{ "cells": [ { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "## The data block API" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "hide_input": true }, "outputs": [], "source": [ "from fastai.gen_doc.nbdoc import *\n", "from fastai.basics import *\n", "np.random.seed(42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data block API lets you customize the creation of a [`DataBunch`](/basic_data.html#DataBunch) by isolating the underlying parts of that process in separate blocks, mainly:\n", " 1. Where are the inputs and how to create them?\n", " 1. How to split the data into a training and validation sets?\n", " 1. How to label the inputs?\n", " 1. What transforms to apply?\n", " 1. How to add a test set?\n", " 1. How to wrap in dataloaders and create the [`DataBunch`](/basic_data.html#DataBunch)?\n", " \n", "Each of these may be addressed with a specific block designed for your unique setup. Your inputs might be in a folder, a csv file, or a dataframe. You may want to split them randomly, by certain indices or depending on the folder they are in. You can have your labels in your csv file or your dataframe, but it may come from folders or a specific function of the input. You may choose to add data augmentation or not. A test set is optional too. Finally you have to set the arguments to put the data together in a [`DataBunch`](/basic_data.html#DataBunch) (batch size, collate function...)\n", "\n", "The data block API is called as such because you can mix and match each one of those blocks with the others, allowing for a total flexibility to create your customized [`DataBunch`](/basic_data.html#DataBunch) for training, validation and testing. The factory methods of the various [`DataBunch`](/basic_data.html#DataBunch) are great for beginners but you can't always make your data fit in the tracks they require.\n", "\n", "\"Mix\n", "\n", "As usual, we'll begin with end-to-end examples, then switch to the details of each of those parts." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examples of use" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's begin with our traditional MNIST example." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from fastai.vision import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/export.pkl'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/test'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/history.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/models'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/cleaned.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/valid')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = untar_data(URLs.MNIST_TINY)\n", "tfms = get_transforms(do_flip=False)\n", "path.ls()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train/3'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train/7')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(path/'train').ls()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In [`vision.data`](/vision.data.html#vision.data), we can create a [`DataBunch`](/basic_data.html#DataBunch) suitable for image classification by simply typing:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = ImageDataBunch.from_folder(path, ds_tfms=tfms, size=64)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a shortcut method which is aimed at data that is in folders following an ImageNet style, with the [`train`](/train.html#train) and `valid` directories, each containing one subdirectory per class, where all the labelled pictures are. There is also a `test` directory containing unlabelled pictures. \n", "\n", "Here is the same code, but this time using the data block API, which can work with any style of a dataset. All the stages, which will be explained below, can be grouped together like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = (ImageList.from_folder(path) #Where to find the data? -> in path and its subfolders\n", " .split_by_folder() #How to split in train/valid? -> use the folders\n", " .label_from_folder() #How to label? -> depending on the folder of the filenames\n", " .add_test_folder() #Optionally add a test set (here default name is test)\n", " .transform(tfms, size=64) #Data augmentation? -> use tfms with a size of 64\n", " .databunch()) #Finally? -> use the defaults for conversion to ImageDataBunch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can look at the created DataBunch:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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Hxsb43tfV1XEBaVNTky1zUjsEs4hZ24YpoXV3d/Ni9bHHHmNno0sxNPp3FxcX2fGsrKwUfKEjUpsgCILgKGUX8eSLXoHuVYShVy11dXXo6OgAADz66KOcXFBbW8sbhcvLy7xKGhsb46SGdDpdjplPRYFesfp8Po5czUMGzYQUs67CTYd0CXuH/pwmk0n+LCeTSZbiFxYWtpXatES3vr6+bSJAVVUVy2vmoYNEZIugtGIyOjqKoaEhAJYNFjriEcfjAGZPNi27HDlyBA8//DAAK1TWRmT2BIvH41xYOj4+zudpiNNxP6lUiicMff/X19dtKfTmyaha09+a9WjuMWrckA4r5Ia5cNWfWzO7dmVlxXbPTaehv263GFFKcQlIW1sbzysej4d/N5FIcE+2wcFBjIyMALDvGxUKkdoEQRAER5GIxwG0dFZXV4eDBw8CAM6ePYvnnnsOANDX18f5/Ol0msPg27dv4/z58wCs1jhu6D4t5EY8HueNZd1GJ51Oc0ZkOp3mDLfW1lZbd3SzU7levWYyGX699fV1fh2R5ooDs9O9eS/zRUfJHo/HdpicGfHo147H45xQMDY2xuqJGxDH4wA6rba1tRWPP/44AOvo5VOnTgGwJiY9gSSTSe5QcPnyZbz11lsAwOe/CO7FzFRsbm7mXoD63BSv18uLB7/fz2eyHDlyhE/ANY/IUEpxxuXa2hpnOgH3sqUKLZkI+4/Z/UDbRktLC3c46ejoYLsjIpZtp6enWV5z23EbIrUJgiAIjiIRzz6hVylExMcvHD58mOt1Ojo6WGrxer3cq+3u3bu4evUqAOCDDz7AnTt3ALivNU65oFeYWw/b0tJoOBzmmoz6+nqWP7q6uji61e3oAfvRFXpzuLOzk21h6zELOrLZ2jpFJx1IxFP6mDKsltTa29tx6NAhAFb0YxaN6uhmYmKCC80LddLo/RDHs09oxxMIBLjC+Pjx4zh8+DAAq3DMbBSoj2R+//33ce7cOQBWoah2OIWuNC41/H4/pzvX1NRwP71AIGA7d8k8LkF/uE3H097ezkcat7e3s7zW1NTEUpp2XubpppFIhFOrk8kkv6ep/ycSCb7vZhabHKNQXuh5oqqqynZktl7Qbj0rTEv1/f39nEKtF7ZuQaxXEARBcBSJePaBYDDI8ktnZycnETz11FO8IVhTU8OrkOXlZVy6dAkA8Oabb3IX24mJiQfOfhE+jsfj4dXjsWPH8NRTTwGwkj50VALc68UXjUZthZ36OWb38XA4bJPgdCJJY2Mjr0L1CnRpaYmLgGdmZrguK5PJcHJJOp1mWSQWi3G9RyKR4ESDVColtVxlhLa7uro67uPY0dHBtma20slms3xUyrVr1zi5QCIeQRAEoayRiGcfCIfD3MDvySefxLPPPgsAePzxx3lz0Ov18vk658+f56Osf/nLX9q0f2Hv8Hg8vE/T1dWFkydPArAas+rIxuPxcGSRzWZtdRP6d4PBIJ+1E4/HOXJZX1/n6Ke+vp7rsfTBgePj43z0+cTEBNdYmLVbyWTSFuXoPR7zOZlMRpIKyghtU01NTdxaq7Ozk6NupRRHzOvr6xgbGwNg1QHqzgWf1Ai5EIjj2SPMfkldXV18mNszzzzD2U0dHR0skSwvL7Oz+dGPfoTR0VEA4MlI2HvMD+jc3ByGh4cBWJu2OinA/EDX1NSwg/H5fDzZr66uctJHNBrlhIGFhQXe8E0mk2wP+udjY2MsfYyMjPB1JpNhp2I6GGmLIxAR1+j09PTwvNLT08O2mUwm2cZGR0dx+/ZtAJacq2Vbt9mSSG2CIAiCo0jEs0t0WqtZtX78+HGcPXsWAPDII4/wKthsDjg0NISbN28CsCQYsypd2B+UUhxxLi4ucpQZCoVsnQDMmggtUSwvL3PXgdXVVa6ViEajtlobXcfT19fHZ6Po15iZmWGpzUwukLY3wlb0vOLxeFie7+vrw4kTJwBYnTG0BBeNRjmh4Pr16xzJLy4usu25zcbE8ewCIuJwt6GhgY85OHHiBJ5//nkAltavs1ISiQS3Rr99+zZu3LgBwJJ9RLPff5RSLDksLCzg+vXrAKzWInrREAwG+UOvlLKd4KjvXTQa5fvl9/vZwbS3t7O0kUgkOOtIv+fKyoptP0i/j9tkEKHwaNvw+Xzccqmzs5Nrxkw7XVtb40LR27dv855iKpVyncPRiNQmCIIgOErOEQ8ReQF8CGBSKfV5IjoI4AcAGgBcBvBVpVRZlNfrEDcYDKK7uxsAcPr0aZw+fRoAcOrUKa7j2djY4LqQgYEBrtd55513MDg4CMDaUHbryuRBcaO9mBFPNBrl2oaBgQGW13w+H9/fiooKW2dpHeVks1mW7LxeLycmVFVV8fOz2SwnKZgrU13Hs76+XnL3fLe40WacRmdR6sSUiooKltrq6+vZpnw+Hz83Foux1DYyMsIysJsj6Xyktm8C6AcQ3vz+jwH8N6XUD4jofwD4OoDv7vH4XImW12pra3H8+HEAwIsvvshp02ZRYTQa5Qyo999/Hz/84Q8BWNknuk15iU5ArrYXM8MNgK1QVF+bh7WZzmZjY4N/VynFDqmqqorltXQ6zZOBmZ5tXpfofd8NrrYZJzA7UQNWsbJu7VRfX889/UzbWVpa4gzJsbExdjxutq+cpDYi6gLwWwD+cvN7AvA5AH+7+ZRXAXxxPwYoFB9iL0K+iM2UF7lGPH8K4D8BqNn8vhHAslJK74hPAOjc47G5CiJiGaWtrQ2A1W1ay2tm9prP5+NMp8nJSVuh6EcffQTALt2UIEVnL3p1aB4r/Unn0puFpeaBbvoI82w2y1Lqdpu9EvF8jKKzmf1ARzr6fKaDBw/i2LFjAKxMNrO1kp5jRkZGeI4plohnR8dDRJ8HMKeUukREn9EPb/PUbf+XRPQygJcfeIQFxtRcdeaT7jD9/PPP44knngBgtSbXuqxSimW0a9eu4e233wYA3Lx5c9s296VEudiL6Xj0dX19PU8SU1NTXEGubSGVSrEEK10p7lEuNpML2vHoTMkTJ06wnN/Y2Gg7MFJL+CMjI9yFem5uztV7O5pcIp6zAP4pEb0IIAhLf/1TAHVE5NtckXQB2LbkXin1CoBXAICI3OuChb1C7EXIF7GZMmNHx6OU+gMAfwAAm6uR/6iU+udE9CMAvw0r6+QlAD/dx3EWDLMjsc6h1yuQM2fOsLQSDodZmonFYhgYGABgHeb24YcfArBkt2JYjeyGcrEXbRehUIivvV6v7eA4XSysV6aZTMbWBVuwKBeb2QmPx8OJS/oMr5MnT3J/tnA4zPL80tISR9Sjo6NsY8Ui4e6mgPRbAH5ARP83gF8C+N7eDMld6Cyl1tZWdji699qRI0c41dHn8/Fhbrdv38Y777wDwMpk05XEZX6KaEnZi5bMwuEw7/H4fD5Ona6uruZFhm76ur6+bmthL+xISdnM/dA24/V6+UBCfWrtE088wdehUIgXt7Ozs+jv7wdgLWj1IqcYnA6Qp+NRSr0F4K3N62EAT+/9kIRSQexFyBexmfJAWuZ8AkTE2SWHDh3iRIJHH30UgL3bdDqd5sjm3LlzeO+99wBYvZPcdt65sHu0ZNbU1MRZjn6/nxMJEokEX+ui0cXFxU/MlBPKE7N2RyssOso5evSo7fgDnbE2NjbGGbJTU1OuO/ZgJ8TxbMG8+XV1dXxK5ac+9Sl2PLonWzqdZvlsenoa58+fBwC8/vrrrL/qrgVCaaEzGFtbWzmTLRwOsz0kk0mW2PQJpHNzc0U3QQj7j9mXTcu2+qvH47EVLutGtTdv3sTFixcBWI5Hn9tULEivNkEQBMFRJOLZQjAY5ELQvr4+fOpTnwIAfP7zn+eW93o1Eo/HeTV79epVvPvuuwCACxcu8CqlWDb7hNwhIr6/dXV1OHDgAACrzkJHNPF4nK/1c5PJZMnWbwkPznYRj05eISKeQzKZDMu3AwMDnDmbSCSKrhhdHA+sm6tvfmNjI0snp0+f5ky2trY2lle0Jjs3N8fy2ltvvcUn/5m9vITSQd//QCDA11VVVbwgaWlp4YXI8vIyF4lqe8lkMmIXwscwM9n0/nFvby8Ay+Z0xtri4qKtULSY+/6J1CYIgiA4SllHPGY2ic5S6u7u5v5rzz33HMso5qmUmuHhYbz22msALHlNr3aLbfUh5Ia2l4qKCtvRCbqWq729nfuyLS4ufqy2QmQ2QaNtiYg4K/JTn/oUXnjhBQBW70fAkuH08R0TExOcORuJRDhDshjnm7J2PFouCYfDLJc89thjOHXqFACrQFS3JE+n02wA+pjqy5cv8ymiCwsLRZdZIuSH/oD7/X7OfDSPM6+oqGCtfWVlhe1F/56p1wvljbno1Wd6ffazn+WTi3VfSCLifZ3+/n4uGl1aWipqWxKpTRAEQXCUso549Kri4Ycf5ijnzJkznFxQX1/PUVE0GuU29zdv3gRgRTxTU1bfwkQiUfJ92Mod89RR3VMrm81yEoGOggArq03XXOif+3w+KSAV4PV6bbWCWs5va2vjx82efnqOuXz5MmeyFXv7rbJ2PPp46pMnT+I3f/M3AVjnX+hmoETEcsno6Cg3+9RdCfr7+7mSWPT78iEUCnERcVNTE9/7ubk5LhheWlrijgXaRgQBsJyKlvB7e3tx6NAhAFYxss5wM89t0sXo/f39fKqtlneLFZHaBEEQBEcpi4jHLNDSSQStra0srz399NO86mhoaOAwd2FhgVcbH374Id58800A4FYVy8vLIq+VCUTEG8LBYJCj4t7eXpbdlpaWuLZiZWUFo6OjAMA2IlGxAFh1YDpifuaZZ7hWsK6ujqVYLaVFIhE+XXRiYoIfL3bJtuQdj8fj4VTo6upqPj305MmT3Ift5MmTnFni8/k4zJ2ZmeFGfOfOnbOdqyOUF6Yd+f1+XsAcPnyYU6tjsRhntXk8Ht4fLPZJQthbKisr+YydF198kfd4qqurOTNWH7EyMDDAe8vz8/NFL7FpRGoTBEEQHKVkIx4ti4TDYbS0tACwjjbQ8trJkyfR19cHwGp1omWQ2dlZXm384he/wBtvvAEAuHXrFhcHCuWHz+fjjd9AIIDa2loAQGdnJ0c5CwsLfG1mLklHasFsy1VbW8sRz+OPP84Rs8/n45odXR/4i1/8Anfv3gVwLzuyFCgpx6OdjdfrZVmkr68PZ86cAQA89dRTfMN7e3sRDocBWIV/Zvba5cuXAQCvvfYa7+uIXFLeeDweniCy2ey2jR3n5uZ4AWM6Kj2ZCOVLMBjk+aa3t5el/crKSp63stksdz+5dOkSAODdd9/ldOpSmoNEahMEQRAcpaQiHr0K9fv9fGrfsWPH8Cu/8isAgFOnTvGJorW1tbxRF4lEuDDLPD301q1bJbXKEB6cTCbDtrCwsICJiQkAwODgIEu58XicE1N8Ph9nuwnli45mqqurOcp56KGH+LqiooITCtbX1zE+Pg4A3Ol+fHyc56lSyoosKcejJY/m5mZOd33ooYdw8OBBANaRB3oyUErxns3g4CCfpfPaa6/hypUrAEpLUxV2x8bGBjueRCLBxaFDQ0PsbBYWFjjdNZvNbttYVigvtONpaGjgE4zPnDnDR1srpbj34+TkJC+AtbwWj8c5Hb+Ye7NtRaQ2QRAEwVFKakmmN3Pb29u5KOvo0aMc/dTU1PCqIZFIYGRkBABw/vx5XLhwAYB11IHOQiqlFYawO8yebEopzjpKJpNcDLi6uspJKrOzs7ySFcoXHfE0NzfjueeeAwA8++yzaG5u5ucsLy8DsJQXHfHMz88DKK2EApOcHA8R1QH4SwCPAlAA/iWAAQA/BNALYATAl5RSS/syyhzRHQcqKipYdvP5fCx5ZLNZm06vm32++eabXKS1vLwsDmeXFIu95Iu2nUwmw+eiTE5Osn1lMhmWRTKZTNEdR1xIStVmzBTqnp4eAFZ/Pz1XZTIZ3LlzBwDwxhtvcDabbjBbquQqtf0ZgL9XSh0D8ASAfgDfBvCGUuoIgDc2vxcEQOxFyB+xmTJix4iHiMIAPg3gdwFAKZUCkCKiLwD4zObTXgXwFoBv7ccgc0WvLsz2JmbEE41GOYTt7+/n7LXr16+zRCKHue2OYrKXfNGRsFKK7UTsZfeUqs2J4t+8AAAgAElEQVSYbZMqKipsBchagkun0yyvnT9/HkNDQwCKv/v0TuQitR0CMA/gfxLREwAuAfgmgFal1DQAKKWmiahlu18mopcBvLxH4/1EtAYfiURYRovH4xy+xmIxzmSbmJiwyWulmDlSIIrGXgTXUFI2o52Kx+PhLNra2lruZEFEvI+8vLzMKdQLCwv8eCmlTm9HLlKbD8BJAN9VSp0AEEceIa9S6hWl1Gml1OkHHKNQXIi9CPkiNlNm5BLxTACYUEp9sPn938Iyilkiat9cibQDmNuvQeaKPnArmUzycQbnz5/nSEjqchyhaOxFcA0lZTO6tVJjYyN3nj569Ci3zMlkMpw8MDIywr0h4/F42SSk7Oh4lFIzRDRORA8ppQYAvADg5ua/lwB8Z/PrT/d1pDkgDqbwFJO9CO6g1GxGZ9R2d3dzCvXx48dRU1MDwJqfdCnHe++9x9fl1Ew21zqefwfgr4koAGAYwL+AJdP9DRF9HcAYgN/ZnyEKRYjYi5AvYjNlBDm5mU5E87D0W7edL9AE940J2PtxHVBKNe/8NHfgYnsB3Gkz+zGmYrOZKKz6H7fhRnsBCjTHOOp4AICIPnTbJqAbxwS4d1xO4ta/gRvH5cYxOY1b/wYyLjvSq00QBEFwFHE8giAIgqMUwvG8UoD33Ak3jglw77icxK1/AzeOy41jchq3/g1kXAaO7/EIgiAI5Y1IbYIgCIKjiOMRBEEQHMUxx0NEv0FEA0Q0SEQFa29ORN1E9HMi6ieiG0T0zc3H/4iIJonoyua/FwswthEiurb5/h9uPtZARK8T0Z3Nr/VOj6sQiL3kNDaxFwM32IzYS45jcWKPh4i8AG4D+DVYfZkuAviKUurmvr/5x8fSDqBdKXWZiGpgdcL9IoAvAYgppf6L02MyxjYC4LRSKmI89p8BLCqlvrP5YapXShVNa/gHQewl57GNQOwFgHtsRuwlN5yKeJ4GMKiUGt48a+MHAL7g0HvbUEpNK6Uub15HYR041VmIseTIF2CdRYLNr18s4FicQuzlwSlHewFcYjNiL7nhlOPpBDBufD8BF9wMIuoFcAKA7or7DSK6SkTfL5BEoQC8RkSXNs8YAbacSQJg2zNJSgyxl9wQe7mH62xG7OX+OOV4aJvHCprHTUTVAH4M4PeVUqsAvgugD8CTAKYB/NcCDOusUuokgN8E8G+J6NMFGIMbEHvJDbGXe7jKZsRePhmnHM8EgG7j+y4AUw6998cgIj8so/hrpdRPAEApNauUyiqlNgD8BazQ3VGUUlObX+cA/N3mGGY3dWOtHxfFmSS7ROwlB8RebLjGZsRedsYpx3MRwBEiOkhW2/MvA/iZQ+9tg4gIwPcA9Cul/sR4vN142j8DcN3hcVVtbkaCiKoA/PrmGH4G6ywSoIjOJNklYi87j0vsxY4rbEbsJTdyPY9nVyilMkT0DQD/AMAL4PtKqRtOvPc2nAXwVQDXiOjK5mN/COArRPQkrPB8BMC/dnhcrQD+zrJb+AD8b6XU3xPRRZTZmSRiLzkh9mLgIpsRe8kBaZkjCIIgOIp0LhAEQRAcRRyPIAiC4CjieARBEARHEccjCIIgOIo4HkEQBMFRxPEIgiAIjiKORxAEQXAUcTyCIAiCo4jjEQRBEBxFHI8gCILgKOJ4BEEQBEcRxyMIgiA4ijgeQRAEwVHE8ewAEcW2/MsS0Z8XelyCuyGi/0VE00S0SkS3iehfFXpMgjspxzlGjkXIg80DlGYBvKiUervQ4xHcCxEdBzColEoS0TEAbwH4LaXUpcKOTHAz5TLHSMSTH78N62jYdwo9EMHdKKVuKKWS+tvNf30FHJJQHJTFHCOOJz9eAvBXSsJEIQeI6L8T0RqAWwCmAfyfAg9JcD9lMceI1JYjRNQD4C6Aw0qpu4Uej1AcEJEXwLMAPgPgj5VS6cKOSHAr5TTHSMSTO18D8G6pG4SwtyilskqpdwF0Afg3hR6P4GrKZo4Rx5M7XwPwaqEHIRQtPsgej/DJlM0cI44nB4joOQCdAH5U6LEI7oeIWojoy0RUTUReIvonAL4C4M1Cj01wJ+U2x/gKPYAi4SUAP1FKRQs9EKEoULBktf8Ba3E3CuD3lVI/LeioBDdTVnOMJBcIgiAIjiJSmyAIguAou3I8RPQbRDRARINE9O29GpRQuojNCPkg9lKaPLDUtlmfcBvArwGYAHARwFeUUjf3bnhCKSE2I+SD2EvpspuI52lYvaiGlVIpAD8A8IW9GZZQoojNCPkg9lKi7CarrRPAuPH9BIAzn/QLRCSZDAVGKUUFfPu8bEbsxRVElFLNBXpvmWOKkFzmmN04nu1e/GM3nYheBvDyLt5HKB12tBmxF9cxWsD3ljmmRNmN45kA0G183wVgauuTlFKvAHgFkNWIsLPNiL0IBjLHlCi72eO5COAIER0kogCALwP42d4MSyhRxGaEfBB7KVEeOOJRSmWI6BsA/gGAF8D3lVI39mxkQskhNiPkg9hL6eJo5wIJgwtPgZML8kLsxRVcUkqdLvQgckVspvDkMsdI5wJBEATBUcTxCIIgCI4ijkcQBEFwFHE8giAIgqOI4xEEQRAcRRyPIAiC4CjieARBEARHEccjCIIgOIo4HkEQBMFRxPEIgiAIjiKORxAEQXCU3RyLIAiCUDIEg0EAQCgU4uuqqiqEQiF+fH19HQCQSCT4Oh6PIx6PAwA/JnwyEvEIgiAIjiIRjyAIAu5FPA0NDairqwMAtLa2oq2tDQDQ0tKChYUFAMDi4iJfz83NYWZmBoBEPLkijkcQciAYDLLk4vf7kclkAFiSi75OpVJw8pgRYW+pqKgAAITDYbS2tgIAent70dfXBwA4fPgwJicnAQAzMzOYmrIOQw0EAshmswCAjY0Nvs5kMnydzWb5WmxEpDZBEATBYSTiEcoeIrJtJtfW1gKwVr46yqmoqEAgEABgrVjn5+cBWCvftbU1ANYKd2NjA4AVFXm9Xn4cANLptEP/I+FB8Pv9ACwb0FJbY2MjWlpaAACdnZ1sD83Nzejo6ABgyXH6en5+HtFoFACwurrK19FoFCsrKwDsdqK/lhvieISSh8g6ENHn86GqqgoAUFNTg5qaGgDWROPzWR+F6upqnkQaGxtZfvH7/fB4LIEglUphYmICgKX768nDlFMSiQRPNNpJLS4usswicov7MB1PfX09AMsGtOzW19dnW0RopzI8PIzx8XEAwNTUFO/3zM7OYm5uDgDg9Xp5/0cpVfb3X6Q2QRAEwVEKEvEQEcsQ+itgrQTMzbhSxuv1snTj9/vvuzlZrqH4btGr14qKCo5mKisrOUOpvb0djY2NAMCRD2Ctdnt7ewFYcoqOcrxeL197PB6u20ilUkilUny9tLQEwJLgRkZGANyzZf07gBURlfuq120kEgkAVmSqI9qamhqW2iKRCCorKwFYNT06Gk6lUvxZDofDaGpqAgC0tbVx5tvCwgJfx2Ixlmfj8bjNDvTn3bQpLdGm02mOuMx5wYygNjY2bFG1WyPsgjgen8+H6upqAFa2kP5Ab2xsYHV1FQCwtrbmuj/WXhIMBjmcr66uRjKZBGAZl2mU+nEhP8yJQDuhUCiEw4cPAwAeeughNDc3AwDq6urYBisrK3niCIfD/DqBQIAnmmAwaJNlIpEIAGtimp6eBmA5nvb2dgDg9xkeHuafj4yMyL11GXphYO7bVVVVsT3MzMzw3o/H4+E5rKmpiR1Sc3Mzv048Huf5bGVlha/n5uZYftW2A9gX3lrGi8Vi/Hpra2s8rq37heZi1XRObs2kE6lNEARBcJQdIx4i6gbwVwDaAGwAeEUp9WdE1ADghwB6AYwA+JJSaimXN21ra8OTTz4JwNq80155dXUVw8PDAIDJyUn29MW4MjQlGr0aqqmpQTgcBmBJPZ2dnQCslbWZ/TI2NgYAGB0dLbr/+37YS65o2bampgYHDhwAYEU2esXa1NSEgwcPAgC6urp49VpTU8ORTTAY5Aw3n8/Hkkc8HufV5sLCAssyZu1OKBTi6KapqYmjK52s0NHRgcuXLwOwVr3Fdm/3i0LajIm+p0tLSzz3NDU1oaGhAYBlJ1qqTSQSfB0MBjkBwe/32+QtbTPRaJRfc2RkxJaMYEYjOpLRku3S0hInqUSjUZ4nksmk7X307yWTSZt6oqMfU7Z3Q3JDLlJbBsB/UEpdJqIaAJeI6HUAvwvgDaXUd4jo2wC+DeBbubzp008/jd/7vd8DYDkh/UebnJzExYsXAQCXLl3CtWvXAIC18gfB7/fzxO/3+20ZSPoG7Ufhn5ZigsEgSy4PP/wwHn74YQBWhsyhQ4cAWAatw/DV1VX84he/AAC89tprtlC8SNhze/kk9P5NXV0d/50PHTqExx57DABw8uRJftycRMLhMGcZxWIx/kAvLi5yJtLc3BxPRslk0uZs9AdaKcXO5uGHH2bHVltba3NmgCXb6NeuqqriyUVw1mbuh14Ap1Ipvp6enuZF5PLyMu//9fX1IRaLAbDsSu8D6VR8jbbPUCjEC6OOjg6WbZuammxzj7YrU243+8NpG8xkMjbHo59jynFb94fM/XO9oFpdXWXHZkr7iURiX/fZd5TalFLTSqnLm9dRAP0AOgF8AcCrm097FcAX92uQQvEg9iLki9hM+ZFXcgER9QI4AeADAK1KqWnAMhwiarnP77wM4GX9vdfrRVtbG7ehaG5uZu/f3t7Oq0OlFLen2E3EEw6HWdKqqamxtTfRG72RSIQfvx9ExPUgeuVibjhXV1dzdlRNTQ3LOHV1dejq6gIAHDlyhOWXrq4uXokHg0FeySQSCY5yLly48MD/bzewF/ayE3o12tTUhKNHjwIAzpw5g1OnTgEAjh07xpHH+vo6RkdHAQATExMcZcZiMV69RqNRtouJiQleAZo1Ol6vlyNan8/HMsvi4iLb7COPPIJjx44BAEdZHo8H/f39AOyZdMI9nLCZ+7FdRu34+DgnAgwNDXF0kM1mbRm5WkLfGvGYdqKLT2tqatDd3Q3g45GLqcjon+u5KZvN2q7NTLbFxUUAlgysI2kzQkomk7boR0f4k5OT3PpnYWGBPwdm1Lcf5Ox4iKgawI8B/L5SalVPwjuhlHoFwCsA4PV6VWVlJRKJBP9nvV4vh6kVFRXo6ekBYE3MelInorykMK/Xy0Zx+PBhPPPMMwCs/SQ9kUSjUZ4Ebt++zTfClFHMrBDg3iSnnWNtbS07mEOHDrFTOXjwIDu7jo4Ozn6pqqriAkZzL0H/H/VXPcZi3gPYC3shoh1vuv6gtrW1sbx27NgxtqlEImFr5jg4OAgAuHr1Kjv4WCzG0kM6nbbp62bxqb5WSsH8/2i9fmRkhCcUn8/HjlBPRqFQiJ2Qln+FezhlM/mQyWRs+3w6zVopxfbT1NTE+4h1dXX8Ga+qquI5zOfz2WQ3c07Qz6moqGBHZc4Hxv/zvvOgWaSs7deUh5PJpK34VS+6zDlsenqa52Vz+2FjY2PPyzpyymojIj8sg/hrpdRPNh+eJaL2zZ+3A5jb05EJRYvYi5AvYjPlRS5ZbQTgewD6lVJ/YvzoZwBeAvCdza8/zfVN7969izfeeAMAcOrUKV6pKqU4qgDy722lo5xgMMhe/Nlnn8WXvvQlAJbkYebJawlvaGiI5ZX5+XmONILBIK9GAoEASzb6scrKSg6f29raWDpraWnhlW19fT3/ns/nsxUhaswutplMxraJXWzsh718EnoV19bWhhMnTgCwZC694T81NcWZkoODg7h16xYAYGBgwNY7y5QhzI1ac6VnyiBmVKyjl1QqxffdXKlqGeTu3bscHRXjvd0vnLaZfDCzwdLpNM8ZZvRcV1fH8019fT0XJjc2Ntp6/el5o6GhwZZpqaMfIuKIJ190lEVEfH2/rLZsNssJCJ2dnRwhDQ4O4urVqwCsRAo9J6XT6T2311yktrMAvgrgGhFd2XzsD2EZw98Q0dcBjAH4nZ1eaGNjA+vr67h8+TKHqVNTUyyFmB/oc+fOsV6eq8ymb3JPTw9nj505c4YdWygU4gkhnU7zPtOJEye4v9Lk5CS/X3NzMxuUeRKhDpmJyJY2rR830xvX1tb42u/3s2H5/X5+/vr6OmZnZwFYso02bh0OFxl7Zi/5sLq6yn+3mZkZtqOxsTFbK3v9d45EIrbmnvoe5dotwpTg9MRx9OhRnDlzBoAlFWvb0A6uv7+fnaDZxUAojM3kgikzZTIZXpQuLi7aipS1DdTV1dkcj16UmI6nvr6eFygNDQ3sKKqrq/n55gJVzxN+v9/W7cQsbtbXtbW17NS2Ysp32t7X19d5oVtdXc3z8q1bt/jzsR/dU3Z0PEqpdwHcT2x9YW+HIxQ7Yi9CvojNlB+Ot8zJZrNYXl62RQF6ZW9GNjdv3sTy8nJer603lJ9++mk8//zzAKzaiu02cj0eD680vF6vrd25ZmsxoY5W9DjNTKdUKsUrhGQyyddra2u8AjIzXsyVTCwW403La9eucQGprIpzx0wWyGQy/LdbXFzkiGM/5C2fz8erzYMHD+LZZ58FYEkY2k60fU9MTHBkXcyJI4KF/uyvra3Z5gGdGTY/P2/LajN7Buo5KRgM2qIYU03RX/Vzw+EwZ8+Fw2Fb5qyOoFpaWjjLcivma5o94cyMWjNrbj8p2LEI+oM3PDzMRXXmvsfKykpOH07tGCoqKjh99ezZszh9+jQAK0XbzEYynZs2ikAgYHMKZoWvaVBm0RVgZT1pmXB2dpYzS9bW1njsHo+Hs+oef/xxNjIi4teenJzEpUuXAADnz5/H0NAQAHE8+TAxMcHOez8IBAI2W9MLlY6ODi4qfPzxx1nmMBdU2vFFIhG2ETkiufgxHY9eaO6mKHhrI1r9VS9cOzo6uFuCeSR3W1sbZ1MGg0F+fCvbOR5zT9PMfNvabHSvkV5tgiAIgqMULOLRnnVpaemBVwk+n4/DzebmZi4afOqpp3gVqjf09HvqqGV9fd3WlkJfm0VXW1ve62sdSq+srHDG0uzsLOfAA/dWF+FwmNuomIWHGxsb/De4e/cuzp8/DwD44IMP+PVFjtk/iIhlDfN00Ww2y6vBqqoqLvQ06x0CgQDLtI888ggnrxw5coQzG816IB35RCIRjpC1jQmCxpTuTXSN4fz8vC0JRkciRMR2er/jNszi1I2NDU6yGRkZ4eSbO3fu2CJy86iWvaaoTyANBAKsbR45coQL9rq7u1kW2XrMgP7ALy8v8yQwMzPD6dSmZLawsMA3PZFIfMwRbK001o7p2LFjePTRRwFYe0zHjx8HYGWNaKdiOqpLly7hzp07/J5O6azlTDgc5mMp6uvrOStpcXGR73lTUxPr5Wa6vNmx4sSJE3x/fT4f70uOj49z6raWUScnJ1nW2KlThiBotM1EIhG2TQC2hZPe377fYnVjY8NWJK3nu+vXr+PGjRsALJvVTigej2979s9eIVKbIAiC4ChFHfFUVlZyFlpfXx+vTisrK1nmMOtihoeHuVdXIpGwZZ6ZvbrMTWG9wZ9IJLYtaNWrDvNwsLq6Oq7nOHnyJCcuBINBzmq6cuUKd+K+cuUKP74fnbKFj1NVVcURTGdnJx9dEIlE+P53d3fzc9ra2tjWzLqJzs5Olt0WFxc5MeSjjz7iXnu6y/rs7Oy+1kYIpYkp9+uIp6WlxdbCS9uj1+vddv5YX1/nrM/l5WUufr19+zY++ugjAHbb3+/kl6J2PEopls7i8bitUd7t27cBWBOAPgPll7/8JT9uVpZ7PB7b96YW+kk6JxGxXGNKfc8++yweeughAJa8pg1nfn6e+8NduHABH3zwAQCriFbfcJmQnKGhoQGPP/44AKvw88iRIwDsvfoaGhp4X8csEjQdj7ngmJ2dZe18cnKSr81CYN1dw8xqFIR8qa2t5Uy2Rx99lBdOeuthK4uLiyypDQ4OciHz0NAQby3EYjHH9pVFahMEQRAcxfGIx6yp2S3xeBx3794FYK1U9cryzp07No9u9mEzD/DKdwx67HqF6/F4eNXx/PPP49Of/jQA4MCBA/z4xsYGy35zc3O80Xzx4kUMDAwAuL+MJ+wf4XCYkwJOnTrFySBVVVW2+gWztmynbsnV1dW8ydvT08PyrX6N2dlZtkXzBEmJcoVPwqzp0dd1dXU8x5w8eZKjcV1gupXFxUVOYLpw4QL3DJyfn+fIPJ1OOybzO+54tAZpFmk+KKlUiuW1RCLBGUVXr17lolTzrJ18j1cwMSuI9fk6XV1dXKj63HPPsdQWCoX4febm5nD9+nUAltb/4YcfArDCXZ1VJzjP+vq6LRVef3Bramr4PptNYM2zV+5HZWUlF/v19PTwYmK7nl5mA8dEIsHa/crKiq1hrCBom6mtreXr1tZWlvmrqqpsHVa2K3pfWlqy2buW16LRKNupkwsgkdoEQRAER3E04jFP8dwLzJY25oFfKysr29ZLPEi0o7PWvF4vr2a1LHP27FlbvY7e2FtfX+dsuFu3buHnP/85AOD999/nlYZuoyIUhkgkwputwL0z7tvb27ntTWNjI8uqZsFxKpWynUZrFp/qDMa+vj5OTNBd0KPRqK2mTEtx09PTuHnzJgDLXnTCzH4cwCUUHzprsqmpiaVcM+Kprq629YTTNmParFnsPjMzwxlu6XS6IDYmEY8gCILgKI5GPEop2+b+Xr0mYNcz9wqPx8MrjM7OTjzyyCMArO7XgLUprVe1wWCQdfrx8XHevHv//fc5bfrGjRvbnusuOM/KygrXMqTTad5graurs527pDdzM5kMR9EVFRXb1vc0NDTw7zY1NfHmr7ZRsyN5PB7n97x16xavWLd2C9bXa2trHEWb51YJpY/ev6mrq+MGoA0NDZxIEAwGbQkIOslqbGyMk1nu3LnD+97Ly8scbRcKx6U2HQq6deI1a3oqKyu5z9qZM2e4F5yu/+jt7eXJKJVKcSbdO++8g/fffx+AvWuyeciYFIkWlrW1NV4cLC0tcXsbr9e77cFfyWSSFza1tbWcVHL8+HFONmlra+OeWXoTWL8mYDks84gO/Zxjx46x4+nr6+MkmcXFRZ44RkZGbMXPZq2ZUNro9kzhcJgXwvX19SzB+Xw+W+dpLeMPDg6ynDw0NMTtcNzQJ1CkNkEQBMFRHI14/H4/Ojs7EY1GWTZIp9Mc/bghCqipqeEkgt7eXj7Y69lnn+WVrd58VkpxssD4+Dh3SHjrrbc44kmn0wVJVxQ+mUwmwxus+utOaDkjm81yhLS0tMSSR1VVFa9O/X4/JxqYh3Rpia6lpYXtqLW1lSvPn3nmGZZEJicn+X0qKirYjsx2T2YktjUScsPnSdg9OhoOhUJsU9XV1SzBbU311zLa5OQkd0oZGRnh5Kv9OBAxXxx1PIFAAF1dXZiZmdn2sDWn0eGpWSTY3NzMezmnTp3C2bNnAVhFWlsPRpqZmeH9m3PnznGB1sjICEs0kplUOuj7GIvFeH9Iy6uA5ZD0JKBtHbBqegCr95t+rLOzk6/b29ttLe7156GhoYGLXMPhMLdhWlhY4AXP3Nwc9/mbmpqy9fVyq5wt5Ieen8ysYCKyPW6i73s6neZ5yG0LfJHaBEEQBEdxNOLx+XxoamrC8vKyK7yulkUaGxtZ9jh9+jSeeuopAFbzSL1aDYVCnIWkpZDbt2/jnXfeAQC8+eab3BpnfX1dWuCUMBsbG7YN2u2OFN7Y2GA70avO2dlZbpPU0NCAQ4cOAQAOHjzI2XDV1dUspzQ3N3MW08GDBzmTbWJigjuuDw4Ocg2QeaAhIMdrFzNmFGPal/nYdo8D92oXzeQYs/mtGxSYnB0PEXkBfAhgUin1eSI6COAHABoAXAbwVaXUJ4qHHo8HVVVVrFkCD9YzbTeYN0l/wA8ePMiFoJ/5zGfwzDPPAABnKAFW8Z9ufaNPC71x4wZLLpFIhJ2NSBx7Yy/FginB6utMJsOauv5q/ryqqgrHjh0DYGnxeh+otraWnU0sFuOjPkKhENtja2srF63W19fzvtHDDz/Mcu+NGze4j2GxUE42cz/MBYj+vrOzE4Al1eos29bWVraHdDrNe+br6+tsb6urq7zfYy6G3bDoz0dq+yaAfuP7Pwbw35RSRwAsAfj6Xg5MKHrEXoR8EZspE3KKeIioC8BvAfh/APx7ssKGzwH4vzaf8iqAPwLw3U96nXg8jgsXLmBpaakgRwB7PB7OBAkGg5xE8Pzzz3ONTl9fH+fKr62t8Sbu5OQkzp07BwB4/fXXAQCjo6O8ojAljnJnr+ylGDFXk9tFvjriTiQSXKOTSqV4pRsIBLgoubW1laOfQ4cO4fDhwwCsRAOdmHDgwAFOOojFYpxNubq6WlQRTznbjImen3Q7nIaGBo54ent7OUquqamxRTy6aHR5eZmzNFdWVvjxtbU1TjxxQ8STq9T2pwD+EwCtPTUCWFZKaa8xAaBzu18kopcBvKy/11KAxsk/gs/n45vV2NjIRYC//uu/zh/eQCDAmUmjo6P8Qb5y5Qrr87qQz+zsKtjYM3t5EDwej61oU0u7iUSCP3yFSjc2pTizg7p5RIK596iltjNnznAWU19fH09M7e3t/H9KJpO8UNIlAUVEQW3GLegCYy29dnR08OKjpaWF7SEQCLCdxGIxWwq+LlhfXFzkbiqJRMJV3S52lNqI6PMA5pRSl8yHt3nqtp9ipdQrSqnTSqnTDzhGoYgQexHyRWym/Mgl4jkL4J8S0YsAggDCsFYndUTk21yRdAGYyuUNnV5lmvnuTU1NOHHiBADgxIkTnL126NAhXmlEo1HenLt8+TL+8R//EYAV8ehWJrolhRtCVheyp/byIAQCAW4n0tLSwkkkU1NTHBG4oc5lp2g5Go1yBtKNGzc4Q2llZYWTCzo6Orj/GxFxMauW7oqEgtuMW9ga8XR1ddna5Oj7ah4KF4/HuR3OwMAAhoaGAFjZt+aWRqHt3WRHx6OU+gMAf6Uk3y8AACAASURBVAAARPQZAP9RKfXPiehHAH4bVtbJSwB+uo/jfGC8Xi/foJ6eHnzuc58DALzwwgssV9TV1XHL8GvXrnF/o8uXL3Oq6uLiIqfQisO5P26wF4/Hwx/g9vZ21sVbW1vZ8Zgt4xOJBGcFra6uFryyW08Q5jEK2WzWNnat+588eZJlRa/Xy1JyKBTix9004WyHG2ymUBAR3yed9Qts73hqa2t5D8gsTI/FYlxEPDQ0xKn2kUiE7cdtWwK7KSD9FqxNwEFYeuz39mZIQoki9iLki9hMiZJXAalS6i0Ab21eDwN4eu+HtDdoKeLAgQM4cOAAAKsPls5e6+7u5sglmUzyKuH999/notCxsTEOYbXMIeROoewlm83y/aqtrWVJVffdA6xiTp1VNj4+zvd/aGhoz48kN1vWm9dmQoHOdvP7/ZwM4fV6WUZramriKKe9vZ3rPMwkCXPz2Ov18uZzMWVcFtMcsxf4fD6+T4FAgGVhXdDe2dnJ19XV1RzlmD36lpeXbT0jtV2vrKy4KqHAxNHOBfuNWRyqte4nn3wSL7zwAgCr/fyRI0cAWB9wnXa4sLCADz/8EABw6dIlzryLxWKuC1GFnclmsza5TKcn9/T0sIRhnjt/9+5dPjunq6uLHY/pHJLJJEtW5tEJ5mmzZvNb0yHoiSUYDPKCKBAI8KRgOoaamhp2KhUVFfy7Bw4cYMnw2LFj3FTU7HWYTCb5Pc2TUYvJ8ZQbXq+X5bPKysqPOZ7e3l5bY1Dz/DF9X03HMzY2xsd9ZLNZ18qs0qtNEARBcJSSinj0yqGqqoojmxMnTrDEUldXx5HQ+vo6rwz6+/tx8eJFAMDVq1cxNXUveUYSCYqPbDbLiSDXr1/HD3/4QwDA4cOH0dvbC8CKbPr6+gBYmWFago3H4/y72WyWV5Wzs7Oc1ej1ejkSNiW7iYkJvk6n0xzR6AgmHA7z5nEoFOKobGlpiSOolpYW3kyuqqriLKaDBw/iscceA2DV8ejXjMViXMS8sLCAsbExAFaSxNZ2+YL7qK+vZ5vs7u7meUs/1tjYyIkyFRUVbDPz8/NcuzMxMcG2adqvmykpx6Mzetra2vhD+sQTT/Aez8bGBk8GY2Nj3HPt7bff5qJQtzQwFR4cpRTv8dy9e5c/oD09PXzMwPPPP8/V/w0NDbxoMU+gTafT/CGemZnhzEePx8MSRiQSYcl2dnaW38u0Ie0kampq2PFUVlbyc9bW1vj16urqWBo0pTbz2ONQKMTOZnZ2lvchh4eHWSYeHh4WmbgIqK2t5WaxJ0+e5LlKNydubm62ZS3qgtBIJMKLjMnJSXY8hc7IzBWR2gRBEARHKZmIx1yFmptqyWSSV4fmaaA3b97E22+/DcCKeHQkJNlrpYUpu01PT/N9Xl5e5nqttrY2W62ElmNNaTYcDvMmr9fr5aior6+PX3PrsQQaHbVUVlbakgv0a2QyGZbaQqEQSys+n4+z4Mx6tI2NDY6+xsfHuWv6L3/5S1y7dg0AbP0QBXdgHjyp72VlZSXXE3Z3d3PSiG55VF1dzfZlSr+RSIRb40xPT3NPtmKJckvG8QD3wszV1VWWzi5cuMBSyNraGn8Yr1+/zrJEIpFgR+WGsyqEvcOU3ZLJJNvCnTt32CF0dXWx3NHe3s6SVldXF8tx9fX1LIFlMhlb9piW6cwGn+bkot8nFApxqrR5do+ZGgvcmzzMVvbmcdfxeJyP4xgaGuJmoKOjo9xVw8xwE9yBdjxmmrzZc83c09OPmSfSJpNJltoWFhZ4L3pmZobve7E4HpHaBEEQBEcpmYhHKcXePhqNcsaaUorb3qysrPCKYWlpiXPfzRWsUB6YtT7z8/MsZ0xPT9uSAXTCiinBmZgSWHV1NT+npqaGr7Vs0tLSwq9trmTj8TjLwalUiiO0rTVCWl5bXFzkgte7d+9yu5R4PM7Pd1tvLgEc5VRVVbGcGg6HbTZmHo+h0VJxPB7nU44jkQhnUM7OzrLU5taC0a2UpONRSnF2USKR4BtuZimtr6+z7CZOp/ww7WVxcZE/0PpnWwmFQuw0tD0BH+8uoDX61tZWNDc3AwCfENrd3c0FrGbh5+rqKk8c5j5RLBbjhdLq6io7noWFBZ50pqamWGYR3I153IXZmUDvLzY2NrIT0s9Np9N83+fm5ngLYXp6mhccCwsLvHAplsWGSG2CIAiCo5RMxAPAltWm5YqlpSVbfyyNueIUhJ2i3lQqxTKWWetjHruRSqU4QpmamuKaHR0p1dbW8mNKKX7PRCLB9ppKpVgCNK+TySRHQmtra7zClSxMd2O28dJJKI2NjbYWTfpwt9bWVpbgzJNqdTQ+PT3NmWwzMzM2qc2c+4qBknI890NnD0nGmvCgfNKeiZ4kVlZWMDs7y4+ZzmnrY6ajM53Q1uvtnrP1Z4I7ISLOYvT7/Zw2feDAATz++OMArHR8Lc/6/X5bBiZg2ZSW1wYGBjibcWZmxrYvWGyI1CYIgiA4SllEPIKwF9wvyjAfl6ha0JgRTzAY5Iint7cXZ86c4WstxXq9XpZzdTQzPz/PGbpmxBOJRIqiJ9v9EMcjCIKwD/h8PluqtM5ybGxs5H2dlpYW7sVGRJzBZp4VpXuymWftxOPxopTYNCK1CYIgCI4iEY8gCMI+4PV6uYi4tbXV1g/Q7MdnZr7p+kOzJZKOeObn57lmK5VKFU0G23aI4xEEQdgHzCxGs9DY7Fqxvr5uy2LUjkdnsg0ODvKxF5FIpGSKhUVqEwRBEBxFIh5BEIR9IJvNcpbawsICZ7UtLS1xEkE2m+VMyEwmYzvNFrASCvRzizmLbSs5OR4iqgPwlwAeBaAA/EsAAwB+CKAXwAiALymllu7zEkIZIfYi5Esp2ozpeADwkRyRSMTW2FVLbel0mh/XKdQjIyNcTFpKjidXqe3PAPy9UuoYgCcA9AP4NoA3lFJHALyx+b0gAGIvQv6IzZQRtFPrDSIKA/gIwCFlPJmIBgB8Rik1TUTtAN5SSj20w2tJn48Co5SinZ/14Ii9lByXlFKn9/MNStVmPB6P7ZBAndXW09ODnp4eflyzsbHBGWz62IvJyUnuop5Op4siky2XOSYXqe0QgHkA/5OIngBwCcA3AbQqpaY332iaiFq2+2UiehnAyzmPWih2xF6EfClJm9nY2OCjVxKJBF/PzMzg6tWrAOwn1RKR7Swm/XvaF5dSV4xcpDYfgJMAvquUOgEgjjxCXqXUK0qp0/u9ahJcg9iLkC9iM2VGLhHPBIAJpdQHm9//LSyjmCWidiMMntuvQQpFhdiLkC8lazNmjY7ZeVof/Feu7BjxKKVmAIwTkdZWXwBwE8DPALy0+dhLAH66LyMUigqxFyFfxGbKjx2TCwCAiJ6EleoYADAM4F/Aclp/A6AHwBiA31FKLe7wOq7Z+CtX9ju5ABB7KTH2PbkAEJspJXKZY3JyPHsFEc3D0m8jjr1pbjTBfWMC9n5cB5RSzXv4evuKi+0FcKfN7MeYis1morDqf9yGG+0FKNAc46jjAQAi+tBtm4BuHBPg3nE5iVv/Bm4clxvH5DRu/RvIuOxIrzZBEATBUcTxCIIgCI5SCMfzSgHecyfcOCbAveNyErf+Ddw4LjeOyWnc+jeQcRk4vscjCIIglDcitQmCIAiOIo5HEARBcBTHHA8R/QYRDRDRIBEVrL05EXUT0c+JqJ+IbhDRNzcf/yMimiSiK5v/XizA2EaI6Nrm+3+4+VgDEb1ORHc2v9Y7Pa5CIPaS09jEXgzcYDNiLzmOxYk9HiLyArgN4Ndg9WW6COArSqmb+/7mHx9LO4B2pdRlIqqB1Qn3iwC+BCCmlPovTo/JGNsIgNNKqYjx2H8GsKiU+s7mh6leKfWtQo3RCcRech7bCMReALjHZsRecsOpiOdpAINKqWGlVArADwB8waH3tqGUmlZKXd68jsI6cKqzEGPJkS8AeHXz+lVYRlzqiL08OOVoL4BLbEbsJTeccjydAMaN7yfggptBRL0ATgDQXXG/QURXiej7BZIoFIDXiOjS5hkjwJYzSQBseyZJiSH2khtiL/dwnc2IvdwfpxzPdk3jCprHTUTVAH4M4PeVUqsAvgugD8CTAKYB/NcCDOusUuokgN8E8G+J6NMFGIMbEHvJDbGXe7jKZsRePhmnHM8EgG7j+y4AUw6998cgIj8so/hrpdRPAEApNauUyiqlNgD8BazQ3VGUUlObX+cA/N3mGGY3dWOtHxfdmSQPgNhLDoi92HCNzYi97IxTjucigCNEdJCIAgC+DOusDcchIgLwPQD9Sqk/MR5vN572zwBcd3hcVZubkSCiKgC/vjmGcjyTROxl53GJvdhxhc2IveRGLieQ7hqlVIaIvgHgHwB4AXxfKXXDiffehrMAvgrgGhFd2XzsDwF8hawzQRSAEQD/2uFxtQL4O8tu4QPwv5VSf09EFwH8DRF9HZtnkjg8LscRe8kJsRcDF9mM2EsOSMscQRAEwVGkc4EgCILgKOJ4BEEQBEcRxyMIgiA4ijgeQRAEwVHE8QiCIAiOIo5HEARBcBRxPIIgCIKjiOMRBEEQHEUcjyAIguAo4ngEQRAERxHHIwiCIDiKOB5BEATBUcTxCIIgCI4ijicHiOh/EdE0Ea0S0W0i+leFHpPgXogotuVfloj+vNDjEtxLuc0xcixCDhDRcQCDSqkkER0D8BaA31JKXSrsyAS3s3no1iyAF5VSbxd6PII7Kbc5RiKeHFBK3VBKJfW3m//6CjgkoXj4bVjHCb9T6IEI7qXc5hhxPDlCRP+diNYA3AIwDeD/FHhIQnHwEoC/UiItCDtQTnOMSG15QEReAM8C+AyAP1ZKpQs7IsHNEFEPgLsADiul7hZ6PIL7KZc5RiKePFBKZZVS7wLoAvBvCj0ewfV8DcC74nSEXCmXOUYcz4PhQwnrr8Ke8TUArxZ6EEJRUtJzjDieHSCiFiL6MhFVE5GXiP4JgK8AeLPQYxPcCxE9B6ATwI8KPRbB3ZTjHCN7PDtARM0A/hbAE7Ac9SiA/1cp9RcFHZjgaojo/wMQUkp9tdBjEdxNOc4x4ngEQRAERxGpTRAEQXAUcTyCIAiCo+zK8RDRbxDRABENEtG392pQQukiNiPkg9hLafLAezybhU63AfwagAkAFwF8RSl1c++GJ5QSYjNCPoi9lC6+Xfzu07Ca2g0DABH9AMAXANzXKIhIMhkKjFKKCvj2edmM2IsriCilmgv03iU5xxARiKyPodfr5WsA2NjY2PZrMZHLHLMbx9MJYNz4fgLAma1PIqKXAby8i/cRSocdbUbsxXWMFvC9S3KOCQQC8PmsqTccDiMYDAIAstksUqkUAGBtbQ0AsLq6WphB7jO7cTzbebWPrTaUUq8AeAUojtWIsK/saDNiL4JBSc4xyWQSyaTViDoejxd4NIVhN8kFEwC6je+7AEztbjhCiSM2I+SD2EuJshvHcxHAESI6SEQBAF8G8LO9GZZQoojNCPkg9lKiPLDUppTKENE3APwDAC+A7yulbuzZyISSQ2xGyAexl9LF0ZY5xaC/ljoFzmrLC7EXV3BJKXW60IPIFbGZwpPLHCOdCwRBEARHEccjCIIgOIo4HkEQBMFRxPEIwv/f3pnFRnZe+f3/1crai8WtuTSbTXaz90Xt1ki24MiwnSCRB/EYyAwyCAwnMaA8ZAYeIEHs8VMe8uAEyWTyNIFiT6AHB85kFtgPQhIjHhuSIMnqbqsXNlsim81ubsWtyGJVkUXW8uXh8hx9t1QUq9Vk1a3i+QECb1/eqrriPfWd7/y/c84nCEJdeZYC0obg9/vh8/kAAB6Px9ZugvD5fHy+VCohn88DaN0qYEEQhGaiKR1POBwGAASDQXi9XgCWE6Lj7u5utLe3AwAikQj8fj8AyyFRFl+hUODq4a2tLW5Rsbm5ydXE2WwWmUwGgOW01tbWAIDbWgiCIAhPj0htgiAIQl1xdMQTjUYBAKFQiOW1zs5OxONxAEA4HLZJbS6X5UePHz+OgYEBAMCxY8c4+onFYhz9lMtl7vxq9k7K5/MszW1vb/Px1taW7Zh+0rEZKa2trWF9fR0AsL6+jnQ6DcCKoOj6Uql0IH8jQRCEZsPRjoccRnd3Nzo6OgAAQ0NDOH7cat907Nixqo4nFAohEokAsJwNHcfjcYRCIQCW7EbOrNo6EZ0nB7G5ucmOx/xpHpNTWVpawtLSEgAgmUxiYWGBj+n80tISrzltbGw0ZftzQRA+G9Sd2uv1IpFIALAm1TRW0XJCIBDgybK5pq21RrFYBGBJ/7QkkE6neUlgZWWFz5dKJdSzWcB+iNQmCIIg1BVHRzzBYBCANRMg6ez8+fM4f/48AODMmTM8AzCjFq01Rz9utxtutxuANbugBAT6vfkawpxV0HXhcJijpb0iJIpaVlZWsLq6CgCYn5/HkydPAADT09N49OgR3xe99+bmpiQsCMIRwYxy4vE4XnzxRQDA9evXWc3p7u4GYF8ecLlcNmmfxpiVlRXMzc0BsMabxcVFAJaqsrKyAsCS/5PJJABrnGq01O9ox1NtgHe73ew8wuEwh6wej4ePC4UCh6Hlcpnfx+Vy7SlpkRNQSrETqnRmlceVv6d/h0IhPvb7/bwmNTAwgLNnzwKwjOL+fWsjxbt377LhpFIpNi5BEFoPt9vNDmZ0dJTHhMHBQZ7cplIpAMDCwgKPWVprXh4IBAK8gdzp06dx6tQpAECxWOTxY2Njg2X+d955B2+88QYA+9p1oyR+kdoEQRCEuuLYiMeMJir3KDe9vim1USSSz+e5LmdnZ8d2jXlMUY55fT6ft11DMp3b7bZFV/ST7qWtrY2PfT4fAoEAAEsmPHHiBABrpkGfk8vlcOzYMb7e3P5WIh5BaG0oQnn55ZdZdtNaY3p6GgAwPj4OAJiZmeFxyuPxoL+/H4CVWEUJV4lEglWVRCLBCQqFQgGjo6MArEiIsmtnZmbw+LG1ozll39YbxzoewEo/BoDV1VUe7Ofm5jjbLRqN2pyAuWZCA/z29rbtGnIkphNKp9Osha6urnL4acp0puOh9/B4PLZ1KDIgcz3I7/fz6wKBADunSCTCa1WlUomvSafTrMUKgtB65PN5Hqu8Xi9nt87NzdnWg+kcobXG2Ji1HZHf77cVxtMa+PXr13HlyhUA1joRObhkMskT2rt377LU1ijHI1KbIAiCUFccG/FordkbF4tFPi6Xyxwyzs/P7xnxULSUz+dtbXXM/Hk63t7e5vdcX1/niMfMfTez0CjicblcHNYODg5icHAQgDXToPcwIysz4nK73SzBRSIRPj89PY3bt28fwF9Q2A/zedKxy+Xi52UWGZvH5mvNhBUzAcXlcvE1dL6WOopKOdiUb+n1m5ubIsc2OVSwnslkeKy6c+cOHjx4AABYXl4GAM5QqwaNaz6fj5MVyuUyqz2XL19GV1cXAKCnpwcjIyMALCVpZmYGAPDhhx8e6P9XrTjW8QCwFVgSmUyG/2hjY2O2AYAG742NDXZUmUyGQ1Kv12v7ItODA8BZcIVCgc9Vri2ZToN+0trM48eP0dnZCQDo6+tDb28vACuTjRzMwMCAbVChzgzRaJQLSymNUjh4vF4vS6NdXV04d+4cAODKlSu8JhcIBLjo7tGjRyx5mGmq5rMLh8N8fSaTYXsMBoNsG5Qqv7OzY3Nk1bIjTTv2eDws33Z1dbGNPnz4kCUYKTxuTu7duwfAktbpuS4sLLDkT87j06CxqlgsYnZ2FoBl4yTVe71edjaxWIzHpNXVVR6rGoVIbYIgCEJdcXTEU43NzU1uCVEsFm01OkQmk+GIh649LCiC8vv9PJs+deoUL+pdvHiRJb2BgYGqcovW2hY2C4dDMBhET08PAODatWv4xje+AQD46le/ylGx3+/nGorbt2/j5s2bAKwFWaq78ng8vJjb2dnJssjKygo/x/b2dm57Ui3i2UuWM4ucfT6fTUqm2fDCwsKeRcxCc0AS10FIXVprW39JsjdzHOzp6eFIOhgMsm263e6GFJM2nePJZrOsj66vr9scDx0XCoW6dQIwBxWSXI4fP26TS/bS9k15j66hgUZ4Nmggj0QinAV59epVfP7znwcAPPfccyxDBINB27odfSnPnz/P0ufVq1e5GM/r9bJ2Ho1GuTlsLpfjzzXT6821pGqp/abjMaU2t9uNiYkJAMD9+/e5Ut20dUFQSvGExuPxcEat2+3m8clcizTXDjs6Oljmryf7Sm1KqeNKqb9VSo0rpcaUUt/ZPZ9QSv1cKTWx+7P98G9XcDpiL8LTIjZz9Khlel0E8K+01reUUhEAN5VSPwfwTwH8P631D5RS3wPwPQDfPbxb3b2ZYpEjBadm9kSjUc5wO3HiBC9Em73fgI+7XKfTac6qM5MbmhRH2Iu5KeCZM2cAAF/72tfwyiuvAAAvtFbDTDSg686cOcPPy+122xJWSKqo7ABsJqYAny1SIXuYmpri2Wuj+2wdAo6wmWaDbMmUZwcGBvC5z30OAHD27FkuLPX7/WxL2WyWox+KjurNvo5Ha70AYGH3OKOUGgfQD+DrAL60e9nrAH6JI2IUZko2DVJ9fX1cSXzhwgWcPHkSgGUIsVgMwCcbkdJAYu7f0+zNQp1iL/RF7O3txeXLlwFYEihlmtWKmQpPz9rsaAFU7/NnvrZaI9taMQcLyu5sdhupxCk200yYnVc6Ojo4S21oaAinT58GAJw8eZIltWw2y1mZk5OTmJ+fB/Cx3F9vnmpBQSk1BOA5AO8B6Nk1GGitF5RSVfOAlVKvAnj12W5TaEbEXoSnRWzmaFCz41FKhQH8FYA/0lpv1Dp701q/BuC13fdwzk5EzwDNpoPBINdZXLlyhVvgjIyMYGhoCADQ39/PM2XAHvVQksTq6iovXFMxWbPTaHuhyGZgYIAjnt7eXp4BVnYb3y8qMeu4KnmWiKYSrbWt1ock2GQyyTPWXC7nqE29DopG20wzQH8Tsw5xZGQEly5dAgBcunQJw8PDACwVhuxnaWmJ6x/v3LnDrXfo9/WmJsejlPLCMogfa63/evf0olKqd3cm0gug/qkRhwQNTmZ3AzMjiTKdent7eQ3gypUr/PAHBgY466mzs9M2SJA+Xy6Xec3A3E8jnU7zANesWr6T7MXMNlxfX2dJ06z+39nZsW2PbvbzqzemXWxubnLvromJCW7suL293XKFo06yGSehlLJNdKlTiimvXbhwARcvXgRglXLQea/XyxPasbExTE5OArC2XKAJbqOktlqy2hSAHwEY11r/ifGrnwH41u7xtwD89OBvT2g2xF6Ep0Vs5uhRS8TzEoBvArirlPpg99z3AfwAwF8opb4N4AmA3z2cW6w/lLEUiUS4KNTn83EkRFlS165d47YriUSCZxqxWMwmr9HMWWvNxYnJZJLbsYyNjXELjfn5eV40bNKIx1H2srOzw4vyqVSKZ4zlcpkLP3O5HC/ImltUmFloldEPRbFm6xszCjGjXFM6M3fDrVazVSqVuC5oZWWFF4GTySRHbi2Io2zGpHLrE/peV+5gbBYGm735KHrN5XJPlbFq1nJRZtrw8DC33xoZGeEi9cHBQVZeOjs7beMHKSk3btzA3bt3AVgFyI22pVqy2t4CsJfm8JWDvZ3Dhx6o3++3SWoUznq9Xs5Ci8ViPFCZjofWcp5//nm8/PLLAD7ZANIcqMyeXKSpTk1NsSHcvHkTb775JgD77qnNiFPshQbve/fusbwWj8f5S1wqlVhuUEqxTHr58mX+EicSCbYFsw39zs4O99LKZrP8Wdls1vbsaOJgFphS54SzZ89y9wOTUqlka5NPRaOtlslm4gSbcblcPMkMBoNcAhGLxViG7ezs5MzV7e1tlmrN/by2trZsheH0/IrFYlXHY/aPNMchupdIJMIT2nPnzrGzGR4e5nXkaDTK7z07O2trMEoT2unpaS4UraUP3GEjvdoEQRCEunLk+rPQjKKjo4NnMu3t7ZydFo/HeYbR2dnJ15izEbMLdWV21KdhZk9prXlGXCwWbR24hWeHZqNTU1OYmpr61Gu9Xi+3ppmamsLZs2cBWHIGtZuPx+M8C85kMpxhlkwmeYa5vLxsi0xo5ksR1/r6OkfL0Wh0z4iHZJBkMsm9BlugsNjRuN1uVje6u7vR19cHwOpxRjsF9/b2cmLR5uZm1f5olWqFueMwZbHu7Ozw9WYWo9kuiQo7u7q6OFHJ3HrF3Gl0c3OTu1M/fPiQkwgePHiAR48eAbCi50a0xtmLI+t44vE4G1R/f79tS1k67uvr42vMkNgsICUqnY4pr1U2hKSflfv7CI2hVCpxK/kbN26wo0okEjzxCIVCPBhks1l2CBsbGzy4bG5u2tbl6LmbmYym86qG1pqdzNbWFg9QrZbF5gTa29ttYwBNOM6cOcOSWiQSsW2BQX38Kre1MPvumesz5Jy2trb4madSKVvBOD1jszDZ3L6DJrodHR3s+La2tnj9b2ZmhrfKvn//Pp+fn59nqY/uwymI1CYIgiDUlSMX8VBWysDAAGekDQ4O8kyiq6uLZTdzcdnj8XCEY7ZIIfZql2Iea615xtTb28uhd1tbGy86Lyws8IxldXWVZ0a0QC0cPOVymWeGq6urtkw2Myo166soKqklEiG78fv9LK99WlYRRUjb29ss3bRiwWijicfj3Nrq3LlzeOGFFwAAX/jCFzjSNXcNrhUzw40k362tLR5LOjo6OAIxs+DMaIlsJhQK8ZjV1tbG5xcWFjjKuX37NicR3Lhxg6XfnZ0dx9rNkXM8lJnU29vLadHDw8McWnd2dvKDNjPfzEFoL6dSreDQdEjK2LlyeHiYnd358+c5w2psbIz3gHnw4AEePnwIQBxPPTHTYc1jcgjm+Vogadbv93Oq9l7bX7hcLr4mGo2yvGfKusLBEAqF+DvY399va6hJz6cybboWzNfQ86MJJ2DPcDMlu0p7A6zn0QAa+AAAF9JJREFUTmOWmd5vpv1Ho1Fe7wkGg3zv5XLZsWuDIrUJgiAIdeXIRTyEy+XimUFbWxvPSBKJhK2fF4XN+XzetukbYEkhJJdVymxmEgHNTAKBAM9eYrEYZ6uYrw0Gg7bdKGnGkk6nP/H5wuFTmSDwNFRui1BLxKOUYhuRiOdwCQaDHOVEo1H+3mUyGduCP1Eul21RL2F2Lg8Gg7bxgd7T5/OxbLqysmLb1K9adEXHsViMpX+/38/nzWgtn8/z+3V1dXGRulLKsRHPkXM85kBOmUyxWIyzSIrFIhtCoVDg69fW1jiTiR7sysoKpyiaA5PZX8nr9fL6TV9fHzubWCxWNSuup6cH165dA2A5RNKCl5eX+fPJyARnQ4MEPf9z587xXil0rpKdnR3uaPHmm29ykfFhb+F+FFlcXMStW7f4mAoyJyYmeD0mEonwZKFcLu+Z+kzfa4/Hw7J5Lpezyaz0XZ6enuYefIA9q9XMgKTPp0lxPB5nZ9Pe3m4r9TCLmGniPDc3h48++uhZ/0yHgkhtgiAIQl05chGPufkaZY9FIhGe4XR3d3OUs7Ozw1JaMpnkTq/U/2hqaooLD808eZfLZctEGR0dBWC1STGLyMwdBM090OleXC4Xz37v3r3L9y4RT3NAM1kqQn3ppZc44qEZciXb29tcAPjWW2/xzFgKjA+ex48fc8fveDzOxb1DQ0P8fNrb2zniMXc/NvF6vbaejmbRqCnN0fc3lUpxBFssFm1yLo099DMcDrPcevLkSbaf69evc0LBsWPHbLsym8lMNGZtbm7asu0azZFzPDTwLywssBSSy+VYdhsfH+fw2DS0dDrNqc0ktS0tLfGDNdddKqU2CoOTyST3ATOLVo8fP85hfigU4tcGAgFbhh2dr0zdFpwJ2RENYqOjo9zkkQYNwJq0kI1MT0/zZGNpaYnTrp2q1bcK+Xyeq/+z2Sw7G5/PxxNEMxvNHBtKpRIP6sVi0VZQTMef9fklEgnOhE2lUrz+19bWxmOJ2ST00qVLfL3H4+HU6tnZWVtvuUYjUpsgCIJQV45cxEPefnZ2liWriYkJmzRGM5xSqcShsim7UXSTz+f5nJmPD9izYT788EMA1gyEMlQuXrzIrfhffPFFbosRiUT4tWbE09bWZmvZIzNgZ6OU4oiHnu2JEyc4qYBmroCVRUWz7fv373PEk06nbfYlHB75fJ5lTXPhv9Fks1m2AY/Hw1l4WmtcuHABgDVmkJwbiUS4g3U2m+VebblcjscViXgEQRCEI8eRi3gogkmn0w3bbxywEgQoiWBjY4NnNSbBYJC70b7wwgusO8/Pzzf03oW9oUgmHA7zGh6t6yQSCdsmc0SpVLJFNrQmNDw8jJWVFQD2bsgej8dWB2RG4mY6rqwDNj9mE9Hl5WWOxlwuF48fQ0ND3PoH+HhtsaenB8PDwwCsMYaSXWiNupEcOcfTaEgiy2QyLPVlMpmqhah+v58Hr+eee44Xmj/44ANxPA6Fai5GRkZw+fJlPqbf0ZfflGJdLhcnjrS3t/OmdIlEggeax48fcyJLOBzmBWQAXEu2urrKC8jNvqGg8Em2trZ4O45iscidtdfW1mwZsub2LbS9w/z8PNcXmfWBjUKkNkEQBKGuSMRTZ8zOCTQzWVlZ4bz+bDbLco3P5+OFaaUU1wyZqbhC/TH3UvJ6vbZ2KbSwe/XqVVy9ehXAxxFPIBCo2kjW7/fzonG5XOZnfurUKY5yzJqTWCzGMsv29jbu3LkDwJJlzHRfobUoFotsP21tbWyDe7VzcrlcrKSsrq5yarcT9v8Sx1NnaEBYW1vjcHdhYYHz7dfX11lGicfjnAUXCoU4V58GHaExmAWDiUSCdfRTp07xVhtnz55l3Z1am5iZbCbBYJBlk0QiYduZljYny+VyXOtjyimrq6t8/ZMnT2x9uoTWolwus5Tb09PD48BejsTj8fB4Y9aEOWHtT6Q2QRAEoa7UHPEopdwAbgCY01r/tlLqJICfAEgAuAXgm1praZu8D1SPsbW1xbORbDZrWxSma5RStjoektiohYaTaRV7ocjB3Aiwu7ubF21PnjzJ9RQXL17kLMTe3l6Wz6olFJiYEUwtz9asKfN6vbaZb+V+Ls1Eq9jMQWNuTEjjQUdHhy2SJvVke3ubO6ysrKxwlLyzs8PXOKEm7Gmktu8AGAdA6TT/HsB/1lr/RCn1XwF8G8CfHfD9tRxkROZe7pFIhKWbcDjMhaKmgWiteXCilFyH0xL2YvbQo+LPa9eu4fnnnwdgtcGhTQTb29vZcQQCgU84nGeRv0xHMjMzw+s6t27dwvvvvw/AklPIIX2WbRwcQEvYzEGilGI7crlcLNfG43G2u7a2Np7E5nI5XhdcWlriHn87Ozuc1eaE4vOapDal1ACArwH44e6/FYAvA/jL3UteB/A7h3GDQvMh9iI8LWIzR4taI54/BfBvAFA6VQeAda01pc7MAuiv9kKl1KsAXn2Wm2wlaOYaDAZ5xhIOhzniiUQituJAmsmYGS1NsClYy9gL/a0TiQQnCzz//PP48pe/DMAqDqXnYhaFVtse/Wkxt0I2G1FOTEzgjTfeAAD86le/4ozIjY0NR8xmPyMtYzPPQqUtmVJqe3s7S7y9vb22ZCN6XaFQ4Bq/VCrFEr7b7a66iV2j2NfxKKV+G8CS1vqmUupLdLrKpVX/b7TWrwF4bfe9Gv9/7BB8Ph/LMn6/n8Npj8djk2jIcNLpNKdDOplWsxca7Le3tzkraGNjgyUMOkfQs/N6vTyBMKWS/ZyQ1tq2LQc5ldnZWS4mfffdd3Hv3j0AVlYbafdNKq81jc243W7bd9PMPjwoKuX0UCiEM2fOALA6WVCW4+joKGdTVsrztK5jrvGUy2VHTUpqiXheAvAPlVKvAGiDpb/+KYC4UsqzOyMZADB/eLcpNBFiL8LTIjZzxNjX8Wit/xjAHwPA7mzkX2ut/4lS6n8B+Eewsk6+BeCnh3ifLYfX6+WIx+fzcWjt8Xhsi9G0WLy2tsazaycXB7aavZAsYUY85jbo8Xico6JSqcQz1WAwaNvTBbCeeS0RDz3zTCaDqakpAMDbb7+Nt99+G4C9fc76+rojpJNnwek2Q5GruQ11sVjkxfrKqPdZoIiKMlg7Ojq4APny5cvc0f7EiRMsteXzea7fWl5eZtt48uQJt1My9wtzAs9SQPpdAD9RSv07AL8B8KODuaXWg5yKz+fjwaivr493Jj158mTVolCtNUs65gZhTbobZVPaC8kT6+vr7PDfeustbt7Z39/PKa6BQIAHhjNnznDWoinPkCyWSqX4PbLZLEsi2WyWn286nWZ57f79+/joo4/4Xkh2bXansw+OsBl6fuFwmLtKlMtllsWKxaJtI7j9JE+3222bjFA/xt7eXi4kpgzKnp4eDAwMALDGDDpPqdSAZQ8zMzMArF2RabLy0Ucf8XYbThsznsrxaK1/CeCXu8dTAH7r4G9JaBXEXoSnRWzmaCAtc+oAhepmj63h4WFcuXIFgFV4SK3wAfsslmbCjx8/5hkvdbUWDh+KcjKZDEsr6XSaN/eLRqPc1qijo4NlsoGBAZ7VmvIaRSqTk5O4efMmACtxgOSR5eVl29bJZkIDfX4ts2rh2TC3mKbne+nSJW6JtL29zZHFxMQEP6dUKrVvEpDb7bZJsrRtxvnz5zliHhoaAmCNE2bbLBpLtre3OXttfn4eDx8+BGBFOaSMJJNJbsXlNHsRx1MHSGqLxWKcDjk4OMgNJUdGRmx9vEjeKRQKbNCrq6tIJpMAPnZGQv0wU5vz+Tw/o1KpxFJbZbFftfUcel0qleKmnw8fPmRJbWlpifX4QqFga/rptMGjlaHvYyQSQXd3NwDg3Llz+OIXvwjA+g7SNblcjrsF+Hw+nqz4/X5blprpbGgNJxKJcJr+8PAw7yRKn9ne3s7OxtyTaXZ2luW16elpdoIPHz7kSUw2m3VUCrWJ9GoTBEEQ6opEPLtQVGIWc5qzFDPbjGYPhULB1iOJ3sPlcvF7xGIxXggcGBjgiGd0dNTWa6najpKZTIYjnnQ6zWGzRDyNxSzmLBQKLKl1dnaylErnKjF79ZmFfiSfrq2tsX2Zu4g6bcba6piFnBTF+v1+Hg8ikQhve9Hf389R6vb2Nj8rpZRtGwN6bSAQ4PEhEAjwONDR0cH9/cwejfTeW1tbLLc+evQIv/nNbwAA4+PjHD1PTEzYonGn2k3LOx6lFIet3d3dvJYSj8f5IcdiMR4ofD4fh8dmwVixWGTtlh5+KpXidMVUKmUzVnrv7u5uzlQZHBzkDJXjx49zhgwZGWAZCzmWxcVFltfMPXuoqFRoPD6fjzONzp07x1IJDSx7Ye7pY2597bS016OK6fxJrjInHGbvvra2NtsAb76Wvqsej4elOb/fb5Nh6f0LhQJLdpSNlslkbFmOdDw1NYVHjx4BAObm5vh1WmtHNAHdD5HaBEEQhLrS8hGPy+XiWejZs2c5a6Snp4dnLF1dXba8epK9zFB5c3OTi7So/sJc4FteXuZIyefzcR+2np4ejrJ6enpsi4YUemuteWaUz+c5bL5z5w4++OADAFZoTZEWzY6FxqCU4mglEolwksiLL77I2yLstUss2VY0GmX7m52d3VOaExoDRQ07OzusQCSTSYyPjwOwkn3MnWfp+bW1tbFKUigUOII129p4vV7+vm9ubrLkurq6ymPL4uIifyapKul0mlWPVCrFUU4mk+ExoVAoOFZeM2l5x6OUYqlrdHSUU5j7+vpsDfdMLd0Mfc09c8jQqHq5vb2dUyHz+Tw7HrMrgVntHIlEbG3zKfQul8u2dZ25uTkAwK9//Wv84he/AGDvXCA0FtPxhEIhlk9Pnz7NEutejoRe5/f7ucA0GAza1viExmNKnrS2evv2bczPW1174vE4P79oNMoTymPHjrENVK6x0PV+v5+dxurqKr/nzMwMf/dJapudnbVls9IYUCgUeJxqBmmtEpHaBEEQhLpyJKZZNPsMBAIcfZghsVmjobW27TppSir0PpSFYs5olFK2BAWKZnw+3yey4ei1lH2SzWZ5hvPkyROMjY0BsGZA5gxHcAZmxOP1ejmRIBQKsQ2YWySY0Cw1m83aJBR5vs6CIh5zA7WVlRUeMxKJBEuliUSC294sLi6yzFoqlWw1PWY3ekoS2NjYsEltppQGWEoH/X5nZ6cpMtZqoeUdj9aaZYy2tjZ2PGYKs+l4APuOkaY0Qmsy5u+rFQlWvtdehYQUbq+trbF2fOvWLW55b67rOLkx6FHD5XKxrGpOZgKBwL6SGTmeTCbDEk6T76NzZDBl+FwuxxOHTCbDjmJqaoptwJTqzTGhXC7bulPQes/W1haXZ7T6912kNkEQBKGutHzEA8C2H7k5S6EMkra2Nl6g01pzlBMIBDhsDgQCLKOQjFbLxl6VEQ/NfIrFIsto8/PzePDgAQDgvffe455s2WxW6jocBNmFuRPkxYsXWXKptAVz91DAmgGTlLK0tMSZSxLxNAfmIr5Z3C08PS3veMrlMqc8+3w+djyhUIg111AoxIOK2+3m7DWz+JMa9dH7mO8P2IvLzAGoWCzyoLKxscEDTzKZ5P5c09PT7HiWlpY4DJfByFmQjXR1deHChQsArMaRlNG017oOTR5yuRx3KFhaWrJNguRZC0cJkdoEQRCEutLyEY/Wmju3Liws8Kw0kUhwvn0ikeDoJxgMcr59X18fRyixWMxWMAZYWW+0kOjxePj3Zvvyra0tjmDm5ua4zcXk5CS3Mh8fH+dc/XQ6LfKawzHb2re1tXGiAWBvl0K1WdSmfnJyErdv3wYA3Lt3j6XeXC7X8ovJgmDS8o4HqL5/jVlVnM/nbVXFNKjMz8/bij+pAST9DIVC7ITC4TDLcV1dXeyENjY2eICZnJzkjLUHDx6w1Pbo0SPpRtAEVMtmNOU1pZSteSilwd66dQsA8MYbb+D+/fsA7KmzZjGgIBwFRGoTBEEQ6sqRiHiqYS7o0swTsHd3dbvdfBwOh3nDJspoam9v56y3UCjEEh3JdoC1H7rZcZb6sM3OzvLnymy3uTAjnkKhYNuZlGTV9fV1Lgp+5513AADvvvsunyuXy03Z6kQQDoIj63iy2SwPGJXQoGI2DAXA6zBU5BUMBlnfN9d7lFIs45mtzFOpFDubXC4nDqfJqCa1bW5ucn8tl8vFa3gPHjzgTEVKj0+lUo7dEVIQ6olIbYIgCEJdqSniUUrFAfwQwEUAGsA/B/AhgP8JYAjANIDf01qv7fEWTYVZ+EdyyMbGBkcxFPmUy2VbjYbMYi1a1V7o+ebzeY5iFxYWuOXJ8vIy7wp548YNTE5OArD3/RIbqU6r2oxQHVXLF0Ep9TqAN7XWP1RK+QAEAXwfQEpr/QOl1PcAtGutv7vP+8i3rsForT+91cIB0Kr2Qhls8Xicd4+NRqO2LY2pG8Hi4uIn1vCaVFq9qbW+ftgf0qo2cxSpZYzZ1/EopaIAbgMY1sbFSqkPAXxJa72glOoF8Eut9Zl93kuMosEctuNpZXsRx3M4tLLNHEVqGWNqkdqGASwD+O9KqSsAbgL4DoAerfXC7gctKKW6n+VmhZahZe2FZNdUKsXdiM1+fWarJMlaeypa1maE6tTieDwArgH4Q631e0qp/wLge7V+gFLqVQCvfsb7E5qPlrUX2oslGo3ynkyBQIDPu91uLlZeX1/nLZPNBrRCVVrWZoTq1JLVNgtgVmv93u6//xKWkSzuhr/Y/blU7cVa69e01tfroRMLjkDsRXhaxGaOGPtGPFrrpFJqRil1Rmv9IYCvALi/+9+3APxg9+dPD/VOhaagle2F6rR6e3tx/vx5AEBPTw+3TQoGg7hz5w4AYGxsjItFqVC5WCxK1FOFVrYZoTq1FpD+IYAf72abTAH4Z7Cipb9QSn0bwBMAv3s4tyg0IS1pL5RcEAwG0dXVBQAYGRnhrTO6u7u5j18gEOCkA0o4WFtbkwawe9OSNiNUpybHo7X+AEC1MPY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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data.show_batch(3, figsize=(6,6), hide_axis=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at another example from [`vision.data`](/vision.data.html#vision.data) with the planet dataset. This time, it's a multiclassification problem with the labels in a csv file and no given split between valid and train data, so we use a random split. The factory method is:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "planet = untar_data(URLs.PLANET_TINY)\n", "planet_tfms = get_transforms(flip_vert=True, max_lighting=0.1, max_zoom=1.05, max_warp=0.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
image_nametags
0train_31112clear primary
1train_4300partly_cloudy primary water
2train_39539clear primary water
3train_12498agriculture clear primary road
4train_9320clear primary
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" ], "text/plain": [ " image_name tags\n", "0 train_31112 clear primary\n", "1 train_4300 partly_cloudy primary water\n", "2 train_39539 clear primary water\n", "3 train_12498 agriculture clear primary road\n", "4 train_9320 clear primary" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv(planet/\"labels.csv\").head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = ImageDataBunch.from_csv(planet, folder='train', size=128, suffix='.jpg', label_delim = ' ', ds_tfms=planet_tfms)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the data block API we can rewrite this like that:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/home/ubuntu/.fastai/data/planet_tiny/labels.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/planet_tiny/export.pkl'),\n", " PosixPath('/home/ubuntu/.fastai/data/planet_tiny/train'),\n", " PosixPath('/home/ubuntu/.fastai/data/planet_tiny/models')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "planet.ls()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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image_nametags
0train_31112clear primary
1train_4300partly_cloudy primary water
2train_39539clear primary water
3train_12498agriculture clear primary road
4train_9320clear primary
\n", "
" ], "text/plain": [ " image_name tags\n", "0 train_31112 clear primary\n", "1 train_4300 partly_cloudy primary water\n", "2 train_39539 clear primary water\n", "3 train_12498 agriculture clear primary road\n", "4 train_9320 clear primary" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv(planet/\"labels.csv\").head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = (ImageList.from_csv(planet, 'labels.csv', folder='train', suffix='.jpg')\n", " #Where to find the data? -> in planet 'train' folder\n", " .split_by_rand_pct()\n", " #How to split in train/valid? -> randomly with the default 20% in valid\n", " .label_from_df(label_delim=' ')\n", " #How to label? -> use the second column of the csv file and split the tags by ' '\n", " .transform(planet_tfms, size=128)\n", " #Data augmentation? -> use tfms with a size of 128\n", " .databunch()) \n", " #Finally -> use the defaults for conversion to databunch" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data.show_batch(rows=2, figsize=(9,7))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data block API also allows you to get your data together in problems for which there is no direct [`ImageDataBunch`](/vision.data.html#ImageDataBunch) factory method. For a segmentation task, for instance, we can use it to quickly get a [`DataBunch`](/basic_data.html#DataBunch). Let's take the example of the [camvid dataset](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/). The images are in an 'images' folder and their corresponding mask is in a 'labels' folder." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "camvid = untar_data(URLs.CAMVID_TINY)\n", "path_lbl = camvid/'labels'\n", "path_img = camvid/'images'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have a file that gives us the names of the classes (what each code inside the masks corresponds to: a pedestrian, a tree, a road...)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Animal', 'Archway', 'Bicyclist', 'Bridge', 'Building', 'Car', 'CartLuggagePram', 'Child', 'Column_Pole',\n", " 'Fence', 'LaneMkgsDriv', 'LaneMkgsNonDriv', 'Misc_Text', 'MotorcycleScooter', 'OtherMoving', 'ParkingBlock',\n", " 'Pedestrian', 'Road', 'RoadShoulder', 'Sidewalk', 'SignSymbol', 'Sky', 'SUVPickupTruck', 'TrafficCone',\n", " 'TrafficLight', 'Train', 'Tree', 'Truck_Bus', 'Tunnel', 'VegetationMisc', 'Void', 'Wall'], dtype=' in path_img and its subfolders\n", " .split_by_rand_pct()\n", " #How to split in train/valid? -> randomly with the default 20% in valid\n", " .label_from_func(get_y_fn, classes=codes)\n", " #How to label? -> use the label function on the file name of the data\n", " .transform(get_transforms(), tfm_y=True, size=128)\n", " #Data augmentation? -> use tfms with a size of 128, also transform the label images\n", " .databunch())\n", " #Finally -> use the defaults for conversion to databunch" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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ZrQ9AxfrcBrabU7Qp8pmLpmk6tNZzrm272RCCIBDONZRSGMeBmKL8HRj8QF9ZavJMCxQhLpi6Sbx+40khKQSPdRZbnUMuGTWzDjONFQRicxEpFJpuQeMT+GF+/teOv0jJEFPkg5//lOVqRdctaeocNbagVaR/3COgO7/8Oea1/wnze1/g4Ilj0lcqBKfUx5zPZA/+6TID7leByX6VyOZR1/u0VGvzkNc/9r4MJQlFKKck7DOAnFGqYBpLq4T5Nm7PiX5kcmXGGOH7x0CqdFFVVIVH6iSJAeOcOCYNyQdyTuJcnAwmnTNZaVIcGPsNJQcWiwNhv2QZlI1TGKUIqdAoI2ydnCi5XIIAJPQXOKMwjiMoaNuGzUWFDetE8N4To8J7mahd22IrW8kYRcxphu329tu1nHdRSbm0wXFOamdCjJS+8PwLLT/8W8PNm4E//mNZeJRKfOdbK9577y28htXTL82suhdekPF186ZHFz3DxTsG28PICgLXpSSRfYxxhtesMayfeYkflcKdm98jxczXn9Gg4O+qA1JK0eAqhRzeeD3QPfUcT6pX+NM/EciraRzOeYzW3H3jPxBj4rlnW0LwjHWRH/sNOQUUWerbVgey+JbCyYnU4V1cbEjJ42r9TkwRZy1jv6XfTnTlE6wzNK5BqYI1lqZp8N7P8JofRnIuWONwzs5zR0dBM0DYo1ZbeYa6MIaBnCPOtaxXUtoiMHzGGkO36Ci54EMkBF9hc3k+1lpSzNh6T123IMVCTDLfcogslwdY25JT4c5HI9vzDdvNOYtaK5RzwPthjgYfZo+FA/rog1+I59eSp2jj/wGAaSxhygdVe5Cm/LCF/teB2D7r8Z/F2V2+vynqmf6WuBo+NFbxNfPf8OP2X7M9G+jHuujmVCOZXGmToJ0hFy20aqgUSCnsyzlTUDP2Pu3SjO1Q2gh+7Qei7wGFNgbX1MFUElEhFNNhwDpLSpGkCzUdgGsswQtUYG1LYx0xQqrXlluWHZnUTkQUisY1WGM5OxWGW6o72lLKDCUYY0ApRi800LFk+TxX7JD39pu1kqeIQ91X4wPyfYUQefPNAaUUf/Af/2PuvfF9bt8Wp/DMM44fq+c5eM5wdutVzm6+xgsvtKSUyVM4r0Bpga8+yQEB1QHKayEkmj9+BoD1wUrGdo2glsuOv/3bwjCM/NE/eAOAhWn54U1DLhljDFppLt5+nd40s2PNJVFK4vSt76OUbIaGMTEOA+fnMk7HYUtJGaMVzlms1ZSSGIYtBZmjx8eHdIslQz9wfnaC315g7bLC5VOdVOTk7h2Wqw5rDIvuABcbNpsNIch4t66laZ04HWvR2hJCweQ0w2s5J7QppOQZtz0Xm3Oadsn64BrH128AcHpyiveDFM6mgDUNWhtijOQiDtooU6OsUeA0BX3fc7E54ezso/p8IsvlEmsVbrEk+OtsNmdstqfYA2HKjX1ie3HCwdGD2eydPRYOaHN2IrUiKLS2HBd5WIvD9ZUL+aMW/gdzQb8OueDT2KdxeLt4Y+d8poLayK626aH3raAURSoa7lMwSCgDKQT8sCH6kZIiijJP7FKS7GyUmhf1DJALFTnBNq3sKqMXxYXgcW1Hs2hZHIkDCoOXgtYUpF7HGmL0kkdt5ESjFmy+azqytpSmgVqoOME04ngEYrFGwvwCDKPfwTwKnHO0ruXs/KJGT6Vi23KQ1rri18tP9R3t7dcz5+w8IC87hZwzIQSCD7zwQsvNm4Ff/Pu/QtfkPVDzdEWiniTR8BtvSL7S1HFhjZkjrB1J4eEza0dkkD37arUg/ewHAARnCUjxqzH6Uj4CfvyTWpqQEl97SuZF1xk+aF7ko9e+Ry6Jl1+W3fq77xi0asmV6p/zOd/79/d46qlMjOIUFKCNYdj2eN8TY2QYR4IfZiJF07T0fZJopkRSTJyfnQr8ZScVCI0fB6lTappavyObr7FStbvFEm2cfOaS0UbRtg2j97OTKjmjVKp5X6AYSlb024G//8XPASEPBO+FMp8LqjVyHymjykRcEpTCOA3aYrRlHHooiq5b1+90QfSRe+d3uP6FJzg8OsY1LZvNBeMw1HtecHh0jDFX52kfCweUY2RzdkpOGW3snBPQCpY//lfEr/z5zDpQfNwBPSzfchXs9eva5RzPp41+JqhtQlh3SKtEPlP084lOs1bqlVwHnJJJnYJnvDgjpVo5fd+bSy3qMyhl0HoKs3cX0cZSsiQ8Vc5YY7G2QaPZVCghhIDvNwS/pWkEg/bjgCoJhcAo1oDr2joxCqrCbUqreSFIKQkEYB3WOpyVSGocPSHtKr+WS8cXfu8JUi6cX5wT6/smL9U0ThLg+xzQ78TUJfhNEvJT3i4xDmPN3ynJHWqNQc2Fn++9X4jhFYGQnJ1zPNbqS2SDQs6Fay9+m9O3XqtQ3CfcU1XrEOmfKXKpkX4ptG2L0oqDZ1/mzqvfEycKlbEXhZnW9/zw3/2/PP+COMmPPhJ47e49YfTFlHjqqULXndEPWy4uAs66+foKjdYWP3q0ySgUOWWG6hRCkFqcCW1ou6Zu9iKnNZKanmsICaMLvoz4EMgl4kMlGISBlBPkTIqKlALjKKSCWFlnOXu0NiganLUsF1Jo2jTd7OycSfgSWC2XWNPUAtyMtWbeWBTAj4N819rQti0xBJxrmRaNw6NjYCIIZdaHLUfXrnN2csZYiQo9G5xt5s3nw+yxcEClKGJMnJ/cw1rHlD/XSjBLHRPaPfxW71MKqParRECX7dO8/1Fw34OO0HC/A1Ls9OsuQ29XOc0fm+/KZwyFFDOx7opS8KJMED2lZIx2oAqlpPkZUhP+sqtxQqeuEzTW/Ekpg0z26nyMbeZq5lghLz/0+GFbGUqGsd/ghwsaZ2e9rmCl+LBprFBSUyLExDiGmc0zEQmccyy6BmtNjWzirOs1TYR+GGbCRElR4Icp92ANlMxQE8J7++3bjkq/mx26yl8YK6oX3/iG4t33Ss1pTPk6sytMzplnn21RKG6/k2ZoVvJKipM3XxG1g3I1/FaKRFGKyeGUea7kukmbJYNqUavSap5oN775J7Sbv2YcCykknnuuZehHttst26q0sVppFouOd25nzk41fgzkWAg+EccdpKQQzbNJEsI5R8kdvioGhHGkkFE6VwkjTUoJlGJRJWqGoTD6gVIGGtugUAQ/UnJiuxWmnHWOxWIleeCcsI2j5FGkyaoTT7GQKVgjt3Pt+g3W64Oab9sVnecCx8fXMMbivafvL7DOYqw8IHHQWj6X0oToaSqd/PTkBIBm0dG2Ha5dcH52Sr/pUapw7cYxpydyntPTu5R0D9dcHQHtC1H3tre97W1vn4s9FhFQ23ZCHTaKcdhy98NfAuKtlXYc/EFPt16h7CXuf/2peHgE8Wmjnl+FRPBJ53sY2eBBwkGsP/MDhUzzey9dTAExFULIxJBINXKJ4yDyO4UKsSmR2sm7Aj/KTmbHVEmQgkB1kxapqkWoOSeM1bRdizauwl4SlaToiUHyNELRVjRNKzUU9a69j6QY8DFQimb0QYrakmDgIMSHSaQ0hiBRrjb33XOp/+WcOVgvaazl9PRUIjY1fazMpt+Sy6OLFvf2m7GJ9qzmQbqLgtq2oe8HNhcX5FJQONrG0dQarVy1/Z552vD6G56bb+UKh5U5Ms4511ojs7vWIzA4hYxboxQKYcWB5HdKAWttVcaWezfa7PQjjeHW24F+GPnG1zUUsMZxeHDED/9G7vmFFwVWfvnlwtAPtI2hcR3W9DOleUrQBz+SckQrgzaqipLKefSo5hq2GMJMmZY6uKY+2yQQXPSUUmialnEcgFJ/Qr/d0LgG51q0seSEMGC1ouua+bPHGIkUdGXLdYu2FrBu63UVq9US2xjIhRQD282GYeznKGmxWLBY3mDRrfAh4EOuskUdMU6ae5lcBlarFePQs7k4I6QN1jiOrgmTcLlaQG44O7t35ff4WDigg/Wai+2GnBN+GGZcfxx6tLUo9T/TfOFJusPFx1hx5YHfPwvc9lmczycd86Dzu0w8UNxf4zM5nh+Z75JTPaAUvhz+DF3hLBFkFPgtJ6mlkEmpmTXTSq4aWFomLhVeyztoI+eMcQ2L9RF+7EEVchTBdF0u5YMoaAXBDzXJ2ZArxx+E6VQqSSRneb3U4taJJh28B1KV0xd2Ugj+vpwBtQg2pUjMCVtsJR3YHUYfa3FfKSwXHYuuZei3jI2dJ4lWisFHjo4PP8W3tbdf10qum4KS7ytIjTFO/BZKUfzgB5rVSnIv0xh8+mmD91mELEtBV1XtWDcyMMGuO4bmlCe6XJA6mVJqdgLy752zMtZw/Py3OLv1mpwvC2ydK2sS4O//w1/x9W9o/Ghncc+uW/D+32gODuW8i2VtpwAcHx9htCGmxNnpqeRiEEcWUyT4ET8MLJZrUm3FsD7o5nsbBoEFXWdYr9cCYQ8eH0RDUWtN1y0ofcZ7X7dfoqs4iaPmnMgpYTpTczq7VEXTCJQXfGYcR2L0LJaS04KCH0eGXq41DFtcY7i4CFLDExJaG7p2MZdKlKJIMWBsIY9SQ2SNrC9tK3PUB892e0bOI6iA0YXtZlvFSyfh4cz6cMVqvbpyXD0WDujo6IAQPX0fZ4ouwDgOWPfTip0abtgnhK1h9H2O59PaoxzLZ42YHuZwLv9++W8Twy0/8G9VJM+VkzBe3h//T9qF7Jz+qPzXFK3IiPJBCpmSqop0FQQsKeBTFBkepStJIdfam0mhuqBKlryQKjjbUEzDOPYovZPHKVkUE/xY2zbYhhjzzLDJOdM0C5TpZIAGTwzSj6dEyUkFvyUlT9O0NaLKbDc9zjUzc2+SYEkp4cdJ2kcz+nFmz+RSGEdP329pG8Ny0aGNqvUPk2KvwiQzqzDs7bdrRbjNcx5nmoDee/wYiSmzWCxZLhXWGWIIfOPrMgPGQRhv775b6BZtrU2TuHkukHzpO1y8/XqlYNe8jvp0W0pxSLsEuiT0g2wAtdQF+RD48NV/B0gpQIqiSm2N5Z13CsYkcvY89Y3pehKhTIWtoAibLUUxq8hLkbesV957mi4CmlwSOlUlklrIqNQuFyZzoZDiTp2gULC2kXmx8bRNgzWOlKa5NUVPVbDUGjJTwe5Uk9XgbItrnDjM6PF+rP2KdmtqiKoSgFpKFkKBs3YWYh2GLX70nNy7R0oF6xxGKYZhmElM1opySo5CKLrxxBfoNmvOzk65d/dDQApR227Bcnn1JvGxcEDL5YInyjXu3lNsNlt8dUAlZ07v3UFp2TEZo1l8caBbL2ZWzqPsKhjt19GPe9DRPPj75fqky47nQckggRCmBZm51iXVEPcH+i8haxSFcevJMVfWzTQxAQolJ0IYsa6t0aImBT9fTZUCJZKjRDZKCXW0oSVUyfgYI6VU2Y+6QzU2kNKlhl/G0a1WuO6AnDPjdoMaMu1ijR8kcSvnk52vVlUq/+iQtm1nlYPtdluTtkooq8HTOEcIO6KCMQ6tHeOYuXcq0IH3sqDYyqYrudA2hk8xDPb2GzBt9I4wkMs84P0oEv3WNrz/vmh/pZiq5uDlHlAW61SNhiUaUVrhaqX/5Iik5YA0tXtY9PNJ0BxFmGvSrkFg5TuvfY/Re77x1PQ+i0JhG+mJpbUQXL7+dYCpKZtBaSqdWtimU7Q3ffjtdkPfbxiGXsgQKVUdtx3BZ0rmbzZnMBRp6Ogact5pMU6EiUl9pOtarG5Qdtd4RmqtPKiC0tAPPd6PHB5d26ELSgsD0Vgp0E2pkirULLdVshAVMEII6poO11hQmqarc6u0DP0g71VaJH4MnJ8LWQHg6OiY1fKwpk40i0XLcn3AtevXOb0nZTR//4uf8stf/JLV4emVX9dj4YAEq+y4Vo6w1nLnnjAtptzB6b27daA4bnxZOPRuUfHTh5zv0zDTHmVXHXtVlPOw802Q22Wncx/jQ8FX8j/jJ/q7kvvRMsmnXVGJorOWUyb2omKgreycYqVm5hQk4imZGEZUkl4/xjqojizFMNfuKG3kQjnIIjKpiJZMjlHqiGpEEn1AmQZVpdSlg6qoKkTviWHEuJonqlCecY7G1R1sSmgtLRe00nPk4pyd2VEpJ0yFI2THK09I1w6O1kizr2HwdYEqM7U3poI5dJ5rAAAgAElEQVTTau+AfkcWY5op8IUyV9977xmHEVrNN77hcE7z9u2EtZb3369wslFoLUoKqeYp9KWFEkQLbsoVhRCEQv1AH5nJOTlrr9yAKqU4v/naLGGTqjMqOfPOOxWmMxprNU9+60/58NV/TymZr39didxU/VzDOOLPRtqupXECR4vOWppZgH4c8GPtm1VKZaLqqrPm6x3JfNJao5W+tBmT/A1ISYHSkosquRBzYrlecLBe018IlHf35KQiQwLFa2OxtmC0nTeJMfnKghWmagyJsVQ5pAobtq2rJRANSmtc43BOCnA3F+P8zIyxWOcYx4Fx6LFO07Ut774rz/Xb37G0XUPTtbimZRw9q2VH2zYcHUqn1/V6zcnJPX7xs59cOa4eCwektGiILZeLueId4PT0DB8C0QdO7tzD2l+weOt/4dp/9FX4xr+o730IRsyvR8Oe3nNVnmdyJlN+5zK8djnieZjD4tJxP1HfJadJqFOK6qaJnWJCKRnQ7aIhRU3wgRQSwcvuSiIdiTZSiuQowovSB65CcHXAxuBlgVdKxA8n4B4QJQVxAKXIhNVW1/4vtVhOGWlCNww1UoLV6pCcS70P4NLuVOQ8pKeKT+FSJCVECVBIh8yWmCS/NEnwKKNZHyxpm46Ls/M5IQu7hmGya/yUX+befm2T8Vh/T4mTqlqx2fREL1poFDdDaIXdopfrgriT0EloLbDzU0/JRvK117azYO36mZdYLheMf3NLIiV1GVeQDIl6xKw2Rir7n3qqUNCk6EjJ8N578rquUlAffP/f8rWvFYK3WCs5kDCVHVTYalJK8D4wDAMhiLwU1KLqen+mNuAL0Vdlgd0x0sdL3Tdep2cEopg9rXtGGU7PT9lszvF+QE81kfX8pYgcjrUNh0dHGNPMMLnkf0JVp7ZVJLWQS2JT+2g1bSulLboW8yLF45fzasvlgsViRfCBszMYx562XXB0eJ3TU1kPVgtZ54btQN9vMcYSoqFbtBxek5zPct1xeHhACjsdyAftsXBA0s5WoZ3FWDN3z1RAPwaMXfDk732J608+wfH16xIB/fQvAAhf+m8rJKCk8PFSweon2VWR0lVRzfT6g47nMrw2/f4wZ8XlY6o4AUVyQQYJs3MduCkEKAHTNbjG4hqD0pqRggmVXWQcYeyRXZYihURKkaKZIwOllEyYUkAjjLWSK2topsEJKcFYlDaV7abqB6nYulIy0Iu0IXZtw/rgkBDi3OxquVyhsiclgdPm5aKU+X5SEgeqlCJXRl1MkZjiPLHXy0NRVG5kQJ+eXMjClfPs6a2V908dGvf227X7oS9FrMSTkgtN2/Djn3TCegK0mSJr+ZFy5plnLKWA0S1vveWlfXQY6fuq7B4k2d02DePf3CLWfEmMaS6itFZYco+E4JDNiSgxwBtvDJQCB8+9TNPemj9LGAIxJl5/3fP004aU033SQLmIisEEjcl4VhhjiRWKtLUwUyIGyUuL09pJT0mh7rZq6RlxYHWFcLZq5VVCBiXjFisUirNzITscHhzWJy45nbPTe/ixZ7U+5Itf/CKjjzuGKVJw7v1ICoGCMExVUrNitjSrVDSNqHI7Jy0nNpvtTC5ZHaxZLFb0/VAJQo6/+1FDvz3gqf/8nwDwk3/7bzg/u+Cpp2pOLQjR4d7dj1jXcbBcLVgdNXzh95+88rt6LBzQzL9VIsmxXC7mvy/GCKbj+hee5MaTN1gfLSkx4KeE9b3/DZBWAHztf4QrClYftKvIBA/+7apjHiadAw+PfC47LB54n1Jyrugz0Qcus7xCDAQvCtTGWowVrbxUJ6QQDzIpiXhoSQlyRGFQ1s5XE8igVCaTkmMvKQirmsjURrDgnKEozaQbB6BKRmNx3YKmW2KtoVk0FLVLSK/Wh6SwIXlFq1qsNeQkTnGC6SjSO0Ymu8Azo/czjAHUAlVpvRxTrKyoQky5Fj5WGRHFzNzZ22/XmtZV5QGpuJ8sl8Kqbe9rj6BrdH3tpe8AVNVr2Qy98ebA175WaqGmmqOel15c8HP3EsN7N+fziNq6mRdGpaaczKNNKVl433knSiSQMxe3Xp+p1TllxtHw9u0afdjdPZdK67dFKOIheCHaFEFbnHHz/J7mlbV2Lk/IOQB5bodijSMahzFlLkIVyraiaZfzM1RKk3Nk9CMpS7txq/QsPOwaR84j52dnkjdtO7b9hlJ28HbbLWAA7wd8GrDOEWMg58LxdXEC4nBc1d000hG1TMSCGt2sV8SYMVaLM1quuHMPukU7d6p9+eUFMba8czvx1a+OXGwuSCGSU2G7FShvuxno+2GmgD/M9oWoe9vb3va2t8/FHosIaKJ4TjHDxH5ZLBc0baEoR9dZumVD21g2H90jVQgg16Sg7RoWP/iX2PXHa4UeBac9Ks/z0Hvl/nzPp3lf4f6oJwM/tt+VpCOy00sxS73CREIoGVVEKmQbe2LwaGPQ2s1FbqUel2ubhVK1ojB61p6akqbTLklqkHLFfG09j+y2lLEobUXaBxE+nQg2umRc41gsl6AdrjX40YsO1DVJOob+FAUslh2r1YqSpA7I+8BY81aiuptnaM/7SAxSt+Tq7nZiHnkfKrxhJPLLD8SbSu9JCL8jm3vzKCqLTb6L4APNdSnIzDXP99xzEi219jYA587x3nuikTbpoJUC778vTQtBFN+vvdjgL2nOgTDoLtcdfVrTWtE0jpQ0X/+64pVXN7z6quzEX3ihxTrRnNRa8YP3JRKPscw5qVkAN2f6bV8LR1tc02Cr/HvMmuIlzyOfSfJTbetoqor8drullEyKER+9ULhLphgzF3kb42rPImktYa2jcS3j2FetNjg8PGLoe/rthpQiR0fXuLg4x2g3C5Zaa6WbsJIi85ySRKxFsVyJiOik47jZbugWCeukFqqwY7z2fT9Lbk0lFd/+juWdd3Z5wKax3Lo1sFx1LJYKbTXOOoYpRwy1HlEg+6vssXBAwH2r+VxUZgxKZakk9hviuCZbjT89vdT2tcgxtMSxxS473E//gvDlP7/PGdif/gWxOqaH0ad5yO8Pg9ce9vuj1sCrHBZUSmTMxFDVC3IipR0FnTxVkUdyqswht2OLUSncpUxwWqn5nEsp2iJQ1dSTp0w3orWw5eBSmwZpu11yRlmLtW4uClba0C46lusV5+fb6jRHhn68lJwO6JxIGXKOGKtx2tX+P5U5FDRK1YZdNc8kqsW7jpsxC64dgtyvsQYbDUMZ52eXi8Yqxdw+d2+/VdvpvxUuNhcMtSeOVtK64LnnWm69HXnuGccwjOScCHWO/vE/rPMgFV57reetNzMvvbSk6/RMqzdGs7n9xq7OCDBmSpZ/lvuc5oOqCfskPaVGz6TYYbRAXy+/bLl1K/LySwtefW1DzpmuUpFHPwgBgjJv1oyxBO8Za/nCOA6kmGZ2nOSoNMMw7pQHjME1DUobLrYXcj4t89vX82gda58eYZ+tlg1aWWKKc23cOG5xzhCiFfXx4Ok3FzTdaobOFt2SFAP99oIQAyohuo6unRl3wqIV7Ug/eKI/wViHUnp2QOcXF7RNS9O0vPmmEJacsywX7Vxvdfu26E0qBctlx9u3I88/73CNpanab4tlx8ndc05Pd6KrD9rj4YDKfT/m35SSnjQW8JtzTn6ZUdeuEdP5rAgt0heKEhPZ9+R0iNZCUpgeeazO6CrHc/lvD+ZpHlzeHpU7+oSP97F/fzn8GX8T/y+CT4RB+vCUib5ZFCjJocQgeLlQOdW8K+qWh8SxJ8YRSmTqNCp06V2TqulZghT45VLQMO/ASinVQQgfXBvpB9IuV/QbEUM0rpH/25Z470R2XSWSwjBfS+WA0Tuc/uzsgnEcWa6Ws+xIjEkiGaWleRaFnEQpYZJT8WPAuUhKhbbryKow9v18HaiU3JQeyYba22/ORCBUKu3Pz87Y1gX2+OgYYwzeB776pZGTky0h+No6ujYz1DK73ntX8cTLf4Kxhr+5/QbWuXmsTJuPUjJHz3+Ls5uvzkytz2JTewhjKusuJt56K9N1uwr999+nFl0WFosF1hpefnnJK69sZgKU0QZyJoZI23YzZVwptesiahw55fm81lopwE55ZsGpFLGuoWkaoWBfauUwRYxUBIKSca5htTqg7RYE77m4kBqa05O74gi1IqVC8J6YIgtj52ZzTSOKIbmkSnM3WOeERp52rLwYA223YLFYiHMyhuADmyrE+tqrI0eHR/yT/+QaKOmIevjcy4zv3WTK2qSc+dpTsF5LhNo2Dc6ZWY0cJDqWhniPeQQkuU3Zzs87dBCJDcQJpRjYnp+Sw0hnMq2VgWl1kYrgHMjB14r+HcMMgJhRlW2GejTLbbLL9Tv33eun/UxXnG967Svxn+FTEDZOiJRcsNrMtPLtdkOuFIYUE8Y6jLXYxl0auFlgOWMJwyhFaK5Bm2YuDi0l338vU0uHkmtTrB0lVCmDa1uMaeR6+vLbCsFHhn6U9sVjjzOJxuqZYaOVyO03zjE1L9OmYbVcsVrIYrRddNy5c8J2HOjaBmOswGzWzw7IWNGGM1qz3WzYbC7YnJ+DZpbCTymTcqZxV7f73dtvznLObLdbfvazn3Hnw4/m3jHdF78oyfUoi96774Axy/sozdu+5+mnNTkvZXy0Lb5p5gJM4NKcV3Mr6s/ifA6efRkQNt2d177H0fPfpJTCR6/+B6w1D5AZJlUN2WK+9tq2dgmVDR7Aouv4uX6BL4yvSCFmjXK6rptbi8jGLWOtyEjFKE3spHOpjMuhv6gF2gVVCQvWNnMUAlJ2YK2tIEaRDsY5slh2lFqrd+/uXYwztG1D27hZ4WC1WnBwKPBaignvx1pwLs+xVARl0uJXaKxtWXRLjo6v0bWN9B0yfobF/9Gfrmnahtdf73nxhQ6l4af1886kpBoVvvHGUJmCkVu3hBz07HO75+NatyMgPcQeCwfkvch9PzjgLjuBAsQQ2cYL6Jy0HgCWnROmlNJocq0ukbbTE8NG/92/Qjn9sTqcB+3TRDtX2SdBcg/+7UfqXzNkz7AdBPIqGa0iTd0tbPJU76NRWuiSi9UC1zjO7lZ1gHFLyaLlZGxzSaZmN3kVu8hnuhFV/5sW/KmuwjgLpWCcJZfCyb2PmIMOrQkx4GaaeKSMA6lE9ASjWMGytTaklHCupV04Vqslq2XtE6+F8rkdBpbLP+Pg8BhjLefnG36x+KGcu0SaX/4VKU/aVrKL7Lp27oh6sdnSNI7Vct+Q7ndhm4sL7t69x/mZMK+mXf/J6QlN0/DO7cLBwZrFsgOliCHOO2FZsDPPPus41G/zg3ftpYhnt6BNbdrPbr5a4fdP74BO3vw+IDVExu6cjRReSmSS5o2bMNJyziKDU6YeR3nOV3Rdy/HqmIPNmovzqsShRKvtsoLBBLu1bUffb0WQN+0aMFrn8OPA5uJMGjNqPUNXk2klOZVshKY9jkOF4wxNK46s7Vqg0DgR8p2gtpKTaDwC223PvXsf1dojmflSWqFoa5vstllgrLTjXiw6OcZC0zWYOo8nAeGUEu/cFibh4fMW/wDT0RhFKVIYrFvm6PGdd+Q5v/iC+8T83WPhgE5OTzk8OMA1TS3Iml6RHEFRExwnFTVjKGgtH3LRyQArGEqO5JiwRiKJijCRYsHoIrmhr/z5JzqUq6KeR2UbPgtQ8BPzXXKQavKcoqgeZA94StfW81UHqqTlb86R7fk5SsG4EQcUxp4YRigJ6xpSFs2tSXIHan6nCjHKelBJHsrMNQ8pSf5JGwNVXSFnKCnPEjqSm5JzGWNIRTGOnpRGmonO7RxGa5mkQNN2LA8OaTo7T0jXWLq2peukIvzs/IJt39M0Db/PHwLw4fpHlFIY+h7vRbm7WzZoY2bhU1CslkvW670D+l3YT376U07vnaK14/DweBbC/DsDrVH8/j8+5uuNLGjvpcD560Ukl4DlUvOzn8G9ew1tB1rnuWncZFlJNDLt3D8r9DYtdNt33qRxjvNbr3H4nERFpULO11/8NgD33nilymCJ+kHJmRde6JCN2iTICf/AvU1oW0IIbIe+9jTKs0Cv9xL5C4W6SmXNTR8nx1qYRIKnotSJGj7le4m+9t5RogJRnYdANXKerlsQY2S77em6BqUVQ9+z3ZzPhajbzVZycyVjnaPrFljXslytODyUeiLr2hq9aGIthyhVTqdpKrSYIsMwUrJQtZ2z9VmlmSDmjOEnPMPR85rw/i2yLNWkXFg/8xIA/fgmfvRzndLD7LFwQB98cIftdsONGzdqiFsLtO6r1gcK6FpM2Y/iXe6c9lw/WuCsosRAOD+hDNKsLFQ5Gh8Vi/WaxXqJ++lfkL7851dGOw+z30Sa+8Gc0SSnqopUKot8R9ipWCM7ChmIpbJnJgLCLlMlxZtFeP3KVhkccSIgEFwpBbSBEmtUKHmkKX+SkkAMyhhRyVK5Vl3vBpwqhehHzu99KEXDRgmOXsKck8pNJCY566JrWSw6oveMJMgVBy5FEpVty2b7f1P4L+jHkfVqha1OSmlVK7lFf2vRtbStw/swf/blcknXtTPjZm+/XTs7vcCaluvXr/OlL/8+J6eSmzjwnh87iZ5nx1ES1jku1+9MBY87ZfRJdHRSbY+VdbWT55nTkp/hPidHcHnnLUXfirObUsMydU999tmudhSNiPPTTPoEIQaMNcQ6jxrnpO6s1rVNx0xz0rkqqaMUMZpZZxElwp9T+xJhp2pS2jBU9poxmn7YVvZa4WB9gFKKYdzO1+oWHUpZ7t29Q4ipMvgyZ2cnNK7C29t+lsDSStO0LQeHRxweHrKsSEE/SATXtQ3j6OfvwJjLEWJl1BkzRzogMOfZW68CoFH4229UJe+JOShs5un53n4r8swzLTzuENwXvvoUH/3ix4QPPuTocM31a9cBCV/VpNgJu8JJtcvi9D5z97Rn2TnWqw5//hFBSeHkJOy58YoSPU4XKb7623+Jspb4lX9+331cFelclQ96mD2KpDD9nApTFTXfkSIJRSmKvsqm51SZPEAO0h1RWVGVnhQDRPVaZNpLEAxXzf1UKnZW5e2V3iV5C7U/0QQlWItpOnIWOrjs4EQIdValUAo/CN6rtCLHQPAjijwLL+YU8WOh66TjadtYis+1SG8qsJVrhuC5uNiA3qK1EYruJZmdkqUqPcZUO6FKweoUtV27cR1dVbP39ts3qx1HR4ccHR2QcpyZTk3reKmx0rqjpHluTvJaIN/n8Yvfqk7gVXIRRmaIQsGHSZ9NzeNUjhEFjSkv81miIqUUm9tvzvmah73/rZvThk9yP88868jVKcRhpB89OUlrEWNdbd2QZjg7V023lFJN/k8btzI7MnGqGqU6rLF1/MoGa17PUHXzmarunDhjjWK9ksilW6xYLNZQNGfndxnHDU3TcH52In8HtJ42AVT1+EY29I2bZZGgoJWmax2Ns4xj4HyzldzzpTIHpQpPPQVdl1mvDa++KsXEel6LFa7CgdP3p3RB50L/zhsAtM7SOEMIV5MQ9oWoe9vb3va2t8/FHosI6Itf/Qc0Xcvm9C4X/Tl9/3MAnnjiBsvlEmNslbi5tIOZyDMZfMhYnTBrCOOGmCKqFEqQ8LXfZFIIGKIk/JZrmtWay2AWfHao7VFRzsP2atP5v+T/jB+Wv0Qhu4gcAqFK1F+OJibBOIEiAslWfbVLXUqn5nO5aknN2m1lR1lWlYGjatw1sXeoTKaS03zeVBLFGNpuiVJlPp+iEPyAmiqbcqKtcEnXSoJzuVqRfMQ5J8lSRZVoKYSwa1p3vvnP2PZnpDSiSTXHtwvdpVmZsBkniZepJcc4To21elKIqM/6pe3tV7Jr1445vnbEcrUQdlONgGIInJ1dkDMVinKkRrN83jHemkoA4PTNVyTxX0RSKYRY24DIF2ior8VarFpzRMboGZo1j4iApujmMtPt8s8HrUCFIYR9m4HX3xjmgul/+A81fT+SU5zzNlAYx8x2u5nPvVyuCCHIeExREvzdai472GzOybmwXK05WB/MVGdjDKFSsiU/pKStijH4ccotCU0c4ODwiKbteOLJJ0AlfvGLc1Ce8/Pzub7p8OBYGIk507Qd2mhCjGz7/lIUqdHaEaJA99JQz9N1DlMJUH70IkSsFc5abr7l6VrJO12ORh98sloplFGYiaqtMqIFebVc1mPhgNbHRzjn2BwesT074fSOtOS+e/eMfjtweHQgHQNreF8qyw3kQ8eUOd8M5BxZdg1d06BKZhjrojf0bIeBnLaiNXd0g7U2LLm6SPSyXXYq6iH/vnzMZA86tknxACCmTD96xk1PHEeiHwh+Sw5+pkbnGOR3NTkMdtpYszpwEadT/99pvKlL1654e5ZBY62bB8XEcBPcPAs0qHUlQOyq1uU8UouUagM8TaGpLZTnPBGZxbLj8GDNatXVCm1da3zkPOcXG7bbkZJF9y/lSCxSuzEtRjlX2rwygm+rNOeyJge9ubigcY7DR3Rb3NtvztpOFADmXj11jFlnaXNTtzfCZAvBE1NgHOT7tMbO73n6mdo+oGjevr1rKDiVH5RaLK21qhsQdZ/O3CfZZ4HpDp/7JikX7r3+13Nd2bRYWqPxPtMPA87a2kZemLQTuULXmjmTMkEpYky1VmdqOifN6wRiTqIabQWAN9nNTiqFeMmBGlKKjH6gaSxtVYroOksIAygRO20ah/eebenn+zk8PK5dWgXmi7WdTSm7zZ1zomUnea8dfPbmm+P8XXzlK4F338kslw3donKQS0Gzc0BXPv8Hfs+pzGUaD7PHwgGNmwuatmW5/hL5C19kVftJ3Pvlzxm2Z/g7dzk8WLNcrqRyukCpW1+F0Cf7fuDk1HP9aMX14zWHy3ZOjlvX0A+BzcWWtnGYZqAbPfqH/zv5q/+9nMfY+7mRXB3VPCzyuSqSKpd+mbDjlHL1GZnoB3x/Thy3pBjuy91MDqUUyNRcDg2FKnOfIiUFcu0pr2rGdiYe1OvrmkuSEiBJFEqtVKWKWksKuaoImyr+KfIiUzW7OEahuGulKIpa0Lrb4fR9Dwu42G7Y9lv6vqftljjXEKcIKEiCelEUDCPDZqAoSYBefm5N44itI4yeojR+9Ayjn3NJoFguFqxWexbc78K0VtL7JiVQO+HK5WrBYtFJPicmWaBSYRw8X/3yrq7LWce774L3LUYbcVyN5viFb9crFDa33+Dg2ZcoBc5uvSbsLPXbyRIo4OLWa+RceP55x7YfSAluv13JO0ihqB8GgioMfV18M6wWq/kkqUS00bRNy6Tq4cedHE2KEdsKC7RA7RckDLhyqTTCGEPJbp67jetonCPl2sl0OOHi4pzttiflUVQRgqp5KFmFgg8420gr75RmpQVrLK6ya7tFR1s7vE7Xd87xzZda3n1P/v3jHxcODqxIDhUIURxkp3f5tEfZjiIlhbuFq53WY+GAYvAyoF1msV7xh9eeBuDg6JCPfv5T7vzy55yebwkhsVot6WpYKlZmeObuyTmbzZZxDJjfvzHn1Iw1wiTLioPDY46PDzA6sfno72n+P/beLNb2677v+6zhP+y9z3AvJ8maKEuiRYmkKFGybMdxpNqOgaYNCsRprLRFELRA+lCjfSgK9CUPfclb0SIo6gSNkdaNGydBgqIF+pDGs1XJtkSJIjVQg2WL1MTLe+85Z0///xr78Fvr/9/n6pKJXJO+Ls4CL+855+6zh/+wftN3GERNu1kek97+t4BXb6PdGXDuVjndGaC+YX6FmDKuWvDGIDbZKRDDiHcDwY+kg5aEBBJRKKhDTaVEI22qFMrgUhA8UhsKcCFfGigevtOUYglGcbowZOgfxQHSWHJWhODEyC7NiB+jRDKnKeCQFAI+Z1RBybiNyOyPRYolZwgRjBkn1QUfAyn+C0bnWa9/kt3gaLpOPmeq0FXN+qE/h/36v8IFcXd13rPdDiW7gydvGR5QHQv3/WCkrtYfd33pS5En3mfF4wnFM8/I5pbynkfeJbI7XduyWCx5e8w8n9KkhNB1LbZp+NCHNLoEny9+IdK2hu0XPwtINQKw/rx8L92x19bzSSGB9Rv5cZyNYDJKybA9x1QIqw3ej6KI0La0XSP7SVnr9RoQcNPSrgrxM2JsgU8vlrRFCUEpQz6o9qtidggR70ZyhYHnRNt1aNOitRzDzXZgsxXyKTmV9rYiJiZlhkowzzkRnENlAUosFssJ3GG0mZTkU04M+4EvfxmWyzxxjmYPMDh+/ClufPqTBwjcV16101MT7fc+3uB8nCq9u617IgDFIH4bab9nc37G6kSkJU7uu861+69z7btv4I++/CXW2w0xCEGqXtwVKae1pm8adtsd39h/Fx8j9xWOiFIWYxu0bTFNBzmxvTjDjQMLJxvaSTYIE+DfbC70/dwXD8eP8bX4y4RRsplht8ftdyKj48c5KHB5djN5sChV5jQZUphRjdKlkJ/nAt3Wsxjh4fucCed5wv7Px19ey1rRrYpeAksmTWV5cCJLX82utFYEJZVR1ZCSICJtkOATTSssa+/DxIqvbzQ7kaRfLZc0XVvcT4t8iRIjvBATu/2AKeZ4i76btMNW45Ku78lXMJrXZV2/7z5eeBFx7AyBvpcLKqXEy8vHCT5w/uzTvPe9oygCWFis5B5t2oacxZunXj/GGnSe+T4Xzz1d7ELkujx5/ANcPPeZaZN+rZZCzN4a5LN1ZRP+xjcgpYYffMeS89u32e+3klCFMM9uckapPPkFHR+fErxI3dTP0TYd2hjG0dF1Pd4H9vv9ZAIHck8aawoZV9re+2FA24aFkut9v9+QksI2C1L0aFNMJclTNVq9i6a5mm1o2laM8koHwtmAVlo81ayVe6+RJP4w+VWUdrxS3Pf+D3PrmU9d2hsP9798x5/H3yfn3RWEY7/oX/H43xMBSLDoIkGxXa8nO9twdMTyeMX9b3gQrRUvf+vbvPStFxic47QEqcWiL31NRdf1RB9x3vHSSwGWx20AACAASURBVLfQxUvj2rVr4knfL8lKoJAp+sLzKht+aSlV6PO/bt2tDQc1IMxfT/9+kOGnINpvftwTnAOladqeqMTqGoQXMbXRclG1JpKNntoSQjjNc2sNBIqtZkhlneXUKiqGoo5d+3BACAmlBV69Wq0EGqsVaZemwVXfdhijD/xDRCQypYwvUGitFN56rJ1Z7CIxYtist+X9ZYGA50Tft8QiAyTeK1UxWzTevIuMo6dTDV0rkM+aeDAqnA+XAunVeu3W6fs+xO1nfl/aTbajV7KpKKXwL/wBR+96jLFf8LWvJXK22HdHnK9ST0wbrNYaFDz+mOVznxsvJ3tpJqKS4fSJD7L5wmdf88+2+8JniSlz/NgHpnlTjJFHH1V86lMDw6B45JEl4+BwbiDGOZlKWVrSR0fH5Jxx3rHbbyb4t7UiN7XbnZGyqFOP40jfd+KlBUUvrSkVn6I5OuaiuKJW7TjvQ6E2dIzjDq3nrXtynkVmpMZYdBFPVdqQyVNyd3GxlnGHWdJb0ad78v2KL34xTpI5OZe9kMTZ5z49+RXFNLu4aj3