is None: - init_g = jnp.ones((self.rank,)) / self.rank - lambda_1 = min(jnp.min(a), jnp.min(init_g), jnp.min(b)) / 2 - a1 = jnp.arange(1, a.shape[0] + 1) - a1 = a1 / jnp.sum(a1) - a2 = (a - lambda_1 * a1) / (1 - lambda_1) - b1 = jnp.arange(1, b.shape[0] + 1) - b1 = b1 / jnp.sum(b1) - b2 = (b - lambda_1 * b1) / (1 - lambda_1) - g1 = jnp.arange(1, self.rank + 1) - g1 = g1 / jnp.sum(g1) - g2 = (init_g - lambda_1 * g1) / (1 - lambda_1) - if init_q is None: - init_q = lambda_1 * jnp.dot(a1[:, None], g1.reshape(1, -1)) - init_q += (1 - lambda_1) * jnp.dot(a2[:, None], g2.reshape(1, -1)) - if init_r is None: - init_r = lambda_1 * jnp.dot(b1[:, None], g1.reshape(1, -1)) - init_r += (1 - lambda_1) * jnp.dot(b2[:, None], g2.reshape(1, -1)) - else: - raise NotImplementedError(self.init_type) - run_fn = jax.jit(run) if self.jit else run - return run_fn(ot_prob, self, (init_q, init_r, init_g)) + """Run low-rank Sinkhorn. - @property - def norm_error(self) -> Tuple[int]: - return (self._norm_error,) + Args: + ot_prob: Linear OT problem. + init: Initial values for low-rank factors: - def _converged(self, state: LRSinkhornState, iteration: int) -> bool: - costs, i, tol = state.costs, iteration, self.threshold - return jnp.logical_and( - i >= 2, jnp.isclose(costs[i - 2], costs[i - 1], rtol=tol) - ) + - :attr:`~ott.core.sinkhorn_lr.LRSinkhornOutput.q`. + - :attr:`~ott.core.sinkhorn_lr.LRSinkhornOutput.r`. + - :attr:`~ott.core.sinkhorn_lr.LRSinkhornOutput.g`. - def _diverged(self, state: LRSinkhornState, iteration: int) -> bool: - return jnp.logical_not(jnp.isfinite(state.costs[iteration - 1])) + Any `None` values will be initialized using the :attr:`initializer`. + key: Random key for seeding. + kwargs: Additional arguments when calling :attr:`initializer`. - def _continue(self, state: LRSinkhornState, iteration: int) -> bool: - """Continue while not(converged) and not(diverged).""" - return jnp.logical_or( - iteration <= 2, - jnp.logical_and( - jnp.logical_not(self._diverged(state, iteration)), - jnp.logical_not(self._converged(state, iteration)) - ) + Returns: + The low-rank Sinkhorn output. + """ + assert ot_prob.is_balanced, "Unbalanced case is not implemented." + init = self.initializer(ot_prob, *init, key=key, **kwargs) + run_fn = jax.jit(run) if self.jit else run + return run_fn(ot_prob, self, init) + + def _lr_costs( + self, + ot_prob: linear_problems.LinearProblem, + state: LRSinkhornState, + ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray, float]: + log_q, log_r, log_g = ( + safe_log(state.q), safe_log(state.r), safe_log(state.g) ) - def lr_costs( - self, ot_prob: linear_problems.LinearProblem, state: LRSinkhornState, - iteration: int - ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: - c_q = ot_prob.geom.apply_cost(state.r, axis=1) / state.g[None, :] - c_q += (self.epsilon - 1 / self.gamma) * jnp.log(state.q) - c_r = ot_prob.geom.apply_cost(state.q) / state.g[None, :] - c_r += (self.epsilon - 1 / self.gamma) * jnp.log(state.r) + grad_q = ot_prob.geom.apply_cost(state.r, axis=1) / state.g[None, :] + grad_r = ot_prob.geom.apply_cost(state.q) / state.g[None, :] diag_qcr = jnp.sum( state.q * ot_prob.geom.apply_cost(state.r, axis=1), axis=0 ) - h = diag_qcr / state.g ** 2 - (self.epsilon - - 1 / self.gamma) * jnp.log(state.g) - return c_q, c_r, h + grad_g = -diag_qcr / (state.g ** 2) + if self.is_entropic: + grad_q += self.epsilon * log_q + grad_r += self.epsilon * log_r + grad_g += self.epsilon * log_g + + if self.gamma_rescale: + norm_q = jnp.max(jnp.abs(grad_q)) ** 2 + norm_r = jnp.max(jnp.abs(grad_r)) ** 2 + norm_g = jnp.max(jnp.abs(grad_g)) ** 2 + gamma = self.gamma / jnp.max(jnp.array([norm_q, norm_r, norm_g])) + else: + gamma = self.gamma + + c_q = grad_q - (1. / gamma) * log_q + c_r = grad_r - (1. / gamma) * log_r + h = -grad_g + (1. / gamma) * log_g + return c_q, c_r, h, gamma def dysktra_update( self, c_q: jnp.ndarray, c_r: jnp.ndarray, h: jnp.ndarray, + gamma: float, ot_prob: linear_problems.LinearProblem, - state: LRSinkhornState, - iteration: int, min_entry_value: float = 1e-6, - tolerance: float = 1e-4, + tolerance: float = 1e-3, min_iter: int = 0, inner_iter: int = 10, - max_iter: int = 200 + max_iter: int = 10000 ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: # shortcuts for problem's definition. r = self.rank @@ -371,91 +362,105 @@ def dysktra_update( state_inner = f1, f2, g1_old, g2_old, h_old, w_gi, w_gp, w_q, w_r, err constants = c_q, c_r, loga, logb - def cond_fn(iteration, constants, state_inner): + def cond_fn( + iteration: int, constants: Tuple[jnp.ndarray, ...], + state_inner: Tuple[jnp.ndarray, ...] + ) -> bool: del iteration, constants - err = state_inner[-1] + *_, err = state_inner return err > tolerance - def _softm(f, g, c, axis): - return jax.scipy.special.logsumexp( - self.gamma * (f[:, None] + g[None, :] - c), axis=axis + def _softm( + f: jnp.ndarray, g: jnp.ndarray, c: jnp.ndarray, axis: int + ) -> jnp.ndarray: + return jsp.special.logsumexp( + gamma * (f[:, None] + g[None, :] - c), axis=axis ) - def body_fn(iteration, constants, state_inner, compute_error): + def body_fn( + iteration: int, constants: Tuple[jnp.ndarray, ...], + state_inner: Tuple[jnp.ndarray, ...], compute_error: bool + ) -> Tuple[jnp.ndarray, ...]: + # TODO(michalk8): in the future, use `NamedTuple` f1, f2, g1_old, g2_old, h_old, w_gi, w_gp, w_q, w_r, err = state_inner c_q, c_r, loga, logb = constants # First Projection f1 = jnp.where( jnp.isfinite(loga), - (loga - _softm(f1, g1_old, c_q, 1)) / self.gamma + f1, loga + (loga - _softm(f1, g1_old, c_q, axis=1)) / gamma + f1, loga ) f2 = jnp.where( jnp.isfinite(logb), - (logb - _softm(f2, g2_old, c_r, 1)) / self.gamma + f2, logb + (logb - _softm(f2, g2_old, c_r, axis=1)) / gamma + f2, logb ) h = h_old + w_gi - h = jnp.maximum(jnp.log(min_entry_value) / self.gamma, h) + h = jnp.maximum(jnp.log(min_entry_value) / gamma, h) w_gi += h_old - h h_old = h # Update couplings - g_q = _softm(f1, g1_old, c_q, 0) - g_r = _softm(f2, g2_old, c_r, 0) + g_q = _softm(f1, g1_old, c_q, axis=0) + g_r = _softm(f2, g2_old, c_r, axis=0) # Second Projection - h = (1 / 3) * (h_old + w_gp + w_q + w_r) - h += g_q / (3 * self.gamma) - h += g_r / (3 * self.gamma) - g1 = h + g1_old - g_q / self.gamma - g2 = h + g2_old - g_r / self.gamma + h = (1. / 3.) * (h_old + w_gp + w_q + w_r) + h += g_q / (3. * gamma) + h += g_r / (3. * gamma) + g1 = h + g1_old - g_q / gamma + g2 = h + g2_old - g_r / gamma w_q = w_q + g1_old - g1 w_r = w_r + g2_old - g2 w_gp = h_old + w_gp - h - q, r, _ = self.recompute_couplings(f1, g1, c_q, f2, g2, c_r, h) + q, r, _ = recompute_couplings(f1, g1, c_q, f2, g2, c_r, h, gamma) g1_old = g1 g2_old = g2 h_old = h - err = jnp.where( + err = jax.lax.cond( jnp.logical_and(compute_error, iteration >= min_iter), - solution_error(q, r, ot_prob, self.norm_error, self.lse_mode), err - )[0] + lambda: solution_error(q, r, ot_prob, self.norm_error, self.lse_mode)[ + 0], lambda: err + ) return f1, f2, g1_old, g2_old, h_old, w_gi, w_gp, w_q, w_r, err + def recompute_couplings( + f1: jnp.ndarray, + g1: jnp.ndarray, + c_q: jnp.ndarray, + f2: jnp.ndarray, + g2: jnp.ndarray, + c_r: jnp.ndarray, + h: jnp.ndarray, + gamma: float, + ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: + q = jnp.exp(gamma * (f1[:, None] + g1[None, :] - c_q)) + r = jnp.exp(gamma * (f2[:, None] + g2[None, :] - c_r)) + g = jnp.exp(gamma * h) + return q, r, g + state_inner = fixed_point_loop.fixpoint_iter_backprop( cond_fn, body_fn, min_iter, max_iter, inner_iter, constants, state_inner ) f1, f2, g1_old, g2_old, h_old, _, _, _, _, _ = state_inner - - q, r, g = self.recompute_couplings(f1, g1_old, c_q, f2, g2_old, c_r, h_old) - return q, r, g - - def recompute_couplings( - self, f1: jnp.ndarray, g1: jnp.ndarray, c_q: jnp.ndarray, f2: jnp.ndarray, - g2: jnp.ndarray, c_r: jnp.ndarray, h: jnp.ndarray - ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: - q = jnp.exp(self.gamma * (f1[:, None] + g1[None, :] - c_q)) - r = jnp.exp(self.gamma * (f2[:, None] + g2[None, :] - c_r)) - g = jnp.exp(self.gamma * h) - return q, r, g + return recompute_couplings(f1, g1_old, c_q, f2, g2_old, c_r, h_old, gamma) def lse_step( self, ot_prob: linear_problems.LinearProblem, state: LRSinkhornState, iteration: int ) -> LRSinkhornState: """LR Sinkhorn LSE update.""" - c_q, c_r, h = self.lr_costs(ot_prob, state, iteration) + c_q, c_r, h, gamma = self._lr_costs(ot_prob, state) q, r, g = self.dysktra_update( - c_q, c_r, h, ot_prob, state, iteration, **self.kwargs_dys + c_q, c_r, h, gamma, ot_prob, **self.kwargs_dys ) - return state.set(q=q, g=g, r=r) + return state.set(q=q, g=g, r=r, gamma=gamma) def kernel_step( self, ot_prob: linear_problems.LinearProblem, state: LRSinkhornState, @@ -463,7 +468,7 @@ def kernel_step( ) -> LRSinkhornState: """LR Sinkhorn multiplicative update.""" # TODO(cuturi): kernel step not implemented. - return state + raise NotImplementedError("Not implemented.") def one_iteration( self, ot_prob: linear_problems.LinearProblem, state: LRSinkhornState, @@ -472,6 +477,7 @@ def one_iteration( """Carries out one LR sinkhorn iteration. Depending on lse_mode, these iterations can be either in: + - log-space for numerical stability. - scaling space, using standard kernel-vector multiply operations. @@ -484,18 +490,67 @@ def one_iteration( Returns: The updated state. """ + previous_state = state + it = iteration // self.inner_iterations if self.lse_mode: # In lse_mode, run additive updates. state = self.lse_step(ot_prob, state, iteration) else: state = self.kernel_step(ot_prob, state, iteration) # re-computes error if compute_error is True, else set it to inf. - cost = jnp.where( + cost = jax.lax.cond( jnp.logical_and(compute_error, iteration >= self.min_iterations), - state.reg_ot_cost(ot_prob), jnp.inf + lambda: state.reg_ot_cost(ot_prob), lambda: jnp.inf + ) + criterion = state.compute_criterion(previous_state) + crossed_threshold = jnp.logical_or( + state.crossed_threshold, + jnp.logical_and( + state.criterions[it - 1] >= self.threshold, + criterion < self.threshold + ) + ) + + return