vk9/TDarzLKN59jlPTvDud9792N8TAai2kCiM4l1h+scohlCL5Yq273nj297KG9/2Zr75h3/E+dnLAGx3Wx68/35SSlLmHhs659gP0rKDmYhmjEiILPqGxlqid+iC5lHGEkvFcTfl7Ff7mlf4+QFUgIfTX+d580vy84mrI1kKSRPcXgik9TRO5bQ8LsVIImOyhlKV5Pq/onaQ0WiUlNn14i4XUyWcpanNpaYsrRIIMzIvQwW0m63RgYOZj6gAqzqbTLNagu0ajNIoxFwrJwlz2pqpfPfBkRUs27+KMYbBOQbnRcSwJAMPXryVW6ffxFrL9WunGCMXc4yRR75ZkpNeFUHMqwD0eqwq7V8zrDk5EhL07vnP0fddAc0kVOf5ZmHW56+2aKNZHUnyIHMkxfue7Hn2c2UzV4BmCkDamNe0/Xa4tNYoDfsvfW66gRePvo/1+hPs9/uyeZvScVCEUANHKiADxWIhnRnvx6JMUOcrLdYucN5Ndtl1DlRxZNa2RT5LPv9isWQcB7bri6lzkLO4rZIjOYHRTdFR9IWgDYsyL+77JcvFMeUJpao6ECPVRhdTvkqkFUWICibS5feqiKzKmZiiGEIW8FejzSsGoGeenVXrbaPJr9KmuCcC0OTpY4vvSxl8e+cJ7gI/OvrFgsVySb9coG3D7RtSAd148QVeeuklVsslXb+i6ZYiZLlpiAXjttntybbDplA24SQXgRYtI4CYwbzwi6IZdUf76s5158/v9rh6r75gfoUM+CajkxxuERZtiMEX1QIBHKTaZmMGDORcYowgA6aKBiSQ1cpN+s4SYJu2LcNJiDqhUsXrpcl7nqyoMqWttWQy3nl2e5Gl99VjqCoqlHCltdggxCgZk9KatiuSK0VfShvD6DwhBmyy5BhoymO0UZAT+/C/09ifJeWEG0cKmXt6LaUVi2WPMWVgCzx+Q6ErcigE9rsd9soQ6HVbs0JBniGdZdAsclgFWBMS9kCPjWlmItHLGMMXv1htre38nAevU503X6917YmnuPnZT03J1K3P/j6rt3ne9UiGrAsZ1bK0lqMj2XuGYZBE1w1cXFyglMhrWdtOQWEcb4v7a4bROUIYWfRLckhTUtaVazoVuZ4QvFRiVQqCYq2Nkvm2sTRtV4LeepIEEqmfogIeA6YxQvzOeeoqxZimliGlDyLnab6PtL6MdhMF8KJgcXieOJz5KJ58suO5Z0fmIcJBkvwK66qDfrWu1tW6WlfrT2XdGxVQ4bHUYVwlWuoyGAs+4I0rCJzMYrUixfsB2J6fsT8bSqaeBeK56NDWst+uAbhxdsZyeYRpGjZWM+48SYkUCMVd9DRY7n/gAXTbEWJk9Y1fJL31P/4eXsH3k28LCiwXSKRnv5XSNKdM07WkMLLZryFFKJDruQ87M5RUFmGcWFAydejfd530jY2BGEGJzK7SaiKVKd0So2RWBiPSOkqL42HJPxorpnFuHNnvtvgoStzaGMahwrAT2hr6tkG3lhRFSfhiu2NZpEAWiwVdYxlDECSOT4xqJCt/ULVplDYsVx/j9tkFZ2c7UInezIKlIFlX17WkOJJy4F0vDNiun4QNd9stIQQae5VDvR5L6wKZVnDx7NPTdapUQUfp0roxuriJpqLmDvg5w65t8DvX9ys++ie95jlqeZ/Wcnby48QYeXN4hu1uJ/fPAccn5ISNnnHck0K5f2NiuVxSVbWdd/iQ0EpjjCYly2azBgVHxaXU2h5VBLqSH9kWsFXTtLSF5K20gdIKExJo5uhohQ9u0pTb7bYsl0fEGBjGPTpoxnEQtfwC5lgsl2ilGN1ITAKPt9aW83jQgjtYCrBaF8FYWfWuSwVp98T7OlH153JVkzK89z2vHGbuiQBEmVHUfuQ8UM+TtUCKkRg8F7fP6JeLSSCvXy5Ie5Eqz2UWorUSMcRyoY+jI+V1+V6x321IKhDJUCCHW5cxTcPx6TVcTHRaep91NgJzycnB96+2vmFF9TrGaocwQ6Gj93jnSCGgiNPMpw4Ly4GR/7L8rki526mfm3KmMdA2PfsywI0xClSzvkdVHSVzURtQaGuw2U5cBW0ELCAktkD0guJpu3ay+/XeYxQcr5b0bYN3nrPzdSHpFcn4rkUryE7etw+B/TiQksjIA4XHpeiNKS6TpvT7Z3uIGGNpX8+oqLZt0UZNBnuuAB+SuQpAr8cSdBXc+uzvkVOa0Iht20yzwjq/aRqL+9I8d+h6WD76PlKMDF/5/B9L7fq1XufPPV3ef5lxNA39oieEyEvph7nxxU/ivOeRRxLeF73GHGd/HzcCisVyyWJ5xMk1uSe0NmzWF4Ki8yNGa6LWpUVf+Ds+CDrQiI/WMIj54iEyzWglXCMUznvGcYdtDF0rNtgg5GyjLU3TCjChxFNr7GTJjVLEmBjdjq7raGxD01ggTPPhgAQUrRSff64AjLRC37Hj1b3wve8xQhC3AvGeuuKqhOFXAQrdEwEoBk8FJAITa15r8bIQu+j5w+822+mx3ke0bdApTBh4wdirGcIZE8l5Uohoa1G2Y793jGEklwA0hlsslku5MLTG5o7V1/8+tuvwBZDwbxJ8DjlEBshakbUiGTMxlmMhdtm2pQ090e0nHsxMRJVnyaTphVXpt9cMJaUottUpl+OU8G5kjLH4h8xVZIqSnSigtQatGja1T+1H1ELRLhaEbYCU0IXno2rlUgi/KHmtECNnF2vavisX8AwDra6IwQu8vGu72W7bB3b7AWv3NK3m+HhZ2NrpAAUXcN5hgj/YxNoCH5djEUrAtVcYhNdlfed3f0cU1MvA+snH5Jw//3xGm8vOpdZaqSgqvkAphuefBTUrRt+LS2uFVWVOi3CSRuc4efwplNY88UTHOI7kWNGsqQh7LnDKkJLH2Bal9YSC67qOnAWcsHa+JM55QvwB5dqPE+gihIAbx8uCpeW4gyTo8vgirlwSbZE5CmUOpUlZAEIpJRYHe6tzg8iVIWomoWuLyKrsPUEpQrQTBeNuq86lfYhstyPDMJBJvPXhFcuF0F++8mXJTcyrzGnviQCUYiSlgEUk2+suk6KUtXKApQSt7ouVE6VQpTyNJVuWSoiCpIGZqCUlsi++OYLoUqoGMset27eEhdy1qHCEVQew31dYdVO/23pr/Bh/qH9FIM1plp5HiXx9Q0dwA2EU64NDGfmqqjtVTqpCn+dXizERJtBCntQQjNGzRloulWHKaCsIHKMUSjMR6kKK+Bhx6404NSZxefTDiEo1A9OFlzOSYsY5L/5Nxf9HXkpeJyZ535ksqB9rJsSP9wHnI2e3b9P3HacnK27emp1s5bxL9WR8YBgcmcjt2zuMsRO/KVPas99DuL1ar8V68skFSim+8lXNux+duwKCjJTK4U4gwfH7pQoYvyBOudI4yjO/7R5bE9gHpMoofjlvcJ/hgUc1MQjirbrwNtYSQ6Tr+qlzs91siDEyFmDA6AZyFi047z0XF7fZ78RbqMpcSVUSCMGL06kbGMcRpRZM2gNKTQRSVRSvFYq2bWmqHQpiw6C0Lhy8gNvvUVoxjsP0KbU2peISiaAYA489Nleyzz3reOazg1BXzJ11z7xSTLTvfA/rm7/NZrMmRM9+v+foSKqth99+xNe+qvn8FzJv/oG7P8c9EYByiuTgIWhMjrPdQPGEr+05waMDiqmnKReyniDB1V7aKD27IvaLqaQNzhHdQHSORKT42qG1YdztuB3DJBDaWMvJyeldT8ChpcJdP1P5+63xY3wt/3IRYKyyGgPeDbhhj9ut8W5PLm2nSfcqV22q2poEY8U0aqhipC5KT1iOBAAGCQB1wxfeTqLpWrpG+DS1iqkEMW2tiENGIbnmlCfkTt0ojDX4MbLZ7PCtJ1YxwzA7X46jk4u6BE5dSIcAoWZXUaqrYfxnpNyyXP5NlLJ0XcdyKRInMQkX6MbJU+iX/xWZxHY38JkPfnQ63tut3FDWKv79VzgHV+tPbn2r+QC3n/kUfd/x/POXUWu113MYVNTh9wVp+j097HtoZSTvjakaMAoHrrWWvmtIjeUP03t4cPcpjKqiuWIgJ224RhLCGNntNuwvzgChLHTdgtXqCNs0NLbBGYMbRzabC6C2pXNBqAWCD4jEWC73GWgdp9ZltcMWU79ZLss2HW3X03YL2m6BNoHRib7d1JbPgr7rF8sJAacO9kqQwGqtwejvFRytK8XEMIz8wNnHOd9uGIaBYdzhnJva9sMwsNmu+NEfvf6Kx/2eCEApJWJwhFGi8UQOLRl1yomcDCk4kZBJEWWrx/ncvMs549xARrFYraYWXH90PGX1uShHpxCkqqjtLaMIFTpsLBlN03Q8+NBDUxvq8g02r3yX76c/WZR2U8pTdlVVr91+ix+HogEnQWYimVbf3iy+OCqDTho/uun9NNYQdp6RIPMUJYNCaVvNd3pMiaZr6BY9R33LOA6YrqMrleEiw269YbfbEbwvMPUD1WyqyoFo7sXiizLNxspxaaylX60A8e6pYAnnvAA+ENULbRQqiSPjxcWGtm05OlrSFd7It779DwhHf0FUsUMmq8QnH/9xtD+Eqedy0K9mQK/H2n7pc0XSSl1qqRw/9oG7QKaV/De1mOav78XKB0oyqZg6B7UbYa3GWoMPmTe5z7EPYQoKxhjh+TQWozWjGwnFJ2vYyybs3EjwMmIIQTy7jLF0ncIUMmmOou5eyeIheLS2BxBnpnltzmJUqU1DSh7vwiRPhS4tOjRZi/7bioZhdz7BsMkJYxtJ0tuWxdFKqC1Kz6MPIyrkr3ZnhRi52Gw4Wq8Zhr3QQVJiHMQUD4Rw+/Db5xnV3dY9EYBUzrhhIIwFRFA/ekqigK0UKQb8WPqUhTcjv4ygvzJkBX4c8KOHlDgc56sy+LNNR9dFvHM4N5JUeZTJ0EBMknmY/Z6Liwsuzi9YFUZ31zavSL66Mx+EogGngADf4xuQZ8XrnNJEhpver5ILnJxJpEImTdKCmgaThmvX7y8BTohwKQqpM4eKoguyGRQZHVHR9QTv6MtM6r4HHuDcdqQYGYc9CrFTTjESy2dXiHq18DziFFwgT9pRxlgWnbg+Sj9a3pMCutIT34dYWNSK4AO3b/8S1+/7GwyDZtgLAflrvIMH93tWqyXnD3xYhq2K8vkOHVDVPbuh/f9tnT7+FGfPfpqabtWMuf6d75KkbT7ry89e17f6x1oKivunfJ5Wi3zQ0WPv5+sh8NLT/w+PPCL3UVvmq23bEVLAxYiOkYuLMzabC8ZhP4FltNKEEDk/PyMGX2y4sygfTFWHqNR77wlB9BRtI4CpmrQS6x4hPxdckZpa9SDWGD6U+z+Ljcsbnv8k33rXU4zbwq30EW2aSe3Ah8jeedlSctVrzDT27vOfGuyG0fPmN++4uNgQgxPQl9JlztdOxyeFyJe27+Bdr3Dc74kABBlrNG3TklNgu5eDVTfm+rXSGqPAxUiqvh93PFMKkXG/JbpxIi3qpsXaduo9a21wIXB+cTGV3F3fc3R0QraalBVudGzWa16+eRNdNsa2Pb2rfcMrEVHfEn6Or/OPhfya06TgrXWFoh4EUorFRH2MokDTU0GEVRPvwz67Znm0oml/lhAC282vcH4uqLPqoRJTGRJLSopC0bQt3nvcIGie4D0hepEBKWi4tmnwKkAJQAIYlMGktYZYCb3G0Jc5WVtsl21jaZxlUIqYQrFjKG0658hlA8sKbl57ipv5WdSeSblivx85OT3m9FRsOmxjinWRkgERZaNTl62Xr9Zrty4+//T3BBuA9ec/IyjVMiAvp2X6GkRodPzqc38mAlFdOSUefY/hM5/+XZx3PPzwwDjmqfUPMOxF9X232xK9Z7e9YNjv8M6xXAlZ1RgrCVtwBVAkShExhgmq3diWpmkLuMBNhE+jFbkkehkFulix5ITK4j6afJzQaSkG9vsdyyNHn+eOywPP/Q7ffMeTgFhJWGtlHOEjo/OiAuMD/No/BeDdfcc33vEf3PW41P3ykXdrvvudXFqH0g1RRtMYy2lf/MGahuefXmP5DXjskbs+39Xde7Wu1tW6WlfrT2XdExVQzaisNVhjcb4g02Lp+WfBlltjUCqzz7NxG4WASpGc0Fr6zzEEfMkempiwRyJYmrO0tibv+WnoWODDPoBJxJzZ5czNmzfp1kJoPTo6hrvwTu6cq6o7/qQkpW7VkMpaE1MUlF+MRZ6mWC5MkALJ7lXSE7y8QqkrUMG2DV33V4vcRZq9fHKuZqcTEk0ryepG7+jaFqVg2EsFtL44LzLv5bHlnKQDnTeQkrrrutLvFqMrrc0E+SZLid62wgVRBbnjfWAYpCceSyV7cb34wPgwqaHXOREIV8FasX7QRtLqnDLRX562/VnKqv8srzu5O3ci3uoMs/ykzFTkB9F8jpzvvXZpBj6SPzkBlypsGeC77/lx9sPAI++K/P6nNuzWO0IIxCTWCSD3U4iR4MM0W9ZKHFHrLLeiX3Mu964x+FhmqGXvEb04Q4q2tOJER1HADuX+i+Igm4zGajH3s9rQtVXDXwAP0Y+Mw47V0WmxVsly75TPbKxFWyvtN+/w3tEvOvwwsLt5E4DbKfKt7kVOT09ZLVfTfnM4k9oPe3GtVaCM6MJppWnbhm99RV5roSKLmBj8Ydv88rpnAlAq3BNrzUQy9aFYSStFjgHvynC9uCaWX55mIpkKUSz96QrRTWlq2eScyjxEi+5cRdwd/EkpE1Ukezg/O+P08/8dAO7Bv0Or20tN7VfjA2kUD6eP8UX+VyGRlr5qCpndZs047AXhhi4w1jSVuEoptNHoNLc9VD1W5fO2jWW32QowwDtsLxYHPkVilHemjZkEEHOMBA+tMfSNIYcyl9mu8bFwqJSCLAq7uWjmUY5r3y9YLleE4JHiWY5lvWljiOT1Fuc9u/3AC+17cSrAAvbtLKqojcKWG0NNPZv5SGotsFTxYzEChDgYasvxkbbEq5ldXa0/uXUYgO6mQG6M4dIcSM0BqWoavvLd8ie/ciFxf4RPfs98SlE4MykxxjjNUWKBJIMkZ84Jv+UdDw+s11ucG4sj8Pw69XdzyiUPlmu58teEAhFL4mhorSV5R4TJ1TmU5E/lxKppySESUoSUDmzIsxDjc4YYyUrRdAuOu5aFl1HDxXli7yNuHLj22V/F2JaMLntJaR0qoUvEEFC9AEqMgv4T/wfnE6F1zZu//Es8e/+/y7Xr13jg/vtF6FmbKWF34yCArxyF86Q01hge2noef1NRXVBa+IK7ex2EoOrfJZMqUT/nKMgCMimK10yKnpz1VHbkIuEDAmbQxmC7jkaZCcEVU2K/26CUJkWxFtYoIXHFYr6kZ4xo5dSQM8G5orqMKARYO5/MV1mFkcQfmV9BRUXf9xOx6+LinGG/ZRwHGq1IpcpJBfUHAkE3RvgVMg6qmzDT3Gh0jqYTh1FVZkXirDjDN40usxKjyUoRUmI3iJeIn3hJVIYGKQvRVfgGcWaGty1NK+oSJqeimhBRSrMp6uXeC+DBGpFuH08q6sdMPiuh2EFM8PoSWOpsrq4YE42V13ODI6ti2VHFUevv/usvr6v1J7AuB3/1PUGonsN5s2c6OZtnvChh/H+09nkl642Pqt+985ESYEJkCOJXFUKck82USiVe3nOiKFl7nJMA9PLNW4zjIIFoHHCjK5VMFAV7mOzoU5KAEGMuElZzsE6xWobIribgHY3VoqAAEL0EtpBiueY1OQaCc5iqIGKszJBLImC0RRlL2xr6Xg5sDA6/2RK8I+WEzkCxa6n7QQyOGANNk8h5hXcjN7+7I954id1uAwgn8sVvvMD1F/8e1++7j6+//WN0bUeIgQ9tfw2AmzdvkYIn/NhfYrnqJ6fq4Ae+vRN4uRtGLtYbbq/XvPsVzuk9EYBSDHgHw2gwngnGp4pGXEr1wAvvBG2n7EFLOjMx5LXWLJarCScPMA579sNQbpBIl1tSipM1cP09qVrKynLBhxAYdnVYH6Arra47PsMEST74+hv2n/D28Nf5w+Yfk21mLLi8G1vxPIohYEs7TClV9N5KUEhi9ysy6AHnnLiRWju9gvMBY6MMM0NAW2hbi3OWthc2snMjISWBrWsBPjgfGZzDlM/eWiGL2gzaRaxtpGJzarL1DSmyH0dG53Hesd3v8T7wneP34Ypt8JAFFgqZo6MlfYzyfnOe1Bu01qXdmYrygwSSzOVNznuP84IYGsexSPqkqW0hz2VQV2rYr+uqycOrVUMpJY4fE94QwPEHxHBw/ML3RwKqVUxNMD+SP8EU1UqSGGJkO6k7M7VyQzGuTKVbkmImTQmmKALkPHcU/FiM5MpbvNhsCwo0Ako02ZQqXQE7vb9UFVgQUFBzoGwNTEmaMRbbNFINmYzKnoqptVozRhhTptEGa2E/jpimpSn3sTFGEs/S9jZth24kWLVFD9HaRtrjwUu7vrBYdYF+A3gXGPcDWhn8OLDNGf3r/5zdZo0rZNWYhLPoQ2S//xZHN/4HtDEctw1frfxCbViuVrxpgCY5wOG953y7Y7MR76/1Zst2s53Uuu+27okAFL0nx8hGZXKRkoACQzYUmZnZ0I08fz1rtakCWRa9s6br0OWgK2PZ7vdSLlo7bXyKPKOjk5S3psjK1wtcsO0F0z+O5OXirnw6dcfXufxtlKI1mrf6n+Pr3S8D0FlLaxu81lgj8xJTMqnaLQ05k8rFtlyu6NpOyvqUJl21kMG5f0G3+Dly14pZXdng92W+c+iAqnTxIokjm+2ObrmY3q81RpBzWdqV1lqGfZ5cDcf9SEYuLHGlleccRz/p11VBWChEPvLUWhQ7cfH1mThPRnBAOWnc6ObzqwSFuN/vxBrCB8ZRFBhqjNJaI0XhVQB6vdfUIShf3zkfOnn8qUkCBuDiadE66/v2e57ncH2ET07t9HI7S2VSgsva+4Iok4BTYf6xUBDkOedWuiSRIuOV8uyFlbO4juZyfQrCVE37BYDRVuggNqPSrM6CsVOCLJBpCVqKao9iSpCoaM0eYxraVqqE/W4jm3sMU0GolMb0K9791ae59b6PsNtvYbtFWYvt5R5t2m7y9UIpbLvg/md/E01k/eSfA0AbK/etH4ulzeyoXLX7cpbZ8/XP/WYxwVOMIU50F4CcchF+powtZB8chnEKvouTFcv3/BRaNwyDYxj2eO8Z9sMUgM7OLnDjQPMqle89EYBCjOiUSPuMc+O0payWiyJJoYuKrvQZZdBeeUBmCkKZLDDd3Fwiv0mmrCGJ+RPFeTDnmeCVcsY2LY1SMlgD6iS/Ss3Er/094gf+q+l9vxL8unYf3h4/BuVvpZmIn8u+Y7HoCXtL21gWfUdTpDl8FVD1AZ8yWUn7LgYRGQ1unDD6OmeiMXC8AntE2rUoJ6S3sVQuqgSkGCJdr2nbFqul5RiKMsM+ZxpjShAUMtl3Vk+w6TasV3IxeS8ZY0qJ1OTyORU6zBpSlwbTB0ckFiVtkFZCnWXJ6cnEEvSqLFIFiAzDcGlHSXWWBzJjyGnaeK7W67Mq5LpWopm5Kqr/vn7uMzJDLNdDKgolMRo+yicPkrd6t8h9uA1BFDmmKiYUzlmtbuL0+OpBQ50Fzrkph3Etl0lhfW/171w4dkqrwl+pwp/yWGPtBBqIQXhzIbgyByliwDHK71aFAm0mQeSc6mvJrNnYBrQR/lsIjN5Nx6dtO/rlina54o1/8BncOLB58J3c9+zvcHztGgC7D/87hfoQue/Z38TaBlWSubGCm5TClvvYDTtsEfolZR547remzx5CIMXM4KW9WEVY20Kn0MagrKFLyGcqILDlcsXyvT8FQL9YgdaMPrDdbNjvtsQUGQbHbivJbwweY+BodY9bcjdGCFa+sIwP2e4hREG1xcI/CR6Uoil9WFNkZbJSUNQG5AKYWzVKQd/1YtpmbNnshFdTkXIpSaZlGgleGhmkkpMwfYHN+oLTr/0C6on/8hXz7kNrp8O2HMDbSkC60f9djo+OMG6PbQ2nJ8e0jWG32bDb7effTZmInt6TMRpl57aTRSyym7ZBNwajPkYYf4nVasF4Jsi9avYng9LE6Bzjfst+HCddNVDcuvZBOtuQjgQJFNYXjKOTtiMzR0duPmnBpJxKhnl5dqZLb90UzSlBQBVgiBFpIAlaeXqPWoloqzxRIiUlWlNtK+eqDLGniqe0ZvyrIGyu1p/sElRk/J6AI7yWedf/CJ8AYMyF/JhEVLbJlouUCF7Y++GO1pnzXirnS2i5Q4CKLsFmbrnPlddlgIqskiapyoOrj5RrM5NFO03V5xQbCYCHvvBxbj3xEbnOY5D9wY2X3lsuM2etxQI7BpmBaqUnvqAu7TeldVF1ged/+yEe/uE9rkjooDVv/+rvo9sFSmm6EgxeeuzHWfyRWJKfPvOr0ywnkXAxSMcnJYbd8azJnwAAIABJREFU7CBtjMZozbBbi5iw0oiqUL3/NDEMKIok1jDStG0JPsWepW1ZLBYo3ZEw5BTQOnJ8cp325LQcRIMPftKza9oGtwsM+2FWPlCJxpqp+rrbuicCkNGS/WqraU0/X1SltE0pEYsop3dhImYCJUsKIhSYxer3kvAngFKTHpxYGsyELFuRaVnLxl4yPKVKe6e0AQDOzm5zenLMEZdnPRy4lKYUS4sqiu1BJX4Bf2R+BYD3fCeyvW2ImyU0ihtvP6WxGhUDP/TNImIYNGNMfPHNPevNhnEYMEDf2BmAUSo+EwIqReIwsB/2iLZTgcCmVAQFZZ7jfWC33eKK1hqUlsRuz24/yAVjDCl4gZamOYuVv/NENryz/T9bP5TzakSEVM7n9Kan3rsAPVLRjZul50MS8EkIgUXfF5uj2VIdyjxOqUkH62q9tiuVVlgMkY/kTwg0nnIeoSQEcn1clNbYWCwzRudwzpNyKtqBs83K5Q1dIUgFzeGlpe74qqLs6k/uDC7y9WXQxJ3X5qV/K3dzTnl6XnENlcrHDXvcuC9q8FAlS0wRJVVKY7QhqSB7VgH91H/TpW0VouxTfsiMwzgdwzd95eOoh96IIouSSRBhYjeOvPCODwDwxi/9rrQFtSYEEVSu/jt1xtJazeLoiGFwvPj2pzh5+tdkFq56kpLqpmk0TRM5WjRcOznm/GKDj4d1IjRGYa0mqwatGmy7YvXoj4m3WJUG8jLDrl2JGBL73cB+GAhJznvbymw53blRHKwrIurVulpX62pdrT+VdU9UQKrAgOtAcEI25VLO5tkPaIJ6HmTmNVMPFXVVJ0LTPFMVdIoiBoFJ2qbBGDtDf0sVU7XmshKPQhTTcHyz2XKxXvPg134B/YP/qZhLxUD6g/9xygyiF0O3/UP/IW2/oFsIaEH90f/EW8rcah8cWiv6+66zWDa86VzK9/UNTyikThMi2UVSFii0MYbgPKmBSkRIMZK8Z+mdzH32A9vtjsaaCSoeYmQYHc1+T9pldvsB5zwvn35ogmaGktnmnOnahuWio20b0X3K9fActDS1Ktnb5SbjNB/IApfWprbn5oo0E6fH1DJSEDqRqOosSa4JQTzOHJQY4wyCKvOjcDUDel3Wj4y/Ls7EPrCJaYLDV13AEESvMKZ8AHcu92jOpXJWKGXmlvmh+GGtXFRl0hz++M722swfmx57qRUnZfZ0dVZawVwmoVCEIMrNbhzxXv4Ovsx7YyB9/cvcevwnhJhNRluLVmbaMwR0UCWvqhdZOnz35Bw5febXQQuwRl9sCONfxA8OXdBrzjkubt8uSDfZz45e+AM27/7wJCJ6fusm1jYs+gW7nXREYgx0bcvJ7/9LeZ4f/7fpeyGLi+eWRmmLD5b1KO+5bQInK5kBN42haRpCKorepWozKhK8Q7cLmral63ti0qQ4A46GwZGS7E1KK4ZRjmVKUUwKETBYVorxVboU90QAktJXLkCl5stNoI0CI8xZLriubS5dcBKQinL0dEHnCZlSH0OSIFdnCcbYiaRZH6PLa1eWsiouhRUa7Zxnvdlx89ZtmuvnopT74i+Swm7iD+SY0CTSV/97YrciLVcEKwrUuVzcYdjhxwFztALToWxDCpGkZgJbhZLud0Ggz84xbPeoHMVSAbkBnPPcvO/bhJhQN/4lw+Cg76YWJWi+uXiC1kgADstI6pI4sk7zHeknK6XwKuKMR3cNB3ii+RgVfpKxBp9j2WRmYIDYDuepBdPY4gNTNyznJvRR9Rgy1hQBU3maSu7zIRREUQlAKU1QWq11ASb8MS+6q/V9re++dEvspf0McQYm4rcq958qgpTAlDRWLh9KZr2XA4as+rM6n63UhPp9fdC8N2QqVeIQPCCQhPKEOReBzxFfBHhBeC7eOVGtHgcRJY4SSCvCLeWENYbu4/8ny77n4oN/kUlPsXJ8hJgoCVeZE6kyd5rmoimx26zLDMkzDiM/cvpP8V9w3Hhc0GvffuSHeejzH7+ke0nOdE1D+4n/C4CNEy6VyZBDQCeZlWk9C5aGojLSda0oeTcWMFgUJlTTSJmTpwyj84Ryn4aUMQ8XdRLE5uH0+hvprz1A0y7YbddUs0+AGIpja7kvt7s9bnQYo2m7GfaWilrCK617IgClEixUyuQUSXU/OzjZU5MZyoVYEVNlNpAUVpkymxCRz0Ohz0wqrOTE1MotA0KAnH2xhQh4N8oMp8AX27Yrj8mMo+OFF7/JyfnfkWCYEzq7KTPo2pb7rp/KhqsSbr9mMzj2w0D2BZkWAhqwrSWlI/7OD/xHfPPFFzlrbzBG6ed6Hfn5F3+RW0nUdGMQBW+BNcsxaZqG4W0/xRtu/D77Yc/Z6Dl78EdLECiqtYPDjx4f0jSTEZLebP8dQhCB1FLB7AcnaJk4gzkqbFohAqc5FB4FMyRX+D4yY0spsdsPtK1wE6ZqVFtBJikwVuF9nuY7Wlf4fbHhKMHJGCM3pzoYOheVkjwjKa7Wa7jO11vyBPHUmAlbqy9tygo9B4q5kVHKWpnhzYHlsLKpSLJDxYWy4R2AXCqvp+4HFRwwBxeP9yPeCWrNe1/mFTNaMxUiaizOo8M4FKRouFTpL5dL9IlGr46479nfEtKhmjk1Lz/254neT6oh5MTp079GTjPpNcXEWUloRa4noa3lcz/yb/G2sQAwnOOb73iK+575DUzT0BpL1/cs+x5bkGlBGVptsUphm5aAEjUFY6f9YByLkra1E9FWay1z40ndOqMffpKtjaxTYsQROpk5qTPxMDJas1gtC7nWM5wP5DwLQwNoLVWw23vOz9cMw4A2itXRYjrvQjr3d5291XVPBKDRuSnjEaRb0XAryszScUvTRZuzmnzHZchdA0q5cBGcfzWAy1n8d3QhYoKgsVBqauHsh4GcEovFsly0HjLYA/+PalAHYio3rM/YbDYy2Cus5munJ3SLjsE5kV8Pkc12zcV6MyFGOms56XpSgmE/cnZ2zgsvfouz27cnM6eM4m/f99f4K/GfSXsxRJQ2hAR7V6qkrDC2xfnAi93jrI82pJhKBTFr3NVstX7+XOCcM3ciT39q9bIvP6+tRWmPzgCCGMu+cJAX5JTJusoewTA6YkyTxL0c91CCqCblKOZ5VWq+nFNrzYS0EudGO1U800aXpfkyN1qu1mu6tJ06BMDUbZDzUQf8VQ1bT99DgednSRZt26IPUJRpugbLdRtr++zAZiBcrlyC9wdfCxcnBD+9lmi0hSlgpXjQXoeSnMr33rkJtZUrmqK+83K9maaZHuf9OCU98YX/jVRAUP2ip2lb9t6LwWYNZBWcEUtbSys++6GP0jQNTRnBp3EkAU3XYo3leLHi06uf4j3W0jQSgMYAn8g/jt6pwmNM5L4D26LK/fX+JB2BxaLnwfWAevuPYtuerl/SBXnMfhhw4x7nxYHVB1FcyGkm6rZtx+rkWgFPVLdWqQ4r/aVrFSoGLrYbNhdrcoq0XYsxs7xZTTpeTbD+nghAijiJ98UYJ1RHjIa2aXEhogpp0xS8froDBlo3XVNaac+98+fn/qz3vPtL/y2L1TFQ/H4ME5oL5OZQWmYnlmaeLWUR7QPYbrdsN2tMa1m0S7rlYnIATFEe43wQv3YUKYm3xhgyIRuaks2cnJ7SaY2LkfOXXuZnv/Lf8PPqp1ivNxMsVRvDcrnk5Yc+KPpuPogdhZ4RbsYY7ouB76zex3B+Lg6qKV9C6B1WCIraj0/Tn3IQqYzwuqX74nhay2elhIw36V5xgIKbDmLZcMpxjTERnRMb8IL4USkIGTZbctYoZbhkpY60XHUhxupiL2xNcylrNrokJlc9uNdlGW2msqaatQETh6bK2kytbDUnFM65oqGWCG4oCVCRnqrSSikLsbNsjNXYrQpmAtI283WekKdrFkTQU96dCBEPw+6gkrrcFqsiu2IbPwfI8qzl+RXeO8b9juAc+92Oi/MzoncTT03Jk5WE19K23SRKOn2uWr0h5E5tDB9+7uM888M/TVOoqDFlgvIYa2mNpW8arvVHtDpw8eRPAvB7v7emo5JDMx99GOxxi1n0Yl4JvGwVKcPRYkE+PcWFhI+ZW7dvT4RyaUkG4RCVPlJMmcVqyfGpJNeLfsHp6TXavhcFiZiKZUugbWflmOATF+dbgvfi9tq0hJAmPb1Mpu3stF/dbd0TAejaqiNrQywGS10p76uCs8qZnAM5gCvcIFt4QLbA/JwX2LAxmq++8+dZn92asvfGWp5/93/B+7/5D0s2EkDN2RfUQCatIWss2cqBPMSwi93ujsVqRV4t6bqeptniRk+mKsZqQsygG1yMbEePiwaMZCsAXjU4H/n2e34CV+Y7//X//U/42yd/mTiWTI5EDInfuP1OfvqBb8jnLy3K+caC4GMJkvLvFTZ9OJOtM5TaCklpHgpPT8Tlmy+Vm7L+TBeHVp0SUeWShTG/yMHS03Pl0v+fx8pSVSWIBVwh+5e0+PScVFgrgVYUdjvRyCpzI3k/6mr+8zqupm0nyPIlwd9i9JiKuntMoVThc7acSgsqpSSBxbtChpx5QD44gnOMZR6jtLoUqICDmQ8wXYMyL2nKLLdpWtrGoA3FOTgT4mVPqhShaYSI6gO4UYt0zWFLEKk0vHeFXzO3sNTBPaHKXKtyabRSlyqyWO5XSRxNEUFuaLueNzz36wAM48B3H/0x2q6ja1pU07DPkePzPU0hmf7ME4uS1AqPagwR50dIYeIc7U1P21p+kIb2vgc4X2/YnV2wH0ZUef/9aoHKEJDuRlXk7/qOk9MTOYbakmKQ2VVM08zPh4DWRdPRZ/a7Ae8DTdPQdh0pwrAfSIX/1fYW0K/aJr8nAlC3WGC7DlNd9coJ9uPIbr3GGj2RxmwjG1HNwGRmMbJeX5Biouu6Iiw4ThecouWRL/99fLeY0FTDGHDDMBk+aTW/Rtd1rJZHrNcX0jYoPd+mabh2/X6apitSNKUyUBpf1Kc328Dn3/ABhmHgvD1nXHn0QxbTzMCAnJPchDduCdomRi4e/VH+xsXX+IXtQwCE0dM1VftJT8gx6ZPLa+msCzJM2hklnZPHTVWSnucnzBVQZn5MjRWlvV++L7VQvbprtagVOisJQtPs96BtUR6X0pxVHvaAFZIha12zw6KIoGHidpTWm9EaV3r5IfrJIrk+U5WBuVqv/bLWFo5bIPlDYc9YqmmZuYYQSovMFzmYIt0UouiQ+T3ejygUpm2mpKN2MASNmTAIYVlayPI89RLRtT03/UmoJGx7o2U+Je9L2nAmq7JnlNeKiaaRWZT2Ae9aQnXqnfbKOUFLKYpcltGQ5uvNGF0suRuMbTG2QRtL+9YPTYlb5R7ab36G9JanUC98GqU1umk5euwvAnC9aXhDSqQP/uWpXffEeMH5OFvZe+8LD0lhlGY37ok7QYXW6s8fNxy9vOX8eoOP4GJms90TgUVB1y5XS+LoUCEAEZUUTdtglMLW/SlFfKzgCuFghhBwo2MYhPQaQmIYRBdSKwTo4R0pRky5R3OrCP7V57T3RADyUTFuHAkptef5TiabFq2lrdRYQ7/ssFpNfcWUoWkt6/WGcRjpG8vjX/8Fnn7z35pOnuijzYoGRmuBYls7o3lKa89oCUBHRyeMBZ55iKbTZLQSf5/Nei2ZHIpvP/oT8txmyfLlC4Zhzc2XbyDjI1WEVcsNmTP7/Z6MpusWtK0lhXD5BijokhBjGdqrEvDUIeAHH7wMRcuAuEJOp425tM5qq6J6k0wbfzk+tZ2VqVbgcqNWaLQ8vsA0rUFnPc2mLoNDmCCvEvfqjT8/KOdM9JFsFFolkp7tMuQtC8pOa12qVXn9nKsci5xDYbRfBaDXY53deqnMDdOlDD94YcPXysaX4BO8mzZwrQw6W2JUJBXBlE1ZMbWzcvSkEMhBdCGxVgb3IaBKS8eU6wnkvq/znpwS0ZXWXCfqHIfgpToZVgfXl9aKvitivG1DbS1ObbpSdSkt13nXNTSNJZdECOD0iZ8G05ByAeb0PW3bobRhUVxBRYkgsev/Aq3RNI//DFpbfmy7n2R2Fn3P+fltNpuB3X7HfhgZ3Ijzfu52RKmkRHxUgmEi4UY/VTcPbBz65JTRJXa7HednF6AVi8WSxUpETfu+Ze9l5t61LbrXqLzCKM2wq7Mw2UyUFoJpiBHvg5CJq1ZyTKK6HTw+ukkhwxhDVnJc9ztHHXm80rq6e6/W1bpaV+tq/amse6ICGj0Mu5GQKvpl/rdY4FZtYzk+7umWWtpAtcWkFSEI+ck5L5UMmqbt0EbKTpl/GPzoyES6toPSJqoVkEi3R1ROtLbBXmsEkednYRCVE94NGGvICnYxE30iDY6wLkrRCwsq4oaB7WaDiy05GxqrWC2kVG60pmmkHWiNWEJ4Jyq7VeNOMruMc55hGAQgUMuLKUvLOOewTZ561FnJMaxZmip8COI89K2V3tSmK9yMWARHZ5LvoeaXzF1yUhP5bPr3CalQq5+MbuZsUlBtTM9TnWnfdOPRQnQTl9VvP/g8IFlR29jigKtoGku/6DHNfN4FFacuXStX67Vb3/nWC6XKCRNAAJjkaSY5G2Y0Wa1Wclb83u2/RkqQTeJDp/+oIETdZNNBDBBE7V0hc8JUKt5KpwjlHjBGM1/Wcp+E6tyrdGklK4iUuWgqGpH1pSL7IBl9RmR0GiMosOk6rU7EKeOdY7FYznPQcq+tjk/YjQ4/OI5XK65fu05WmvVu4Nr16wBo07Le7Di7WHP9+BpHRytu3rjJ/Q/cNz0GZXnp5g32o1Q9PgYxnOv7idQZxpEQZudVaw1d3+L7xGp1DMDx8QnL1THRB/a7Pbv1Btu3wqOsCMAoVSwp0S9XMltFQ8q4wguMqVQ4Zf4j4rBxQhOWkyoq5CkWuHUSzTvTUI2fqimmepU6554IQBnLMA74dNkCWi6yinZJdC4y7B2pNUQzi+uFBE3X04ZMVoaUZaig1Lw7ffmR/5yUIj/0lb9LhmJQlWfyYxTnQpSG6Bl355wsGlKzmh4TolyMylhCSkRVBp0ZVLlJ2obCRra4CLfPI86D6Swn10Va3Rq5iI3WGCXth91uwy9/7Xf5K2/+OQB+68v/gBADH37XU+TcsejEEK5t7HwRABThw7ZtyLmQarWazO+0lpZdTIlxdNKOLAKvNUhZrXjjtx8hjCMvPPAlEpSN32ALDNTawrGCS69vjJ02Gkr777AVOFmLT3iHXPr9sFytWC6XrFY9N97wFe7Pp+U9a46OFgVRpfExc+3aMUqtJki3VhqtNE3+wPd7uV2tP8a68dJ3pq/vTBLhcM5XwCs5TdeFVokPn/4yGU2c9ODipVaxLryvGCJZgU4RlCHHKR9CZ+R+QWxXYoKYJdT5ycjRiJ9NiOx348TPSQct5KbtMFWj8W0fFtO55371MgdJFwBNTAzbHY1pSDFjTDPNhG3TwSjmb9dPr9H3C1xMHB1b6ozAeSGv7/c7HnjgOqvVktu3bnG0WnJ6fATArbMLmVnFWMAKWsYQWk1alTlJgupzUXxpG46OjoiZYssNWluG/cDNGze4OLstJpBKk7wnlhgge5Yi+MjF7XMWixXLfineYKHAy6MY81Vl8hjTBEqqrXTRAFSgrNiMIcfG2GZGw070jldGC90TAShlcLGg0A/kOVQBBQvMWn6SsiKrhlTclkLMxAz98gRjFzz9A/8JIVQC5QwXrgrZisyi7wuIIJFTGeAB5OKXkzLri3O0kgpLFy+btm2x3QJ0y3a9kZ6uFiM5W7xOmgYaqzC656RpUceKIWjQlmv33V8eIxeYSAoF1ue3+Z+/8knc6PjqRpjP169d42J9Qaee5Gj1Iou+oW0My0U/y9oURnNMmb47ESmNQuarlZ0MhSPDOBbvFEvLBy4hfkgJ0hqr4S0vP0pImW+/4StCTi03gDEWk56Q163nR2ty+/kDmOUM8a4AAxPfh1ZmnqMBymgevBk4Oj3m9PSY87d8mX7sJvHKGBM2vY/GakCj7HM03YdZLJpJBSIlGUhfFUCvz5pN18BaPamjhyDnWgNKFRh/rSLqWdcK1TYy11OGPFaYLlNQUFrR6gbVSFWfy+xQ1JdqsinzYQk+mUguFo+zu28sJpbeR3JWrFYnLN/zUdq+nxxIbdNgrXRMdrstSp+xNaLGcYjnVMKj4Df/8AE++vBN8duyDd0P/XkAWuDN9z+I6jve8OBDXGx27Ndrrp2cTMfAO0/btrzlzW9luegZBxH8bazcywA3XrqBptAPdCFdx3m/AtmHYqFhCK1BPMWUsdNcZtjt2A8D64tz9vs9vhDStdEcH0uVFLUVBXAKKT0m9sMAThML4q7Oe0Lx/bq8alWT0dqiECoFOR34tlVo/dxxeaV1TwQgpTVoIx+KmV9SA6dIntdsQONDmqTMRb5drKSffcd/Rtzspg3w0CagwkSFMb3guR/5aQA+9MxvleepCB4hsLrBYY2i77upvZVyYrfdEvMWyIwxYIH9D/8M7aK0+4wqRLRM3m7odEe7WBGNwo1ieWtNj247GeInxf/yB59iux/ZDfvptZqmoUrW9PoD9LrFNJ/neLW4xBQPMTG6yKJraHlyylZCqch0jlgVGN0WHQZsiqAvt64evOlRTccQIi54Yspcf/EHOXvTEarALjXNYZorxzcmcnh0IrAdytpP7Q4l2liHQ+EYA9+9r2e9amj3gf4rb0EPg/RiKdXa4x6ju5JIKnyIuHNHheWlmC9xoq7Wa7t8EGWQfmH5avM3eQf/EIBx9EVZJGOU+MbkAteuSLAcBWJpbEFk0uJ9wHtRJABYdD3H/YLfvfk23rv4PC4EMIbfaT9CRUB3t17kg9e+QnKJ6DORNHcnqqpH2TB/+ztv4aM/8B2uf+AvcXx6nXyI3cmZkBJJa0zXszg6kXvUuzlbz2JXoIwW1Odk2qZpur48JGE1HB2vyGSaxtK1lpPlgu26oMWGkbZpOVotaa1ifb5h2Xelk17esxul+4K8R41UhCFFUmmLaaVojClBSuDf292I9xuR30LI8aLHVknoiaaxLJdLFosVAMa0JBLWJAnKTYMp/MddASFsNnuCDweUDnVQ1Rzwm7Kcb20NOSliigIkqZ/l0PX4FdY9EYC00bRdQ075EsR6irZK2lZd34JKIiBYTkxKWbTEVGYchyKLcUAw46CNlyKfe9vf4kdv/iM++Nnf5rMf+ug8OykIDq002liMkhae1nbC0fgQcH7A+SjZXIpk4PT3/iXrj/x78lkaW1jaAbc+R/dHmEXP6AdBpQDkE06b+/Hes997fvZN7+didZNffO5XJyO5mJNkc0jLQRuNTU/SpKOpDZXJWJXw6mlMfAJtFRiFyQrTzMdu56R3X11MUxFvraKKqjgpKh9QKWN0RpNpm3biWx1yjybfn8otOjyXShfr79q/T5DjTN5TlRUeOH7xgnG1om8NWRlikXEXczAFmKn6FI+Vea6gLVOb8Wq99itE2ZBjyKgW2kbO57aoXtSkzygKnP5gx0fmtKbJ6JLUyXXl+Y38YQA63bLQHb36Fh9f/CQuJnwILBYdy+OCOjtZ8OnvvIPNmeeDJ98gplnAuKI8YxT7keADq/d8hNXREYujIzb77cQ5imX+471YV6M1XdezD2GCjlfkqdVqQmFWJGoNrENw6AFO9f1ENxL2A51V7G+8TNjKZm5CxHQtxshzdY2hWy7p2oZxK0Fq3G5xu910v2stxpta66n6s9rQLYXoGst8ZrPZMo5u0nTMKVGtWHLO2MZydHzMycnppKiglC57ZstyteLo6Ji263CjJ/qX5Vz723Kn6ZlgLOdzNoDM9b5W0tFIOeDH8XsqnopAfKV1TwQgaw2LRScXOeouWa0MH0PKKBIxeMadVBMhJmzb0rQW70ZyTIXDoi5F61wIbSEEhmHEWunpugLfFNdFxacvHuGpoy8Tc8INI/8ve2/WbMtxnYl9K2vY+8znDhgIghQJYhLFSeIkUkO37HZHUGxRLdsdntsvDrvD9pMfHP4h7gg/OBwd4fabHa2BbKndasmmKBEiIYEiCQIgQTUpgATueOazh6rK5YeVK3NlVZ0L0sbFPe2oReKevXdlZWVmZa55OD9fhAzboiboQvqOrivgggpoVrTyXATjnWdwuwaVpcT8tC3OT4/h2+Dm6Fs4V2C1WmG1WmO1XOBfd8/CVV/B0Zk4M1BQeXgWoyl7hicfaszrqiBY9Rmd+yY2yk+AWdw+uU0bZblaoQnZrq1btBKysnKoqgpFXcN1nSo+UVZCmPQx8sxQtydwiFoAUAdkuSWJ4U25vxSu316B/TlWhdRjKtwcrigxD66rq1Cbfj6fYWO+gW5Ro5zNxdBJqVx7XVcmQHCC+wnOlcFOAcADf7X6TwEA73f/JHC8FOPARHVbxfRUZVlh4+lfRT2fS0aTHz0vsTVlg22W4Meu8zhZLLE8OQWdL8GPvx9d61HOK+xeFyboelHibFmiWTt8Y+9z+OX2K0CICVqEEAvvOxS1ZM2Yb2ygriucn5/h7OwU65DmStzIQ9YQciiKCtWTv4z2/Azty38s42mkpHVRFPCO4EmIwt/s/hp2QjaGc/Y4XSxw5ZFHUHrG8vQE21f2UECSgALAYrVC265xerLGumBsbWxie2suRSNPZMzUduB1g6KQ81Q4h6KeSTYQwwAuF0ssFkssV6tIQOWaZrGmYJP2mM3n2Nu/gv39K6iqjWRrC8StKEtsbW9ja3sbZVnh1o078JqEE4HwOFMA0sZ+IEhCnWSwJ3YSF6au6xn6/jdAAoJ6eDHgiTUeMbdTQPT+jsT7SxMCOkeh5G2JlE4mcenSvc8Cw9brdfA26fCvXnsEgNhKfvXRH6Mo6hgD03SSAFQ57aIsxa+/8/Ah1oABLD1jGewXa8948V++im61QnNyCgLhw//ux9CsziL3cHJyHONxPIsHzxMvHODLuw4rjWfgDj7EzDQh1qKgAqt1Eo0T0kdQAAAgAElEQVQB4P3fOMC3PhRKKVADJpE8tJ/z8xVOzxZYr1tRd0Q1GYOh9h0XPGw4rBOZ1Cr5q4rVaoG41uRSlHj8QwCF5LCp6BeC5OTQUUgGGVL1zGY1NjeFAEn0eQfftWjXS3TtGr5ZAWDUczmQOzub2NrcQH2PaosTvH1wdXcPXduhIKDgCvNaiMvefB/rdQvftSgcYevZvyWpk8oypcchEqN/VWO5WoOe/NvY3NzA41tb+P735EzcvHULb/7FV3G+XqOaVajC+VivV6Af/QgA4H9mjY1dwuJYmKXCSxqnP9/4NXy6FMJRliXKqsJ8Y0OYqqLEyckZ2nUH3ykXVcLBgUnUSO1qBXQ+eF/Kfvrx/t8BQeLN1jdexFfu/gye/ejPY0YAkWbMJnz/1iYee+wEWxtzrL1HUc0w256Dz0Pm+/UKq+U5ygKotzfRrM/RrQm7W1dx665ULUYj2QhcWYkNqC5CVdKtSMTPF0scH5/hfLGMkltMfaTORAiZUJzD/v4+rl1/GJvbe2hbH8/xrC6xs7uLra0tzGYzVFWFxWKJ1XqNk8D8VnUdHVuZPbjzEtTr5T3LBXHs8K0HcyN4IzhlscEDeabyIVwKAuS7LhmpA/IC0uCj2ct7sBMuXgmQuEVKzfWirMC+AXzynEv9aN4qwovv/0f45M3/Ndp9ALEB/dmt92JvH3K/76Lh1ZuxUVhoV8hzGcAf33wvii+9JG00a4HvwIUY37/5L74XiY+O4+lbdzF79jeEA4MQ3roqo8rpPz/4PMqyxOz1ArNZh1l9jsIBzp0l11UCVoDkeWpaPPX1N+HZS9XThUgR5ck5XvvgVpCcKASdBkNxmPt6LSl9mpA1mIhw9/oGylAOQeZl7T8pB5uVbkSyii8vbHoVwtV13MdsDG3bYb0u9O1ia1MMpYUj7L5xgmZnA4UDwB264puo8NHEDBChIEJdThLQOwH72zvAez+JuqrwaHkYue6tnf8Yd7/9hzg7PUHpCNff/R7MZrPApQsSruoKm5tbqKoZjk5OJbCxA5rW499+IiTJfPce/ue/KrE4OwXYo2xblETwTYvlIuRiPBAbw2rZ4PDuLfxu+0HUVYntsolptdarNeazDWxsbOIrz7+Jf+dXd7BarNGsTWaGZgnuWtmLnrE+X4QyIR43rv1dAMB2KKNdFAV2dnbBEOeFjY0Zbq/FW9N3HrN6LelnuhZ3Du5iY2cLZV3ivJHzd3J+gvXyHJsbFVZLB9SlaETOznB2eCBjPj0BQ4jdbHMTOzt72N3bR1lWODsVNd2NG7dwdnaG5TIkK474iaGuE22Q2PZ2dvDII49i/+p1MIs6cjYLjNvuFjY2N8WpyBGqArhzeorT0xMsg+ZBS+Kour7rGnSN5LtM9p3g7RgtwAAC8Ym4POKDiwnQFIg6wQQTTDDBA4FLIQGtV0t4LhAi2iIF9UGl5ojgKJQGCN/V0A0KlDY4EXTUQlO5Z5mgQ8ZeF9ysVUL6pUdFvG/bDl+99TO4c+cOFlcWUXys6zoZ60NJgKp2+NrhB1CWcq2eJ7uHqBsg6qwqZNwlgiurzMXz1UevAYd/CmbGU2/exY/+o3+I//I7v4V54FTqKwXKsowpaRQY0V0/Btm998/ehZc+9gMcHi+kPkrbRdtW0zI+8I1jo4dl4yYtIzp+71bmPr372jl2Xz+PMQkKf/0LV9I4GFHC0Ta2OGkKhKXgrKAqUMbNqxWu3JDksW/uFygK4NG7JzELeuEIXVeibRoUheiXNX9drEnfdVit11NF1HcIZk/9spzFUjj45Vo487NmjfVDPwv3ELC/fwVXr11DVddYrlY4CyqdxXKBW3duY293D7s7m6CiwJtv3sLZAiZeTfb5Rl3j+t4+6tNDnO8/BEYH3wSHhzslFssGi71H0BwvsV51WC6As7NTnJTi4LO5uYm2aVAVBT7x7A7e+P6rWKwadCzaEwAgboLzQglGkTQCDGzOJFZvY2szVEAl7F/ZR9t12NnewqyuUnkRBsBzvHbIeM/uIXzX4eToCD+4s4nNTvDK7Vu34bsWs/o6Tk9PQLs7oKLCyekChwtZw9vnZ2gdYXM+x9Wr1/DIux5DVdU4OjzGYajRc3xyjLbr5Dx0HbqulRx5nOKbHEldrb39fWxtbmFWVTg/PcGsqrC/K6l49q7uw3sprnl2tsTi7BxvvHETN2/ehI3xAYIZYnWGZr1A1yxjbj0gnGtXyLNDgKDmoFS7bBmqU3f3OKOXggAd3L6N+dYuitkGkpUBQacY1DdM4vvPHRx5UFCMOQjxkfKwDq4ogu86Z/YLAuDBMbGnEpNv/MLfBgB85Ot/HApVdfjKrffglx76IWY0R16R0eG52+8DnENdl8E9PKi0zHOevnWE7z68D2YpQ1AETxodj/jyA7rrbzz1Hmx848vYOTyJBGjzg18AQ+Id1lqFUqO6u+DxE6Kim67Do//3ddzlsyAGa98AqIArjNqNGUXB0ZsNALZ/eIajxzdA5HD1jQZclKH2lpk7gCf+8jDT78Z3FAiZEigiwvuevxO/d+YG7zmVhGBG2zboujaUChf1wuGjm9iYtbhycobFcoXOLaSSpmuxWsuzisKpteqtN9gE/5/hzomU+3CBIVIVL1FIJOo9jk9OcOvOHezvX8H7nnh/3CISt+Oxbhq0JyeYzWZ4z3sfw2q5xt07dwEAX/7ajzGra1TYwvXrV/HUZ/89FHUN7z1e+pu/BAAcHR1i+aMX0W1BKg87CklSPf7EfQYA8LFrj+B4cwu/9FQDx4zTkxN0cCjqGtVM7CkFzaMae914oPDgwgMQ5wlACFld1yACPvzxX8APXnkZO1sbQrjMnlNsdXP5MFa8h8NbHsABmIQgNitxjX7zhsfuzg62trewubONxdkC50EFftKssLW7g82NTWzM5kDX4dadG3j99R/h9R/9GIBkzNbCd5qDTY3+3ge7TMhosrmxgaossV6ucXD3Nq5fv4q9PXHD3pjPcHR0isODIxwcHuHo6AiHh8fBfbsNcwoqvWaNZn2Otlkg5o+MRacZ3reBsXdIqveE51wo4MfRiDGES0GAjo8P0XQdtvcIZTUXf3sAYgwH1IIg2Vkl3gCkBEj8TRiED/71P8a33vtfDafLoqn0XReJzDc/8t/i49/4n2Kg1d3mPwTw5ZTuxhX42u33SWAnadluB1cmScqpmyIbfE9AXRbYmNVQDxAt5lY6jcQWaUhtUDrdF6/tCbEC8OwL/wdAkgV3cb6Ee/LXg3bVlPuF0bCSeiH1UbJxOuA0UPsvFQ7Xb4SkovONsNY+V91GZxi9k/UNxSZP/tUxgmI49c7AB164m7iqkAnh6HHxavPhELbNGt7LOm+/foSjx7YACtnJ6ydQ77wKV38DtP5oXGdOw5ngPsMqlFKYlQWKusTiMIQLeLHf+s6jLEs0XYejk2McHR3i6rXrAIBH3/0YHn3sXfjRa6/j+PAQZ45iyYV5COD+tz77OE6Od7BeLnFtbx935lso6hpd1+ID75IA6LOdU5xdexpffeEP4ZlQlaIlIKLoQbm1tYWqqvHjs208eW2BcmNLUkwVmiEFYBay4b2XQHZyQCU1wN69J4RjXV3DxnyGMsTjPfOhDycknIerQs95WZTwJOlp3jgR5utsWeOh+S2AQ52ixTkW5ydomxYowplwhNl8ho3NDRSlw5tvvIEfv/EGbty4GYvl+XB+JXu+VBmtqlrmEdMQQZwv6hpVVaFdSwC59x7r4CR1cPBj3Lp9gDdv3sHR8QnWq3V0ckpxw8LcrVcn6JoFgE7qQcnqhTU0ZTG4A2nCMk7VmNtWtFFaiHMMLgUBWq0WkqcMjK2dqzHQy4UiWGpk043TiRUcANBxK4jLuUh49GXF/EfsY6p47z3e/fJd7L3hcbf8B3EMjhgfvfYr+PbdPwUAfPX2+0S1Zzy4YqqOYHiXUgcigVnRtCwIH7x9hO+966oRPzmqi7plihS24qkSEAA4Olngu49cgZ/V+MDxOfDylzB79jfiJgwDigRIjH/OGBDDU1VCIfHjV/cWaxp0LBmz1WdAif247dBHSSoRQCVGMk8WPw08+Y2jpOpTFSUYRNLH/o9W2H9dDtjRezYy1et7vnYLr3/q4ZghvNpiuPbDoCp507Vtl3KATXBfwdU1/HqN8/NzlG0y6IMk9o1AqGYVwMDpySlWiyVOT0UFd3R0iPl8A76TSsPL5QqLs1PMqjJKHKv1WiroesbR+Tl23Z+ixgzee9xaPgEAWK5WWCxWeN/2JpqmwA9Pl2jbFjvbO/jY448CkGJqZSnu/SgcqCrhQx6zzmsxO8EjWjrbh6rB7LsYAuBqDuU/Conwbzu0bdMrgKiahLBGRWBIvcPOrjjUbG1vAnwdi+UK50tgv1titVgBlNLslIXkO9za2oAjws2bb+LGm2/i8Ogo8wRmU0FWvOQ2xUtWmbiuxd7+FWxubmNjcwtd4UF0HUVFOD8XhuHmzbu4dfsujo9PUkXZ9RpNs4q1z6is0DYNfCeu6FLHKA+n0BLnGnTOwXuM4dGGjAqtKR54EVwKAvTSB/5rPPv9/xH+WHKxbYbYgNl8C67IPSlc4cRfXaN0WdKvaxnOZ7//j/H1a/8Z1ut1TGKY6opIpdIylI0lb+vPM759IMSHY0IzjWsx9owQfU9OiqYRRfIo4yOH7z1yBSDg2Tfv4qXreykZYkxRoemFJP+VqBg5PhMAXtjeBMKmWTeMVx+9Atz+Ezxz8yCOWdRxLaqnPx/GJvYmI4TI+NSdmpVcUFRvSpv4DyjU+WFwcFM1L4oAoAgFApP6QfvRInQS5Y2oYgQlrpFIYy9KlMEuxgCuvdHEYEJA8ko98cIhiICN+QJ3/m6oZBlo75qlwFg7VaV7R+Da1Su4sVri9PgYbdsmT0wA5IUpWp37GLHfrBscHIh67fT0BPO51uKS/GIzeMypQxfcjFt2aJnhQVitljg5PQnu/YzN9ocAgJuLD2JxvpA8aFsbePzdm1IMrZ5F92mND6vqGsAZiMSt36qKFSQ1VbCnhESfi1rO+nzLo2mbqO2QxJuhinBUdyhzF77EBUleulLahFFWNZxzOGiu4uGV2C6VQK+bNc5OT3F6coz1ai0xS+slEHAEkNTlzEA9q7G5tYmtzW0AFIPXF4tzbG9vYzabgdljtllh58rDKIsCt26KLeno8BjHJydSWjyYM4qCJcA4Dp/QrBdy3Uneytl8jvl8E7NgIzs+OsByuQjSTTq3nK3D4MMALgUBYgZeeuK/ATPjo6//L3E+ZVUDVIUXkf4rnI/Gy8o5QKsysiDNpm2wXJwbApQQvma0FdtGSkmT7E4p8lcpvnUzLpyk54hSRnDV1n468iGGxeP0fInlejOmo4jPCnnqVC2msoE9IB4MeHmlL13fB7WSdbhpPb7/6FUAwJM37oKoQPfqH4DBKJ/8XEhtQtFQKvm5EGKWKH6mkKRU5m6ojLXtsKFS/TWK8k/6EwlOeC4zmTut3hx46EYHhFiSqKsb4ZRECJX3+77nb0cV5Q8+fg1hqhO8A/DI9YdRFyXu3LoVi84plGURHWKapsVqtRJba9jwbdfh5OQ4ngEp2c2YEYOcEI6uW+NssUTrPVb1LHLjmrkdAB6fv4Avv/QG5rMZnv3Ih7G9tRkM4ckRZlZX2NrexiPffhnLj2zD+xbr1UIke0XiXrJta35IdXIBgEATsCiPsFo3Ec9oMcgc9ExzdMpRbYORW8L/Gdx2WKPFX/1rwvlijXL5OgCR7MDAbFaBvcfGfIaHH34IN27exjLY2lTroXF1zhXQCs5KfKtqG5sbM8znNYg8JC+oFL9cLo/DPFYoXIey8Oi4A1MHsBARrfHkGSL9sIcrC9SzOWYziasqq5RPrwil0rs2OSf0z/q9pB/gshAgJOngm+/5L7CxKVlif/Hwf0fRebBHLAtLxKgrh6oIJblDZUMfVFOdl0y762YVxdVYPZUlf9nfPLmD1bf+KKaniBAJT5J47J5jFqcACukmnvnxHRAI33vkqrXByWFkxsvX90CdjxJJ8sqzKT0o8g0RDyP1la8R8PL1fekTQNfJBnzl0asAGHT0HJ6+eQiAUT7zeZmz78zh0fmJ9KdcrAad+syZgqJYrZO3iXfi2pAh4qqKpHvThcQYqWRmlj+utRAk5whlVeLJrz0OhOSkAPDEC0dJJfr37/GwCd4WoM5jd76J2UPCUWuGjCLYYM7Pz7FYLNG0XfBQXOHw6AiASECtFxujxK4QVpBzpAiNyhLzjU0szs/RrFYhLkcQe1XJs2Z1jSc//nlUZYlu/TcA3CBTB0Bo2xZ3fv5DoOMXsVic4/z8BFVVxnPddaK6fe1w12xv8Zr9mSvBeYCOJa1PWUamdHBGSYkp9SQsw9KxsH2hSRonAQftYwCA3fI1HB8fg8DY25XUOADFDPQyP5f61rIqEM/f1thYmDtsb89RV5VIpYsFVqslFkvJHLNen2O9XsSM5N6LpNd5H3GwFJfrUFQltnd2sbUl69SsV7E6bVXV4A2PZt1g5aXsecoAaBboLeBSECD7Vtl3sezrc/v/Pj70N/8EoFoyD4BRVwW4TGW7V+s1jlb/QeRi2rYF76i4qmlbQiLNrpM6JKEMNlU5V++irSIqjMy/ZrjpJoAIT964g+8+JNUNfUZAhGiIFi/V3/Ghjr3d0CIs9LkFa2dJmFqf/92Hr0iLzkfJ5uWHruDpm3exeuVLAPSAAE3XxpQ+RIT5z34Ban0iAtavfBH1s18Ih4UGmYHZGo3sopjfkst2rk6MRqFsZVPphlGINjVpvV63oWxycjl1hctUQRPcP7j5o9exWc2kHEjTYtXJGZVkwEA1n+Ph6w9hZ38fTdvi4PAInf8BAGC5XIhGItT2YgBrMJZE2AjBqjNXoK7F5rNcikek5jVTIALKItlh1806MTyhmZb2bpoGG00DZsbWxgZunF+LW/X07AydZ1SVqIJVciHncOhF/b/JwHK9Rs2iRisKB5ALEohZGEoZQZQIuZg/TX5U4mPt0qUrsLUh6qyTxeNYY4XHt9fY2NxAWVRYLI4xqyu0wXNvsViBEZhZZrRNAwo1ilrNi8ktDg4OcPPGDVF5AmjbNdbrNc5D6rLFchEISSf10EKZ9a6NSAjMEuRKkMzb9axGXdVYB0cNQM56VZY44zNJCdRZVVyAwPjyAK8lmAJRJ5hgggkmeCBwKSSgaLSG8Pw+iHnLxRma9RJVXaAsapQFoa5LVJWDJsi5efL34btzsW8EddB7Xz3CK49VaGIqkBpFUYpLc9ugaz32PvX3sPrmH2eie/LUSvJy4AniWDn7V/5+95GrqUaP9QBA0juXIUYJAJ748S28eGXb2C774kV6RuKshio6eyezSFaElLkYAF559Epok2xBT755B8uXflcSCMogJYFkEKPXr3wR85/9QlagK7cn9lVnoU28RqlNXCfThtMa69iUs9P21jbWdR7eS/BgUhu6Hqc5wf2Eo4MDHJwu0DWS9qYIqq+6rjHf2sLW/j5mWzu4e3Ak9lhACqIBqMoShFTdWIGJxP4B2Up6TmWPMG4ur2NjYwN1qNm1WsxB9AMwgKKeiUeoV6lY9oEUUGtRFAVun+1id36IeVVhWe1G6XtzU+INyRUAkcQQkgveczKvqpJ8ds6cgb5qWe0xScWm2MLEICZ/hXhNXecKNwvr47BuZtiYneH1w12AHN69vUJB+5iH+le3/N1UHK7rsF6vcXJ8jKIsY5JVH2zlVVVhPpuDvcdqLSmRYtLlViSeNlRGlbPFmZZC1Tbec7D3rUNuvyQBrddrrNbrGGYC5qglydT4bFVyQ7gUBCghLQUZsPcNXn7yH+GTN/4pZvMS5/yfoHHAHP8Uq5WmVpeSCFZXWhRVKuwEyTUn3jE1mnUybhKRiTlKY2H12uJ86fpD/O7DV/DMraMYjKr9RhVUFqTC+NnbohPvyiLOMSNCPPKqolu1VcflkMrkAh4er1zfSxebNh2U0NcrD1+NvwHAMzclTqf57pdE9I6egR7L7/xefMbs2d9AUThxPy8crEOQXSPVi+uIndFb2HXNl9N4yoEAygNqJS8VUu2hoEqc6gG9M7BuGizOTqMxvGxDjawuVBEuSnSuiAQABaELecvmVY35fEPiTUI2dyLCG2dXowHdLQs8tdEEO65HWVWoucZsNjN2HgrqsCJmCHGxQJtRzQbmZHd7G03zFLquxZUqIULvPep6htl8LoxbsBWTG7FnQgmNM+ffnnUk5ikezsR8BVoKIIUncODC1KHGFQ5VXePG+QYIQhReO9hB025guRI13Slv4Or8BhbLVVCdSVkaahqpcAx5DU3T4uTkDMfHp2IbCnguEVGprbVaraPjSPIfSI4EBAZCRYATHKJtG2xv70YmWishu1AcT8M29B0BktGkKIrobj4Gl4IAydqYejGm4qdDJ1VGK4ZrxLvsbvMPTDJMFVbSIhfBH183rvrPiwOci5LSqP3Bcu2sSJrMRcPlMPC9R67i2ZtHeOnaTriRDRGKP+F9r99AMws1OVwuQcB85N4PKSGrWoHI3MOmnZ1AOmwMAKFgmBr9XXTNljYvP3QlInPnHJ69eYjur/8gIgvth8E4+/Y/Q/X054PA6ITQxw2nh1RZoaExMrpqR4mIEr3OZpA87RL9omxeSpgnuP9AZYmudFh1weAcouYbMJoFsO4aHBzcQdt1IolXqSx161NmEu8lM3Nd13jy4RVuLnfjM14/knQxbbsL5xzm8wplWUQnhKqqUZWllAkpSrEBau2csFHV5gmEjCNFEZkby1jVszk2NzexWCywWq7QtI2RumESaSawgdzSKDFL9jd7/H34wsFNTnFVzkqGGxzBMYG5gxa4UC+8zY0NHK8fwwoNlu0a67Ws//uunccqwWVZoq5rbG5swLkC6xBg6k2NHqkL1kW7kT11uSebeLGWVYnt3T3sX7mKLjiYAKHkRuHQdcJgbm1toiiLWMdI+3FEl58AyeZMLztKLkEEvHX2W6jWNYjEJRFkgkPDJojSY1hs7jiW4HVONo/lvoZEKG2KXHBJm1I5cuVoWgDkO7Rdiw+8cQeAuEt+75ErUBSpqqZXHr6S53Sz8SuGeMRyB1b1xemKxunEawQwm/sA2ciGUEYiFZ7i2YOYemse1Hi+w4vXdvH0jbuikigozMujffWfR+KpBf5a44JZFA5VKUhDpEJda5fEx95BVHWbhexbxANGOtKZXchFTPB2w+buHpZNiwWO0Cxt9voOfr1E26yx9oKg2q4NeeNCBpGyjO+0nlXY3NjEjfPrWHY1NjdMVvsQ8KgpdmQvlRGBqapNVa9OnQIyeTrFolC4JxWkTIxt2zSYfeU5nHz050Itm/xeWNVRnyvUuVuGKKdB6TPJ6FxfLc02ZiZJWoALFWAJYIpj3pgXmM/nYMhZZACbzz2P5fs+HQPwl53Hw7tHkmmFCK8f7eLdOwdBVSnPWC4kvkgCynNNhCVAWt1YceV6tUrSLRCK30mbtuswn9WoqhKlc3j9aDfrx+K9PlwSAtSHRFwAxKR3fduCbSucvgfD4+WjP0P57l9LtSukjgKIgKoqUNUFqqrAivNtKx9yJ0LOrhlXTAMvX9vpqeB64wofotQGPRjeiO4plY59dvzN9JscbIS4qLedFpzrr46G4yRiZriyAOLMlNR0Lz+0H6VLncCztw6xWZfA63+Epm1RfuBz0T0ekE3ZNGu0rbwnUYUU4f2lARE5qTyrsUijNISCysI4qec6u/jOJ7j/IGenxMZ8jtLE+HDbYtU0aDspr61ZSDwhpmBhLx5X5Bw25nPs7e1hNdsXhBUCNoXQVHBOwgGUO7RnnWMoQFD/hZLP4s6ZCAFR8NLSki0qdyf9Gtq2xY0PPYtuvRZkCmTq+LCD05ewUS+SuMdw0uAba5C3SlP5/azSPgqQk6Jxmr8uaQJciF3y8H/nb2HufTwDXDJuLx8CAp9el8CsqvDDg+2Ie65vNSCW9DhN00YiBDDeXFwDIO7u77+qtYekYGazWsXcl0AIgg/u4M16jZvn+5jFFGOhFlLnY7DvRXBJCNA4F2sJjhoDA6OSOw9AJR8R713QE3uf0uCITEDYu1pie7fCclHgbBAs2ePEI7euGDxgS8pVSNaeAlXxhcM4MJJANtFsVqMLBe8iEeqtRd/AbomB/pLUC0JoNNjPzio7Via7QTYyDzD5QKuTPsw+89tXdkAEPHv7EGVRgn74L1F6BoIOunzqc8aRIHCIrKk5lMsTtQk7KbtNQVqTOKS07iplAoYw5hRxyAlMcN9A9Pmi06dZjRhT03ZY8BmaZhFsKZImmJHqaMGLNmNWFThoHwO3+5jNihiDAojzgHPryGhSsO2QcTyJ0HZB8ufIpVv3/MI5gNQpJ0gpnGN7Cml2tp57Hsef+NhwbwVI+zBJBPn1HrsaJamsVf5plIglfBKGmEtJcRypb7V3KeG0we6OCCURbpxfxazyIUAd2NnawPEfvorifIHZLz2G8/NzNI1kPHj/NYnCvbnYgOa9a9YNjo+OMJ/PMZvPoCSjbTs0TYOmkYwzJ6dnOEHIVmLSY42+PwOXggBpRO8AXZKWfw6brCjA3uNbB1/BB/ck+636/f/g41fhigKr/+ufS3ZWY7tRDpxAWK0Y3dEa3hO6tgW5tAQqbUQph5K6DvGaSiqIaqQ+91PVpagPg9EVpn3oBh86PEfXtfjG9gxs4iMscaF8OXS1so/R8ybQFiVEyXj/E70B+ddHXjGOMyNlDIAYL13bi/XqhSCJc4X74b+KB6frPKqnPw82h1eeoaq7BkSSA4uCrU6TKg41a0r4XVQtRvGML97cE7x9MKtr1FWFMz6TiHhNodN1WCzO0RLQxR0s6ZbIloGuxOlgNmtwcHgYbX9qP6yrClUtFUyLUMhR0t5QjFdT1RuRMC3qbOCcCarW60TmvFGmRXBOStBf/ca30VodEy8AACAASURBVM7nOLJaCMX3vXMzJvlYZisRn3gxfbxgTe91NA0WkO/EIFY1vNi3RPNhGGeClOQGsPXV5xVBZeM59h025jOAGbOtDbxxugcPj6718Ichh5tf4JWzGt6XeHz3GOtVkHSaJpYI90GNuVouwcx4984hXjvei04JgGHO/01QwaX3ZV+LBJY2LaOqOWxKyWr9zdtfBgD83P5n8eLhn8L9URHTy7Rtixlz5IqqqpRU4wz4zoG7SjyqmNMChE2aNEUM1uSemhlBCRvrgFPetIimmfHMrSMQCN+5ti2ZCLzOLGzVrkPbNHCFQ1mVWHVaT90S39ArJ8pF99ix0XUcyXif+pFltWUj+nf3CVumskDsGgCh69SYLEbGl65JEO4ztw4jx+Ocg//+H0SVW/20ZGZg7+F9gcVLv4Pyqc+HqqhiANb3VWiZizAYIifKEM/wY6mBJrjvsFxKYGjXMTbmNeo6ONS0LciJ1NMB+PHplUQUzPssyxKzWahxVejfMnlMhWBPUc8KESmceFtqKp7o7uwI6pXmoi0oSQ6qNbH71yojvPeo6gqrX/lF8RS7ezeq7tjqq1WaMRoD+fleEk3ORo6B4Q8HMCR0+bOi/VcZbJcmRvF+cYlWVd3Zpz+ONpgjuqYRW47v4M88tjfFdXvdtFhpDbHVKqQ9Y7x2tIP37J5EZrrRFEws9dReO96J72pjXmTex4oLbEmXPkyBqBNMMMEEEzwQuDQSEJC8XBSUmn//vXPM5hVOn/8DcKiMqRzPt+7+iajmgiFeGYbm238MF2ID5p/8dSzOTsHsURY1tjZ3cXp2Juq7gofPNhxFBpGa91ytrfTNjO9c2UZZVfi520f4q+2ZcYMM4jQk8V/tanz4cIEXNku0bYdcBRkkGUrf0jh642J7QdVVpqntlkWcHzJt9l4esHDK6am2gswd6hnz4rWd6FygqpBnbh2icEDz3S+FJZSBzOoZylBw7PzF30H59K/HeIx1CCCG4YKZHIi84XRphPuc4H7B2fkZvPdYrRYgcCwe2LTi1isZ1inGiDlKEpAEMdaSpboSNZvadFW6cUUR3PiDCs2JerYqyyQBhWsUi8KNv3tWad84HYg2Qb57ZhRFic0/+zoOPv4ROFekMgvWeIrUh9qChrJOH6wkps/ujQ8ahhFlfP0VMXAzaFbSFDg/01bv31OzMQOnn/6FmBTZh9RjMndEPEYUAtCLXHKR7N8+JlG+uajReo/37h+rZz0KJ+9wd2cn3iv10Vx2RtVMcRFcCgJEwQe4LCtBNnrBi1zsnMOdP/1tVFUd7TC66MxiCE1iuUQ4i25ZemrWS6hTZdOuQynbDnuf/BzO/vL/lGcVRdQTxyTQuuHNZhJrvfy9yCOmbbs4hw/ePUPbNnhZ44Rkxnj52h5+7uAUs406RhjnnjEmNDMphDFQl2U/6QbsO3imduJg4ABKBecy+hU+5K6tqV0svcCJQbBE0pMHeV03h+9c3c0yFpAjPHPrQIp3ff8PwMyYzWqUdR29ZRbf+R1UT30+FAzr4BspwFUalU1EQpMJ6B2B1WoZ62m1bRMDP1873MZqWWK9XqPtWlRlCBR1BcoYvzNDXddR/Rbj9ZxDEVysq3DNBpbK+3aDV6zq4XvFgGWXSOyp0VzoxUZS1DXaps3VzVmXI8/I1PRhHIo3otNSPlb72Z7L/uhTHbN0pgwNRfIuoji2wSkNz3fkgIKEafM+MrBSi6uAOmb4gHM8e8GvAObzVAYCyugRocGjuT2cCFevGhMFjZ3Jiz0HgUtCgKrZJth3mM1nKIsiJu0EgMPnvog1GFWopyHGTYPQ4IzQkCivGLqFk6blMrZp2w7n56domhYgiu6kjtXugyjRJOSvC2iLsXHUG+dA0JijF6/s4NlbRzGjbgSWFCLfubqDj503KMoSRdtmVQk5bjIMX2rmuWe71Q3sk+GvP75oJ3KRn6NAwBJpy2MptG/RrRuOjvO/Hj4QKPUM9PBh7LGkBTm8uL8T3+EzNw9RFA7tq78fhzibzVDNZ/Cdx+Kl3wV1XiQk79EG6YiZA7K7FFv4//cgJe8Jd5tHQQ3BLXV/SJaRqirFrTqkbKlKcSoAgLKsAuFx0UGgKIpY0RRADGC0JVCSJ9gQ3ioAWW1BmUelajac7PCqrkIhOs1Yf1GfFA03o/yOHUsgSBfxRTYuL/tV8T0AijXQ0jxj1pGAmCKvaghrzqwqw+eCTdZUNI3SVfovu1XvM57H3oxPuqcg+Ui4RP0nX8Xppz+eGGmkpMv3elWX4vQWVQ32HVxRoihLHH/tiwAAIoeqniWqHzgDi5CtPMDxF1ngGDTFUtJBuGbxTyfncPDV35P+ISWhoxhqJR7CcAGzDQeM7TYOXnA+pLZ55vZRliKHWfMsNVEMHi9dq35pHJF7JrJktEkpSQqQI84aIAh20AJ+duXseianIbuq8ilq6IwaIVxJY2Y9JPmhdeQDcRIk851ru3BBVZdUNsGBgYH5bCbOIrNZHnPEHLjx1ciaTfB2Q7Nu8NrRbsii7LN3SkSo6zoQIlG1KdEBUggFkLIT1Kpe0zbqSBA5f87+DkC9VSPyTBJMfotRacXs+CXOPvMJbH/7FQCITChlzGa4m4Qxjd1njg2JYbXag7wXkw36grn0pbUx9QW54BVriYc28ql5UhUmcjIMX5COKDXJ5qaebK5wUdvUD+bV2mxdKx5y60/9PNiLGUEf5YCRVGc5XAoCBGYRGUGRsgJIiNRsnr400N8zWm9E1FlpKZg7EJNIG2UNogJFNTMirlRWleqqCGwAwssaX8QkI4wI3USxPEThXJhffjeD8a29TWAVaheZl0Wqt1Y1gIrbZO1kPaGewnXr8jM6ZuSEK+kcQ4+B+lg/cJKwvIwjjeqH3twN1bZSFRAOuyFAxFIFUwkRIIft2ZuHKMrAJbsCZS0ITQmQppL3/uI0HxO8fbBer9A2TURw5NR7zUUGKlez2dxpAmVZoq4q1FWFogghDVGl03+iZS0TJM0EB65cicA4wWLzj3LxJSiUBl+GRJy9x+q9AYdknv6qtjLPBAlzrPu8761qcdQoPjZTjR85Ba3aZ2VSWk8NqWvDgTscyjXheziz/UwPkXSEzCXec4hnFCLjPaewEqS1zullni5LfrmYCF0KAsTeGyqbL7oVWZOtLefY9d4ssR4Q682fv/Av0XUt9j7990CuwNGffwlFWaOq6hismrIDiDu0boRoMDSQof+ExXuTku8q9Tx18yDrgULfUUrTjuNbo0CEzHyVG+vJPNmnKCGmAxmlp3HN3WBu+cbJiStR2mK6YdNGticpvqx8WcI/EvuVnEmIU2JYYsJ3ru/BEeHZ24conEP70u9mSV+rp34dBbsBwpngPgG5eBacczEeRNIuqRFaCI+8EnsWxamAQ2ot733IbJ7q/UT1m7J1hkmxe6n/tvtByyp1y1NDm949bdfh/HyBxVNPoG3WUGvrUAAK+znDSebZ4R82aqe+hibrJ4wvGeX7BADRBiR4r4+4B7PPGEJ9rs1gz0hMfNbDCCXU+zwzfKMptqyKLg/EHRxxQ0Tth4tVm5eFABlUdve534kJ+Kz4mJvb0oJ6cEhDk4x4gEYJa9VULcMtGQramHjQbJRoP+rT7T4SRrwOqIAkLyq/Ip90PN+9vpfpeeMr9ekFZfrh7KWbtUD+gse3qHnh8QxxYH6GG8hS0ujAcBGr1tNxU/b2Asc48vwxYE7PY+JIgCTctAMT8J2r4ln39K3DEF8SDsl3vwRHur7//fgDJnjb4OBffA/Vr3wmZqOugu3G9byegJRwtB/V3/lOirepGhaWmRTvttIlLzpF5moTTpmuRZug9s4B4YiIL4ElSewZLbfxM4BQ/bcHoWN1Luh1lksR5vwMEG6PYZZs+8PuZDwebNqODklnQ/1s8AYhsEpEnP1OjESwdcyDhzCSMMBZg5zfC8w5aXc97MN9gjuES0GAiAAKgYdXPv0FnDyvNiC7k8yLzRbEcAGcl79l1no3yQuEwdj/9Odx9LUvCedGmsIDMQOuNYT2sXwkNczjONq8r0wINhtTx5tJcnGqSmR0HuYho+8xEea0WRLWj0Q2IHvP3lR+tQ9PB+mi7RK9fKI6MDzBcEJBw5bmx+K4MPLqshno+KQfkzQWBE+Ml67t4plbh3j10asAUlT8MzcPLxjtBG8nEIDNr34d61/5TEY4bMJKIN/T+d1SToPI5u6zh0UyQXvvgTbdZQmOIlzdF7r/cjBG77CHYnkQZVoz1ZWcH0swdR65p93FWRL6z2WwscsExi+MR9zBzfz7vWQqNaMhiIywFTOG2hldUtVRABQYtZSIFeadKfbwhvDlNCcIACppDjQaVkpMSUwBRA+7CxAXgCkQdYIJJphgggcEl0ICUqMlIByQisXkAi2PojAw0AMrlxJ1r0kiSKJq0ikf/fkXQ+6oXn13lyQeq3emXuoXDRYLj4bmtIrCcRQkrNikOt2cexDeQedHkVvTQctHNhyJ5dqQuIvoGs2xV9Ufx/FbvTADCE4Xsc1beKtko6aoMQd6n0idGaKDQlAzWhXHKAdp3hqr+3ni1IgILz+0B2jxMC9r/J1ru+OdTfC2wvEnP4ayrFB1nVF9IpMSrMotag8CJLtQT8I2+9L7YBcMe4wR8gN2qfaXetHpOWFwyMyczp+jFKxK6KmEtZGR9M2pyc5oNi+jjTACljk39pzmkPrWESPTiCRJJolYwzXtBV6PjAej70TWTBOE+hA0rLGXSajp41WDmzKNDZvrMA4XjOTanYYjKvYLDjwuCQHSejGeOaR1T4hW1GJpIVRU7Ktk7fvTz3FrqIogvBhCqtKZ6a/VCBruHc1GTYi60Wxzj6xxIoWUjS+2DaVsVZCl1DJ/qqqw1MifzT1t/ESwkqhuaV7a5Oombfw3M6KbRjJi9o0jzdYlPID7vwHBH3OwKJFx0N/HtqluYhBLYb1In+Xd+UylM8H9gxSbY0GRUhb9bvZZaqNfOUQAJMIjbZDUZPH8yS7UmLaCKMsNp31rxpP0CI77xkOJlxl4IKCeJWmuqury88yxjRKayAzZ+evgYU6GwSu2Zb4Mae/3z7yqyeIi6GoOCBjitfxJafwAouNH7Nuo0fPg9979OracY4h/7Jji+bXziutwyQkQoJKEEJidT3wOgERHnzz/+zG7s6XqdlWcQ6pgGDdwLoEQAUWI0o51LWzGXiv9GMiNcNYvLBEqzqgdDzimOF7DvaR5B9/6TCeNSAyz54/1Y99tRO4Gy5sGmlkhrV+XmrBxyuC0lBj0QtFkdPG20rVK2CQxA4gIInJ1ASmlh1nSlxP4NPXhWk5w/0CDTIuyRBECEAFFMhpC0TubBjErRy37LyUXzSHYcCE2vsKU3AbUHkTGQUG5fcPfRDzB6RmUnyNXONR1jY2vPo/bH/lgxuF3EVHLNOJIw7yUKClkclQ4mn0bU2ZPNmuh/B9YmOroTxDPuVlDFg9dzVAwJAz5/Aeewua6NWbFGKiMuRh2PMCLSh/lIoD8mdk990AUl4IAMTMcpOg0g6O4SGhDNK2h3BG5681ILwyAJEboT1wkqdPnfx9lWcVDQ1nuORuTQIa6W8ivD7zS4pcwRo2v6SHrPqeQuJC0OSR1yKhMkH8cYzDs4PvXw/jkKUXohsO/Wj9FySr1bsuJ3+DRnB9IIcycBcMSkAQ0BqIXnc5/ZDpjwIMPE9xPmM83Qql7F5k1ICGuoUNALjsn6SH8jVVN5R4XMpwUgeA48x8NDpggTm9LESS2sIcsafBxc3MTD3/rJdDVqziZzUJ8kyDQqNoLjrhjThX9X/oI36qhcgKU95cc2II0b9JxUeIUzRoWMRcbRggNwtyZKCNEUdLRNkqE+tKcSXcQx3uhagIhSNfixFQu4uLVyuFyEKDOg10iMFquugtJ9LJ03nFxDEbLpAakaz1pwt6rskxcLLICBJsI3pwwAUh16VStZydj1Fc6hP4GsJRNDwyzh2clCQF9s26CC8CMPc07R+LEOSrI9iulNWSGVJRlteO41IG5V6PCM+KbCylxUBQXqn8xbFXlKhGCBC1VyeaeI5kEE/V5p0ADTvvcvEWsyp8Efti0QSQ2Lrhwa+ltF6WhBDYVj/SN2I9y2pk9YlQMNpsuMIPK7e99/QVUOztwhZT77tr2AlWRHYNxSw5geKa0zRm5Tcq6VVOS3vKDqAdIylrk62yer20H256zNqp+ZC8VSUFIwf35Yva6yfuJq6AMOXqELP5qcaV5GwTk+vchXAoCBEcgV8SAVF0cSfvRwRUq5ve5GwFV5wReKl0wdGjAoEUhiUybPMZl+CDzYUyqQUZ68ltMv/qC+1teROwwC2dMdwMpxnAnAKi3CdA/JH2dGeu2Cfer/74SfwaClb93wEao9eA8GKIzwn3C6NTTUG1mun5f6BEj2+WIgXmC+wJELjJt8l2vsCE6MGrVhLwVmbqiQF2WqOvKBJ4myKQG+SOMWEaIqPccRPtwuAV20zrdtwYxOic5BNuuDS7aPaZWe+C0yVUicaB8xxlkLXgo1LXSlDWOUiI1ZmOeHSJycuKKrhVds/1NBGfFi9iHyTxgCCSzlEWPOMIw8XHd40tM9+hXy0DEOTJfgH/vcQb7DHoPLgUBIlBIdlgCKOAVwXZt2Nw9ZwH0KHFclAu8yi8kWmz3KlQU0PQyBmfqJ8BsiIybiO8uOScMXwzHNsJRmQNIFILE0mayfvrZXCJj10fA6d7slvCvbTtA6SQBoFwE75jAYaZNGQ6xcqHhdpUl47zCGlH2LjhDEH3pUKZl+olLa4yl4EGVCLYHcoL7Dund5Zxc33ag6rioXnMkSUrLEnUlNiTtL9+znJ0jvZrvZ86u6QceIUAZK0YJT5x/9lPYf+l7OF8s4FlsNppmanzWiSCNbjkjeaDXjyRL9kmqCEy20LO0mmJacDl9AaIWhACQQ8wo4S0L2U9OHNGSrPtwXiqpGIaSerfH9WRgkJEhMdFJW2Q95sK9/q2P56UgQI5C+V2SHFNlIQlCu4ogedw4oTM9BfeYGfc/kPHoMK045kBDStceEeHwnij2622GaNmHWy4EZqhDepQ8gohE/LYEKONMBupFmG9ja5Eepsg83qHspRURWeceZt2fVlR5pGuE3t5kjr/luuN84kOyrOvUO5D6jjiUI6aRPif6847ARcfNEp9kB5KAUU00Wka1m9hc1dAvtxkGZ+xZDONirayIPjH9O4BwDiWxFoM9RzVUG1RuJ5/6BXTHR/Cdj0g03m7OX1ThZ9d0j1unhJGxhDXJroSjNypN9B/Se673nMo26BEOTaO9XImB+Wv7GX2XOS+KpOQwxIlsm0Qpkydub0DoreMITIGoE0wwwQQTPBC4FBIQwUnWVeqCN0yw9xQFmAhXfvELOP76l0Lr6MzZ64UNb4SBVKItMkniIk6AdFxWFtK/nKQlTtxCptTipHKyMrUVzAd+FERZtmdlb4yGGSnodWTYb8FpANzjTeyl3GnCjtGqPHPpbHDDsO+4xEO9ea/ZSA9J2rLSkLqhZjFEE7yDMM7ta5qcwhUoywJlIX8BoHCF4faTh1gmwZDs0J42r/eR0XndhzakYGSEximCAZGACqPfopStvu2k1pF1bc5llvR75nnHqe1FJy+p1/RIj29axTABxRitgGmjUk9U+XHIg5lwhJV44vpkpVf0/gvwX29QrHjnQhHYpD8jNzjnb4WWLgcBItksEmTaZPhp/xd/c4CWkuwZf+m3QIafemqi8cWk5C4cbu5JnRGBq+hNCKnajSpPG2r8Qf7bSGdmCimmAmD4kLQwRJ37YMDsG0LIbMD4x8eRZy6gvX9Hvbz7SmirHiO76gwMSkykw5mFpNrki2xGECX2niUrHuzhu1LXVR9e0OSE8M5D/wiph1vhXCqjXRQoTKxQaInIVuk+MIya8nHR62rAZMhzvN6nW4iUvTL9wZzf8JO1SXWteNcyc1byO8cVIUjAINKx+BqyY0f2ASmWL7lEp+Fz/MLmgp1B337DZgx9b7XIsIF6c+kxC/k/Sb2eoZFwbpmydmNqQ+11FJ28BaG7FARI6k4IgnWUZ3iVjeijHSRR+RjFFVuGqxc8JeezxZ04X5lIf4ABlz7sjSJnkLmcZmKNnUT2pMHQ4jnUuTOZ0rwM5i4602SjGBhrEO/RHyTKKg2K9eNPKUEM4oJ6mMjGEGXUNRte2uXMnMU+9JcucsSU4hrSwzL+eYJ3AOION8greo8FRqcsRfLJvdRsiXiO73voFBMboafPSO2Cs44yiNK/7Stx/4kQUJCI5NeiLOF9h63nvo47T70/c3DK9ie49/eC8VAezmEHF439cQ8HqQLGOS7+y9m8B5oG2z7aly1y0XmYcY0RgP7a6zsMP/mYIYUinhjn25VUmQvmnHpW6ejiU3opCFDXdkJOYpp1e9Xj7nO/jbIoAbMR8nJLyR/sgm0bJYf0wnoILON8dPeMcBj9xgGbp1ihYTfoM3LxZXPc3NpV4qrcIM0M9ykQMdjn7q7qLZYYQlX39YjHAMxGNsR+uJx8j6+Go4vz0J8S15aasTlIiVMWhgNI6MAcaH2SZvedSNA7AjYjtZWqCSTuw05KJXTeg5smtgUQs1EDQwnCRst7pCwimRyge8fnJItICRrMmTX9ps0CADEdzfZzz8PP54gxh5RQaJ+BzL3reojYqhXDgylrbToyYybFR+F4a6RQJsWxnsgeQYz9GOKaMYZBcukTnz4TmNEsfZIlanYNAFK7Q3bc8hiuOD7rTIR7w6UgQG0bUlEUDBQlmKx0I2lqPHvxhEJPoFCpqCdmDCi/Db8POlHDyMXn5VtnzM05bVWrCWTTpM/NAZxHDeuTeuK2fZRzcrAFSQfVHHkYuUBPmqF0SRvNdkDsAbh8Tca4mXCQlcPKmmSMFg1uS985/shm/tZ2w2z0xkjIIpJ3cvFxBNtP/phJ/fbOgQ1AtcyCIsNYNiHi1cRcdMr5Mw/KKSRC5pL0r29Wz87Ia077gwKjaO9NUlZiWENiU6RAz8VnPwV38yY41C8aHIpwozK8SnyGRyghAT07Y2KH3cfioTdkouW7BKZbzYq2iJ/7kpcZciKD9nDmlIizGy44Rxk9MX32GiTirNb5Pp67OF/jpSBA3ndJbOuKuKiOjHkvvIsksfRlCh78lq5l/4RmnL2sPrKlwQYa9CjDUtHeckrZOAwHQ2Yulp+xopK+aDZjCHnuHBFCBbvecAxx7avkoFyc2oVoRLjpu1uY0QyWoU9x7dYOzIBOM3NcCGjJpPuQoVNEPIMDjcQJwg3fFPeL301w36BPgPQ99IvEgdJ2TNuQJF8jUiG5Dh4OLrrhMhhWx6yMid3OWVgDaZ0fB82TJm1CN2OSVtg6WlQPYBSFQ+t9nNeA2EUcNNydfclJCZ81ERCnvZzxhGY+mRaE83OTzgki0bbvw/KFSgBy4qIDzCcWCTgB/SKS/TWIGhXT+SC2KIxb3d51XtZmNQaXggABgqC6zsOF6GQAoMKBCocrn/lNHH3tiz1j+mgn40TICAwjjIn8UW8OjDRKAsa9nx+HcYFiiG2brMtBI9bdQZQKwIHAcGkzWSNtLK1AgHPpZ/XaYcAT94u3xkGY7RdHNRIGO5jHAMhBpdakD86pFSEVlOurB+MD+odg5OFRnTHBfQeKzEuQeNSeoil6nDJO6d1ZZGnzx4GAovfm8gSe+W/9oHPnHKK/FasckfZ/gVApOe49AjmHqpYKyeQcqqoCI6SooVAddeRcxJNgJIWEDnLpLCmxLOM1lBJ66GQARASKVaGz4QS1o06dsxElem8Z7THWEpGYZPPq05TsB2UueeQaMkJj53zPLAm4JARIN6rvWrBvIzdVVQFBeeDKL/4mjv7894a4LxKQ/IXmOI2yP9nv2ctKXHy83hMJRoKClVpkz2D9PXAYcs0elb5kxuH/HDtInBsZumDcWC2hMRJQZvRzCByOD2YwjaEmM+ThpOSxLjs4duz3pEGJykguLE7pVJxdJ3tAzEEYiFcZPdJxTITnHYWwDTXAVEtya7Cp5zyRprUTuaBSNaIDVMug+70wB0cRV0yaiXgJsv89Wp8I2xAncG+Pc1D/yVjPPvMJbH3zJXRdN3RuMUM0D43EbNg0EV7LbGWEyX4JDOUovAVDZVfC8p4Jh6kIF+UbbX2PydnDlY89Iyhq6hhIQOYe++jR5w5hCkSdYIIJJpjggcDlkYCYgzGww8aG6E939hnLc8Z6ReDurVVfWX9jyq1M9tU06Rd0knHklP9Ow9+t5HJhP70m9tIgG2/m1UOGEySMsGHh+pB/klglFd2MfEcA9fgPMeVSvC9z8R5qxcbBcIGqLpF0OmqaDmySMl8j6XXS+8v0GwMO8ULudYK3HYhCqYRYU0u94uS6JulMhvOeJG64dKOosk8wtl/pMbZX+7BRyandxDNn6ruhMlftEC2uvPAtAEB97Rr87h66tgsJj1m13bGHfrhdf4yxf+O0cxEk21UKK+h1279hIPFHyVCfRcYhKyKUfmiKQTjZM8YkOatey8dtvVXzOY30k2mMwjgvuw1IA84YQFV77F2VbADX3+Xw5usOzZoyw+JAoOy7HA6MNRHLo9fCLM7FxOhid4Wx8eSdZCnV4we7QVS0HW5KPTwuEkoC4LI6Qea4I+qgRzUKlGdvGJ1oOiix815f90b3Q7+dRDSMsjC4WXvuonqOyMUgU1aVoelDZ5/OGke32gnuP9RVhaIsULoiYwQUIUpMl+5AhiOHviWUoKo6/YXjmbT/RoRHQKZbp9TOqmyzRLboMSY8ZA69Z6zWLdrWo207dN5HwpA8MfvefolBy+w6hgAx8vZp4hcRHjNqTuOO5Ll32OL+zxgvo5IfMN79a6mXcQI0HPrQ2YCzj+qEZZV+sYVVDV4Al4MAwaMoCECJtmUcHwgBKqsKi3OgbYGD534bxZi+V0GRq7YxBAtIUlYmXcRphAAAIABJREFUSaAnM4QbLRIdfV58wAUcnn1JsdUYS6UHTq6PbyApK0z6PyJoBgLx/MkLx+V92UdRSiEUR6YHM0lH0seIZDHYQ0NCrD8n/bSWXki9y3tw6bnex3l4053nMOdBuobEMHA/xcgE9w3KsgQR0LHYEi+KlyEYghC3F8eDkNkuIiFRZsvuyQEfnyH7C5Fan2kyxHL5S5+SuXzvB2D2oBC3FAtgGrtVrpDg8fNtrivhs33YQXFPc8FAsgVRQuZJ+uEBysiYS4Og7JG+0O4yWOccchbR/G4JsMGf2t4jrVtyluo9+vJLQB2KqgK5EmBgvZRhtasrmJUAl40UV9IbMklG2CIOwVfyYsdE8dC+/wIvaHfxkvW3RN7RqLh6Qdu3BLNpLEem0pLnIDaOHUYzz8Dz6FczRzvbjKcdzn9Ajzh1bn8fZaPyO6PbNXOo3kjZ4YkR4+zjI7IoddvX8GkT3AeoqiIhSQPJ7Zh6DFTO2LHdY5EryRFy1HBEQjPsChjRKmRGfr2Qdjrpngv31XUN7xnbzz2Pg6ffH7J0h3Fm6j7TJY0Z4PtSh8YKjSD4scrImaahv7szCji4nhiAoZSp7eMvlk/+CfAQDcaijxre5yzBVmeTcLcfpm4ZwKUgQABL4aiylJn44GGDLWzMGMRLPPRLv4VbX/lngoz7nAQZyYCEStsNrNwMUVBDhc3fDzS1n0dVeoOWxg+/5xUS7zQvk0b7u3iTZU8MyJpA0UuQGfC+zcdOJnYKMCJP7rJpD0OSUnJuyg41cn6sfeVSUzat6CuaTrBlGWT55T0J50QZ4SbX59Lsatvn0fC3Ce4LFGbPZRI+K/40LtOkkv0FHD/M+RpBaja3moWodgu2SX3u2P26mzlsag0+Tdd9UCQExjVwrllkTxDjBq7gvQ+Zu7cSqt75ETtTkp4onpF8CdSzz6s0oUSZ7MgwvNEuUjJooXeq8zGG9m9NJi4AVb1FYi0TH9qQLu7iUhAgDSYjIpRFgUJdPB0F24BZzICIkwZAFztsOsVjlgFjsykzbiPZEPrUneMG7DFY8WSEwLU+YrfzCv3EjZoRBsvjhHFnTGN+WJlZYnoKgII7aUESMS3EVMcaAk7J9jSyyQyDJX2LSiId3vwE5ZuoT9QSJ0e9xtaxQZvaWJE4Q0rvIIujCjqbnCHQVZngnQLLzcbcYUBAZlJSPeI070063HRPn6Ww7IPiY0VkiX/pxc0YVc+9VDsRwUuVx+x8d52UzOZAFDJbosXbgfjE86V9WNwD9NTa/YGkPzrn4dhzZOVtCrDMRqZNwqnLTApIDFy8L9mW86Njzy8NJZ5A4O8Fg5ifjCkZ/nYRXAoCJPrlFImsKTMODw8A9liulrj1J78dKLtD2v4JrPZRsmunWuSJa8sdDfo2l7hcrJSceldy46e9b2BfBBkRu/8iAudAvf5z+hSRr1JUTx7OuZhlGE6SKvq2M9zhGILnRAjZ/JZYFahUaDlYO6lBQKnOZPAcsyb26WYtMsJr1kS/Z5yqcoKRQNkDNRGhdwq6ziNjiqz6CGP737Sh9KZsvIwgv9ANh92jG4fH9ohlFGlw7SJQhJ80B8IUnX/mU6DDu+Cuy5if/jzsebDfM9Q+wCtpXrHfHqK31/prktTRdhb5GiiOsBLR8ESMrGKibsJAjBChPg26iNDckxANHjyEy0GAihLRXdLYel7/w/8N3nvhvsiFQLLAEZvgTuUsFBIXpRzBiGGwR3zynxOiS+IlcK90P+PU3qqqKP89HDQdxtjtIk3oO5SqjU3D0WjqHKUKid44Hhh1lpS4SJyRRf/p0CknK62S6mFkTGZtrJSV/9FNr4TEzkkm3Sf+WYdR4lG2WHJ1EVwuy03U5x2DpmvDOQIA0rzB0P1icFr605MULPQRch9BsopDNHLfPaHvzZq+VFXIhEAAkYNnb7QrofXI+bbMVOZkYNRisU38R89YmE+PKuWEiM1/obd4hHOuVJ0IIk4iM+YRJjuOYRTys2TRh47Ljq//m22b8MVPR4CmQNQJJphgggkeCFwKCYic1L6RDLAOB3/22wAkSanvROpwRRCdCVm5AREfEHK5yfcB52C4MuUeLCMvFylyKwLGq26M1bYaCB5pE7mjERZAxxDaYaRd5CysTSZySaqzLg3XEiYVcr9x0Gt3bRvS5aunj9F3G05HUuEjqC5t5I6VSgR8cJvOsoqHu1Rdlu4LfdmEo1rILllCsxXIA2+RqU6ytulhE9xvYEiEKPX2unnXaprl/GLqIuPc89Nh7Rl6zkc5aErct1UDXughGZAFe8b2c38hV/b2YuZujGlHZBAqXlwsaKskM5DmL3DOid8pU10zAcym5hVTdF23nqG9LuJvasNKS5jjlNxrr6+VGJNO5d6+BMTponk3PHhPOUq7twh0KQiQXQRXpJLcDslPzSZBlNIEemfCjIrUB0vKSBvFSPVqF5JLHBCj2cqhrXUOiAdMRWQa239kVGeDKSa8aYlYf0Uozi7bzjEKHACxhyPNREywGYFTsByjcClyXZKTqhpM+s3F4B7B4fxnACgS7Yl2Gm0kCRqpl+E6P+RJXaEIZwyBwGx2o3u3aj6jGpjgHYCw4TN7K5BvICaoy3ympjFqJ/vukyav56TQtyUg7cfcH4yRObr0DyMDoqZPdsgse3cYZx9nxN/s/h6zE0UEkRCxVUkOGdNs6PnP1skhHtOE3Bi5OiyO0xAVZVAtE24JR/49j3vivEH8aD1U8zZ90Pedv4tBHTMDl4IAUSAqRIyD534v+x0ucdDqpTWw6USjYF+asBtR32hq039J5H22YdhSq7y39FhKnH969vhhG24CRaSm7+whakMaTFWueg8f0tyTg8nEkZ7vigLkxH7GQKa773vlRSOwnXHKso/EVVoi2jtJEMOmIzd6YNn+HeGm0sNMOTpObynqp/XaxfzpBG8nkDk7lKfHgdcznM7CwG6QaFZE6gSKNXFCw7gv0j5JezLW1DJ/vQ9I2JkdahC43lu4xLRKeqgwNt/bm2ZefftEf6ebm/KfLJNruLihndp0YphZZbjj2tk16/1uXbv78UlJwWDtuWmNo4k1vMO3gpGp9oCyP/2fx+BSECBHQlxsNDWAmIVDM0KnGhhuaGjMCAUNfgOAnAvvxctACRtMG9tf4hJixG9UI5hDYpCktCeMBvNf8K76zQz/n+aq15nh2w5AJ4QGDowubSoglDcno3aUSYxLDvkRY3Acu2eAiKN0mhABekRSJ0dRbCf0n0eRiMEcnlwVEhc0EpuBlMTIEdgE9w36Ef4Jt/HoXwvJ2zL0Zf7Nww3yvnWfWmZueOaGbv7ZX4hX7dbzfwG+di3OpW2laqsP9bUyxkif01PN/TTSdppDXioPljDY1hEnXPzMi5wC3mrtDZsHfUKqvppdumgW8T7013+MKtsXxv0Z5XApCNDYwsdLug+s+sUEUgkiZPGK6+HpQY+Ry0jePMOHWUaN9An2duhbswFvlmoSWcQbNsaAhsi4+8tgmBajC+8H5g2OqvzqCPAiCqVNSdpZRlAHuF6JSWCdIs6wBJlF30wkKrZc/SYdyZh73mojkM11rEHgmpMqMZ95NAZO9Ocdgc64KsPsH0tcrNrNQjSn6HdrVzGM24Aro0D40lMHW98yQki3ZduSmVEETQAgfXrP2Pjq13Dn6SeMlGEki8BA6W9KgPv5B/upt/rQj39KZ+teXqZvQciRCI8l0PZDxsz1mT8IM9m7qd8EF1DL3qDGviQWIxcphnApCFBShQUDO4055xlyK+JH+j0Y7SQJgu5GE8djNygjZofOkXBfmaOLOMKjRUks8RL5a0pcj6olhu8y01ybwZknKQehBE2vGeYlPps92ByaKFl4H0xSFLmRpA/X+Qw3Y6QtPcwfpRYf3hOl+9IBMcgk/t4nVMa+h5zQWdADnFXO1DEMmdQJ7hO0bZs0EG+x6P2gYZdxTyZfGiO6Jydlc5/CpNiW/mPvhRrLwgkjFBxmiqJAGQLck3YgVFNVwmmJlh0Pq+PNEN5KKpJtagiHuZCYTXvuEgGyJoKxfpXhG7sW44q4d4DzUYRnXkBneOwrj16zh3jEOnIhXAoC1PkuqmmE2xibHaC135MUoteUCMVmoUXiZjIYFR2tN0d+0arU7IpytrEM99YjFFEMtZQjcJFWpWARddSns9ZZ17EYZG6IVYyr6B0ky4NIkxCj7gDL0SaOzO6e4a7MxyE2MzYDIQ0UNnavMQ+lwWIpYwBFYA4Eb7jnUfQ0wTsEbSsSkKS0Se9X7bHRBtNjzsKPaQ+rREOK2JV58aMSVv+zwMVvnxEkFicZVDovY1798qdRvfqD2I6IsPzsp+Fu3YxxdRYilrmXdKPzyAbAI1g37+teKjOvXqzcO+v3kCKyx5tPqfuLic+9O8kZyYyQRh4jEZ2+EuongUtBgNq2AUFUR/uf/g0cf+1LciFD+HaGnFjmTLRR3G7diMeXf5jacuB+EDU8dp/kaoD8gKVGWhtF23D+dkxjFViinasnKYAoI6i51MZR4gkZR8BB/RY1v8HI6r0PpZOlckvGmY3ZXsxz86klwpJWyg6pn4QFIin1paMkusV+LUcYVaRaKj0Sal3Cn+5gTvD2AHsfzxgQPMqAsFWtp2rwVr1XXxmnnBiXPtrLCBCF/dFH6rHThNw9c1QZC6GTe9brBovFAmdnZ/Yp+eAiM9vHM0OwEmEcf3ZMNPRj3GEgDX0kmDMex3FJ5ycD2/CnIEaWWejdO3DQ+H+pipgCUSeYYIIJJnggcCkkoGa9jvEsBWW8D4JeB8oQ92FoSQliy4jHTkbxTbr28DCxSxh9qHbTJ+4D7V2/bzuiMHA2olTf2ysyPVZsy/oxqik27slWhccM5g6d70JQbxpPDFTzDFeEWCsz4hgDFQrEqUQ1OkOrr4ZwrkmLxkmkCwNUFWKWet7OUNcHyeMuOZwQBpxpdv9b2yMmeHuAPYva1jk4Z+r4MKNjhgPBkYMXn2xppzeTUcgFFVVUGWsTmCNJQOY7ZlVC4RwNfbvSeABxmtBnuKJA27ZR4mFmLBZLLBbnoV3Yh/cQCEY1a/ZiuF1wBo1djiYG7/vq9hFHBqLoZ5P64OE4emqvJEBJuqSokc80EP05ieOUdmydtH7S4zXI9P0TwqUgQAdf/V0URRH+K0POt3wiSYAe168CED20WedkE+KE1IJaT19Ub6sMxNoBeWMPwI2Qm57qzP4ZvI9cyXDhz5xUckaDkEYUCFZ88c6hdISubeF92kyiC+/iARiMExpEZBA6IUMQ/SFSQDI5IaOsYWaMjB8SSZWPphYL9cZmiJYS0RRASOnET3Dfoes6FCjCO7JMIuIebH0H8kEtxwxPebt4Z+DybE8BZwYVd2jdU0dZVbdV1w72QG/v+q7DYrHAD574GfnuvVRmXa+Nqqx3hvuJiDkkGRko7nPmyrjVpK4i8egHkcZP8btlKvOnJaYzX4/heCJ6sHbYnuo89RmIjKk68JM4moxBXu4iPeNeqsJLQYDKsgBRAeeKUDiq1yCE3VtuqU+OGMg4aCNwxA0ZUXKPYwGAQWrz+GyL+eVzqt2pIkuOqPuHRD5Y28iAZ0m/ZS/L9BsIZ/YbE2IEWTzU1pkC8EglkMk5FIUbLK/eS0rMxs5/hkF6UzATVh24SjTKGfW7yWYfuEGLXlSaUkeMFDjoe/dN8E6AOhkwM3zn4/eMGIWPkkDYBHVT4qQz5Mb5SYq223isOOv4okJpdlMNg6nTXtE0x2z+ta1y+8uwkyh19QjHGORtKOs/IegeRovLYonUkLomHpGGbQKjndY79G/WUsehNZLSXxo8I9ml3hqSS77Br0EKvQjopwmummCCCSaYYIK3CyYnhAkmmGCCCR4ITARoggkmmGCCBwITAZpgggkmmOCBwESAJphgggkmeCAwEaAJJphgggkeCEwEaIIJJphgggcCEwGaYIIJJpjggcBEgCaYYIIJJnggMBGgCSaYYIIJHghMBGiCCSaYYIIHAhMBmmCCCSaY4IHARIAmmGCCCSZ4IDARoAkmmGCCCR4ITARoggkmmGCCBwITAZpgggkmmOCBwESAJphgggkmeCAwEaAJJphgggkeCEwEaIIJJphgggcCEwGaYIIJJpjggcBEgCaYYIIJJnggMBGgCSaYYIIJHghMBGiCCSaYYIIHAhMBmmCCCSaY4IHARIAmmGCCCSZ4IDARoAkmmGCCCR4ITARoggkmmGCCBwITAZpgggkmmOCBwESAJphgggkmeCAwEaAJJphgggkeCEwEaIIJJphgggcCEwGaYIIJJpjggUD5oAcAAP/d//BRbtdrlFWFoixAlK6VZYXZfBOucGB4EBjOOVT1DABQ1TPMNzbgnMOTxT9EVZVwhUPhHIqyAADUdYWqruCcQ1FWcI5QFA5VVaGqKgBA4RyIACICg9A0HdZNi9WqxbppAQBtx+g8w3sZGzOD2YO5gw7ZEcE5AhGByAFEcEQg51CWLnsW/h/23q3XkiS77/vFJXPvc0513au6Z0aci2mJoqCLYdKyLVs0Rx/ADwYskZQgGbBAGJD95Cd/CF0oCAYsw4JsUTf7yW8GDMOGJVIjSLJkSgSHEDnX7qrq6rrXueydGRHLD2tFZOxd51TVDKeaBeNE96mzT+68RGZGxFrrv/5rLRFisM/ouRA7N0IpGSmCiG7OOeO9Jw56X8MQKAVKLuRS8M7rvYfQ7l2vrX1Kc2KeE2lOlFLI2e4rFUouFBEQQdq9CfVlONtWilCyIGinRDtL/dW2i7RnIt27dsA0bXn+7AnHv/aX2G4ncs6kedbrASEOzNMGSiGOA3FYIUXYbjd472xcROIwst2c8Rf+1m90I+ayvYv2J//r/0Z0jOo78t7GcoxIEUqdFE7fMc61fWIMOLBxGIjjyPrgiOADfhwBOLh6jQ9/z49x+OxTDq5e2Rkz76o5eKvr1MEVUI29HiNA6T73bX/Mv02rx9T9XUowTQD8ifQrBBHG4JmnLadnJzy4f4/PPv2UR48eAXB6ekbKhcODQ65cucLdO7e4c+cWd+5+yMGBPudhdCAJJLc5XoogRdcAgJwTpWRdC+w7yRlKIWXt5ZThdJvIeK5cvcLB+gBPZJpmXp4c63lKBgfjOPITv/i3zn0M74UAWh1dQeQlAngfCFG75WOgpMQ8T4xhxcHhIcM44oPHda9V12/he/7v8OP82SYAQtBFOMbIMIz4EAgh4L1OjjpBQF++ntNRBMQ+AypIAOeKbnUqFOyyemRdqL0Km+U7yAC5LAM3OILXq+VcdI13UIpQh6EKQw9u2RZ8wAdHNOESQiAEhwQh56KT33uCD60/AFIKuUBKWX9mHWDZFo1SbGGxASkiKoy6mxCcbs+FUhZBA/1ks+dSj5H6CJe+pDRzdnLM6a//ZeaUEBFy1slQn5v3Huc84sD5YA9Ddt55zplSNuSUuGzvvtXxJKag6FgFSWlnIXfiEKcLdbT5d3h4qALKgQ+BGEcd291ccTEyrNa44BHebgF/k/B43cLvut9vOk8dxlXYNGVzb/t+vy/q53n9OrcPIeDWa/28CZRpZppmvAscHn7A9es3mKeJs7MzAFLOzCennJyckEthvRq5eu0awzgSbU0dgsM5EDzinCq3pQoh7UUpxZThZU1AChQh2Zox5sIwZ8AxjKM+xzwjMhFDVVJsDSgXz9H3QwCtD5BSmDZn4JaXEUJUbbqo5r9aH7BaH5gQ0sG9PTvFObUqvub+tGrGMRKCXxbqWAWPNy3ME2JsWj2oICj2QkqBlItaBrJofVLsRdSRpqutWk0mpMSsnmoeiCzX6HUnEfBOECd4lWq2XxVAJpXcYo0475qFA6jF4x3FqQaai3ZevDRLwVlfpAg5ZSSXxZKpi0gprZ+lqHWz9NmmS9FnUYVVk0+YMMKeh/RHOdtP2gI2bTecnByb5VNMkGScD+2+Wr+dCqKqUTvv9fkDpWSbKJ+HrnzZfPA6TjBFzYaFDt1eOAmSBRcCcVCt+8rV62y2W0QK3jlVpLy+X7y+c+cDx7/x/3Lz7u2dhfxNAuKi7/aP21/0f9BRU/cvvOq36C39txVob/V9p0T+n+E/oHDGHzv931kNa8ZxxeHhVa5dn5m2WwBSSpyenDLnxGa74enz59y6c1vPmWb9XQSC4KLDx2A9dizitOuDrUciZiGVQjTUJKdMTJlSVDHMuVAkKxrkZOe58Jo5+l4IIOcDq4MjSs5qQdiDT3PSBcc5Vus1w7gijit8CEjJAMRh4Gv8AiFGYjDryWC0+gJVo3Z2br1myVm32WMSEwqlqDWRctGHWqSZplJym4R1lHgHUuE2vRvqoxenC38xKKtpGCIULwQnxKBWE1LaBKaevx5vWkcImEC287hCyY6cFS5UoeZAHA5bzL3un23BrgK1yKLxVMitdGZ4b9XovUOpfZS9MdWspEUA7Qhhir5LYPPNX+Ls6VM9X673ZkJSsh1qFpyv59EFzzmnQhbV0igqlC/bu2/ee3Ipr2rvaqrjOqtXUAWoat0uRJybFeEInhiivkdZ3l8cB8aDNS6GCy2J/fa2QqQKhX1I+G1HTj2+F0J1uztnvx+2nXt8j0SIKMQfPKVk5nnL2dkJJwZ5bbdbdKnxrFdrrn7wAVeuXDGF21TCKDiXEafzT6Q0C6iuMyXnpmzr9wrBlVJISfuzTZnNlInDwMHBWtcbF8g5MVWDR8AFz2gw63ntkoRw2S7bZbtsl+13pb0XFpCUQhhG1kdXTPouFgelsD46wjl1uOM8gm8O9BAHgl8TYlSoLUa1VESo8jWlgnOJ6CLBR7V2iulEncqRDaZKOZOyNKugwjylFIOooPp91KryiyauAOviC3HSNJvqV5GiWod4UUsgLDpW06j8QmaocBqivo+6U5B6f3nPmnELNFXsuhUOLIUsZfH7WDeVlKD96tW6RQGTjhDRPTTpdtp5qXW/wjRt2JwqTv3y5p8iPfxrpnEls3rVtVs1sAqVSjYHqK+Ejg7O3LMqL9u7bc5VOLWCqws5RUevq0wV3e69QTw0WFdVYmfWVNZtddp4JdDst95yeW3/foDtP6jN3FtMnle1dtn7eVO7yAe0b5k5EXxWVODr8z/Au0I4ugpOODs95tmzx3z26ac8ePAAgGmawTmGYeD61at8ePc2Vz/4QH3mg7kIfKGUhOQ6/4ohH7n5U3POzeKSorA9RX2/SbR3m6lwskkcXgms1p7oFHkqBEqp61IiCjAu0Pp+ey8EUEmJOK4Z1wfMmw1z2ugXziEI4+qAOK4R58mpkCl8pfwCAEMccWGsbnpwgTAMeISKiglC6fwx5kZQ4VIXbhFyKaSUyTmTTQBJNwWqL6bi4OqfcHjPIiTa9QpCqXMOh1s8QE5N35yLnV/JBN4WWQAfjD3nnPpqRAVJTrkTiPaiZ4MTq79HXPPvNAhNlt5RdMNyHiGnspAgKpLWCZcqQHfOc44wknavwunxCzbf/MucnhyTjT2Tkvqb1PdTfUPOFA+DVeOAd55CMRhAfT8lp6acqAJAd5+X7Z02MV8n0t4v9CrTLi6bcyYZgyt4HdvFfAkpJfPxqZIBMG8ntqcbDo8OcQw7l/5BhMvb7vOD+oR64dNDefuC5yIY7geB+9rv7RYx6JpciMHjHbx48ZxHnz3k/ief8OTpU+Y5W388V64ccvvmTW7c+IAPrhziXOL05VO2J3ZeSUhJTcBUv23vDwcIQf3LuahC651HYiR6fTdzSfgwE8MK7yIhDpQC3o8g6m/anm0pIwxjvvB+3wsBlKYt68NDwjDaQqM3gBSG9RHDatUYMjkXswwq00zpxc6ZUz6I0pw9TaOKwan/xHnzO0in8RvuKVUTKEa1NovEvTp0jDOgxIDKEOn1Itkdmr3Ttn2Nw/lADDDEQPCVcFDsGvWYRUBWi2zxy6g/Spl0C93bucWaqFRq15h6VUj1mG/ZsfTEOtkbNlXw9m6eHWG1s0mf2+Y3/gpPHz9mmrbEQdk8zkdEVONCxPx5dobKJHTKpKrnlkaAKDvPseSLB/Zl+9G2UkoLHeiZjf2iVQ0krwOZea4L0QkpKc2+OAdhwMeoBM86BlPi6Pf/Ifjs+68s1m9avPvx97YLfVUr39Zi6d30PeutXLD/76Q51dD445v/C28+WeeVNDXPW54+ecTDT+9z/9P7zNvUlIDVasWtmzf40pc+ZBwCQ/SQJ+bNTK53WgrO1hhva5N31cda2b4eL4IvdjfevvOBYgJoGBxXfOTgYMU4DgQfmUwR7p9ACKERxs5r74UACsGT5y0hesYxEvwhANPZGcEHEKHMmXFYUUrix9Kfaj0vOfMt+Vsc3/jPGcfAT53OhBDMKVYJBl5hJkEd8jmT56RauFS4TxYnvbHCnHO4sBAVlpiYRQcqUpAMgjnQYYEh7F8xqK80YeJwqIYRvMXqOJTRUOq1qlZirDUpTVBg0Bt+EUwIClXRbUMXDu99lZgdCaE0AZTLLtuPXtjsOJe7e+8ExCKpTLihLJmz0w2nL0/A+0p2wrld2rUPgUzGicfJov85k/IiQkqJklRra0++am7+0o35ebScFytbALeHF0kb807HsXMkg3SOX7ygwW/BU6Iwek9wrlm9kmfSdsuQyyukgTe1H2bB/0Gsp35JLZwvdH6nrZ7/63wDmSdks6Fst1QYx4WR4+MXPHv6iE8+/pjHjx+z2UwgjqPDIwA++vAut25e4+qVNdvNhjQnDkfPQKiUJFzwOFURdoUPIG6Z23UN8UGZqB4huxqzCEeHI0chEMcB7yMiHpnVPVDRoGGIjKthh926394PATRo8Ok4DipBK2uiKHtKcmY6O1Fh4B3f5pcJYjdVPDkljh79dTZ3/wsQZYh479u6WKqqUi2cNJNMAFWLo1GQZQGVvHe47GlrXIUhOg1wwaq0Oa8vtxj7TdxiwTSR5JQ2HoPDBx1j3qN+pKaF6E+aS/O/tGCLGDcVAAAgAElEQVS/Pfijj4/RuJrUYQJ+ESJGpczmy1po2NIJsn3Lp17RtrvzJq9r+yx90BgBHyJxHJugKBZ02ixLEyreuwaN4irE6azPeccXVc+jQb8XD+7L9qNr+3T3xl4LqujlZNaoW8ZjNgv1bLNpMLQGZGeGOCLOq38BKFNic3rGyuKKdvXoc/rzO7gXd8Hn8/brfyr28Lprv86qepPF5URwJePmGVIidOjLtD3lyePPePjpfT57+JDT0w0i8MHRFW7fugHAh3dvMQyeadqSU2KMniF4Bu/auZT5S9UHTKnQzjXQXpb1Sr9qOzc/9maecCGABfx758kpM00zZ+bv3Wy3xNEzrHYh1b69FwIojmuCcxwcHZpzzDTz1ZqcXlLyrI52lJAQxhHntetfLr9AcQVy5v6Tv045/EU9qflZQK38Ag3eS102gGoBVZ9InWc1WAuvkf+6zZmQsH2s/94wbt3Ht+uDQnT6QkuzoLxTWDAGj/caB9Rgw50ZIeRGapAF/ej+FRtADl0UpOwSDHwQnAtNKOSUF8itg+leEULVCunarpixLb0AXswmPfdXfpH44i/iY1zeaSUdFFECAmIEA9+syG68a39LMSe4tD475/BxwMeLB/dl+xE215ainVVYsOwfISz+Obc7TqXY2FfNrL1P6ASbFNI0dUrWG7rD6xfzi4552316yA1+NFbP6/r7db6Bywk3z/icGWJkPDzk+PgFAE8fP+L+vU94+PAhL14cI+I4ODjgCx99yO3b1wG4du2Q589fsDk7Yz0OjMPIajW0DA6g6wzU0BKd45Xs1EhSyGINSTF3gAJ2kykax8cbwhhxYWB0gguONGem7cSpCaCUEjmvXnvj74UACiHgcXgXiOuByZyXPkSG1Uqj4mVm2mw0HigOZIOqSil8P/59vio/x5flF0xb3o9XMNgpJ9KcyXM2J3hZltO6yNsRNeVGXdhpvzvLp/25EAwqLFU6KeUQvAvtUO/UJ6XKow6COARL0bNYEaWUdu2W2qaRDQx+kkVwFLunXgDF2g3n2j2XosGCdcAp/FYWKK89sz1zp2q3ivPtwHPtOduneZ4QBD/oEKvacMmZEKMK/k6YOBzF4oB6KwcW57dqy6YMhIgP8dIC+pyacxpLJ3nvHZkwCcGTOv+l61GCTkmqx7b1bRltJBuf3ZGv71M9397+Pwx8t/+7Z7u9rfC56Jr7fTt331KQSaG3ISgLdJ63PHmsaXYe3PuER48ecXJyShG4ceM6t2/e5M6dG01pffzkGfOc8S4SfVAFFwiu81B36b4UcKnvSi643wV2BSURAZxthYMQEAIQzdes7pS1ZW9wDg4ODt5/CM459QWkpBHxswmgNM+sVgdgFNCT45f4F89VOzcL6FvubzIOa4ZhpWwb71sglQvLEJVS2W2JbGloikgnOtxinuqf1jeWBb8zCZqFYRZPg7NcMdaICQoTar47T6ipeNyi6WsKn+X6lWzQrI42q5Y+VKLEqxYMTSqUXP1ZLEK38/FAD8HZRjHLam8q7wbuLv3aCZ61/kzbDbOxnVJKjWjgfMCHiJRZn3/vV6hacSkUaq4603VLUWq6sywQccCHeBmI+jm1qimHEHR+NVhXKK6Ow2bkn3eG9qmYFRTCkvex5Orj7Mf6W/btDX9ftK1urz+9xVMXY1WB3u5cb/N9/93P8g3dVgqkRNlskO0Wd7gmzTOnpyc8/PQ+AA8e3Of5s+eIwOHBER/dvcudOze4euWAp8+eA/Di+QnD6oCDcSBGVQpcnWMs76vOtaoUqHK7IB5O9u+5UywtEHWahQMXgYDzvimz3nuioRJxCMQ4nEvkqu3Sg3vZLttlu2yX7XelvRcWUIwB5zxpngnBd1CM0m4Pjg5ZHx5xfPyCOc2keWJ9pESFH49/jhjXDOOae+PfwyF8VX5uJ60MWPxM1cItGM6Zv0M3Vk2ofjArhg7ywjVrTFUZJTqoVmF9VjpbZ81I0zQqmSF4p2Zxg9fU0e7domlkc74XC5j1fgkEbM7CSp1uUJgd7xzGlFBjqNGtNZv3YiXVN9ATDzr03p6BPbRmAe1YS8jOeYoU8m/9t0xPnpCPTxrlfbkztQprZmRNu2NZC+1Smo131+8jovnFfFDtqlk/sq+fXrZ30ZwRPryvwdmd/7Ay4KqF3sG+dR92/ApKlInD0BAEzcZscV6V7PO6/vww93DO37776b8v3U8/Vc6D036YawP4NtkzbLcM3uPGkWm75enTR3z28FMefvoQgJPjU0oWbt26xZ3bt7hz9wbjEDg5OcYZKnD12g18XDH4gndbvEHXPSRfRKsJBHu+1WfsvWsZ9OucLh2kWpwSdOs8HteBOLoWr5iy+uhzKczmJ/IxUvCIXGznvBcCSERI85Y8T+oXsZuMQ1QKsThKyhweHplvZDGMQ1Cadpq2fJT+E4YY8GvLdlD38R43xOZTKb4gJZuAWlgHDQfeSeTZZSLoGGoqhHTYCsa005vBu6z+k3o+p8GqlYkSLMtBdSmVLMbK24UppNKu0cm/5HBbHPp1smLCcfEThfZs6wJeal4nWxB6vNE5dmeZq5kYlnum7mK0wleTrEJJmdOTE+ZpUuJDmrvgUd1XKK8SOjozPe9kw1DhpclK1e+jr+LSeP88W1XafAi4nJuCo81yx3cQbfXdAeZftPPYt2meGdcHHeyaKXkmp4TkgvPhB/IDLT25+Pv+s+d84bMf47Ojp73h2m/T2hojgCUIdfOMy4XoPfM88ezZUx5++imfPnjAixfH7chrV69x984tbt2+wWqMyuadZoaVhq2s4hoh4ssWJ5b1uvqHG8FgF/aqCjT9fHIO56WFRei79YjzpKrU94qfc6ScmKeJzWbD2ZkmEgiDp5RIy0t5TnsvBJD3jrkkps3EwcEB68MD/WJ9YElDNY1/CIFp2rI5PSGYJpxcIo4BF3RR+nj8n/mx6U8SY8TZ3bm6IFt2ZUJByi6WvUtJrBMHwHUCSYfqkhHAUepLqhqh+UAoRTPn1lQ9bhFkNSxHg0chizBbWYZlQkqjTVdtsLloFm60bVNh4pqFJDsZHtTHY1kfzGDq3ESL1ad/LB86wkNPPKhJSavGW62klBPTtOHk+JhpnhvWTxc/kKaplcNoz9meWSUqqFD1qhHb5xBV+PQLFpf+n8+vuSUtlIYi7FrGAmYhOUvgu6SMWqzl2iy2K2c0V4sqXPO0ZZom8pwJww9HLrnIH7QvfEL3GxZrp4Y2976fH/Uo+zrfwJVMsjIKscAYB6TMnJy85MH9ezz89FOePXve6u/cunGTL3zhQ27e+IAYHS+PX+BxjDGyXlvYio9Ms8NRFoWxFFOOF/QHaOxZQdcN10lh7x34QMA1pTWEwJwdm43Gdk1JLJBf/eDzNnF6uuH4xUlLu7VaO0oOeL+68Fm8FwIoBLUQUpo5fv7cLBwYxpGqMeOWTNkipaXZwRUVPiHgfFBHey5IWECfUirk41umAKQgJe5o5xqPo+l16gKtjtVKrVYYrMXRVCPC4Di9mBVykoI4kLJE9S8ZsxetJGchpWKBfl1KH2PBKdxhJzDBUaHFSiOvDLmaCaHW7gHTbirEtpNegDbgnHeq7XgboI1YsKRcocY1lWqBLbnwKhwzT1vOfuOXlM2UlUQgommF2kLQYE2Po1Dji8pOTJY9IzCCSiTEEZyjWA7AkpOVpngvhvD/75urilip77Bu1yZSGpzqvCpgzUp1ajFVKwmcWbnLMq8oyMw8TaSUWDHuiKw39+/ibYYPNM2/t3xgETyZiwvM/U5b7YvSrTPMiWhKogfSPPH82SMePLjHg/v3OT4+wbnA7VvXAPjw7m1u3riq9bQ2iXmGVVgKYAKaZcKgjF5ZcMoqsn5oyIjqk7oQLExbez7OWeYRK4hpCEyahammuq4VBpyeU0M8Cmma29NzTmxNe88huDwlVuOaTTjl6ZPPmgC6dutWi8SNYUUOgnOTllywh/Ut+WXcNhJC5Kvuz3BH/mPmOBGGJfaE4AhoATqFvwTEL0/cmq++Iddz4uvwtUmSC2SnQnFJMAdSdSepxoK29scCnRU7lxMTPiaAvANpWr20fHQtTU5Pl6YWkqvWiGqlzhxUnZG0WFtRdb5s+e5a5gG7Rd8ECs2f1NL11MqIvcVoOfAqNXO73XBycqJUd2PcuTpQ6zPuB65zzcrLaW7PGQdSI+990LQtXi2iml+s5IlhXOEu44A+x7a8n4YKsPgFKxzsnaP4pW5Vthi+xaLWxa2HWhHIKTFtt6Q0d0DeuT14Uw/PFT69AKqIcy98Xge3/bBtHwL0IpASLqX2DNM8c3JyzIMH97h/7x5PnjzFOc/1a9f50hc+AuDmjQ8YhsDDFy9IWStBe5ebXxq0897pe9AsK8oydL2yWS0fW1e8B3wX34X9nQtptqfio1o5aWrVoVcHK0JQRTJZXaBShJRyE4iaT86/lgX3Xgig7WbTqp2uVgdcv6VFlNZHh8zzRMmFEAcOjq5wcvKSPCdC1CJMmqQ0gfOkMuMofN//XX6y/KJWCQWtqkl9BxpY5S2xX9+8ZbWuiRJ99fHYoptLgSlRSkIT7EgLcK2tgmVVGWnPvnPWFtGYFkmFnA16MnOqjzivgmepViiL/4kKry0wXLHr1UJ12iHX7s2b72mi5lVbHkANpnWtDzY5miDLzf+03Jcj58Tmm38VgNMnT83yyU2JUOhPHZLaHfMp2dJQsi1EsjzHShXV6rhGt3bai7qPr9r2awb3ZfsRtu4514wGutmWeKHR6r33CEJcKTQ0p4SkZY5UP2KeU8vn54LWkjn8iT/Ila99DR5+91wI7KLP7G2/6KdevwqcnmjwunP27U1Cyp2zjwfImTJt8TnhRYhRn+HTx094cP8+9z75hGfPX4AL3Ll9mw8/vM3duzcByGlis9mqH9wFHB6XTk2oVyKHZq4OrhBjIMSggfCutNRJIvo+ehGtoIeo5QqqxDqt56TITI1LSsxmAR0crRWdcH6xfuZMSYXxQJXCECLehdfG6r0XAgi0iqfznjCMTevebjZstxvSPPPB9Ws4r4GH4korcrQ6PAAX+T3p5yiiA/rZrf+MtE0Lq8PwtJoE04nGk7iwlK529Np5Xax9w7dBs07PczJ4LFtBJ71A49lXAUTl31fNq4PFWsJTw2erdYNCdrBYLTrZNdaiCgfXsVMsZ4FZJJpjbsmMrVgvot+FQYPTUk42/qSdZ3lQJrC6csmggqS5aKzLFGG73fLi+p/S93Xvl7SInwmUECJpnhvpQO/LdefoMjI4FggOG7z7sT4dU64Jn0sB9Lk0X/2n1PeoYyPEsJsmyimbaidfRn19pVBqqiopymi1fHHrccThDYbbEgw2f53A2bdyLtpWLZuy93f/886biLLdUsKJkPPM2ak66x8+VMLB06fPCGHg9s1rfPTRHT744NCQATQ20jnWB4eIBIub6hQA9Pk6KWrt1bWsQf8L1KlZ6G0O9WSpKqRKF/Su5i4ijpRLW1OHYbDKtlqhQFCraxhHDg81EHUcR2KIvI4v9F4IoHmeOXAR5yOrdSA3LVdvvmRhe7Zp9Wo08lofRJoncJnvuv8JcUJ2M/i/YGdeHq4udBlHhhgIYZf663w1F/WlBu+an6meZU4TOc9aT6OrMIpUy4fmj8EvYkldL/0C7pqfpD+/dHhpC9CUGmhaGvGoLuI++B0aLH3mgm7SCyr0imS2pTBXzbMd1gkxMH+ZI0Tf7ksZesosrIFqIsLmm3+V7XMNhNPUG1rnpfbf72jKBrtJDUbs/D52nL4Lhd00n1+hzLlNqB1tyrlXFqXL9m5atUBrWEJ9ryFESya7lHWnft9YLhXiUd+QmBmfUiKlmvleId00z0zbiVURzRDf9+Gcz1XBu2gcnCdo9oXOeRbL77Tt9/Vny6/i8oQrhRgCL47PePjpPQDuffwJjx8/YZpmvviFO3z0hbt8eOcGxyfHPH7yGICDccX6YE0cPDlpGQtvaEctaYGYz0csq0EpDflY6mYJQfyOUteHmtRtS0YVfTgKs0euXPkAgMPDIxUw0VNKYRwj165d0ezYK52j4yoSY6CWZzivXXJZL9tlu2yX7bL9rrT3wgLanp2RUyIOo/lnrObEuFYGWIF5mik5ac6psgS1pZQ1r1oc+Vr800iA+CLhV+MC95i5H5wwRE8wa0fp0tqHghIJVPNXs7bF25j2oBjo3IJaJSfzV3RwQ/3X2HrivEIUdD6gwgI9IS2nG841KKOm8mmBZOYj2qFMV7KAwXceodZ33/E3NTofLdBvp7duYSj1ipDWgK853MoCZdp32+2Ws7OzhgvnUhr91rdUQ4uDtF6rFMvHV4vrWX+qdeODJhkVIyeUNKvTNYQFFuyOu2yfX1t8jBWCi5Y6Se2IWl2zsinB0lUZscehPgZB473mSbXjlGac98zTVmHbLPjXrE4VeNr3LvRwW2/x7De/t/8PYgWdZ21d5EP6Ot+AlJHNhugcLjhevHjK/fufcO/jjwF49uw5wzBy5/YdvvjFD7n6wRHzdkvOMAwakqJMYc/CZtP5HMeBaO6IMhWQYimT1D/jwWC4pfft1bQptHvn0lytuu9cCtN2g3OZw7W9dzdTZpiYdA0mcTAK6yE05Mi7hCupC5J8tb0XAujs5FQzuFqgaS0NOwyDBr85x+nxzDRtWa0OCGFFsAJnYdACZ1+Vn8cFT/DqfMMvyS19KeR5Aq/O65y9OgL7UtqlkMVq1BgjDXTBrGSGeZo1viVrMbucu6DONgJV+Gh9Gw+ClcDeY6/VmkPqvVUhJrtQVZ0UpWKy7AoJRTSWY8WVHaEEBprZINtd7Jf4pkrl7DF3QUhTJhnrJVntpMpPKKWw/eZfZbvZtvLoWi5c8eacMm5Q2nsVtPW8lQFVZ0ApCe+XIFMfo0ZXl6J1gKqgbsQQFAaVPsPCZXuXLZWiDC57/4vvdFndnKu+wloqo61EWhyyBOp7XJziuwHH8zwxTxNXfvKPMH/rX+l31ofzFv7zgkXPEzzn+YnOO+aHbRf6qirjze7v7OyEh5/e48G9ezx+rPBaKcLNG9f44hc/5Ma1D/BOOD2dCH4grM2hXyaqH7nOZ++UDTcMKoBymhHvCEUZiN47XCk7917fVVsjipiLQLq4uiWgvQDTPDPlQoyeUNewecNmFiYqgcmqDAmUjnCiIR7vuQCK49gc1ynlxozZbk6pcT8hRqbNxDgeMK7G5UECiONb/G2+ln4BP+rjLrmQilJ2xWnAp8ORU2KueHbnHXNO6wj1Dvy62FcBtN2qBdTSy3SWRmMF1ROKBWqa/0atpsXJ3pdXqMSFjgOwTNDWP7V8eqdhb33poTUIdYk819O2CwFLcGxdIJZy4tJw/JSKYfRmAdm96vPIbDZnnG3OmOe5qytUVIiIQ8QyWbO7QOwQD2wSOG9l1PssB00Nk4XKXZ1U9fkZDf2yvft27dotps0pmWwJSDt6vnTZOACK4Cy9FoA3gpHzGrrgO59QS4+lh1nG+omc5h368H7b9+mw93mfjHCeAOqtpLr9vKWy78J+l84Tjg6zfICy3eKlcLAaefr0CQ8efMK3f/u3ef78ZZujd+/c4YtfuMvNG1c5OT3BIazGgbA6amvhdLyhZrxPCbxkhmHQGmpW+dlVH3aN6ylLiEZTEhryI62zTa/oFo1eZgxWnrv5hACRhaRVA+prQHqRZS18k2X5fgigYcQ7x9GVq7x4/ow0K8X65OVLhtVITplkgYdzmomr0iopprkwDlp172P+HuRCwPEV+flqRxKcJ8ZIyapB5FzzHy0R+oLTmkPLe2kvsi6wKWXSnBfYqwofnT66b9X8GgHfzl8JC0Bf9mCHRMAuIWHXsFogudpJ6QZKO8Pe+WRvlu7k2Or7g6Pk5X41c3iXbqd/MCIkgyJzrtYMaCCi27X57XrJyjNrnres240Kril2FjBl3m7UinUKy1V40D608+48oMv2TtuNm7d5+uQzLbMhpdXIUj3BxmQHuwXvG0xelR2p+QydqUrSKRBmVZWcLR1PwiP0hVX2Bc6bhEW/bZ+oUAXJ/v6vg9fq9687xoFa7LaGyZw0fubkmHuffI/79+7z+Mkz1qs1N69rHZ8vfelDVquRaZpIqRCt8GaMsRGyQDR7uMX2KNXaEeOwPGfbT/tY14k63/t9lr5K/8c533nntKCdCJrbZVHOW1YMOhSlO8/bIOTvhQAa12um7YactjhXGjPm5GVinA8IMbI521Bk8RcdXTUoCc8QHH5QSqAUjcj/1+VvtMVziJF/kz/X0sn/2vUV3nv+8LMNNEhHY3Iqq012Fmfa5yqMGoiKWliusVBruVu/904Xn1LpoLAeFtNz2d6yP/FE/WEV8uu6sPxe+iavTJsOBxa1RGq+ulJUaFR5WWSJQerzvVVB4LwjfPw3LJNDZ4EYy6nFUrVrSquYqSXF9frOaSGzMAw4H1qWgzxPeD/inNaTVx/X7gSqbDp5m1F+2X7H7er1a0zbDZvTUya21DFYrehs8WHBQht28o8pL9iUjrqQASJkUyRzSoRhhZRCmic++3/+EXfu3nnFSunbBcbRhW3fUnob6+e861wIt0ErrYBB165obsRnzx5x7+OPefL0KYjjxvXrfPjRHQDu3L7B8fEJ280ERBPOpnRVyNx5hjhAGCkCsRRiUBdELUbrXaaIFnlscKnfWS1254yhQru5L00pdn2yZGPcmWtDD3WNfl9K7tKOLSiFAi97DLu99l4IoBu37/Lwk+9x/Pwpflgi2+ftxOrwA4p45jkzz/pwNdGlDuKDg0PGOSBDQNQNr5x5kSUS29LIfNf9Pb6af56f+PQFv3Z9za+f/g/Up/Xl/PPNcmnZotmRM4u/BxaTVr/otD3toxRPKV08DsuL7zMKVKf+At3ZL6NG7/ZBdvvQfdf3c1/8vAocVOhEt/l+0NAP0sX3VH0/VQjlklv6nJoOJ6dk2lDNBm4Crg9M7WHLEC3LgfoGarGzWs/Jxw5KKBkk7FhAzs532T6fdnTlCpuzU6ZpQym2wNo/C0Rqlk+X/08RVSH7Lo4IVdzqWEgpETsBNG+35/bhTULnotGwv126n7c99xv7I8LPzP8Qn1OL1cPB82dP+M63vsPjJ08Yh5EvffE2X/rih6ysVPXz58+IIXJoMY0lbcCIU8WU8RADwzggLpJz0SwqufDi+fPmp83zTKSY71VFag1Ub6xr76iIPFiasA6iq32u87yGlSiE2q1nsijSJUtnCbGjfAqvn6PvhQBaH6y4cvUqaZpZD4Pm/QKmzcQ8z4Zt+iW2wBW2lsivOi9z1tQWOWlG3RBiWxhFhG+u/gYhBn7T/Y98pfwcv/dsy3f93HDqf3FjxR9+um0Qk7YlvLT/pZNu548OonIUZ9kNnFpEJZcdoVPT5zQGitAgtybYbMHvfTzNsune546A7B+qLMdVK2TR83aBhNIfLNIGzY4FZOeo8q+yAfsO1AFa87zVzAm7967bQhwUevMBanohu/dgqXealbMjeBdtrVZyvWzvvjkpDMPA+uCAcRwxRJUak9cgG3tvfTBqQShuTxN2rpWrB0hzQg4EiuaEO/y9fwB39lzT+tRDXtM/ec0++4KGvb/dOdvedL395opAzvicIWems1MAHn32Kffu3ePZs2cMceDOrZvcvXub9Wps0PU8F4botCyNj8zZMux3Ps4QAiFGsni8LzjLoD9tp+aO8CJWQSC0tVLRA0EuirhRWiPLWocJKFnIUWYN+dAFphc9NuMQcTZfgwmqxbdcctHA9wvaeyGASk6EGDk9PiYMA3HQ7KnD+kCd4C6T5i0lJ5xT0kJ96NN2i3O+1ROZ5xnvPauDA0BJCFWSB2PV/bb/ZRNOvmnpCC0if2mLU77f1putdd3cwZdLIaWamsTvlVZYHLeL6cKOAABaXrcmpFy3+zkC51Ukak9KvYLx9hDfrt+Ies/dxcS6UIxEoAO/7MAErp7XtKpiFWi1/PZyfm+TyRtDrpREH9yodYKcCXA7Z1NZlzo0dAvYZXvHrSxZyUOMzaKFCtWodVPf4xCHBq8hBe+KKns7JjUNlp7nSa3eoEGW0zS3Yf+6YMWLBM+bBBLnfP8mgfPa73OClPBGWX76VBlu3/3Ot3n06DEiwq1bN/noo7tcv3qF4+Pjlum6VvnVzhl5yQVl7FaFy3tFChI4imbbz4pC1MLPoc0fr6nGSp0ju0hMtW6qWfTK8uCcWk5S2rQrBZIIFXFvud+yI+MpScBlvBe8+SNaIH0Wrl/w2C4DUS/bZbtsl+2y/a6098IC2m63TPPEdrPBhcjQactOlKa7OT3RTNYhKFuro3/5ENlutsqeE2FcrdmebVgfaqGmEKLm+ky53bH3xbLFqgz+Q0/OXulXZfjsFF4zWMyVmuq8dtTtHLeYz1o9sgaIArsVQi9S4V3FaqVpJL01on+do/edZwm56o9ZdJ0dL5HBbj1sV62L5daXI7z3fPCBpuQ4OT5ZfDrVFBTVnjSd+0K3rvuEoKSDCp2qHyksaXv2kkdVGmlfM6js/b5s77Yp002h0nlOO5ZLzplcNGllNPjUx0DeLn4/WKxZqbEn0Ki/KSWKCMFpDZrtNJEpO7DPea2O6vO2v+6YH6ZdaFWVws/M/wCXNFD+yaOHfP973wPg3r37rNcH3L19mx/78hdZrw7AeVarVSvHoDNZLPGn0tTDuCKMKzaTQnlFhO3W0pGlRHCaey2KW7J8145VskeHEiyRFoYgWIhJT6Ovq1ll22axhP9OEYckUEN8ctHqytmsIq2CuhQhrH2OIRDe92SkL54+sRtZcr4BWoLZeWI40PTgWRicI81TW3iGYTSOOhYA51kdHLLdbFpAawixBbWCME8bpk1htT7gJ1Z/Xjtx7jiXWrGm/Y0tyhV12Gd4NE9LxV/bIFhYQbWGzw6edkFTF5PbkVOLTHhVBOn3nQDvz7XT1y5YtYfabLh03O4AACAASURBVENPFe92Mn+lY7VecXLnF3D/8i8twbxYjnCxZK1loaxXoRLiaJmxhT5OYT+xqF6/LNu759c6YkGPl+3dN+ec+gSMUNLmVoyda1FohITOp1Dfs5JTvJJN9nCfJTegLsTTNJOjBb/uwUf1M7ydMHmbfX4Y/48DJGd+ZvoHuJTZnp3y6MUzvv/97/P4yRMAYhy4e/sWt27dYD0O5JzIeUlWXM/kXG5j3nulV4cQmqs5l8zm7KwlG3WuQm69lrjM31LK4sOBLslxpzgX0aSmNteDCfsYg5apENgWx1wgowp7jdWbpontnJhzbvNSS0A4rRpg7eBgxXr1nhekO3nxAj+s8HEg5aVSoKbid+bE0iCsPGtg6jyrfyf4gJRsjDdpa9iwWrUXk9NMjh6IbLcbnj95TJo23Lp9F7ndae8sLwg0Seq03bDZaH/+2fXfwHnHHzv9txjHlb77zgcC6OJdiQl0/iNZgu6ag78fPNXX0c6jv5QhvUy3fYOpdyG5nS3tkOW7lqx0IRPYX333zdrZtab0HHacc5Sv/HnCi5cmfHb7PSdTEEz4OlwjhISodOuiIHOjdeOWGIJ+YvYW237y0lLSjtC6bO+4CU2Jq01LL0DxC+7vXDfuwfyH6hsKITalbH/MV59htiSlBUGGYTe25NUuvU23tW+v2ee879xrvgMVrC5nXE7kecvzZ0/43ve+w8NPH7b7+uiuBpmuVwObs7NW+XiIgSJqGYTgGWPUcAiq4oX5uS0dWEqUNDPGoGQrceeiC4u+ppaUKsPn9L0pwDbvuoeccyELzOKYimObCylnrhytGFeagSYTKX7GlYxQs6XvBu577zg8OmI1XFyz670QQNvNGT5lk65CrEXGvNfBaLRrKZmcNeV3fWApZabtljiObE5PGceR7dkpB0dXGg17u90orXPEFkU4fXnM0eERkwmXYbVeNAOnwudX1v+cF9vnnBaty14+K6zXK6a8UQEETeDsUA3rSV6RFou4kJ1FfsfU6E+yd4oaD7M/pWTnyP2v6hH1elWplFeuuVhDPfTWvmvyyyqweo/3QYWJ7VoktyJzYgMxRM3tBlhlRX2fO8WqZKGdL0Xx9Hyl5a+TZvnmPCOlEC0NyWV7x80UsxAHpe2HJS1/7piOZgSpsmDqe9WsSylI8OTsdgQUKFyT5pk4DDoWhJ1yKfDDQ2ev3Ir93jPCzt3ndYIpzwlXMoNzPHv5jAcP7nP/3gPGYeTDO7cA+OijO+Rc2E4zPkQOxwiixJsl76MnDoFSAiVlcta0Y46pzb8QHEEgNvKTVVwmdJ2UJYUYtIwTvWLaZE2tI+Y0ltJ1GbNzVtZiEseUHVPS8jGr9UGD3tfFa7ygYJUElqe6ZLcQon+9ivCeCKBTQpw0DsAkN2gAqfeBNGV7aupXcd4TvS5oUjJpnklpZnt2SsmZcVwxrFaLNTSMqnXnTPCRYVwxzzPbzYbf3Pz3ABwO1/gafwZ8oKTEPE/8kRc/zvGLY/6P8iuAQXx4/tmd3+Q/Kn+0DY6mTNQ/OA+aW0yOJfVO9b1UISE7R+x/0mV/iUamO2dt7bJ9tdOuD1JkT/jsNVl6qf8vAxdxbVFxzhGcJwTfcj+JFNXUqsBwTllTYehgumoVLUkqF7hyqSnT7i5rgkURoaSFSYhzeIsjumzvvrkQ8Djz1YU2R+MQ8TkTmorjKCkxbTftPdYkszkls4I8uVfCAEQTkrZ8iblQkiAxL6lmLrB231YwvWm/tzpPkRYw/SfkH7HdnPDZs6d89zvf4fHjxwwxcvfOLa5f14U6VKxeTNBY/STpCLfVt+1cUMs+F6btFu9oDDf93vwzlmpLnIfQFXzL08LIdTX+qmZPWRAe75wGB4v5pU1DbS4Ce4/OB+Iw4Ic1YQgcHBw2RXIQr4mW7XzKcNxzCUiBkl5rpr4Xs7fYolVqiV7rsA++RcLrQ7NM1o5W5XS2Y+rgzfNMmmfyPLdVNg5D86OMq4FxvWa1PmBzdsbx82d2rQiHCu9948q/JKfMvz39Pg6PrvD1l38MgDB61n7NwdkhrPae696Kvk/dlm4x342xqeDWBU36X68DIKTFzahZvQjHZV/ftl2IXO3AbnvX68sqAj4o/78KhTTPLc+byFLRFGg+A6zQnYPmF6rPo55nyfFWraNlW6uF5Drfw2V7562mTwoxMK5WzJOlmpFFq7ac6mSpik6nBNnHEEzTr++4myc5ZYL3DCFCETYnJzhZM5oPIQyxJaOt7aIhIHv77G/bb28lfMSCqmtaqbTlxdMn3Pvk+3zyyScE77l5/ToffXirXWyaZw4PD5dCjVZHzDnHYMXdYox4PAWn36esefO8p1VzqwJCNNNIEuHF8QlhGFgbLDY4sTlngeG5qKLQCRcvSqlWhKFTioXlXVQ/kYtEP+KHyLgaGcdVV4+rBhwvL9ed9zbcq5Wn+/ZeCKC24BR1Vtfsymm7UWiMqgGYtN2rM56yYsbeaar/lCamacOIvhhnuHUpWV/eNBPiwLQ94/jFCwBWB4fk1cx3+DtcfbjhyfWfZlitiMPQJgBI0+BgWcRfiaHZfwmm6dXdaoDmzkHntP517kNxFzxJqvBZxlJXidWw5beZbvt7SP+P+W0qhNYz0uqg1lifER8CeZ5bZUc/xO7dmVB0lbiw9LNkheiqX0BKbuW57caoZSgu27tvVUEoIjvVcqsiVaxk/FKmIbQxWKFf7zwhBnIp+LyQDvq2+rUN1+58QIgvOTs8YBvg8EiVmKvXrhG//II3z5y3az8IpKewojLQfmb7fwNwfPKCTz7+Pt/77vdwqL/n9u0b6Pg3S6GmJur8NWlWhTtYSW4tP5EQcZQ0azFMZ1kFqFZJFRqa21IcZKlkj4pSmJ8tBiRlzaLvbZ7UuYUFBpfSoDOBHcKCcx4XNJt/fWcxBo3RsuexPHfX/bv/aVdAndfeDwFkA7TWC8lGMJidJw4ryy9lOafSjM+hRdd658iWQds5T84Tm9NT0jTzgSX7i+PANE2WaXfW7ArmqKtJMudp4rfS36R67X/6+U/CoC9wqFK/+Yh6/8xixdS7aU78+ifswA271O7zmwNa6jfgDbsv0sqZud37Vl6B3F6vCzbBd64UWj4UC/5t1k29XgiEEM2Rahmtq0Bsg1KaJbYQP+p5F2uodDWTfIi4mrRUQCgtfc9le7fNNeUukVJayAOifoBkSqRxETjoAlE1T5wGR1ZlpaV16RL24hR2X40jznvSdqshFl79tPP6gBXnWz2vxwba6V/Z53W+nv2df7b8I1I+4/j0JQDf/vZv8+izRwzDwEd3bnPt2hWGGNTy6IJCFXLWtSSEwFz7ZJMypWxJR4WAI1SEwC0LfhYUFnM6D8bouRqsJI2JZE8iBGeCzjA+D7IUP6ZmHPG1ArSvGfRlSZDtHPiAE113axYa9ff2T2qxfPYdDrRvXk+jvwxEvWyX7bJdtsv2u9LeCwsIVDI7sOqK2kqeEcnEsFIrwiqjhhCb5ps72etjhDRrPZGc8C9D2352dkaeZ4ZhoBiNW0pZ4L55YnN2RogDz2//u8TjiHtF1VqcJzsWTs9ppvvbLKRKUmhQGK/SnF95Hu2fPevF7e+k25wsGt2ePWYbemN9we7bOTtltFk/u8ppO05Qn87pyQnTNC2OY6tBr+UVoubnq26AimWbFbn4PnsCQm9FFnIqpjmGxXKqMGYppGnDZS64z6eJafApJbabDSuDpZ13BHEQvGVV15pbS65DkFxwwakGjeYsa7n+6gXMh+SsACUGU4fYFa/bL67Wtf1p8aZ93rR9H2pyCJITL58/49P79wC4/8l9gvfcunGNjz66TSXL1BIiegLzvThHDJFxXFGsvEwDKVLGSV8exmhKZlkCnGwTIQRWVv9nGEbCemDeTpSkiFGbVFi6sOacWyZ7fTZ91mqHQnwVAg/RgdPkzrXGkCZqXgJKXVtT9q2f7pm9hY/2vRBA0i18fYLCUgppVn+NAOv1IWmam4Nb98kt8K3SgUsRnBTOTpQ+7bxj2m4pObNB2WwiogFXFqB1b/WTHK6ucXDlKv/e05+Ai6jrDeaqfRd2/D71c9tFdiZjPaYKpGXJXXDc856Pve8dxKK96Iq6UYeevCq02oau6mm33us5uhEjy6H9taArSPcbv8S8Xaii3mAyH5QgUOa5EUncjiey60P1LeSyU0irGE3b+2hFtCo1Oy/PsORXsiZctnfT2uLvnAWKKgkhxgGkWJ5GhWuwQONWoMwEig++KR0+W9LK6gttfkor9mgFHGOMjP/+bQBWN2/gHn0C7PAaLlzj9sGi/f3eYn1cBFEpzNOGB/c+4Tvf/hYAp6dn/Btf/TFu3byGlGyQVjCltMZFaXaIjPrONJRkVgW6xh5SYWjMR6MB3cG5hlG5aD5Xg83iMEBYk+e0QPtS2syqWakFm9du935AWk0n5yBlpVoDivd5kBbbo++1BpnX82giaOx6e5lhqEru68HN90IA9U019gX/T/PEYDE3Ipn1eo0PQWtTgAoZEWVb5aSsLCv3PE1q3ZTnz17xobREefbF0YNfYTt8nQ9Pf528+gPmZA/GuumFw6vChF7ba8KnDgrbui+EqmXUmzosx/W1OazHO3vVfx2aUt1VjLizcvQ8i7XTa5t6gmUCSHfe3orqjKz2ueTC9pt/jXmeLeu3nqem06nsJlcZb50jcsc665iAztHF+GgQq/fBfD46cSl5R1A731FQL9s7bS9fPOXg6ANqdorGWDRLpeSM8xqHEnyAUtq7CcEb80st4ZrGynuvwokavCiGSljsl4PZrClY5sSrXojz275/5yIfkOxtq78XtEA4Oz7mt/71b/Lp/ftEU3p+/Gtf5oMrh8SgTvoqQaQTvsEvMTZZMiUnFT5WjBHoLB9d+83rSfCB0YI4x6ORNM14NFMCLnRKYr+waPxd0/eKxVS5fq7XtadbW2SXtSiiFk8NFPd7ip6I0bWrMu09HssQs9MvFaYXtfdKALWB0qi2rqX98CEorBbD7ohBSGkmxkhwkZAGC1xdMhrkvMSl9OevlVEBnlz/Kb788Bvk6zdIft6p9tc79PWKtL+rYFksoMV8bst6Ez7d8f0AsONbJmk7pqdKq7W0T9eWXeHjpL/N1uU2JuoHm3k7xSZs8vQTUnZOZP+YprMaV6xWayswpi1EzVJes5Y34eO6Ptm1dRLUHHmVRLIIMpHQoLciFnthMWCAfrd/s5ftnbXHjx5yPWdSUrJPG4lG1xK02q0zhcQ5WrC2laBBcrasFxibNCyxJ125+lKUuKBwVuwgHfdWwucigfO6Jt3PfvupZ/8b/8u9LzN+/DHRwc3r1wC4fes63kEMXjOy2IKMc40pqyUJlLxR8gwUAr6aKLQHVJ+oaEoyQdozAiB4khSEgnOxwZk799itHTVgXdcY1wTiUsixLDC47dPmlvMUs3hwDu+MLdcUZlvvHA2C65VK17+BDjY/r71XAqi25lOQgiuFed4SZABETU8c2aybGjeUUzYKYmRXh4cqHupfT278NLee/VOkZJ7c/KMA3Hn8jynXblBK4Tvub/Pj+c/qQthVCq2692LddPE83YvZ9+80uvCiUrXBVn8DS02gnbbcy75QWs7VCZ+9O6d///X7865jGtCSRkja/u2dVCjFOVa//7/kyq//EjjHZltjQsqSYqezTBbm23KuYkpBKUqzpkgLcnMW+0WFY0qhWBBjZUNehD9ftnfTnj15wmZzxvrgYEcRADplSq1SZWAtypLSg1Wb1jxhBtcYZRggJ0vpI5Z/0Vh1pS2meqbz1rJ3NgaksD3b8NmD+/y+j/8xz45P+MqXv8iN61cBCB6GOCwMTptD3rmWgHOeJvVJp6S2gANxmhevKm5zKlatObI6iLhZGWkh+laqXizcIQSH8xEXIq4Izi1Sodo4Uq2oDmGo8UShIT82D+utiuyF+TkUIU2UYgzknLQ8Tr1YU8zbAwMgWqiEtxjBnDMX4RTvhwCyxa0tnHZDJReEhExKv96WQhxHVuuDZrmkeVaN23tKgmyBWlI6feaVxRYeX/9pbjz5J50ZipqvVkLQVbyzIxj0r/k84aP77FKse8GzH7UisHf9vY42SwV4JbBU+1K/2xc+srMXbbBU4bgDr+0YeNXO2rP2ALHs1s45XAiMP/lfMb48Zvr1vwLoO6JzMJ+n+rTnZgF5UsxhGnyjWNeElSUnE7oeb1kxesF2af18fk0ENmcbcsrEGImhOkmdVhw2dbpmYS5lKcOuTuxAQbfVFDu1JDvQfqeUNU4oRrxAFuHpr/1TAIYP73Ln7u0fqN/9CLlIET/PYnLAtJ148PHHHHz3u0ynJ3ztqz/G0eFKqc5ADKFTgWoguNiiq+EdVfhU4QSODDs5FIvN7SEGjo6O8FM2KnfBuerrVisxDgPOR7yP5DzZHFqo7TF6PAuC1ACPbq4ofE2XNskQmrpGOIXNclGyRAgaD7Sdt8wWtrLEA7uWeqsYrHhwcKDXL540JY5Pjvnogmf/fgig2kQ0TTjLYispK445DJRcNKeb0DThlFTDiDHiXGjFzubtxCtDzlbnG0//CU9v/juA49YzHdzig5rJKYGT9kB9p8lJC+zSk6lsWlhu9SI9tkp3L/v32ltEADsU+2a6LNZbXw5X99i17KS/ZIVzhS5hqmmp+9eqB5q1tyc+W3dSSlqJ9tv/nQUUCmW7lEdXYbL0czH3WQLhalods2xqXMOOoMuZPM9IMcd21OJcznn2fWOvse4v24+41bi5yvQCWmYC1xzW4PBIEZLoYiVFiJY1u4iSf1wIlsvMYlhiv5hX+Hw3U0KvcL8NFLff9of8jhK2t+/LFy/5Aw/+V+Jv/xZeMreuX+XqlQOD2wxeq/OqswDUZZDJxkyrAahV4AqOVMAF1wLc18PItNkQLWeizx4psxKsmsJbjOgwmpWp7yKnuc2/JfloqVKnwaE7AsigM++WlUNKXrJMOE8qwjZPbLYbxlHzLTYhA0pgMLSknl+cxTA1f5PFG4WL/bTvhwC6CLTt8GGpGpZouvZ6T6UIedb8ScMw4KOmf1EBtHe6qv27BRJ6dO2nAHUWfnT2r5inaWeEVydr/ey6dDZ2UvosB93FuGh5lEWC7AqInd0X2E4nimP/Iq+cvTuf64Xa3nnrfs2aq1aWoJAjy3f9YXmaOfv1v8K0OSNEtUZyEZxJs2DQ2T4wVqplCQukltNSudEo263KpplzPkRd2Lxfkr1WwWqL1JsCei/bj6YJ+p6LFGU3+iUZKdBIOyBE541Usrxz5x3r9ZqUc5vLPavTW+mB6vfwOPL2DBfHtlC2elHdz3ntvO37o+RVkH732D+efpV724lps+XOratc/+CQIVpAZjupwu0VLUnJrHY1V7TPav7p/LW8bSsLTq0pc8bVipyS1VLSZ4jJ36poi1RijrfrKUErpawlK9A5UVJu60QLNu2EjxZJrWJ/UUhD9C3nXnEBhydGx4E/ZGWpfnR+63lyF/6wkBesrpdZbTV49eg1SMUlh/WyXbbLdtku2+9Kez8soKZzVz9FvxVzXub2ubT6P6YJGfU354zPCVfzhfXacWe/C9K+u/lEITgfHKlq4jlDsMCsPm16c47vGjgXaeEXamg9jGTWysJmu6DZPheqcvVjO589031LzPA3tXpehQpdXzNo7x4EYdpuOTs9RdB8b94H4urA7ssvWLJZq6Uy3Wq+OPMhhTg02EZEkLzU+nFGNuh9V2a6dtqc9e7SAvp8mlnHCC35L9De3zRNzfHugyeEdYNecintp1G4jQ1Xh6APHs2/6fRHHGmaCHFgMEshhoiS8i9ur4Ao3fbzpk7dZ//7Xx1/hqcfPOUPfuGEq4eRMVbvzS5A7ZwG3tZ0YBXq7gOvq++yiJKoYozM89zKzoQY8a4We3NtfapEDWxriBak6zQ2p1WpLTXmKLT3sTAGFZ2pMZINsWnPQAxC80vZVOdxLrIaBoZxYL1e4by3KgPtrIZ8SDtnEY0VWxh32vfRvef1gFqrC0xHydQPYtscwlJdUb9aatHknClnGzXneXXA6TVoDJAnN36am0//iR3vrIS08KXpP4VhqdK4c7D5NRZ64a641EVTaMu7u3iRdPadtCM7vLv91Qja3Xm6adZYcN2z6q+5t2C7enh/OVlguIXBtzuV66Cdp4k5WfwPTtPxW00eHxeDurKYxOoCVQEkBj1UHFufb24546i9rHOhYuf1ebdHYL29zITwubRXCE+tGVutZbEXinhklAX7tzmTU1Z6soiWI/Cd18d7LT0gBckz4j3x8Mhgqwrx+t0hcE4/z/MPvc3+3VQAYFyvWB8MaHhPDdcoxBD5F5s/o/3B89NHf59tUdKAWMZw32WxFhxhGPAhME+JcRwZxpGShRBH65fXf11QAsM8acyb60vP95B1aAUeVVZVAaSbvEGgLTv+zvpTCQf9/db1o026bt1Y/Hx9qxB4PXaZuV3ex8pUfU1Z9fcKgpPOybX7Bc0XUy0QLW2r1knPLvNWxruy6tqA3bGG4IZZPv1Fambnb8svN/rgt+RvW3JMUS0uZ77j/o5lBi6N0li97W0Br9fb+d1PH5a/6/t2u5x6e71Ktex9H20fORcM32HbdSy+Ppdtn1FhEXX726T9V/tTMx/3ZRcW56RZPigRo+RaHyg3QRJCxbmNMWQ+AQ1atdifnQG7RFhL9wyLZcguxja6bO+67Q60niBaSSY6H+pnaQtZfffVlxvMkV+DUb33FiwppLMXTM8eUI4fwPSIcvYZkk6RdIqGPvLGn8KrM4037Ff3/Vm+wc/yDX5GfhUQYnTocKwxPrAaVqyGFQerI/7l5s8xuEhw3uZqwAUjE1j29jio0BlXK4ZxZZaKb88ll8r4LKQ5keaJSnWvwQZSA3nbHNIA+TrXqtXjvVMhtZPqaHkHWKbr4DVo3Fu/QctoZBFA6fEh+pYYtRWZLHn5kfpTmo/qVQ3lvDextPfCAqqU3SooZPdLFjPPYCopLY1EpUq3c5QCUR9gmRrLfVcINZZG1+wlpjQzbbd86+CXVftwju+Ev9v6KaL0wlrvRI2F3nLpYhX2LZ92eTn360WT4Nx3tl+Qq5nA+1ZYfVBdr17ph9vXFff/tiWnwYWlOZp3xORenxaataXbL9kgiAoleLOOFLIoJRMGz64u5BYrrjfZZLGk8jyR09z+vmzvttU0SQ7HwTiwWpn27gJTSrYgscA/HUFELHxCRJXF6nyf50RL5lIEAszzxNlpQWaxelMRKYfWC2Gxk3enyEVWz0VL374aWH+38I4p8R/O/5Bj74nemcKJMfqUvXawvgIixGnFVDLeO+JocYg23mOIhEHJNEE8IQ6NlNHiHU1JqwSrkhKsIiEErf4MbE+7xd0U5ZIXco9udixyX5+IZ5cFVxlwuK4EimjcUcsFF7RCq2a19zpXWwb/3TV1AV7q364RVHyI1EwtF1k674UA0tabctVqYFcg9b6Ttot+bghNpSuG3VvbMcWlHdla1dzSPDNtN2zOzhZstCs3EGLkK+7PWpFDY4x1nGbph3PFYXflw7Ku9k36HZfP9fgaebxzZC/4XLVwzhOumGXi9uSOXaOD6ZydrP6u+PJ2s+Hlr/1FC6hbNCoNMKxFs2pallrrpdYcCcu7qxUlHaaJGcvN3vVyLzTYTYxFV3JCrPpqmadXrN/L9u5ag1uAYYgcrmsy0sCQB2IIpFyaFRRjJA4WkIjW7HI4Ui4wTTs+XLsAIsJmykhOpD9+iyE6wj/f4MyHsD+n26F7f8sF2y86pqlxuTDPOtfnqVZQ9hYz4yhF2E5nbA6THZeQIswewnog+oHgvTJDTQCNq1UTzLWsTBUaSyLkLSLZfMyVPeas/EiF8pZnVPcLfpmntaVS8FhaLNdTrbU1VpxzChtiaEb4/9h7z2DdsrO+87fC3m848d7uVm6FBiGJzknJIJGEB2MwdtmDDcjMUGUGDPZMlWuq5tP4y1R5gqcK22CwmTGFxxaG8dgm2FOYIIRAKLQ6qBWwUtOSOt5w0ht2WGE+PGutvd9zz21RNS1xcZ1VUp/7vu/Oe631rOd5/s//P8o3pfEak0SEUoqmaei7Dh/SXBhDMThKK6FgspaqqodFawiEtHC8XrshDFDMvEgqV/SXeFPa4NpJZsxGAEO8UVYRAVQo72WEei6BpXwUTv1LNE9E3tsk1oX8q9aG29S7KTm1DAzYvLDxjW16COU6FGMi0PH95Is9a1rNv5wePOO/IourNjcoMM3xtmrzsxp9VoOta5M20+L4hPViRUShrUX1QhZq67oUkIYUUsthVGNMIWfMubSQJLYLcSX5esdQ9pwLjEQCoRc6puDdkB8sL/WGiiL/Z95kybVct3SpyHQymRIj9H2fPAgBGkipRKq+14qm7dHaopWi72WRInLtQ98B6O+/yLJtuPzsAmM0L7vvlVDJb3WCA59lWE4NpzN/O2vc5OaD9DOXyzdCQMUABLrOoYl452iajsaIPpELkhvys8BkWlMZ0QJSPhRjaW0t9YXBlQiL66V2LkPYg+sLZNnhi5Ew1grHHAkApQClscbK880ccnmeA7peYOB1XUvNktIFHACDp5JLR0IQr0vySom7z0qIL5CYG4LH9Y6maen7Jj2eIV2SdY7m8y0m0zkDdDyFZf2NboDIK/1hBZx/yJa/5FhOJ/VHbn5ZyUc2RNKyCTnL6zi48CAgxam5eSdx2GKm0oOup7NRvDUMSb6RSdsIapXJPyf5RsZlI9Z26p7GNz/2TkYueP6jNr4aJxNjuYbT26T/lUNvGqIh9Na2LScHRwAsHvv7hCgMCEbJHetkXIZnrYqh0RmRk7jeBqSOhFUy/1V6wemkp5mRRS4j5FBbHBnfc/TbV7UpkrCcrPvJvcYYQbe1TVvCbmUydbJNcD2udxgDyto0lGWirRKAxQWHeWTNzmwbY7ZF0iEE7GWH+St3AaBvhvWDxQAAIABJREFUvRX99BMvmFU4yzh9uZ6Sh2YIobA3tM2avl0Lk0HfUleW3nm61nHXzs8B4DvxzM32LjahOgmu8EhCPqZL0iTCEed7YcbOHmLfygSuUu7GVhalArauipGa1BUhGmxVJ0BDT4hBBAITO4FL9FbGaOZKU0eL1ZtznwqZ3iiOxmdajurksaYck1aJxy8xUxhb0aWxrrRQEaFE/bauKybTGZUdEG85R3hmXj+1G8IAgVjLcdgFslczrHYjlALLsh+j6TZmGhidYtYpxppezDB/y+u4ePgQB4kLLrvJQ9GrKgcudCEJ7h28R6FPMTGfMi7l9Ipr8dWjDyMPZfx9DpWNSR+TYzsYjrGjM/qgNnJSad9Ty7+NgZpHYAZBaIXrHM0n/yGrq4dpG2GYiArwYKhSeCySpRQzXk/kF/TgDcXImMMNNbC45VWWohwmGR2pJs90PESuCSecF6F+9drOzg5N09L1fYLMD7/lla4QF+SkeEgM13BysiTnDQVynAo3+37IVSSNm/nWNvV0trHYWK1l1X14eMTFOPTT8aLvhQIRp787vW/+LQRP18q5lodX6dbH9O2Kru3QO1s4HwlKMZtL2UFMk3lV12htkkeR5rIyJhw5aiEGt8J1LYokqQDJ25F8i55OsEbjXIM1VZFIaJqOk+UJbd/Dl57GO0ffNnTrdYFYa62wxjCd1ESgrwTCnotX853mRXNmdYkogo+ETLPTrlG6lWMkoJUPgsTL6qsxrWCVUlitUjhQimOHwlSNUmYY2Ge08/jFeTtv5+28nbc/kXbDeEAweDHjb4oXlEJvp23p6fwFyEpAaT1sm5AeG/spdepYEe8FPqpNT+V90pvRhQupntR8Sf0S3ntu8+8uq/lrb4JxXJFr12cUUMFZIYN8Y0OILXkwp+4z38OAux9te9YhR+CCIVQ3/l6OoZXG/dE/pW1a+k5WRaaq0JUIA2Z+K/kw6IioDH+PUZA9fqiLGMeF800UaGgJow56QMG7gdaE7OEN3nHMj/W6D/C8vZhtf3eXdtqyXK1pmnaUg5M/WmkpVA1Ca2W1KUzXtTUCcjNGEJB6SIZnLykitX3GGiHcVImWR6nCwLxar9j1nsraMqq893jvyxhdN02BdhPDKI87tGFECvpLKcU7+RDH6wMuX7oEQHN8IN6Lkr4ftSYoj6kMVV0NB4oRkyQjfMoJxRBKalIjITWsxWiTPECgMsKPB1hTEU1VKMTatmWxOGbZuAKKOLp6laPjE1ZNQ5vCkzGFDPO70EoxnVRszad0XcektiJTo83ojjch2zJfCBCoqlcAVPVik1BYyTYbgZtck6QU0XX0XuHWkdXGnKhBaYLrefl1+tWNY4BOQaTluyFZNv4Ode3HjfBamrnPij2q0Y5y7GGCy8cI3tM1DROl0VVVjuP6XpAiMfKF+j28jndfm3NheNUlV3NGQGB8HZsGY9inaOnkNH36d9ky/zZK6Izsymi7oZbmNJQ775R/0yn56VIHVGmQKJvqcyIlzCnaJIPBk7CbEBvG9LkY6XFib3SBEtIUhFvOJUltUA6bpucQk9uf4MC51uvLR/jP24vRtra3mbiJJMaNoesHJgR5vaosLqrKsL01YXtrC4Cbtmt8iPgUkvYh0jlP1w3MAq73hGBTOC+WuqCoFOu1TIxPPnnI8paLvPplL0N/8XPcdM+DdF3LHQ/9BB+cvgOAZt1STSap2NNLQv2MPq+Q8fz27n0AhGpKdD0hnUv1HbrWUFVpdKskkWAK48OY0ST4VIya0GE5F6KNJgQn48YIWbJYJ03XCeChaVtRGe46urZjuVxwcnRI2zvWKfy4XK5YNT1RGerJjMySrUwstXC9d7jVmq7rWK3WVFaABSaxj+cbV5GEXNOjeWEIcWud5oNsyEtN0WisK50YL0w51tAfhhyB5JnCjW6ArjOJXAdwcJrssoDfSavznB3zm0ntcqYYi2HLq4er+w9w4eChEr/OhY5Qpar/HKuVVcVr4/dJnUIxXNkLyGdR117nmfe4aYDU6X+NUQScnr6Hlz02PGMmgeGaNj3M00YrdyJjNPzRP2G9XtN0HSYlSpWxw4SfO1aUuHnWZjK2Ls9UhMf0xtXma5OFY3rOzqViVTcQjuYEKQPR6QbigpRvSjm78/aVb7PZlBgnoCTCkGn5m7YvdS0kLIpzgfW6gzRJ+r7BaI0xFmt1qi2JtLEnv1TvHTGO4PyK0lddWpgcHR6yXi2wCravPIu+/BwxRLpmwdvjfwRgvW6Z6F1m1RYx9vwB337m/ah0nt/xD/IN/vcIAfqmIfaibaWiF4BELbo7zge8j0yMGYh+87wTAWVGi8Y4UN+E4ijRdj1tf8JqsQSlWCbjcnx4yGq1pFmLd9l1jrbr6ZynT/feO0dEs7t/gc9OfrwoyL55+xdplst07wva1TE9gYCj7XONXCwow1xQmhebpRh4vJAcLSp08n7ywn68jclwcT0qdB3NSfk5n7noTe0GMUDjiNXIJcgJxzNCb9fsWwqhSLIAQ2hoUB7d9LIUkQtXBf12mMAIOcyXwQaCZZcWtEKHM2gpTnscZabcDIcNdjBu/LlOlC4dInK2D7V5PymviEzb1x5Qjf6TVzsZ91/03I0UiZ4cHnN4cEjfdZh6Opwjhd2yccjMFAOHmxGtEmuHs54FofciQheT15MlGsYdtbAvpDqh8TkBgWifO0BftTadWFmcqFT1nh68j5Swkki0y/vpXYC1rPAPj45RMVJPaqrKFo9CZKVz+CiHadU4oMCYXV0B3brhyqXnmb705TzzpS8K9Lc2zOeTsr01Gq0iymq6rsUY0ZLKlD4gNS7C4dYTQkfXOfr1ElKdS4wRbQ1Ra7zr6Z0TPkpVjUovchJfhOGy9xZCwIckx5DvwwdWTcPJYsHyZJGKT1ONXSfH73onqDbncN7jfBBkWzqOrWoeDz/ES/b2E6rQ8Bn946y7EwAW66u8cfKTzGczptMpdV0zrtHL91VYZUaL8ByJAPEMfdsStMKpxIAek3JxlmNIhbNajQ2ZGKv8nHN05k+FAcp9roSvyrfjja5/I+O5XAoU2ejE12ycPIAzH056ScRINamHq4qD8NKT9he4jXdvHm/jUs+61lOG59QEejoSJ/ZCnfnb5q2MPUM17Du6DjXqJFpnmd1kgIwg+owxHFy9ypXLVzg8OMLUNbpOx8m2LuvDxxQqY+SBjPNBbEolZMMhobY+wWwHFoOS40l3VUKpkAwUcIqbT4zT9bVGztuL11zviAEqo9nZ3oHkrfRuxXw2wWpF3yVtmghKmVFYRgyOMYZbLu4zrSuRLhh1ncV6LfUmrsP0EjbzaGLQVJUYl4t7eyg8zgWOFyuciygjecnsqXddR0RhjdTgte0REZ3Wsul6Uo3O29vfwfUN2Ihbdah+TZW6YOskjNz3jtW6oe+d1LtYU1gOQohimLywfTiftIC8LwuoZrViuVzSNh2L1ZpVI+E2RnU0zvsk6hdKGC8zD5SpIhWHCnRbyHtRUgRaT+R+dnbmvLS+hZ3dLXZ2dtnd2xN0WxYMZKjfyWhfWdQl/bMU6ekWK7qjY+z2HDWtIXHUOT+UtmTqpbxfZjUJowVpLkj3Nz4Me3PSzG3s2fwxjzLs90LRLzWekHP1b5nxE8tsTMzYYUSGKPHOQf0vbp60HF9teHKDcc3+azxtccm5nvEhxjelznxGp9xmRv8uuZLk3WR3WQ2rlCysZazBGsPJ8QlXP/L3OLhylb53TK0dQABGAsMF5p5DZPLw0jNMuaEYyoVmmHZWiPR9T/B9WW2hUgHxOGeW+KdIHTxD9JUaFDSzyX0hsavz9uI151NYKSIAgyy7rIT4t65rrDXU1lJZi3eB5apJe0tn9yFy5fCY6aRiXk/Ymk4HAmamrPwBq+MjurYtTArGWOb1NgB7F7eojeJLTz9L0zqCFw0e70MZf13fo1WkVZLAv3/9a1I8um6LdLxLnfOLlWVvd4cLr3gZRlmWyxVkaqdErto7T9v2Mh5mE5Q2uEQD1rcdq3XDum3xPnB8dMy6aXC9K+PSe0ff9XRdT9N19L3bWBwDacJOHn6KMmTC5azFU9U1tqqpq5rgUt7UBFQCfQC85eK/wagt9nZ3uPmWm7jlpS9BgM6aOJ5bUMXYeS+0Zq7vcGspsF1duUpbV9QX97DzOdga7zzO+2G6S9eYOSEzL+dGXjZEfBy8prPajWGAxmG3M9rpSMv1DFJxbBjkaIdTjDM10rJBgTzpSSfIksKCMukxSblQa4PO+aVk6RWjBF9qegQeGNucjetWm/8eG5DydfbSTu0w5nUq9OzpcwEc5IRijvEaXTwfpTeNrxTH9Tz/7HMcHhzQO5cM8kDFg0m1PHEAd+Qrs6mY0Jgq7SNvI4bEYJBZDCBxhg1PInt5xQhBqjMZYtbp4SfGheFpFI/vvH3F22LV0Lz3l9n79r+IyVIlgELTNC3aiIdT1zVb8xkm0fcDoAOrVYP3wirQ9w4/FWqaaWJQn08q1Fv3ubT6IKu2Z2ruQ2nD/OIXmPpvA6Cu5hidQQuSE8n5inw93nlCZVguV1y9csC67VmcrGiarvSpyaTGWsv2zRfY2dminlQQAmsFA7eazAPOB/FOkCLstu145tnnATg6OGaxXNK0Xbq3LoUhhxBlIe9NEZWQvJvxGK2sYVLXTCYTppOaOtUHRR8wCXEXUQQ0z2VOvXQ8iTqkx6yVFMQaneYpDwSxpWHwSvICXSlNZTWq0gRj8SrNc+spM6uY33yBansLM9kSry7EYcGuBhG+glotYT5G9+6HfNgZ7YYwQDk5Pg45XfP7H6MpsqEZJQk3DnQGk0LZV3Fw8UH2r3xoM69BTC9TvCShDQLQPKnfw238wIaBzB5HMTybztDw5fhWc9JvY5Oc/CuOGdnwDTuKkbTGbiBdFJT8jtEabcTYDAzEcmxjB4XJyx/6n7jymSdYr5uizKi1Hgx0pkxxvoTOdDIIeXUVQ0CZtNr1XkJtfSKqzIYkvZ5IAiroUVFhptlJCDitDcrYIQGqh+6qkFBKfIHV1Xl78dpq1bA6WVE3PdZGugQP9t7T/vavUX3TdxCtodUe3Tomdc10Jqv3fb1PXTV0fZfoXHp6HzhcrNjfkm32JhU7swnrMMG5gAoyodejYlUQjaDoAlEr0MIoYEZesPeB3sFq1XF4tKTrHOumQyvFpM6sAjXTac2F/T329vfSeIgl/wuA1njkPpu2o2k7FosFWWIEYL1u6Lpe5EkSHFryx8P8khd91hqm04lwx/U9k2nNhb1dAHZ3tlFKYRMJaG0rVquGpmmx6ZpdEJmLDGVXORqhFZkgVChx9AAmSlRg3jnako87wjmH0qJmW9lKcngoVM43WaiqGboyED3BrQrzvQ/jkL5GZxkhmxi+M/KVUcjzBdaI54Wo5+28nbfzdt7+RNoN4QHlVjyhUeKc9N312lkpmOCF7n/Dpcirmzjoqnvv0QnScuHgw1zZf4AS8Is5ftsxmQsdvNYa13fpr8BPP1v9PMYYbnV/DZBVhUrItTHGPjIIDo4vfKjjEer0nG8qukb57lQGDgzJ/eVyyYe2Hmdvf5/7D95QdEAym24+Tf5sRjohKIVPq9iDR/4XPvfZJzg+PsbWNZPpTEgVR6iz6CWcNjDmptXO6CFn2h3XtyX0Nrw7NfxRangWIeKDG3l5jDweQwGKpPsuZ9MGbSs5x3n7ire+a/Fdz/Gv/zsm3/zdtI2sqL3vqYyme9+/p3rndxJijw8KF2CWJBu0sSLEVtVM6gld39O1HV3fc+lgAcBi3TKrLCrcwe5EwuNXj443QlWud/imhUDimgusF2uWRyfMZincFwOu6/AuYJMeTz2ZMJtOmCWPrK4sk0nN/oU9phNLDA5ioI+BRRoTV04WdKsVq7alaTr6FEZ2vSDU4HSdYS6al7xkji7cctMFdrbmMi+gWCyXnCyWVJVlZ1vmlZsu7KbwmaGuLJU1fHG9ZrFcEKUsibYPmGpCv+2wtsbUNVEpfAi8Zes9w2WoVB7hPX3XYyuD7x2LE3nOT33hGY5Ojun6lul0ik1UPWNPcmIriT5ckiJam0hjxxERay3WVKnOSDyzvF0moU25BckRXqdf3TAG6HSep7Qz6oA2flabmYA8kY07biRTh4+Ok6JZ3o8myDLB5RqXiO97Ukocow0+GQXvRZbYOYO1FU+Y/wuA14cfBGXJIK58X6ck1jYm2yEsNnSCurKJey6mqNsgNJWhrrMYeNv6bmZxyiM3fQatFfcffB3W2BHkNCFfPExnQpUeosSCl0vp3V/6wlM0Tct0Pme2tYXShtVyQfARpVNSVrvBAOT80Cl4e4g+JST7wr+XbPkGWAKVY9Lp3tLzyAzCwqA95IAiEaPrRCkVN55bgZmet69oi8FjtUrSCpEMPNE6EnHUFrSRepUQNnNzfd/StyLZrY1mMp0ymUzwfc9iITUsXR/o+hajFdPplOl0ilmugAHB1TQd/arldxuHnRpCgOrKCY/c/Jf5yfBbAMznM5zzWKvZ3t5Ca81kUrG3t8vOzg4A09kEn8TVmvWarlnRrFqOl0sWCahw0rQcrdd0fZ/YrEMJHZdnQhqXShZ3k7pOzAqaxzpByH73zq+zuzMveR1ilDC2ilSJKSJGj3dgjaayhu3tObP5nOmsZZ2uZzo11LMtqnqCRorhg/c8uPOvhnGoJIrtXGC9aqisYYtpWoTK+edbu5wsG0Lw1NNtiNA1Hb1vqWoZf/WFLRarNYuTBUrJgt0aS++6IsVuraWqqkTCKucWIEq1UairFNiq4uuv069uCAN03RDhC3g+G5vlYyS0l1hkW5A6MQaa9brIPozX5KNsysaLzB1NWGwpx6nqWoxC30uC0kly7jX2rwMQdECbSNEZHKPTRjes0uogo9HKKqTQc4h3oZSs9iKJvkRrtBkKbK2xVJXlzcdvomtbfn/3Eb7JPcBiIYiWdbMmfO5ncM6xe8/fYb61TVXX9L3jJK2Kjo8Xolef8khds8Z1EnvPuR8VTJl0CgRAiUbKWCTumrxbKnIb5+WyqKC8sgQuGC0YVMr3+CT9rVTE1JNNMMaw0njBvnHeXpy2/s1fwSpF6AP4QYfJv/9XqYzkHyqr8EHodbxzuKRs4LzDxyCaOl762Xw2Yba3Q51yHG1ClLWux4VHUeEthfU5F736SgvM13ti0+IRZOZ63dI4QdxN6hroqEOF1rrAmZ33tIl5QLSJIkSP61vapqFpOo6XaxbJs2udp227EdGnHhREC8CHAuYRoI/BhcBkMmV/fgGA6XRGXVVUWuN8RrZNQMPehX1AaI4goepWHYuTNW3TJ7XSpCtU1cymM6y24OUZqK5FhzhSEBaAQ9c5jo6X+BDoe7+RI5vO58y3d7D1hO3tvURz1BJazXYy0K945Wt56otPsTjuQEcm0ylb820uX75UnqGxFUpPMGbCYnFC71p2djXWgXdiNJtmTds11FV9YxugcduImp36vgAIrgNUUAgIwOgkBTDev3gTI2qXEfQ3/3b1wgNcOPgImVYkhkDbSOeeapPEoERrqLYVfdLyyLxOmdV2DCAoIaXRzeSOKxXipsgUZ8BDxt3HCKYSb8MqhVUM6DVrsNZQVQbvNOvVitf+xr+lfdWHuPzMcwAcHhziUq3CLa+4wt6+ZzKdEnxg/cl/JJeTBlUIgb7r6NtWOvgYdab05nNPyx6pWcghiVSbkE27kjdTEDvpOWdvp1ROl3BeepeRBFBIcO8RXdLG6Tm3P1+tFn2kV4F2eUL/H36R3T/7FwEwOmKt6Pl07/tVorfot39XkeHIrZRTJCSY1or5fFpqRObzGVVdcbxc0gVBy3kfoO85ORFG9uCmKO9puxZQ9FERjaZZGa562cZWghITgIBIqjjnIHp80rbS1jKta0D6e9c6fIB161gm7sM+BOazmYSKY8SHgI+Rj7U/gE199c1bv0gIQXjwUPRIor4eGSBtK6rJjLqytCcrEWjTBm2UFOsCR8cL+qanaxr6tqPvejwCASdtE7WHEFE6G/+IyjQ4aeiYJIESYqTtHHrVEkIUKYcgRshaw87uNjEGkVGIltmsIsQttrfEAFXGMp1O2L+4CxqmkxmVrZlOZ2WczrbnTOsZlalZNx1KWy5cfAmVrXGdvPfLVy7hvGJ3f/+6/eqGMkAbIbKNH1KIJsU3z3KMSkrBe7xSqBCIDFXNJXyUjFhUXAONvnD1I4kRQUEMZNqYvhOLPpnNkOptMQLBO6aTCa+v/ismU4kvG2tKniWvmMptpbNlj8dYQ1XZgk4zWhXm8rbrEsLGodsuxYhzPDZXN8ulhhjxwbNeNxwdHPL4W9/F13z68wBcuXxACB6lFLOTJds7adUZYZlDACHgul4mDeeTR7NpOPLpxs8+JJj6IIutRhxtsoPQfgwvaJC2MCWHFELYUE0lF9nlOGZABp8Zh9uyt3pugb4abdU2zOoZy/WSoGAruzchdUIFxIDWEVWZJPGcq/hFM0iphMry4l0s1+uyop4mme+qbehXt9P5hr7v0cazSlQzoe/QStMmT/utH/ltPnL/N3PiGn7lNWIQd/d2ePtnf5bFYs1i2QwIt+BpEvWNqWp2d3dQuub9l/4Krm94y+4vobRIGUAqb4gSRn7o+L8k+sA9s19gXtXY1Ic/0b2be+t/iULRx0jrJNLS946n9JcAWN56IhpBRrNctiV3DJrjY4lA9E3H8uBEVJijsEJ47yVxkPq3rSwfOvpLzHa+iDV5YWsIfokxMveIBHZuiqZ19L1nbVpi8qSWq4ann36avu8xVkJrtqqoKsvklZJHc05IWCfTCcZqphMRmdvd3y05oMl0It5YhKj2sdawt7eLQuNqmQ96v8fWzrxwAp7VbggDdCpNfc1vsnoavB+xIWdYobTKygJNG61AndXZ+6bZPI4mNpk8dZlgdV7Rh8Dr4l/jmfm/oa4sMz0poTNtTHL7xdBMpxOM0bRdXyZvm5QOrRUjlPH4RuvCfiswV2EC1jqpJ0bRUEkLOeHUCpFVcLTrhqPDQ9qm49X/779m2Q/GN3sa/nM/Q39yMydv+tvEJ/4pi5OTdOdxACdkDzNmzXi59xhCYSnMz1Bg1v1GPmvDaMWsz6Q2wQvZIimRHY4J3JA9nHDq/cQYCNFj4lCFPsj8nhugr0br+p55vSXeNAKxB1A+EPwAEol4+vf9KvYbvnPQdwohhVINSklY23U961VT+lc7meKjSGK38WFsf1fxrl3ySpoQeN+ywVaWZrmi6VqarsNHz7qRReLO3g6TyZTFsmXdivLopK4IvkFp2UbbiqZzPNz9DU6Oj2lWJ1y+7a/yNf/pfyUHqyZWyzoIqIxBofnk+vsxlSr6NiFEgo0i2haFVcB5YVC4c/5zcs3rHfquI0TonJcwd4QY+pKDDZ3D956oNNEHVPTyPEd51r7vadyC6JbUtcNoMNWE2J1AWkgqPRmNi6RQ7ES0LiTmitVqLUSlRLRXxNix9J5ewcWLN8u9TxrWqxYfPDYYrBFuzPW6LSFTW0VI79V5n4yVpW9cIVmNUXJx6Bu8Duh0u55Byi789fYZezgbbbQiz36ISjVBGwJ3JSGU8z+JmiINAO8dWiWKCeSlnDRrnvrY3y0vZvfe/wFT1WlCB9VJQKrt+yJgJ3mfzK0lhs65SFBDbH06rVLBqHTuHHYLIZTrWX/iJ1ivlvR9h+sd68SE23X9yLMbSAIPD45YL1fMn/8fCT4Mxq4bDPb4ycURPUeIAZ3hGCqpxjpHCL7k2oZwmQAmMhmp0gNYIMJG/Y7WlqhTUrt4MwOXX9kvRKKJm0SQf8wc4Xn7/99ccHjfS/+MkfVv/zsAdrcrqeVSoobrvEPZKkkTyL4hhbBUClcFH4RPLQRIU/461dN0nQBYfJFw98SU89RBQ9vRrRse/Oh7aW3FfR99Lxp49hEZf9X3/iBt5/BRzutcwFYKq6syviOKh27/cY6OG45PIl1jWFw+4CWLJdMsI54iC8YYHtj9v9ERPrz4Xu6bvadQ0vgQaFNZoE/by1gdGEr63rFctkSlMbaS+w9eIg1JZsK3PdF5QAnFTgRljdQ6pRZ8oG97oo/cW7+HR9sfYGosytaFjiqOpbfTf0Km+EnPs+97rLG8dD7l4mTCTGmeWyw4cIPcfdOtmUwNkKM5AZSnqnRRcZXXJ3NI1/XM54q26bny/NUiafHs5afRRjGZTP705IA4ZRQKuGCUq3nB9gK/ZwO2YdjGUO8YiyWLKd4UYyghuOODQ+rpFJTm0+qfEZtI27bYqweSWAQ4OmZ7dw9bWdowTPLG6pQghaoS/ZC2cwnhExM56JibTv5OZzPGlBfGmGIY27YR2pxODJArkORh4h4XxYYQadqO1brdgDSrUV6JOJAqxhBQI2oSZRQxboIFiAycbtnLMRplrHg3IW68v+IVoRIXVTr87JsoWbLFbybvyMjKMlTD/unvo3f8GPd/6mc38kLn7SvXfAgcr08IMfN95bDrMIVorSVBHrzkXFKJQ6bHjTGKZpBKUs9qqOLvnaNpGmF91oJ88yHg+p6lE09B2wrdic5U07ZoL3kMFYGEFlv/8/+DX/n+/xq/51nf3Ik3HcD4gE0or2o6pTpZslisadctTdMSfMcHXv9DvO0PfxaQ6IIwCiiavocYuXv3F/BB0aW8jI8hoVJ9YirIz0FC6yAsB94blK2wdY3OHV5HMsFiUKIMjA9E58Xz8z0+DkSsBsXd9l+A0fzOG76Zb37iV9nd36OaXCCzjgtidChqz6eKIeLSPOScozYwaxu2+p59bTnoeqzWLBfynE9WK7zrhpB/ouqqakNdZ1CE5JsmE832zoTt7V36LuD7Yc7zvWOrmjF9gXLTcwzreTtv5+28nbc/kXZDeUA5NHZWy57Ql/WAxscqH66zz0b4bXBer154gAtXH5KkvIriggLLxZJlSohmF/c1TywIQTyYd2sGAAAgAElEQVQhgMXREVVVY+wOPobkMYDFjASfBJEnZIASM9ZGoxF0DEguyVQVVid3LNFvuE5WigD9y3+Q/qm/R987cedHIcZxXqbcpxrE/caOQySBBUrSOJbvs48SYkSn8EsMCcFnDEGNONxCwFibRMAEMRgyYqeAMRK4Q6WVcfCE+bdIHih7VpmcMXlLpq4xRuiBTkEixm/5vH2Fmxuh2grAoNRlZXANdC5g4jiXNworFDDQ6P+Aj57eCemn1ncQk1du/d24NP5MCEySZ9R2PTiRJNBKlz67WC35/B99gbquqSczJnWNdy13XP4Qj118WzqOwjpSvVCF1h2rZc/27jaffcfflSv2PW/40P+cpBGc5LEQsEzIXU5rMBrvhsLUGCPGDES/uQzDe0/seyojshDKGJJCn4g9AlEFVC/ov55AR6RSOSRoiAEcgT4oUDWmmmOq6UgiIUdTKPWRaciWWqr1ek3oetTONqaagDIEJ0znJyfiAR0vj1ksjgv6VDwgGdeDZyf/Ntayvb3Dq26tcW2gaweW+53ZjFv3L7BbX68M9UYyQCn5PTYV46lFVBKvZ0jSH7W5zwuZKnXq9yw3cOHgIQ4uPkiZfmMcMTcn/YyCEJMwkhqxE3Sf/kmWz+7CnX+H6WwmdTCC5dwgPg0R4U1L4lDVdCYTd5qnM4yblAPyXc/isf+N5dExq5S87E4WoDTa2KHwM4ffBus7yqml85NzLsODOit1tvFsE+FgjFlOWaO1iNQNfGwBqNCmIrPwKhXyZY2efBqYCtj6VnBJjE7lpEEYBlB61g/f/iOiN5QtZyI1VeqF3vJ5ezFbXQkrskfyFeOmUq6xdxHe/ucR2JBs46PkJ4rgnOyQly1ANlYaY4T1WRubGNRbcrD4N07WbCuNSzmlLGCozKg0QVvW7/019Lf9JbQH42X8Kmu58/ijckZt+PjN3wAxYitDldg/qmrCdCXncn3k95+7lW98+VMpHC8FnlrHgWexsMDLQFVKQm5GDwax6TpWTUsArK2wsynRmpSf8ulcwrpPCAlwJSFwr2BSao6EczHEyP0fez/6woXC/5ifc0g8dTGx3udYXGQQbmyaFuMDcToj7Oyy0lYAUr0rq9L5fMbO7ja7OzuEGOm7vhjiHOb33rFcNqwbqVm66eZb6NbClhITCGw3witjZKftrtunbhwDdEaNT5m3snE602MZtTgYobPWxfGsfxfjlTpzHGbinDOKZWUu3+bLuO0Lq1SbNFyO63uODw/pP/r32N7ZYfuuv8N8vk1VV1QpTxSUBi2DRqqhFaZKglkbtTaU+z7+2P/O8089x+LoiD6DIhJ8ORqF1k5ixjmZf6pmRuQYMtOAKkSf8rtMHq5rCa4vKx8ig0ZIs0Z1LcRANZ0XI6e0Hun6ZHZikyaNfB2bHlBZAidIrveb2kA6+EEKOG2XFVDzfUWlR6b1vH01miJPhLp484OrLf1o+q3fxckyMKkGWL2KMSXg0wJCS8YkhJjIa5Osg1YE1yM5/pRLUqpU1jfZ+GhV6uVm0xkoVSZGYy3G1KA0LhOExoCqKqokbtWHyO1X/oCt2QStNeumZb1eYzrN5+bvBODwg7+Gqec8fv9/L3VHKcE/BkgQA6vf+hXsO/4Lmvf+e9588Y/EKEDxitq24w9u+kGsNexub7G/t8sbn/gpOU4G+PjEbh0iKuQ8dC5ST8ClES2X0Ykip7JpXA2idXnO3Jjr4qAq63pHFTycnKDaFqcM0bUb7CN1PeHCxX1e9apbCV7qEX0WyXMDCe16teLSc5fZ2d1mPpuzOjkUtoT0fPbnc3b296i769Nl3RAGqDywL5dQPg0YOKvFkft55u+bMagSQojDaiFf1ZlGLBmh1z25Kv8Wq5cnRgi+Zx0XxOCpPvUT3PSaW6ne9N/S5RVPu2I6nxd3VuyM2phQVT5XjHjnOLh8yMnxCX3bFqMgHU4JUkgpdGK2VdqWepvMUGsSw7VPHShr9+SHJiwRnSB0Mv50ZGzJujzZ49Fy61obsENoUWdUTkwJZqXF5pTnnVUv0+dE31O8TFJ+Nt1DJEpoMKakdfGgrveGzttXoimlxJORD/iMFHWBUGlMYkNw7/8PTN/+XdTV4M3jvST1URDDIGYWY+FMEzYBQ1WRSh96vHeALsd5x86M4B2/dXRCZSwow/xbvwelNYvf+lVAwkXT2Rxrarquo2mEj3DRLYkq6RMlTsV17Nne3mZrNsVozXq1ZtXI4u6tD76are1trH6KVVyiQo/yLX274g/qOwE4/vV/g/eeSSfX+oHnXkkIQYo93/U9gIjNxefXItfgHE0vkOw4Hu15AhxFeTItlx7kV4kKHrrrG5ntbPEdX/qUjL2NeVDGZ0bbQoqehFC8Le89ldUyxvpAbxSqmohEWfLsbGW5cOECr3zFK4CEYkxckJkHzzvP888/z+GVE2bzudxf29H1A13PZH+bam8f19zgHtD11rGjCNEQfju1ugc2EF3piw2DdnpFcPY1DJNbBA4uPMCFqx8pRazDBcl2OQwlhiJsHAcVy0potVjy4Tf8DXZe9kQJGdx16Ta01Wfa2wIzTvcVvGf9iX9As25wvS+wSsjKrwLrVFpT1TOUkdxL5lXLJ9HGQozp+xSq09kIkNBmMel+hFKXU1ZgxpawXVYiNZVGV/XgIaoBpk1Gv0U23tcQGUz5pLMWE9vfBu3vyv5hc4DlZ51rlK4blj1vL2qbzraJiDy1USIuB3Cy7Ol6T10bbFUzf9dfwYcUhkr7xrSqVwzFy0pFdFUXL2D/mSXNXhK6S5BuedeRi8/LhKaNRhH59pv2OQkBW0sOQ2mLsQPx6cXv/n66rkc1a0zf4fqO1dGqwMKVFnkE10a2t2ZsbW8nqYQOk4rsPjt9gEm9zZvcw0zqmj/Yrgm94b7nssieMBX0fYd2SePI+aQAWjEpchWD8KLQAfUEMr9j/knJ4ioOmc1MYaXLiEmCdUTh1NMSAXDObcxPWf8rb0+qSczPGSKz6ZR6OsWoisYKWlW1bSEI1loxSVpOMV1flRBxk1GfuHrlgOADdT2R+q2mo28bUZkFpipCuyK2N7gHdL12OmRWXkWZ5fhjgRJyKG2083U2lG2EEeFBioMzEkXTWvO1T/XoyQxtLNpofk+/jbeshAxxHAqYbm3Rty3Hh0fUeyve2twth5nkosu02kkmUo3ux/U9y5MFXdvSXr6Cc30qTBtuJYehopE4cFCe6BxWVQOxp9JsSl+nCYFYYvIiHNeLaNwocayNRaeBbWw10Hnn5JSSuH0+fghOJhY94o1TavNFppwWER6540e58zOfAKVLHF0ekIWdd8Hqt8vzuefxn+LRO3+MmPNEMfDI7T/K/Z/6J9d/n+ftRWuzrT1671Fty4U/9z3l+/Vqzclv/FtMG6lrjek8WgskOY+01z10Ge88SsHn771QQkWV1uw/LWwAnffEKLkTpx6D8KaUxxjYlYXTLPKSA8dSWxGItJX08dG4+cCT/4K/oN9G06xoGuGAfKZ7Ka/YvgLIZNx3LV6JpLYxOqm5VoRcY7c6QRGxtmV7f59KregVVFVN5mcbKKcSR13yaqQ4c1go2cQUjdI4n+qjRvNWCfVvgH/YEFzOObMIvPnx30ddvIBzjuVylaiJxHDUVUU9nQhtTlInFgVnmZc+dPlWXmdu4fnJBd71ikv0Pbiux8UWndIQxhrqxAhR0hElYjFcdLOWouCqsrje4/qAVoqtyQyAraqGzqH+VOSAYPBcRk1szWmPR+y7/DQEYl4wR3Q9wzPuCCkPpVBphV+hleKLX7oPkEnYVjVXdqtRiAu2ZzW7E+F+Whwf0zYNfdeX2p2jK1eptq/w0Zd/CoC3ru9MOY18j6Oellb47bpheXxC17a49VpURZP8sE3MutZonHNcfuOfQSFEh67veLq+n8lceJ3u++TPJraC7JqPyVjlpN55XNfifS+1SAyqquNQf0avEbNcuS8GKl28/C9nbfO7GYFLYoRHb/9R+Rxc4rOyKfeWTqXHIIr0hGKk59EhvBBlRfzwve84+72etxe1TaczQtMQTE9V1VRVEij0nsZUQMQlZJnWka959JAn7r8JkDCdDzI5hcyKoDcj6SEGLjy/xDnHpYtvEI65ynDzFYethtU4UYAGVSWhXmNtChvLBKu1xvctoRImEa0MykpU4HIvlDWu7+naNS+bP8+l5y+xXC4x2tB1LY1PNUe6lSjzVsOkUljvqaY1u7v73PfEhwD4ba2xVY2xFRHFK+58F1987DeIbIbBJhMpxoxR8jfee6IfhO0K4/1o/suRgXFGPKacsE3kvX3vuXzlKsuVIHOt0UxnU3Z2dqjriSwEksfps5GKU9bdmsWqxrcdTlXJQ3JlTpvUdfGAgDKO88Ixv/f1ei10YrWl7yJVNWUynTOZiFnZmm3JsvqGl+R+gZY9n9OGqEyiZwEUvuxBz9425ziMrWg+pXnOvg2lDfUsxamNQWsrife0T/ABlOGh3W8H4EH+I13b4fqedrVCKVisP4i9dIF59QAAvWmwkwk5ZjsUjsXiKq8XC1zX4rqO5qXvxh/9YyFuVIr+G79b7t1oTo5PUItVCm8IFPvW8DjPhbeW+/K9GJbMMyLqhgFGnkss16JHIelYckYPvfGHufcTP13yO77veeSO/4YHP/vzKFNtvLEQgkTrxrmxvJIKofDDlfBmFIRPSfGV3NMo9Blj8tCGzqyU5f7L119dnbcXr0lCXOEST1kuu5R3ndQxleZrHj0kk9t+zcMHAHij0QwFzrc9esAT911MYbbcL3KYblDILeErRtsgntD2u76H1Xv/PVopFr/5yyUvaozBBwmNhaJenJnkTdrGUtU1h/2cqwuPWkSIgVdsn+BJZKRRmEuO1Yrw/POcTDxvPe5ZGltg2EplMuAKYyx1NeF19/05FNDMkhfQWvzM0Xc9VSVe2yde+zd50x/+gwGmnhdsMdOMIjQ4xCGSoDXKKNFVslLI3vU9R0fHHB8fpeshqdBqbr7lFgCc6/ntL9zM8jhRhakG53vWruew71gbhXM93jkm0yTHUE9KYf1GobcajMlqtWa9bqisaD15F5hvb2FUYDtNB7MLO0Tv6Uds3Nf0q+v+ct7O23k7b+ftvH0F243jAWX0x/V+TtsMMVF1+tdrj3c67HYq7nr6XMnD5dlLb2OyJfDOGMLGqjtDgsfnbpoGMxf3/qP7387XHv5SUmXsMcbylk98gMff+i6ee+pZAA7ne+zffBPGiLJq33Q8slvh+74Qqb7m8pVyPcTA4/f8bb72A3+fq3d9C7dMZXVi64rF8YmsGENMno14Cr4Xl9v1HSiJS4cgQIPghcMtFmXHAfkmjNwp2T+6Tdc1+L7DVBOUNoJiyoV3aZUWNIKWS2CBmBA8D9/+I6NwQ4DoIcHdH3vt1xEj3P35PxxeyNFvgtFgq5IEheQ9jQEfMdA2ZxDPnrcXveW+pbVi8Ru/zJ03/RkAPvXGmdTC2cQAvwH8GTpQznmK8mbF6z+2AKU4ePnAPp1Htzcfx4Q78T5uyjpEGbUhRJr3/hoAX/voAY+oAVAjuRbRkYox1Qclz2sU85YyhFryl5n09KmTm9D7MiW+fvm7xAYunVQcHR5wsD/h6onD2oqQvD9tDVvf8l1E4Lb7/hx1PZH8Ut+xdyCRg9VWlTSzHG944qf5zBv/O+GSHHt/pHxPjMR09JhRnqNJKiJ6YdpIBMb1opVUctQmP4N0zyFIKP8k0rWJCVxFHJE7Lz7BJbdF23nRW1IUaPtkMnhA45axRZA8oNWare0ZtqpQymGUwoTILAEwZsdHdM7TtNcfozeEAVIM+ZdSD1R+HD7H9FnAaENyrNQint53DDw4FXa7nqHLiTvXNQTXEUMo6qJ2skXUpiQh8wztfRDtDmC+vc3W7g6LwyNCVBgljNaqux0qOc77Q8M7r1zFWMvJ4RGHVw746OceERhjYlT4gA/s7O/ynXe9jcf3JtBLEtdUdhDoCo62WRO8T4qNMkm4EHkZDwHQLhfJGMRkpCIQynf5OT12z9/iwf/0f/Lw1/8wvhc4tioBEUQELE9CSShL+NxGIbESIpbn/cjtPyITTxiMWWbSzoM+Zsr7156U61H6LRhb8ZZPfmTzjY0StfcfrIUZQd8QXfg/+5aLr4f3m75P71FFDVolwba8Td6IUh8meVSRAwkhsP2kgBAOXl5z6eYJwVtMuEvOGEdhWsRoaG24essMN2JQv/cl38Sjl34HkGLPv6DuIwyka8RYos/pmjMIJ0LMfIMBp3p2rn4WgKfai4O+lVa0s5blssEaQ50q++99yTdx9UpL7xw+RNbrtcimVJb1bpIg8L4s8Lx3fO1/+gk0SrjZhomLnJstxieFNIci08iH7/oz6FwKEQXwpBQDQWiS1a5slZSJA23b0LaRPjFUV1VF7xUHywa99olLT2GMLcfJ99f3fRH4lOdGWQCu12uadcOFm/aIgaGezzssqdxkJQvqxbq9br+6MUbv2ECczs+MPpc8/XV+H2p7Nre/Xs7nzBbh1lc9zNPPvjnBO3VRBUUbxuJoeWBkFUKQBPojF7+Du/pfFSE7bfCup/UPo5s3A7BaLvj1+QxrDE/+3m9ycng8TPR5ElaK1cmSD9qAW3c0raDULnzsd1k88K0AeNfh+172yZCZIMbCdXIc1zU8dtePl5vLk/8m6kZhoMA38zOLCrx5XG7d3cEjd/woD3z6nyVZBhkwj9z+Izz4mZ/PR+eRO35UDJVoA8s1qMc2jF1IcsjZeF0j44Auxm6M/ingBsoXGwi/8/aVa5JTiWUSiiPj4hNp7exd30P81JIstpU9V2MGr+j1jx0RtSIGSZqrtNK+5ZLnwjNLvHd89u59MXghcPnmqrCt28pibcXrHznC3/JNfOLw9zFaoXXFAy+TMfHJ4z/gpuNXcnX/mSG5fw0AKW5cf+5/xpii9eOTN+ODI4ZAZRVPn8xQwPHXSh3QXc/2VHWN0pq+61k3a4yRGqPMmE0qbQg+8PHb/iZv+txPScFp7zCJHFWTCJjF3ZcHpRVm5AFFrYT8lER2WlfouuLmW26mT9Bx0dXS1JO6SGD83vOvQpsrxJT/8sCqaVkuV9i6ZqanaAx1ymWBGKmua+n7nqqqmM+3Ti1goVmvRdZ7khSiQw+xIyqPT+97aQwuRJb6BveAMhzmLDMxNjob3eiU4RE39gUMzQuE4za+Tp1TaYvRNrn2GVKckSCJTp686o84J8dbLdfM53M+uv9nuf2ZXwZtIPYofwfNWmSyu65jebJAa8XyeCk3l2l6Rkn7tm05uHxVVkWIBILSlr5P7ARtI/OwGk3KWkEYwoSP3P4jqKzpkwb1xgAEZMJPMhXZCOWfx2HLZLQUeUWr5FmU40MXHyH4/pRxGYfzUnguhFGYVM43bJJX2wJQYHy+1D5605wHrq7PDdBXqfVdR0Sn9zRaFCYaKh88r3v4Kr6yQw1ZYTnYLDuQeI+8epu+ysWOIXju/HRHJPL5e/ZBaV7/uOhWVRa0CsSpGIdJXZXuk1fpd174Bvq2IbgeRvLZp0d7wc7mMU3SxKpk9W9tlaIEslC67TKYC4YQAlvPPgnAZ+5+EygI3rL7dF8WV33f06xW6ThWwlbaoFXFwr8bgmdX/fPCBYeSELo6NQdqNoehD5E6jQNthHF7Ut98zbsyxuCd5x9tb3NbiGhToatUZGqEmd9WlrquqKwleHl+2QOaTqdYWwnizo7qCUcLj6ZpqSc1k+kEjac2LX3V4meeYyOLikZpTGV53+XX8MDZ3eoGMUAgru4ZRugsb0apgfpcIZOmTquYMvH9MU45PlcxdONjFA2bU6vuZA1L6JCh0Gt5soIYsZVNnoAnEgle4rsgEEbnPNoYvA8JoaNT0Viuc4mlujimDvfoHT+GnUx5bfhkOk6etZN4XCIk3SyqHRmQEnYYOr78FQ8qxsA9H//HPPT6HxpWXmkTbx7HhrtTOE2QT/d+4mf42N1/i4fu+gYA7n/0/ZIDyh6QAgmBjO8LstLk5psYeTdkoxU5jcIpHmLZ9voQz/P24rXbL3wjHz/8ADqF2bKn8MZPLPmgEk5EnfqpTqv4/O4KxdSpxcQ48KGSB5I2QGvDXZ/xhOigPpWPiAJnvuuWd0o+sw/XdOm+bzEml0tk2qbB6AzyIKPjqmHMS5hfg7LJsPh8aWT+wb3Pf4Irr3njEJpObA8h+KL02iUpB6WFastYg/Jgo0kaQMM9FTUwlYbkaB0YiIXYV8JdfiT2SHrOskD4+1VN23T80Kc/z/sXt+A8BalaTy3z+ZSXvcxRVRXBBxYnS8wotDiZiLicTnWP+XnESKEBywZoOp2Ca1FdT2jWuK6nSwuPvq6orSFMxijZzXZDGKCzDMHp709/V7ZLnclaUSJ1G6Gc8Y6x5I/iWccpmwWefPIu6pmnMF2Mc1C5o2evq6SD5Ehd18EiMp1WaKPpmoZH7/hRjOuJldQBaXM7OgzeSPBJd2fDS1Op0NWmQyfKHR9wPrncLtUTpPvL7jHAk1EkoEQTbjMHVobieNDGSPQOkYIb+5vDNXnzeAmFKW1QMeL045hYp+OeNirpaUvcpnx/2pvdyC0MX6Z3lkOSbLZRCPG8feWb0RGjIhipQ7GjOriqrvBOtKqstcXbGfcglTbOABdZn2wWX+atZZHUDfZiVIyWvfjDi8+S6/Vy7dp4m5JrTAuhIYQ78qaVSovC0/12dPVlH528f41O+aUYNRf/6FOltODi8zcVQtDF1wko6WWfvMzTb7pJAhwqYoxm176H2GpItTlZsytGAWOoKDkreYpyrrzMuvfh32E5n9ORmOcTKz2I5xN84ADDDz7zHJe7lsOjGmOFGTw3ay3z+QStDc26pes6tne3S+3PpJ4Im3fWCEsthMAqeXbNei0e0KRmcfWQ9dUT+tUKHzzOZgFBj5vaDeDQ6XZDGKDTbRyKOZ3XKd+XlqWjQwqhqjKhDUZqc/YaBwOuPblKHWpghx0MUN5zdISoy4oPpC6oa1tUDGhjcG3LPY/9Qz52z99G9bfL+bVKxIsDv5o2hm9+XeR3vpBfiazalDbcc/QhIvDozoO4vqGPXTlXzCGRyIahebX+BABf5M5Ei5MN82B+T3OpPfz1P8Ldj/8Ujseg6M4PA1spxcP3vIM3f/LDqdZDC/tBGGSy73/s/Xz49rcOz7l4O/kZxmsfvgI9qgiICFdYLPvIvSmlyYjEGOGR3Qe4+/DD13uT5+1FbJPKUFm4Y1/Qb2EU+nzrq74dYiYITbmCDbRiTAqpabJXA8VMroXJCEettYigacPRxecB2Lv6UgCObnpOxorW1Gqajj10fQCUHiHxck5zTLLF8O9RWHiIdXHqH3GwoOlERg0Tfl4I+RBoX7kQKh7vqCq5ZmurJBmhyICNw/jXRNivEm9i37wnHUc42nRx50bqADn0jUrigH8ZpTT79b8qXkqMkos+wNKsG3xw3Lf7OR5evLFEaO6af47tyZyjoy2qqqJte3rXoY0qIbiqrtJ6cYhA5HtcpTTCumnY2pkTnaddNLSHa8JaWL9dmsL6DuLKcXS45HrtxjJAL4CAk8/lP5tGJUE4g/flRf1x18ZnbfeqWx/l2effBjoW76KcP0KWZRji2sMZxRiKzPX71NvpaNj7+rtRzhOsJPRfevIaLs12iSGye8d9HH/8Yd7x6kCImm+8Nbn6OhnVqx8UTSItxJzee7KsojaGqJSw88ZME3+KFy/AmD8tj6fkOm4+yziwaZ+GgJbVWPFMZNKI3hOUJFwlHzPytHJu7wxPZbCXIwbvvG9I93JqoaG0IhYxFnnuj+2/+Yw3eN5e7Gat4d5b3lnygLlz5NxLSKirvu8TzDgUIxWBk5svJf5EWcjsXXnpRqj45CWXSwFrBsNIeEmzeMlVOZeqGBacMW0j15ch/yTwjHgVmWFg8ILKBeWm0ogZA1w2WhozKWR42lnKiyOjk5HBYLzlwtETADSv8Fw4PEgGIHJ1z5R5Y/eSFIuGzM8YB09NRQEMFKCTuEcs47uZ6v+nPIeD9nvLM/i5vV+hiR39qqPR70ZXMjfU3UeKl6S1pnc9JycnoJSkAIwmRkffJkXUw+exRsAf2pg0FhWuD6zXyQNqGmyliQS2btqjntW4pqH1gSaFFn/z6Vdy9/YfFvmYs9p5Iep5O2/n7bydtz+RdmN5QEAGI8ALezHj6JhW45XQZq4D2NSRSfsONSunWw4fxJIzGv+WspAlhpxROBteUop1KyW68lEb4U4jhc7wG8d8x62OECt0HLyV4IZCNRRoo0ocutTeKIUymroSPrcQVUGpmezcdHlVNX6gpxMq8l15Xhvx73HwYoBxS3gjFb4m+vXgXUmKFfQb8PqPvorP3P/F4dibqS5KZi5dwOs/+irx7mYfJnonP2X4eyZEjVpqT87bV6klrzt5JVkTxye48cH+M2xfuhnvPAcXnhGtqwTrVdowjdN0HBk7q5cfUMg8ARtPq2YqskahGhWQKjXsA5vDDpKnMr5klaMYpw+ffPqRDMi4a0YY5S2RXNGpc22EWtK1aXQCadh0fam4NJyqvSNiE+JuGf/6gPpMIYrNu4RAwFZfRFvDIv6AAB5G4TGAd1/58zRtz8/v/TLTUBfl5bqtk+4YqPkPoyuLsla8VyfRk+N+ijqR2qWJ/WUmtXhAxgh9kFIaHxTLxXE6Z+DSpedZLI+xymC1wSiNnlhMnQAP8xkfvPJqfHeZ67UbygCd9nAVFJTZeDKUz2mbHAqKX4YPLo5ddrVpNDYuQklYyTm0tSL3u9HjRpGrjWOPryvFmlOeZLBSsseleldkBvQwiYYYiH64R5U6dPDDse45fohHdh/gyXAHAK9WHycClkSPH/PlRPLcrJTitkcP+dw9+6MHJtdY9IDSpKKU4sR/H9o8WW5m8xFFTLiTR+64j3s//tMcu+/jjY/5YrCO1XjkdIgAACAASURBVG0AfN3DEeIAjsDAGx597fghn3r+p96DkUnrqPuro0ca+LqPbvaOyMmZpvS8vfjNexFkU1rjvePy3tMAG5Pg0U3Po5RipmfkYklpKdybx14O450ZKz/1pdJDicLIPJRxfNbu40UiIyO1kQseTSAbR7rOHDKi947XjP/xlWdDMlyPIhdzDmUMKFi//LDsF5JG0lDrFtm78tLhqmLk6z/+NUSjcMGjXeJuHAEwjNH861f8R2ZxQu0qmT+CPL4MiTfGCCbIZ7SwLGJ971mtJL/zBf+dTGqbJNazuqomRsXlpUDi+2qBd/+SxcmJBFJTjtYYjdJigJruJpbLJc2fGjZsrtMnYcMbOZUZkPjoCP1WJr5roFM5cxCvOdY14d0YBBZtNn9Rp3tdVENupPySCtvS6r4QLKYVVa8ew/i7hG1bC96ewFjXrjRtIKq04oyKe48+MuQ9IgTX08SwgYAT2Lf8+7bHDlHG8PqPHYm3mIpIr1lJ5oSjUrzxsdde89w+ff+X+LqHb0WxIBI54nuTPJDl9EQgQ02o879cMi6ezllx7bpAQZqE9Magz/H38/aVb947QhAqneObLxELO05eIIlxEhhyRp/lmXo8yatifMa+9bDNMG5LjjUPtbzdaC2aFXOHCIfsE0JM8gKDMcoGKGyAJNK59KnxEAPaZGPJmf0sltlExn+hzxnf10YbgSHGa9ZkNPUYAq5g+dKrjEUjM4gjlgUk7B+8fABdKMUPHn5XyqOp8ve+l7xT6HlG1xEi4v0phQ5yL6tGvNm299hkfDYW2xHaRLNTVYa1+uslIhNijo78f+193ZNdx3Hfr+fcXXwRwAIEsLTs5EEmAFKmJH4AJEHaSSS5Kg+xpDiV6MHJS/K35C/xSypJVew4cuUhtumKRYAg+CHJJggsIFfismV8Y3cBLIC990znYaZ7uuecu0ASArsP01XA3nvPfJ/+np4e4C/vfggA6PtVnNh3Cj/d+GBkLRLsKAGkeGU3xeHwDZDPWwQrOMtprAevoPh6UjIvqDvcSl6uCSKIZqPFGODpY3STTt2DRAELeB0AMMXPU/t9j2gYv15BDeSNU5l/EazMjEd/lm5/vBoIL/+jf4hLf/a//BwZ6qp67fDX8yZiEghfrJ4FCHht6Tfd5Is7xApx/Q/HP/k12JBo59Ygv/bJjZq/zZUPmXzVvPTWn1ngQpEWBu7RBs8S+nwR4u2Dfw9+BN3U1qhTi/85mMAeRlZc0I9UKRoZ70RmBBEclrS9ogdgJMSXVJjIzaO1n46YNBJ179kLePje6RJUlJFv37lP8fDMKWWsMq+6L0VC6+KHCMI0IadkkaczP39poxz2Lt4U0jUv681YPXTNrU+MYqkSlu68pEEdMeZMCH2PLnToJl2OzitBR3qn0owxyy5HCXtnFTDmPTnNID2LzJrPsp/1CPsIb/7KdzEPdoQA8qekCz/Tmza3jIx7MhvyFpPreIDQ8vwf/NrnuHbjXQBlz4WBEp1VMcVyQDIdGvvabsLjGLHOwNpffY79r35LETiEDgGd3r8Rslmc9k/kBUu6nGxRST41kgNpQBc6XP2Lv0WXfckyJCLCiRspRfvlXRcgQolzXwBwce2sWRe2lKMC7zeW3scXd38yWMNE04kAXjv0m05jZQBf3PkJvrH0viH8Eu0knYiVKpF5QyI1b2TkFdOgXINnCesv3gAzMKGJRqABsI4H1O+KHFNmIwdI1DZtZ4wfixKz99wFLSLCIrWfLPq9Zy9g48xpMx65Dp40n9rusx+XMnnvcu9PPkZUAQpEnupkpqdeB81S5FoZd5+w1FoF+kGULvb4mi2voqum50nwmQhOPXZgmbpZwywgCYSUVcfaWlw+qaCI6gad9T2m2XKRfagudAhdcpnJZ7nDKIRy43PIwlxYpWOXYsgZocQM/MbB98383UoNYEcIoD8IP9IF/xf4z7qgYlrKFdQKhvFQXhkeESauCsxiFRVbn9dWVowpLQjFrpRi1ouvCEgHtZiLGwBJWBAB69NkxaTLqUIKZ8xlQgjY/OC/p/0lMM71Ee+8fiRlERCNLkoqmnqcXguhQMaKKGVXjh3U7wTxFQMvX7uLK8tLzoI4fj3d23JleckIBOCLtbPQoALrNiDZP2JcXDvr1lmKJMFl3od1UeY5fWPpNyFH7L7IZjuQBF8RUuXFUCWYvlj9MAm6Bs8cnOarJnCCxOiyEgKq3lOunx4WbbkuYK3evIdJLME+pZ995z/FxpnTKjQ4Mu6/+5bDL3EXRZZM9oT777yVvAr5GQN48G5KEMOzdHldPWwqkqcwWypWO+v/mQfl74PZ28kbPrH73CemTFSBVAISysKIN4YISJcCi2U0XMiQ3dUPlu+odVMUWwlhT3zk4O1j6Psp0tHCShkn6PXfFLzb3rr5NHyeQmIXI4biPNgRAkg1BDD+C36E3+X/mB9khEYhgFxBP8pBzvrWPWtTDdnzyBhs21mLmm1uIiGmHPQqGaEpBHSThSSkTIZskYP3Zow4mwEhbfJ1kwk2sutM6iObvZEjYs5CUGtAgxEzSu61GEDUYVDSrpWxaIhEMFmLBCqQAEIg4OUbd8HMuHLs0ECoH79+F1eWDzk3wNAZ5gU7uNKGM4J+uX4OomGSOXV9cf1cGm/+vxB6wQN5dnHVC8AGzwqKq80qa/JexRUn+7H1HqO+ZyBlsDe/A2oYOEHU5zRW9995C4BcKpciu+SixELjNQ6SNkhig6mQzF04x4s92wcncBwJMqOmyVHsHzAadn833js9t77yIBTy032fLMN03V19HzREIonJ7uVQiqjNtR4s3y28VZQMsBGo6ZeDt5eV3k1RAOm9BAmIsAeBcyofuU1gDHaGAELKZ5S8TmaSeXNs+C6NJmHtcXmm7db9eOS4c6hOkUd46dZJXD96GQAQ/2aK2HcIElJpMjhL66FbSC8r/x7Nu+z7CPQRHU/w4H/8oWOyiU6j+wczNjtnZ69Zf3smcnU5CGeoNFRbN2F2facRYPdxVo4c0NUigzzHr91NApUIx2+s6u9Xlw/pSDXUNG8A1yHWZYLCcYKWRVVEtTxjYTrNGw2eFyRDIoA5Hzamsh9R/oqAkn1P1wIAyomyuRJCVvh45VJuOgVQDi84TRy53YIrqrAwgUj2GkvTqsKMeEycopslgLXorAtORqsRfbae+cHaS86iqZ84sk1044Zj6FYFkViJQNnDoqy2GWWBTeheWSOoBeN6VkGU/2dg/cgto3SWidrvS3eWAfROmS8Hi8ehHaRo0KBBgwbbAjvCApKUHBQIiMAf4EcAgN/l/1QKWXWq1lzcvs6YZTMGY4Yzu3DmF769iIdfRNAkl+0mABX3WR8ZHGemPtR9Z89HyH5SGbd3ECYNpLq2QNwHam7n9mLeLwJw4a/W1AVXzOgRu4CyxjRwZ5nReAUNsv9kW7t87EDS2rLFdnX58GBFiQKOX7+jv115KVlHx6/dcWWTGy+iVh61b2nReC5qfbWymxo8Q5D7beQcS5iU/GMuw3oGawSkHH6haOMwODewHOp2qsDmXFGMJX2u5JcbJSQ81UE4I7+y0qFl3FgYioIanToH4UaOrFdN1p+GDbF9Iv+ZS+vk6hexbDjv5VgLiAjF2lMr01qVnm4S6/Qvwrn3xH3HjN0ffqx85uF7p1OEoOwngVN4PvNwz34L2BkCSBBYwy7H3DGGbY9tchoCOHT3E9x9ohAamt9d/CZuHw1YvnkSN46tAEC5bRQAdZPkguDsjkN6QbEvd+KkKxZ6pKifFMkWZxEPDy2DcpTJ7jvXXWhqQL6OoWdQVzIJnPv8JoiAM2+mvFnpaDiU2FMetiTATt5cxZXlw3njtQjkRJwWucr66glzZ1X7kznOVcfl+8rRgwBHh+DyClaOHXDtHb9+13VxdfkQQMCJG/d0c/PKsYN6OG5o5gvj847DJnyeH0jGA3Hz9Dn7RckqzZrrTRmjopec4jSKlL5Ir1ja7B/KJAdCynwO1tWLLDAYusFjSUYzKoRCH5XbzwkjdT8bRm50IrJ/rU5ZtVNoRLQoFkIcNiBFiOqWsndNGjRh0bLOzHqWkFDKeiGU+yjL5VxtgHk3BBeBvHHmNPacvaBrSOCcbV9KpDZCVxoauu087AgBBGTpzxWDoWF2A8LTWjhPCwURi5YTsHzrVdx+cRGMTzSuPcS8mBFZleCEpMwqgPqckFOyycrsOBL23L6W2s9XElva++izG3jn2y/i3GfXy4+5q598/HeQQRKA9956qQydk6C7dORAvnhOUM8iXEYxRySFGch5o1RaNC07/jwHu17IfRO5qxYGwDEJK/ssp8hPgiq310fdIzt+4642d+XYEgjAy9fupHnlga4cW8rrOh+5G3x1wEZBtHsi8j10HbpsFfV9RKCQs2MDs1nUyKvSIJwyJX2wQSWW6725VCoMOGE5M/JlirmQHJ60TFG/iHInOw8mW0I13zGB45/J/Gn8IQ/Ljrdgv3thXCvaaSbGIsprLzQgEbKcGYdkSghGmJcmyVhapT3pW9iXFU6MEjmIaLw1FS8rykD9LoawMwRQnu13Dv11/phmdhtvjfK00eqomCK2nLdBHCOArBvvyK6yQZfHIznPUjeZoDRwwrSbXXCSpcFv1CIHLJAKschlBk77yEKxHJBL4zn/s9sA0nkiFxGYr12gEHDyZjoHtLK8lAXkcAMRT1wnS/1zShmlwbYmjGMkhEQJxPpEGARJ33D58AsQ4qdsQUnvV3LAAwE4fn1tyAAaPBOQYwTFPSPvuYTnShQZUdCDnkC6shtIeSyK/sMOP0ogVuZmikDpDp7Ul1zAFnKEVZdTSNmjGgancsYDcRtaFaqO0hPreoCteTh1rJM+Zh6nDJLqNPx5IA1Yy5IdgQtpc8NxIy35EdN/4jEQ6yia9xYkXJpEoTDrYCap3gaWzr1KSm6h2C1cxWWqsh52hAD6x0u/SAulk83wtMqt3XPxLTiYxG9lvmY0H9VUCHJ9yNLfPcShXz5CSv75st6l8SldTWUHTK94qhOCBey69Uu8fO0O/vIbrwJg7L59TV1wKoCMzUtEOP+z23pQVPshcoRv9Ivhdk/+HmPEpSP7AQAnr90FEWFleSldXsexaJHskWqI2qPNV79YIkkRLydurpmQWbhoppWjB6EROs48r3sVBhVx+cj+Mma5mRLAytEDaPB8IITOKEPwik9WpPoZlNv0PQNTydeDYklUm37WdabtgQAKoDABuFeLxV7XECYLoMkk5yljuaEEmkVArCPkK0P6XrMmxBjL7asOCiNVkUDWmwD7UOdqbJIRc8k1OIe2dHEG3VhDLkUQ+mquFZJDr6xWoThqNMkxCIE4JXnN4dPB8ZfSuAhLG3jvDMvhKOuHw7WrYEcIIGfKzXlFWwkW7xIw5p8p08VvumdOU0d5eV///K4+Z2lHhVvWyKiDbNLvupWSMtauwh7A5aMHsHjrl9g8+quqtZUxknYuhzyLfmbWQQhAqsArRm4dTDVBuEsvvpAsomt3ECNj5dhBHXu9Zn73xw+jfDEMaGQc4hJZyWeLjt9YBYWSHfi19ceIscfKkQMuQzDrhmY1wUrzq63Jrey3Bl8dkKUJrlfdBLdEGOEkxCVPM4aPBMqQwaukDE40WW+wNG32nBAjOFCykFTDz61lRZMBUJc2xmXfKs6mij+ShUXxqxrXYP9irplUZlIzYGm9MG8uRevqW4AtPo71xioNaaBpx4ChpzyY0YuHhvsskEJ21VmrdqTpLYRfmd9WnHoIO0MAPQU8aUpEhC5+E4J8w1tgDUMnqLBxkj8zeRYqqyN8ctGFm38LMPD4yNcQxaLI8PL1O7i6fLhYLRSw5+513aDVhtyEyPw01LaE12tuRT8jjJOOaSJGXHpxP1TTIiMEUYSVMJLiPhv2YhXWAbHGdHP9pRdfAE03ASJcOrTPVaRAOHlzHSdurmkrkRlXjh7M+zzZfRAjVo4tqaszNVGIRMttscHZ4KuEes9ghOkq3cjPZGoaJctEXZVWkopfBAySgMnZQoDkwlMXnFEwrRsstcw50IZVmQyhK9yOgX62WfaCDDIPLbJydmmA9WaeZa+KdQwAdC+41CQVemOCbOxYt+uv5huuES5WmLrsodeBp/GkTAjFOoopH2UkPSAuFpF3uT4JNJb5iSUt7DwBRCoBFHQDdKSMdatZpKH67RLh1z9fLcgCg+iA0WzE91lekI6nM2WRrR9KaXaOX08HM698bRkdUTpfiUJQOm4/KN+geXnGC1voeoTrF/1NiLtuGzp+p4FmQSBw+ch+s5k5RLh687eMw6y1nV9euwGN9YSLS3sghBi6gJM37+HErfUUnGHG/OrqhhH+pJm80+MU3r7yYnPDPR+wWEEepwZKAHveqJKLB7RQPFRcLKAuuftiTKl0RLyoB0I26B2KF/+BYrHwCQq56WwlhS4flzSHVG09IAtDOyMjTJ2McpU9LbpWRbRwoWW7PGb0zNIHm3798vpeRpkCiAj7PvoERIQH76ZsEhRTBrG0ZoCNCJR2Yt47yvlgjedoyBc85xr1oWwJ7SBqgwYNGjTYFtgRFpA9uOkgazAu1QSy1SMwT4TazUNpOoyYiHYjvvwHZsZf3f2J0+52Wy0uuwIkEucXX0uHQ4NRCcRtpD8MfKiVVllGUuk0XOoPPXRPAd7kV/95dr394leO4JXrd7Fy7CBin/PdVe+DXUtP0SvBqHnWbSGmZtL0eo6YzTYR4kTDdtO9MjApPHQxy2rlPFOvrs6/b77BVwuORnQvLvrIzfyuUkRmsRo8Ng1pPZUw3gJxq9UauPUoCE47XCWPerktu5dLXQob53w4XD0Lo2YGQVJXEcilEGKLk8xj5OkdZBKNpz71qoy6AXnEyilTwUg/pQlOgQUhpMSt77+N3R9+bCJtyxrat2JD4GULIoIRqFOr0m5HCG8bUvjTcSSBHSGAnPBhzns58rvxPXMdmTFoSetpQIF7XEsrQyTZFpfzSCT7DS6PUTHwiQLCJLne0pkUmUsZx5agCDjHjJ4zO+ftqAnG/mgx1RCYdFn2hYAwm2Ll2EEQBbxyZx0rRw7owUPXPmd/eD2OwSDr32hOhYT0l1884P3NgXD8+iqIAq7+ymElmOM31tyG9JhLoMEzAtWBhDPWKgkn166+f3OGT3CdAETOd2AF2H3IJLhSGGrsU2BKkLDrSgHV0P8c8EBkldTcmhNAlZyiFCIe+3RlgU7JCU3PVPXYBck45Wvt7B8TtVmUKBnIXk0RMdpfFYBjw9SHzF74YXoSAmHf+c+KDEeK+Lv/zlvuevFSs1IMDSsqe0QzlDuPy/QIwLw9ogGlb8HadoQAUsgTCrAHxfJH72qtK4II+Prnq05bslgoy+Su07VaiGglXC7RIgnVdHSUmGHXTQAiXNGN8gLarHtrI/7cWmWaY+GocDTE7crX53tsI5b42VYvSBVjurM+hIArRw/ilVvrYDAuHd6fUgiZxsr+z4AMDHOqYODwLuNPN2CKBpaDIXrg0uEXksU6nSpT+XJpj2Ei6b6SEzdLUtQGzw4ktFfOuTn9nCw2GRzjorzZaHuJBh3LcO806yygXKQ+xSIECECIQAyGjgFJLUPCPwS/zHxsNvoyhCIQh0IHisfuNtVKMuj2jVkPy5Jqay39L5ZFnq/0zcO61m4RFrcnX+tA+dqEjTOnsPsnH+PBu2+lW08tsOiDNc+y65BWT45QANHJJwvJ2qrOE9nOlF+Mw44QQAv0bdUm7CZfbdbZI4/1QdKv/3TVZZsGjMZkf/MFSi+GAV5cO6fSXSPiUq8IIeDkrXWsLB9CjL3b/HR9uI68laMxMYZwCyFXtZTeR+Jj7BK4l2yFkJU6PKhqIYVFT/Hl4X04eWsdr9y5p1UuHdmfnwM1RvmjqOOWzigMFyr9XFnEUbF/yLAuHtwz3naDZwIl4qxYI/Y96hsyfE/xOGvhE0qHWksuscTUCQDk3izDCzzP1ssVDEqUTAgBYUtSkJGG0KGbTHKqrV4LkvHpW6tEzz85QTaiMcJkp9diRYq4TAbGnBDqTkuZDtfuOfuxNvHwzGnlF3vPfZIOnzJj48wpd7XD3nMXMJvNcO+dNzTjiBOGAJi9/VN7k5StaLWcsT46MQXR2WOUCwCrODixkMaYTYYdIYC6rktXyYaQMgsI9lZStwgnwq//tNJ8iUYFTlUI2jCgZ1AELq6dQyDCiZuruHLskPFtp3qhCzhxYx2RY3JR1dF5KIhU88ohC04IxBKSann42LB5HN21wDzeb+Y8b3XsqjAz+n6GS4dfAHVBr6J49dY6wIyLh/Y9Of2Nf2lPBUkry0xMXrNhGFz+y308YQwNvlKo5IzB1/JO9I2weZTrMgDKEVd9ZHRddsFRsrAjc7nTiwhEAUw59FraHeBSFlQBKO516zXJnESUt6p+N1lE6CL66RSz2SY4RoQui1ciVLkbyqTMZglDFOcy1/SXtR0VjnlvZu/ZCyMWRbnY8tF7bwNgPHzvbc29tufcJ+k2WDDuv/NmqdZHbR/MuPf2m6BoCEcGRaWXrgtZEIml5bMsqi1G5oxQ6LDvo0+0UfFY3Hv7LcS+R99HHzGX10ramQc7QgDFdHl7OqkPjCi6YuWkPR15UeUJgMEkxzhyeVFycdbFtXQTJ4WAkzdWcWX5kBE+ScDI5viJG2tYObaUkmZmrSgb6V5rqLq1m7RuHNIFkhZiw5u1RNa6in5pmbBp08Voj7zwmnGMr4xCZAbNejXhLx3ahxO37+HEzXWsHNlfsjnUlp7jQgN7rhKkHvHd8lS+V5U/hZIaPFfIGFoxWFH8hMcRCAgjrycyOACBivtNzvQAAIl1LRvdBFAQl3nZc3HYI+l8mEBZadU8CG6PsKIHa8VQQLewABD8vueY1lihNMsYnJnGQrKQrrtsbaX7xBgPzpzCng8/xsP35QZUlA1+IgS5cJJZw6fBAMfeZMQ24zJ3ITkuYMvI6wrA/gufYdJNwAz0fY++T56cjXdNjk0CJpMOk4UJXjj/GTgyYuj0WvM9Z8+DucP+859iOp1h7dS309wpKxq5DXJrM4QdIYDYJLO0US4pK4GRoHbTcp5apEzKMnn5niV+ZHyx+mGS8Dk9zvEbd7GSb/pUJpk1Fjnjs7J8CNxHSEyMCg/HZ+fbKY45V7uk5AuYOWYhV2mVo23q1CsThPzXuquazuroGCAh6pdL+5ApvhDo6HzNaQi7EW0G4HTmseXi0kr9e4PtArZbFfbnTHblx+ErldP4UaMcraKR0l4lxY5Fi1P8spdAGtyMKYhAzwUhWyOZbvQwpYzNoKLdY0m3Gy+CQigX4DmiE0Za42Q61Ck3fo6hcSDCZGGCpU9/jtU3volpnCJyj3vvvAnkW121dqbdOHbgD2VJhoKR7B9bvKoIcATW33pdc/sxM/acvQA5MwQA3aRD102SxRUZ66fewO4Pz4Mja2Lmtbdezy75xFND7LNVx+XSzmT+jGa+ENgRAujln63hF28sZYQIaulAfcEZrAZcM9JqP8eVyYxQM8WqYLmriLty7FDO2CvNUc5eEHAlR7kJclLVXY0uzmxXYqpLjTFub/AnLbEIZ8WjkcgT1+y836s1eypeXu3HuG7ylPw4vDGflNmRzuv3OBjP2CArqTkfrxs8AygYPG/h00sp7qdSj4Jk2RCBkoWJuyqk7CC4wAMzAHd1DZcEoko55j4CtbJESMH3ZXUzCoSOFlR776fTOYqbDEIUNUoMl0q/bs0ooMs518TSiSMu7MJPsleAYGhnCGMiitj2n+erynQpF5kRZ+V+p9npNxxvJUqWKj3ehNzv8+jNbye1e7pZRpBfYSrfqRUnHCDm9Z+M5t1L0A6iNmjQoEGDbYEdYQGBCC//dM3ozkV1KoJ5rn2Zvw9+MAZRibahQLi4ehbHb6zi6kuHVVurwzFTVudVXD66VExK23VlZdlotkpJG0TsOVuaigOOwLZEmZYxBrwDa57Lo/KPjGmTI6WfbBGxmxyxX7ehYixpesg0Lu6SMQtnzL9I5S/74luleW/wVYPX7utIRaVR2RtlrkjER0RFLoFG6XE60L2VV9ZhtUSmqU/KUkbFMrhQjD7kkuyquhBUKzFH9cCUaFfvBhS3nNhixOTocu+5j7GwuAt916GPfbIi7LBdj0g+Mkp7bYP9ZOuB8G4hGbErr5FpbKtWfoZssdjdI45AVLcna2Pz/C5EIR+XTCHZktH/hfOfQk25ObBDBJCMsbZf/18aE1dVlV2ZCF+unUsRhUTpXhlDJPryiNB1HY7fWMXlowfBvVzyNj6eJ53xpgop5o4ZNH/7SHBDCF3DOi1R+PYqz4X2MjbGUmNwPM1fYzzS5miEi5UbIw5KoT53Dd5Y9AagbpahaB8SXINnCYb9VPqNRoZZIWTqFP2Ds841xLMgiCYJyGzbBduML+kpmYOJVPNVTM5HyCHa4j5M5MaF3Ayf8D1b5m1xU4aZ7hObzXrdM3HKWJ5LObSbBB9D9rBKUUe/Qluj0pM9z3dKIhXhDUu/W9PSvNUWF+dkYYLFxUW8cP4zfXb/t97Drr/4aJBVxcKOEEBDdPRPnw6y4JG9HhOLf3E9neuRK3aLdsYOFygQXrl1D1dfOozLRw6YQ1w16zaIVmlsiheDOcyZH8Nk250/39TuiBVI5YNFxZpHuGo5LJUGhXjYR1XbznN4o3cWiJlpiN/fJXR1Lc2RblXvpWQZ2+5/8s8g+3oNngfkN0FwDMxoIvDvdEAEHurjC5EB6nOAQk6QSZSvZLB9VB/J/64WCcv1bmKFl8EOslEz0nEIh0piUbFuotd4Xq2MKpB2SEILMcbkSWEeJwVIXeFRGOJ2Zi7F+qmDl/w3r+LlEkbAwoyz+jX/PzJWS+MMUAjYf+Gn2LVrEV3X4f67p7D4Pz8CAGw+2sTGm9/aUrDtCAGkUCsSNZcTdWTAI5N2LILni9WzJfoF/mH9DgAAE4NJREFUSHfSIN2maV+q08C6oKbjbDarGObWTDn9Qu6xP/y/NZMcC5dMD4aCq4gaOy6nb1ZaWnE1+HYswVmBaqio7l2qjFgwZtBg5DMPXcDxG+lmVrmLZeXowRFhlNuohN+e736/9MXQGy6BHBASR7JtN3gmoOyYoS42wAqCdK4nZqXNqkOF2c2xlmGZeFZWcoi2XJwmg/DUS0YDnNc2qzVjZ0OBvBADAI7ujJuwH2E584z00mpRbCWYIVCivpgzwycZUgk7o2XpeTe9MVbKpEJezSw9u49cfRxVBLg0WymDQtkue0RFs2k4BETg3unX8WhhAXs/+gQPNx7i/huvAQDi40dpHDvdAkrMx3+tH5e/taYi1k7Al+sfpft5iNI1zuQZazlRn0BCMAHg5K11XDl2CF++uB/WhZBehlDJHK1ubOzGJcEZk4eusrGGeOQzYxT3a+KFXDRlW+FyfmautTDye43nI8qYFXL2Dzj5kI///V2gS0lGQ9gFAHjl9n38zb/6Pd0DkJDZ9Nm/L45shE4mbrdnpyTd4BlDUTzgLSAtwEOhlEFw0++H5icV6iTyTlcuSFJcyvegyP1akkdOGKSPcAvDAQw0eQYiqZXl8Fjd2lAkd/uYBGMRSdkS4Ue6BqlMCCFxkFjhqiFozuunX/SZ1WTJPC68JK2paZdtW9ZiFI2yGoNdH6tDj9B7zROIOeXti+lg/qPXXwM0Sg76ekePU2TYGQJozvhEA7FFlMcZRsQc8eXaWY1tf/naHVx96XAKswZKslB2LYNCwIl8J87lY0s5NDG3azDEMvNRCWSpc/R3Gq83XqF81ku1yq/1XezMKRljkXuFKIbNWhvJtGmHWReVr6KhIV26RyBceemwb7/C2stHU4LTk7fW8Mt//W9Tkcig2COwvR1WBA1rGc4ui1rQ2Cu8m/vteYKnQK4YrYBsBxWkSrgoTNoLL6t1FyteL6WT9xujKd0nqyJnVRAFnvSyrqhqP5NNllmxQa54PeVx5vLsxljRUj38vNEPhgpZFUBdQOxzKp3p40IjNBTG7vJL6cIFR8lvIihl9MbSdB4eLnMcIZWy4mxfl1mnSmWu2Vjmb0UxFIXCtswjCkGBHSGAHCJgXPG2gse+qKR5yD3oPUCk0W0rR5dyHd8fgXDi5hooBBVO6YzP2Ajmjfb/fpL1gVWr/T+pri1ZRjmsa2wI88sQ+wZ3y5Oii0Ef017eECUi/P2/+XcAEV4Y6Tydbk8Xiol2eY1IM3RFYnQhqIUT+z5fAdGbvGCJmgdWDg8FbbOAng9Unln7JP+12nYRPRaKwl2YUtn6lHc/9ALUTDgZE73urzAlvErPDZcUiwnlTEoZB+k/G0agLt6s/CjznhdyySMfcxSttBMjm7yRcjauEolU/ZX2hrK6WJPgQTs2mKqsoCjPfvZesy19EJCdPb7UYAXGwwe1pDicttqC2BECaJxFerCCR1xNAPDl6tnMHBN1iJY1bFCsnpRn6urXXlRNuzyX0Yx9x5zvtv15z/OLcmMy9tRcmTYUIlz9PFDUBqb2GIV4ePn63ay4pOhAcXWE0GHfb/9zAEDoOoTJBKGbIIQufQ8dKP8FgBAm2eWQI3l005Uxmz4GAGw+fIjpo4eI0xn62RSxn6Hk5LNjLQfairJRvVdDJA2eMcxd6yIknDsV1s6mIZHLPs8oeWXhwCJQ5jBPEQwml5kwZumPXTixWAqZX+SM2GK5lHBqZIGRotEmCxPs/ehCSqPz7illqAyxRARF88is5UUEIBaBlmm0igGFDFlGWtNqHTBUyhrhY/bDamPFVi7uu/lcVzLwO8WiGtbW4qd82uqW1HYQtUGDBg0abAvsCAsoxt6ZzlxJezVdYxL9F3OUGwCV9qCixZSq1psJzep6/PpdrBxbMnsMMOKd56kOKEqB+NOkbGU3D+o/wZQd+1EMmdpYHqhHNFBLfFPetpYNXAoBe7/3QwDAjS6g6yYI3QKOTibp82SSLZq8ARxCcq2FLkcYBkgG4pJWPiemBLJ1mVxr4mIDgOmjTWw+3EA/m4K5V5eHda+hem/SrlxQJuPpujSmBs8ehDrZ/gCxSrOjxeCmT6vDxpoxYFxFuqGf6SlZKKlunUvM2jKyH+ICA5Q0C40a/0HxJ5rrBcrYC+6FLl1Kuf/jz4BugkgxnXOxFnme38aZU+p02Hf+UywuLqb5dJOU+kZvIBZaGbEUHFkLIVs3meUFZS5lq6zcZeZD040TTK2vmkn5g8NpWcXCs1aQjLWs91im8dLS1rAjBJC610DDUEcjVC6unS3mbV7g49fv4upyyl5tCcDJEUqut9B1OH7tLlaOHkx3gIxJgqHFPzJgQzCu4rw6Dv1LoaFHaXQ40lvKlh3HK5D5WyH63u/+ILnMugm6ySImC4sIk0V0kwUAQDdZQOgW1L1GOQeeb1iDM1UpYJZM5pK9F+AYEfsZZtNN9NNNzDYfI842MX38EAAwffwAff/YrMZ8N4D0ryMwkVaIEXFGAKZPqN/gq4DBW7J7sIOyI65SLlicfvK1yCOwMkrAKKSZpomC6SMLvxFK0jJZ+DmXoFU2K75RPiR2u376dRAF7D33CdL+TlZ6qODjCx9/jo1309UE90+/iW6SWGs36bDrL86nMOwIMCXaTAlMhytnl5dyH+kJ5RtbfS3WqI+6meJ8RBX4YXJAQFyr1p1YrwGFgNCFlDXbJI52pQwiaE9bscQMO0IAfbl2DgzGbyy9jy/ufohvLL3nnl9cPZv3boYzurJ8KK93vXhQIRVCwMmba2AGLh87MEit42BIabZBZcP6dMQI8nWfrAXA1h/lx4U8d3/nd8pcQ0AIAQ///I9BBOz93g+zoOlSinkAk8kuTBZ2pbtPJgvougmomyRLSDywlWYq4a0+7Dn7sfuY7v+YTdHPkqCRDLlx+hj9bBP99DE4CyVBcCtwatZjVwvuGxXewDzyvMFzAwbYHiWwm9w89PI7YcMoRwEs1JVsGH6MijU2vX9quWa45nvQWqUOV9vgeS4ytrExqUXFjL7vQYi4d/p1tcYB6H7yvo8+Qdd1OPjpzxKq9uWiyo0zb2PjzCmE2Qzc5c7nguMs7o+3+0xJ01xJTVSUceGNEtVXMkKUA7pOMFfdEYBduxZx+OcXEWc9Hj9Oe7l3Xv+mKro8GKtd+y2mix0igIC0HF/kgIKLq2cHzznf156sAI+E7BvS9uRg6cmba7iyfKiS4HUHW/xmrdmRolbjG4gbZ8KOC6PUZgkpTX8MBuS2d3/nd5LwzBrYZGEB3cIi9vzg99BNFvXfZGERoUsCKHSTfH14QEHgYsWUeebDnjms3X4GctkYzbkcQugmWOg6LOxOt5ISpay/EgaV2okAR/T5fMDjh/ewubGOGGfJZWAuBazEYP7DDp8Lrj+tBdXgq4ASDJDxUvCdsnVhGeEYnqvFYVw3JnjAej5EQ+fBg1AaK+FzsETiPUYqTarJjAxvbENelcscIp2krUFNAhBx7/QbKoRCSK7pnHQF+85/io13T4G7gL7nUXTVACpjkI0JGDtnFyw4QgreQvRzSk2U95iEvC+ib5uBPR99Atq3D4u7dqPPB41DyPOBzyNXzCFABrkVhe4YAVTQbqvnCbia8ACfQsrnRiYUsjdayTwY2ite3Nh7R+YxvqF+T6Zxb03JH9Ur60N68ikAFDr0H/0J9v/Tf4mFXXsBAJPFPehkr6brQMiH9ELnFCcx37dGBQmzBsCSCgUVUrtGAeS8UlZCZIsnhcsywD04zjDbzHtJ3QygHnE6RYzpOmTuYxFWKNqbX7NKwmvU3FNamA3+v0CUpK2eW1Q3v6JQ7IiKQYNfqu+W1vuRxx4v1JVfM4aay6bCifZk8NZ1DXFbUWH8TG6e2gZH3H/7Dd1bVXcdEqPee+4C7r39hqkkVojBbTZOyGy11QKErTDOrKhOBlwEmHGz1e2UFVIFYisFXI5MdAsdHr//DgCge/AA/WwG5xESXcA04FjmCOwIATRgy/aMQF5RjS4emHueDVEgdJMJTtxYxaUjBwEAl48cAJ4ofMY35gZ/hoeKqpdnBuvkCaFunhA0P50EB+z93g8ApLDnbrKAbrKAyeIuTBb3YLKwmKQRlzYFJxMiput5KZZUIAnCEOfrOagQIaeZljDTfDpd5iif2b4T+S2CY4/ZLO0D9bNNTcXTT6cpICGk9Q4UwIGS0LOryKyuDEdkLry8PuHe4FnCuEdliOvlo+COPCiUak/y1/VtR/U9PqUsIe3BpH4KEwxebjnLSrCXVFGqwV+l7fnRqGVH5gkh7YEi4t7br/ty7pyhCAZZA1Y6KwdhpXmjiGZB5d8Blboq0cuiJlnGpa/cji9RTcno2ETA/bffxK6fXQQD2Hv+0/TswQM8euM1eYuZ7odh9VsJH2CHCCDlZJUg1sA4UUKewGyoC3jl1j0QES69eEAvkCtSuiYh+3JHMBblpfnShihGlXBjggqC1pEpSEJnz3e/nzWRVObhn/2RG1+JMmJQ6LDvt3+okV9dtwiENPKHH/wYe7/7fWkYZARbMNFrYukAtSytBeuItqgH6VgjCDlbMQAQ4yztEZnIN9FI5WBeCAHM6fpjsXgUibl0TQTQpCv7UNntqmW4HMZr8HxArIUn252G0WNcH5MHFUWgWNe5JXtWqEgELV+uxC5KECxbFPwRqwIoLsNaW3djL1aE7gdlXjRG7qP3TfqR1FVcoXqKxjnp14cGtc3jwpNcRGKZunsm8QuFN5qxGWtqNp2izwfGFyayt7yALnRlPz0LUaKSUzMdwh1LRFxgZwigDLWLzImNyowskA9ddQGv3FrHleXD6GeZ+Q17gH3Vw3P/dR8m+mzwbE6zow/hmGWxpBgbf/KHo9rOQFByBNBjc2MDfV9cESH7nR+u38XD//r7qbT4rKUlZi+ciLKFlPp7+MGPNW0RAFxZXkI5HJrK7PnO941MSqj5q//h97W8DjkLKtG0AEI36XDy9n2t2U+n+N8/+j1s/OkfleaAylVv35OoB3Y8v4NHH/y3vEz/Hg2eE9RCX91ChoL0axFElfcIek2JCxdGaWee5cPA+Al8L+XKforrNH9kACFlR7AtEDnmWaZchJtk+BjrunwtGatDFzL/YvQm8ta7CK30slb9OFNxmxVOushEocJFaxSZ7YqyWdNSxrgamRF7xr3Tb2C2dy8OfvpzAMDmb53BZOMBptPNbCklnhMjgyHXmmdpt4WO2A6iNmjQoEGDbYEdYQEVs7MWlsNDaDWkjBoBYTLByvIhxNms3LYoZQa9Db8/2a0AlF0i8QeO+BbUX2d9runTMFyfS1PwioyvzRDfx6M//+N0Fw6SVqWh6fZqgkCVNUnF2rBjNHDp8N4ykekmirmfftv40z8sdggDx6/fQQRwdfmwJou0vsr0PgPCpMMrt+9p07PHm7h89ADwp39U3veYxqcGkI2xKc8ffvBjzD8A1+CrBqUS5wITsK6toeOodl7UPgcpxaJJA/keoEwzVn2XrJ/OZVt5C9yY028uyivvl9q5lONl0RaUDuwft09UinHGe3IutW4ywWSyAIAx3dzEdDodWgUusahfE29ZDhYxfbTBgSbzkAsOGq04/EkmxDrh1Ofmo0eYTCa6PpOFCSYLC9jc3DTzzesR/fvY8UEI3aRLab1HfIVzI7fkjE8+xR8jo+9nSiPe2WbX2H7yu0O+tBDS2OptsaIVgY7lVJrfhnEJODdGEj7ijHr4wY9dIY3t51KWjD94sIpD2jcuM/ujEHz+xbgsriwfhkrPQeoKlUDgyLh0+AVFXO53Q53MsO+Ax5ckr2dJic8jzxs8H2Ddix2cyWNoqHJB20JbAxHhEL3Gv5xRg5Azj0pfZMiTC380+CRlNDjHtG6mAdmvsANjo7iSHQ1z2lOtosWsi9p2Y3+PfQ9MFvRg6mxmsn/I6NgI0WqhnnTlva/DZozGRVfvPXP1u3Rt29HW0/99jJjNZnrmb//Hn2P2xmuYLCzkrCbjfHruTccZdoQACvmkbez7FGf+NKFNuUy6j2KK0HWYLCykDMsxZq3cME/U729cUxsTVPqLXcsnhHc8mS+OCKXaQuIaR9IAiiZW1XdnLOrxjJ0WzwSwlSFhrBpUuF3XMi0CLPe1VKGzdQUV8VTaG30phsG4MTR4PuCVE4sW+ubdRsPwBWlZZ42Uh6qrQZqjHI0s5gnnYFKzm0qS/slyUYNHehZleIjWHiEgIB0sNePSWZChRXMuzc6pyMX8KU9EUvAwy/kgwkyo2UQD+XDlokhaAT9c1VptLPyhHFWp9t/M+g55kOeXHGNek9THdDrF+ukU3Xf0L7/E0mc/x+LGBm5961VIkFDNEZi5XCg4AtTuVGnQoEGDBtsBLQihQYMGDRpsCzQB1KBBgwYNtgWaAGrQoEGDBtsCTQA1aNCgQYNtgSaAGjRo0KDBtkATQA0aNGjQYFugCaAGDRo0aLAt0ARQgwYNGjTYFmgCqEGDBg0abAs0AdSgQYMGDbYFmgBq0KBBgwbbAk0ANWjQoEGDbYEmgBo0aNCgwbZAE0ANGjRo0GBboAmgBg0aNGiwLdAEUIMGDRo02BZoAqhBgwYNGmwLNAHUoEGDBg22BZoAatCgQYMG2wJNADVo0KBBg22BJoAaNGjQoMG2QBNADRo0aNBgW6AJoAYNGjRosC3wfwAasp6E7xtMJgAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data.show_batch(rows=2, figsize=(7,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another example for object detection. We use our tiny sample of the [COCO dataset](http://cocodataset.org/#home) here. There is a helper function in the library that reads the annotation file and returns the list of images names with the list of labelled bboxes associated to it. We convert it to a dictionary that maps image names with their bboxes and then write the function that will give us the target for each image filename." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "coco = untar_data(URLs.COCO_TINY)\n", "images, lbl_bbox = get_annotations(coco/'train.json')\n", "img2bbox = dict(zip(images, lbl_bbox))\n", "get_y_func = lambda o:img2bbox[o.name]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code is very similar to what we saw before. The only new addition is the use of a special function to collate the samples in batches. This comes from the fact that our images may have multiple bounding boxes, so we need to pad them to the largest number of bounding boxes." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = (ObjectItemList.from_folder(coco)\n", " #Where are the images? -> in coco and its subfolders\n", " .split_by_rand_pct() \n", " #How to split in train/valid? -> randomly with the default 20% in valid\n", " .label_from_func(get_y_func)\n", " #How to find the labels? -> use get_y_func on the file name of the data\n", " .transform(get_transforms(), tfm_y=True)\n", " #Data augmentation? -> Standard transforms; also transform the label images\n", " .databunch(bs=16, collate_fn=bb_pad_collate)) \n", " #Finally we convert to a DataBunch, use a batch size of 16,\n", " # and we use bb_pad_collate to collate the data into a mini-batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data.show_batch(rows=2, ds_type=DatasetType.Valid, figsize=(6,6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But vision isn't the only application where the data block API works. It can also be used for text and tabular data. With our sample of the IMDB dataset (labelled texts in a csv file), here is how to get the data together for a language model." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from fastai.text import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imdb = untar_data(URLs.IMDB_SAMPLE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_lm = (TextList\n", " .from_csv(imdb, 'texts.csv', cols='text')\n", " #Where are the text? Column 'text' of texts.csv\n", " .split_by_rand_pct()\n", " #How to split it? Randomly with the default 20% in valid\n", " .label_for_lm()\n", " #Label it for a language model\n", " .databunch())\n", " #Finally we convert to a DataBunch" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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0! ! ! xxmaj finally this was directed by the guy who did xxmaj big xxmaj xxunk ? xxmaj must be a replay of xxmaj jonestown - hollywood style . xxmaj xxunk ! xxbos xxmaj this is a extremely well - made film . xxmaj the acting , script and camera - work are all first - rate . xxmaj the music is good , too , though it is
1, co - billed with xxup the xxup xxunk xxup vampire . a xxmaj spanish - xxmaj italian co - production where a series of women in a village are being murdered around the same time a local count named xxmaj yanos xxmaj xxunk is seen on xxunk , riding off with his ' man - eating ' dog behind him . \\n \\n xxmaj the xxunk already suspect
2sad relic that is well worth seeing . xxbos i caught this on the dish last night . i liked the movie . i xxunk to xxmaj russia 3 different times ( xxunk our 2 kids ) . i ca n't put my finger on exactly why i liked this movie other than seeing \" bad \" turn \" good \" and \" good \" turn \" semi - bad
3pushed him along . xxmaj the story ( if it can be called that ) is so full of holes it 's almost funny , xxmaj it never really explains why the hell he survived in the first place , or needs human flesh in order to survive . xxmaj the script is poorly written and the dialogue xxunk on just plane stupid . xxmaj the climax to movie (
4the xxunk of the xxmaj xxunk xxmaj race and had the xxunk of some of those racist xxunk . xxmaj fortunately , nothing happened like the incident in the movie where the young xxmaj caucasian man went off and started shooting at a xxunk gathering . \\n \\n i can only hope and pray that nothing like that ever will happen . \\n \\n xxmaj so is \"
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_lm.show_batch()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a classification problem, we just have to change the way labeling is done. Here we use the csv column `label`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_clas = (TextList.from_csv(imdb, 'texts.csv', cols='text')\n", " .split_from_df(col='is_valid')\n", " .label_from_df(cols='label')\n", " .databunch())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
texttarget
xxbos xxmaj raising xxmaj victor xxmaj vargas : a xxmaj review \\n \\n xxmaj you know , xxmaj raising xxmaj victor xxmaj vargas is like sticking your hands into a big , xxunk bowl of xxunk . xxmaj it 's warm and gooey , but you 're not sure if it feels right . xxmaj try as i might , no matter how warm and gooey xxmaj raising xxmajnegative
xxbos xxup the xxup shop xxup around xxup the xxup corner is one of the xxunk and most feel - good romantic comedies ever made . xxmaj there 's just no getting around that , and it 's hard to actually put one 's feeling for this film into words . xxmaj it 's not one of those films that tries too hard , nor does it come up withpositive
xxbos xxmaj now that xxmaj che(2008 ) has finished its relatively short xxmaj australian cinema run ( extremely limited xxunk screen in xxmaj xxunk , after xxunk ) , i can xxunk join both xxunk of \" xxmaj at xxmaj the xxmaj movies \" in taking xxmaj steven xxmaj soderbergh to task . \\n \\n xxmaj it 's usually satisfying to watch a film director change his style /negative
xxbos xxmaj this film sat on my xxmaj xxunk for weeks before i watched it . i xxunk a self - indulgent xxunk flick about relationships gone bad . i was wrong ; this was an xxunk xxunk into the screwed - up xxunk of xxmaj new xxmaj xxunk . \\n \\n xxmaj the format is the same as xxmaj max xxmaj xxunk ' \" xxmaj la xxmaj xxunkpositive
xxbos xxmaj many neglect that this is n't just a classic due to the fact that it 's the first xxup 3d game , or even the first xxunk - up . xxmaj it 's also one of the first xxunk games , one of the xxunk definitely the first ) truly claustrophobic games , and just a pretty well - xxunk gaming experience in general . xxmaj with graphicspositive
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_clas.show_batch()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lastly, for tabular data, we just have to pass the name of our categorical and continuous variables as an extra argument. We also add some [`PreProcessor`](/data_block.html#PreProcessor)s that are going to be applied to our data once the splitting and labelling is done." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from fastai.tabular import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "adult = untar_data(URLs.ADULT_SAMPLE)\n", "df = pd.read_csv(adult/'adult.csv')\n", "dep_var = 'salary'\n", "cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country']\n", "cont_names = ['education-num', 'hours-per-week', 'age', 'capital-loss', 'fnlwgt', 'capital-gain']\n", "procs = [FillMissing, Categorify, Normalize]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = (TabularList.from_df(df, path=adult, cat_names=cat_names, cont_names=cont_names, procs=procs)\n", " .split_by_idx(valid_idx=range(800,1000))\n", " .label_from_df(cols=dep_var)\n", " .databunch())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
workclasseducationmarital-statusoccupationrelationshipracesexnative-countryeducation-num_naeducation-numhours-per-weekagecapital-lossfnlwgtcapital-gaintarget
?DoctorateMarried-civ-spouse?HusbandAmer-Indian-EskimoMaleUnited-StatesFalse2.3157-0.03561.7161-0.2164-1.1496-0.1459>=50k
PrivateSome-collegeNever-marriedSalesOwn-childWhiteMaleUnited-StatesFalse-0.0312-0.4406-1.4357-0.2164-0.1893-0.1459<50k
PrivateSome-collegeNever-marriedProtective-servOwn-childWhiteMaleUnited-StatesFalse-0.0312-2.0606-1.2891-0.21641.1154-0.1459<50k
PrivateHS-gradMarried-civ-spouseHandlers-cleanersWifeWhiteFemaleMexicoFalse-0.4224-0.0356-0.7027-0.21640.0779-0.1459>=50k
PrivateHS-gradMarried-civ-spouseTech-supportHusbandWhiteMaleUnited-StatesFalse-0.42243.2043-0.1163-0.2164-0.6858-0.1459>=50k
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.show_batch()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Provide inputs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The basic class to get your inputs is the following one. It's also the same class that will contain all of your labels (hence the name [`ItemList`](/data_block.html#ItemList))." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class ItemList[source][test]

\n", "\n", "> ItemList(**`items`**:`Iterator`\\[`T_co`\\], **`path`**:`PathOrStr`=***`'.'`***, **`label_cls`**:`Callable`=***`None`***, **`inner_df`**:`Any`=***`None`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **`x`**:`ItemList`=***`None`***, **`ignore_empty`**:`bool`=***`False`***)\n", "\n", "
×

Tests found for ItemList:

Some other tests where ItemList is used:

  • pytest -sv tests/test_data_block.py::test_add_test [source]
  • pytest -sv tests/test_data_block.py::test_category [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_filter_by_folder [source]
  • pytest -sv tests/test_data_block.py::test_filter_by_rand [source]
  • pytest -sv tests/test_data_block.py::test_from_df [source]
  • pytest -sv tests/test_data_block.py::test_multi_category [source]
  • pytest -sv tests/test_data_block.py::test_regression [source]
  • pytest -sv tests/test_data_block.py::test_split_subsets [source]
  • pytest -sv tests/test_data_block.py::test_splitdata_datasets [source]

To run tests please refer to this guide.

\n", "\n", "A collection of items with `__len__` and `__getitem__` with `ndarray` indexing semantics. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList, title_level=3)" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "This class regroups the inputs for our model in `items` and saves a `path` attribute which is where it will look for any files (image files, csv file with labels...). `label_cls` will be called to create the labels from the result of the label function, `inner_df` is an underlying dataframe, and `processor` is to be applied to the inputs after the splitting and labeling." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It has multiple subclasses depending on the type of data you're handling. Here is a quick list:\n", " - [`CategoryList`](/data_block.html#CategoryList) for labels in classification\n", " - [`MultiCategoryList`](/data_block.html#MultiCategoryList) for labels in a multi classification problem\n", " - [`FloatList`](/data_block.html#FloatList) for float labels in a regression problem\n", " - [`ImageList`](/vision.data.html#ImageList) for data that are images\n", " - [`SegmentationItemList`](/vision.data.html#SegmentationItemList) like [`ImageList`](/vision.data.html#ImageList) but will default labels to [`SegmentationLabelList`](/vision.data.html#SegmentationLabelList)\n", " - [`SegmentationLabelList`](/vision.data.html#SegmentationLabelList) for segmentation masks\n", " - [`ObjectItemList`](/vision.data.html#ObjectItemList) like [`ImageList`](/vision.data.html#ImageList) but will default labels to `ObjectLabelList`\n", " - `ObjectLabelList` for object detection\n", " - [`PointsItemList`](/vision.data.html#PointsItemList) for points (of the type [`ImagePoints`](/vision.image.html#ImagePoints))\n", " - [`ImageImageList`](/vision.data.html#ImageImageList) for image to image tasks\n", " - [`TextList`](/text.data.html#TextList) for text data\n", " - [`TextList`](/text.data.html#TextList) for text data stored in files\n", " - [`TabularList`](/tabular.data.html#TabularList) for tabular data\n", " - [`CollabList`](/collab.html#CollabList) for collaborative filtering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can get a little glimpse of how [`ItemList`](/data_block.html#ItemList)'s basic attributes and methods behave with the following code examples." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemList (1 items)\n", "/home/gilbert/.fastai/data/mnist_tiny/labels.csv\n", "Path: /home/gilbert/.fastai/data/mnist_tiny" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from fastai.vision import *\n", "path_data = untar_data(URLs.MNIST_TINY)\n", "il_data = ItemList.from_folder(path_data, extensions=['.csv'])\n", "il_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is how to access the path of [`ItemList`](/data_block.html#ItemList) and the actual `items` (here files) in the path." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PosixPath('/home/ubuntu/.fastai/data/mnist_tiny')" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "il_data.path" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/history.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/cleaned.csv')], dtype=object)" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "il_data.items" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`len(il_data)` gives you the count of files inside `il_data` and you can access individual items using index. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(il_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[`ItemList`](/data_block.html#ItemList) returns a single item with a single index, but returns an [`ItemList`](/data_block.html#ItemList) if given a list of indexes." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/history.csv')" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "il_data[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemList (1 items)\n", "/home/ubuntu/.fastai/data/mnist_tiny/labels.csv\n", "Path: /home/ubuntu/.fastai/data/mnist_tiny" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "il_data[:1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With `il_data.add` we can perform in_place concatenate another [`ItemList`](/data_block.html#ItemList) object." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemList (6 items)\n", "/home/ubuntu/.fastai/data/mnist_tiny/labels.csv,/home/ubuntu/.fastai/data/mnist_tiny/history.csv,/home/ubuntu/.fastai/data/mnist_tiny/cleaned.csv,/home/ubuntu/.fastai/data/mnist_tiny/labels.csv,/home/ubuntu/.fastai/data/mnist_tiny/history.csv\n", "Path: /home/ubuntu/.fastai/data/mnist_tiny" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "il_data.add(il_data); il_data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from fastai.vision import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/export.pkl'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/test'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/history.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/models'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/cleaned.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/valid')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_data = untar_data(URLs.MNIST_TINY); path_data.ls()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemList (20 items)\n", "/home/ubuntu/.fastai/data/mnist_tiny/test/1503.png,/home/ubuntu/.fastai/data/mnist_tiny/test/5071.png,/home/ubuntu/.fastai/data/mnist_tiny/test/617.png,/home/ubuntu/.fastai/data/mnist_tiny/test/585.png,/home/ubuntu/.fastai/data/mnist_tiny/test/2032.png\n", "Path: /home/ubuntu/.fastai/data/mnist_tiny/test" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itemlist = ItemList.from_folder(path_data/'test')\n", "itemlist" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, the files do not necesarily return in alpha-numeric order by default. In the above: 1503.png, ... 617.png, 585.png ...\n", "\n", "This is OK when you're always using the same machine, as the same dataset should return in the same order. But when building a datablock on one machine (say GCP) and then porting the same code to a different machine (say your laptop) that same dataset and code might return the files in a different order.\n", "\n", "Since all random operations use the loaded order of the dataset as the starting point, you will not be able to replicate any random operations, say randomly splitting the data into 80% train, and 20% validation, even while correctly seeding.\n", "\n", "The solution is to use `presort=True` in the `.from_folder()` method. As can be seen below, with that argument turned on, the file is returned in ascending order, and this behavior will match across machines and across platforms. Now you can reproduce any random operation you perform on the loaded data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemList (20 items)\n", "/home/user/.fastai/data/mnist_tiny/test/1503.png,/home/user/.fastai/data/mnist_tiny/test/1605.png,/home/user/.fastai/data/mnist_tiny/test/1883.png,/home/user/.fastai/data/mnist_tiny/test/2032.png,/home/user/.fastai/data/mnist_tiny/test/205.png\n", "Path: /home/user/.fastai/data/mnist_tiny/test" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itemlist = ItemList.from_folder(path_data/'test', presort=True)\n", "itemlist" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How is the output above generated?\n", "\n", "behind the scenes, executing `itemlist` calls [`ItemList.__repr__`](/data_block.html#ItemList.__repr__) which basically prints out `itemlist[0]` to `itemlist[4]`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/test/1503.png')" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itemlist[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and `itemlist[0]` basically calls `itemlist.get(0)` which returns `itemlist.items[0]`. That's why we have outputs like above." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have selected the class that is suitable, you can instantiate it with one of the following factory methods" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_folder[source][test]