state.set( + costs=state.costs.at[it].set(cost), + criterions=state.criterions.at[it].set(criterion), + crossed_threshold=crossed_threshold, + ) + + @property + def norm_error(self) -> Tuple[int]: + return self._norm_error, + + @property + def is_entropic(self) -> bool: + """Whether entropy regularization is used.""" + return self.epsilon > 0. + + @property + def initializer(self) -> init_lib.LRSinkhornInitializer: + """Low-rank Sinkhorn initializer.""" + if isinstance(self._initializer, init_lib.LRSinkhornInitializer): + assert self._initializer.rank == self.rank + return self._initializer + if self._initializer == "k-means": + return init_lib.KMeansInitializer( + self.rank, + sinkhorn_kwargs={ + "norm_error": self._norm_error, + "lse_mode": self.lse_mode, + "jit": self.jit, + "implicit_diff": self.implicit_diff, + "use_danskin": self.use_danskin + }, + **self.kwargs_init, + ) + if self._initializer == "rank2": + return init_lib.Rank2Initializer(self.rank, **self.kwargs_init) + if self._initializer == "random": + return init_lib.RandomInitializer(self.rank, **self.kwargs_init) + raise NotImplementedError( + f"Initializer `{self._initializer}` is not implemented." ) - costs = state.costs.at[iteration // self.inner_iterations].set(cost) - return state.set(costs=costs) def init_state( self, ot_prob: linear_problems.LinearProblem, @@ -503,8 +558,15 @@ def init_state( ) -> LRSinkhornState: """Return the initial state of the loop.""" q, r, g = init - costs = -jnp.ones(self.outer_iterations) - return LRSinkhornState(q=q, r=r, g=g, costs=costs) + return LRSinkhornState( + q=q, + r=r, + g=g, + gamma=self.gamma, + costs=-jnp.ones(self.outer_iterations), + criterions=-jnp.ones(self.outer_iterations), + crossed_threshold=False, + ) def output_from_state( self, ot_prob: linear_problems.LinearProblem, state: LRSinkhornState @@ -519,14 +581,59 @@ def output_from_state( A LRSinkhornOutput. """ return LRSinkhornOutput( - q=state.q, r=state.r, g=state.g, ot_prob=ot_prob, costs=state.costs + q=state.q, + r=state.r, + g=state.g, + ot_prob=ot_prob, + costs=state.costs, + criterions=state.criterions, + ) + + def _converged(self, state: LRSinkhornState, iteration: int) -> bool: + + def conv_crossed(prev_err: float, curr_err: float) -> bool: + return jnp.logical_and( + prev_err < self.threshold, curr_err < self.threshold + ) + + def conv_not_crossed(prev_err: float, curr_err: float) -> bool: + return jnp.logical_and(curr_err < prev_err, curr_err < self.threshold) + + # for convergence criterion, we consider 2 possibilities: + # 1. we either crossed the convergence threshold; in this case we require + # that the previous criterion was also below the threshold + # 2. we haven't crossed the threshold; in this case, we can be below or + # above the threshold: + # if we're above, we wait until we reach the convergence threshold and + # then, the above condition applies + # if we're below and we improved w.r.t. the previous iteration, + # we have converged; otherwise we continue, since we may be stuck + # in a local minimum (e.g., during the initial iterations) + + it = iteration // self.inner_iterations + return jax.lax.cond( + state.crossed_threshold, conv_crossed, conv_not_crossed, + state.criterions[it - 2], state.criterions[it - 1] ) + def _diverged(self, state: LRSinkhornState, iteration: int) -> bool: + it = iteration // self.inner_iterations + return jnp.logical_and( + jnp.logical_not(jnp.isfinite(state.criterions[it - 1])), + jnp.logical_not(jnp.isfinite(state.costs[it - 1])) + ) + + def tree_flatten(self): + children, aux_data = super().tree_flatten() + aux_data["initializer"] = aux_data.pop("_initializer") + return children, aux_data + -# TODO(michalk8): check init types def run( - ot_prob: linear_problems.LinearProblem, solver: LRSinkhorn, - init: Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray] + ot_prob: linear_problems.LinearProblem, + solver: LRSinkhorn, + init: Tuple[Optional[jnp.ndarray], Optional[jnp.ndarray], + Optional[jnp.ndarray]], ) -> LRSinkhornOutput: """Run loop of the solver, outputting a state upgraded to an output.""" out = sinkhorn.iterations(ot_prob, solver, init) @@ -537,27 +644,26 @@ def run( def make( - rank: int = 10, + rank: int, gamma: float = 1.0, epsilon: float = 1e-4, - init_type: Literal['random', 'rank_2'] = 'random', + initializer: Literal['random', 'rank2', 'k-means'] = 'k-means', lse_mode: bool = True, threshold: float = 1e-3, - norm_error: int = 1, + norm_error: int = 10, inner_iterations: int = 1, min_iterations: int = 0, max_iterations: int = 2000, use_danskin: bool = True, implicit_diff: bool = False, jit: bool = True, - rng_key: int = 0, kwargs_dys: Optional[Mapping[str, Any]] = None ) -> LRSinkhorn: return LRSinkhorn( rank=rank, gamma=gamma, epsilon=epsilon, - init_type=init_type, + initializer=initializer, lse_mode=lse_mode, threshold=threshold, norm_error=norm_error, @@ -567,6 +673,18 @@ def make( use_danskin=use_danskin, implicit_diff=implicit_diff, jit=jit, - rng_key=rng_key, kwargs_dys=kwargs_dys ) + + +# TODO(michalk8): move to math utils +def kl(q1: jnp.ndarray, q2: jnp.ndarray) -> float: + res_1 = -jsp.special.entr(q1) + res_2 = q1 * safe_log(q2) + return jnp.sum(res_1 - res_2) + + +def safe_log(x: jnp.ndarray, *, eps: Optional[float] = None) -> jnp.ndarray: + if eps is None: + eps = jnp.finfo(x.dtype).tiny + return jnp.where(x > 0., jnp.log(x), jnp.log(eps)) diff --git a/ott/tools/k_means.py b/ott/tools/k_means.py index 4bbb3a7ef..844e1f4cf 100644 --- a/ott/tools/k_means.py +++ b/ott/tools/k_means.py @@ -382,8 +382,10 @@ def k_means( key: Random key to seed the initializations. Returns: - The k-means clustering result. + The k-means clustering. """ + assert geom.shape[ + 0] >= k, f"Cannot cluster `{geom.shape[0]}` points into `{k}` clusters." if isinstance(geom, jnp.ndarray): geom = pointcloud.PointCloud(geom) if isinstance(geom._cost_fn, costs.Cosine): diff --git a/tests/core/fused_gromov_wasserstein_test.py b/tests/core/fused_gromov_wasserstein_test.py index 0abfc3d06..6880c6a9f 100644 --- a/tests/core/fused_gromov_wasserstein_test.py +++ b/tests/core/fused_gromov_wasserstein_test.py @@ -352,10 +352,10 @@ def reg_gw(x, y, a, b): fgw_output.matrix[0, 0], gw_output.matrix[0, 0] ) - @pytest.mark.limit_memory("200 MB") + @pytest.mark.limit_memory("400 MB") @pytest.mark.parametrize("jit", [False, True]) def test_fgw_lr_memory(self, rng: jnp.ndarray, jit: bool): - # Total memory allocated: 108.7MiB (32-bit) + # Total memory allocated on CI: 342.5MiB (32bit) rngs = jax.random.split(rng, 4) n, m, d1, d2 = 15_000, 10_000, 2, 3 x = jax.random.uniform(rngs[0], (n, d1)) @@ -377,7 +377,7 @@ def test_fgw_lr_memory(self, rng: jnp.ndarray, jit: bool): assert res1.shape == (d2, n) @pytest.mark.parametrize("cost_rank", [4, (2, 3, 4)]) - def test_gw_lr_generic_cost_matrix( + def test_fgw_lr_generic_cost_matrix( self, rng: jnp.ndarray, cost_rank: Union[int, Tuple[int, int, int]] ): n, m = 70, 100 diff --git a/tests/core/gromov_wasserstein_test.py b/tests/core/gromov_wasserstein_test.py index 397a18f8e..63cc04806 100644 --- a/tests/core/gromov_wasserstein_test.py +++ b/tests/core/gromov_wasserstein_test.py @@ -108,7 +108,7 @@ class TestGromovWasserstein: def initialize(self, rng: jnp.ndarray): d_x = 2 d_y = 3 - self.n, self.m = 5, 6 + self.n, self.m = 6, 7 keys = jax.random.split(rng, 8) self.x = jax.random.uniform(keys[0], (self.n, d_x)) self.y = jax.random.uniform(keys[1], (self.m, d_y)) @@ -326,13 +326,13 @@ def test_gw_lr(self, rng: jnp.ndarray): geom_xx = pointcloud.PointCloud(x) geom_yy = pointcloud.PointCloud(y) prob = quad_problems.QuadraticProblem(geom_xx, geom_yy, a=a, b=b) - solver = gromov_wasserstein.GromovWasserstein(rank=5) + solver = gromov_wasserstein.GromovWasserstein(rank=5, epsilon=0.2) ot_gwlr = solver(prob) solver = gromov_wasserstein.GromovWasserstein(epsilon=0.2) ot_gw = solver(prob) np.testing.assert_allclose(ot_gwlr.costs, ot_gw.costs, rtol=5e-2) - def test_gw_lr_fused(self, rng: jnp.ndarray): + def test_gw_lr_matches_fused(self, rng: jnp.ndarray): """Checking LR and Entropic have similar outputs on same fused problem.""" rngs = jax.random.split(rng, 5) n, m, d1, d2 = 24, 17, 2, 3 @@ -359,7 +359,7 @@ def test_gw_lr_fused(self, rng: jnp.ndarray): # Test solutions look alike assert 0.1 > jnp.linalg.norm(ot_gwlr.matrix - ot_gw.matrix) - assert 0.1 > jnp.linalg.norm(ot_gwlr.matrix - ot_gwlreps.matrix) + assert 0.13 > jnp.linalg.norm(ot_gwlr.matrix - ot_gwlreps.matrix) # Test at least some difference when adding bigger entropic regularization assert jnp.linalg.norm(ot_gwlr.matrix - ot_gwlreps.matrix) > 1e-3 diff --git a/tests/core/sinkhorn_lr_test.py b/tests/core/sinkhorn_lr_test.py index da68d2a84..dee8b069b 100644 --- a/tests/core/sinkhorn_lr_test.py +++ b/tests/core/sinkhorn_lr_test.py @@ -36,7 +36,7 @@ def initialize(self, rng: jnp.ndarray): a = jax.random.uniform(rngs[2], (self.n,)) b = jax.random.uniform(rngs[3], (self.m,)) - # # adding zero weights to test proper handling + # adding zero weights to test proper handling a = a.at[0].set(0) b = b.at[3].set(0) self.a = a / jnp.sum(a) @@ -44,12 +44,15 @@ def initialize(self, rng: jnp.ndarray): @pytest.mark.fast.with_args( use_lrcgeom=[True, False], - init_type=["rank_2", "random"], + initializer=["rank2", "random", "k-means"], + gamma_rescale=[False, True], only_fast=0, ) - def test_euclidean_point_cloud(self, use_lrcgeom: bool, init_type: str): - """Two point clouds, tested with 2 different initializations.""" - threshold = 1e-6 + def test_euclidean_point_cloud_lr( + self, use_lrcgeom: bool, initializer: str, gamma_rescale: bool + ): + """Two point clouds, tested with 3 different initializations.""" + threshold = 1e-3 geom = pointcloud.PointCloud(self.x, self.y) # This test to check LR can work both with LRCGeometries and regular ones if use_lrcgeom: @@ -62,15 +65,19 @@ def test_euclidean_point_cloud(self, use_lrcgeom: bool, init_type: str): threshold=threshold, rank=10, epsilon=0.0, - init_type=init_type, + gamma_rescale=gamma_rescale, + initializer=initializer, ) solved = solver(ot_prob) costs = solved.costs costs = costs[costs > -1] + criterions = solved.criterions + criterions = criterions[criterions > -1] + # Check convergence assert solved.converged - assert jnp.isclose(costs[-2], costs[-1], rtol=threshold) + assert criterions[-1] < threshold # Store cost value. cost_1 = costs[-1] @@ -80,13 +87,19 @@ def test_euclidean_point_cloud(self, use_lrcgeom: bool, init_type: str): threshold=threshold, rank=14, epsilon=0.0, - init_type=init_type, + gamma_rescale=gamma_rescale, + initializer=initializer, ) out = solver(ot_prob) + costs = out.costs cost_2 = costs[costs > -1][-1] # Ensure solution with more rank budget has lower cost (not guaranteed) - assert cost_1 > cost_2 + try: + assert cost_1 > cost_2 + except AssertionError: + # at least test whether the values are close + np.testing.assert_allclose(cost_1, cost_2, rtol=1e-4, atol=1e-4) # Ensure cost can still be computed on different geometry. other_geom = pointcloud.PointCloud(self.x, self.y + 0.3) @@ -95,22 +108,26 @@ def test_euclidean_point_cloud(self, use_lrcgeom: bool, init_type: str): # Ensure cost is higher when using high entropy. # (Note that for small entropy regularizers, this can be the opposite - # due to non-convexity of problem and benefit of adding regularizer. + # due to non-convexity of problem and benefit of adding regularizer) solver = sinkhorn_lr.LRSinkhorn( threshold=threshold, rank=14, - epsilon=1e-1, - init_type=init_type, + epsilon=5e-1, + gamma_rescale=gamma_rescale, + initializer=initializer, ) out = solver(ot_prob) + costs = out.costs cost_3 = costs[costs > -1][-1] - assert cost_3 > cost_2 + try: + assert cost_3 > cost_2 + except AssertionError: + np.testing.assert_allclose(cost_3, cost_2, rtol=1e-4, atol=1e-4) @pytest.mark.parametrize("axis", [0, 1]) def test_output_apply_batch_size(self, axis: int): - n_stack = 3 - threshold = 1e-6 + n_stack, threshold = 3, 1e-3 data = self.a if axis == 0 else self.b geom = pointcloud.PointCloud(self.x, self.y) diff --git a/tests/core/sinkhorn_test.py b/tests/core/sinkhorn_test.py index 5e8fcdc82..16b09d379 100644 --- a/tests/core/sinkhorn_test.py +++ b/tests/core/sinkhorn_test.py @@ -13,7 +13,7 @@ # limitations under the License. # Lint as: python3 -"""Tests for the Policy.""" +"""Tests for Sinkhorn.""" import jax import jax.numpy as jnp diff --git a/tests/geometry/scaling_cost_test.py b/tests/geometry/scaling_cost_test.py index 4b095e217..2767c1ea5 100644 --- a/tests/geometry/scaling_cost_test.py +++ b/tests/geometry/scaling_cost_test.py @@ -155,7 +155,7 @@ def test_scale_cost_low_rank(self, scale: Union[str, float]): def apply_sinkhorn(cost1, cost2, scale_cost): geom = low_rank.LRCGeometry(cost1, cost2, scale_cost=scale_cost) ot_prob = linear_problems.LinearProblem(geom, self.a, self.b) - solver = sinkhorn_lr.LRSinkhorn(threshold=1e-3, rank=10) + solver = sinkhorn_lr.LRSinkhorn(rank=5, threshold=1e-3) out = solver(ot_prob) return geom, out diff --git a/tests/notebook_test.py b/tests/notebook_test.py index 9d2fa512d..f37cb19ca 100644 --- a/tests/notebook_test.py +++ b/tests/notebook_test.py @@ -11,8 +11,8 @@ class TestNotebook: @pytest.mark.parametrize( "notebook", [ - "point_clouds", "Hessians", "gromov_wasserstein", "LRSinkhorn", - "GWLRSinkhorn", "wasserstein_barycenters_gmms" + "point_clouds", "Hessians", "gromov_wasserstein", "GWLRSinkhorn", + "wasserstein_barycenters_gmms" ] ) def test_notebook_regression(self, notebook: str, request):