\n", "\n", "> from_folder(**`path`**:`PathOrStr`, **`extensions`**:`StrList`=***`None`***, **`recurse`**:`bool`=***`True`***, **`exclude`**:`OptStrList`=***`None`***, **`include`**:`OptStrList`=***`None`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **`presort`**:`Optional`\\[`bool`\\]=***`False`***, **\\*\\*`kwargs`**) → `ItemList`\n", "\n", "
×

Tests found for from_folder:

Some other tests where from_folder is used:

  • pytest -sv tests/test_data_block.py::test_wrong_order [source]

To run tests please refer to this guide.

\n", "\n", "Create an [`ItemList`](/data_block.html#ItemList) in `path` from the filenames that have a suffix in `extensions`. [`recurse`](/core.html#recurse) determines if we search subfolders. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.from_folder)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/export.pkl'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/test'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/history.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/models'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/cleaned.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/valid')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = untar_data(URLs.MNIST_TINY)\n", "path.ls()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ImageList (1428 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /home/ubuntu/.fastai/data/mnist_tiny" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ImageList.from_folder(path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`path` is your root data folder. In the `path` directory you have _train_ and _valid_ folders which would contain your images. For the below example, _train_ folder contains two folders/classes _cat_ and _dog_.\n", "\n", "\"from_folder\"" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_df[source][test]

\n", "\n", "> from_df(**`df`**:`DataFrame`, **`path`**:`PathOrStr`=***`'.'`***, **`cols`**:`IntsOrStrs`=***`0`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n", "\n", "
×

Tests found for from_df:

  • pytest -sv tests/test_data_block.py::test_from_df [source]

Some other tests where from_df is used:

  • pytest -sv tests/test_data_block.py::test_category [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_multi_category [source]
  • pytest -sv tests/test_data_block.py::test_regression [source]

To run tests please refer to this guide.

\n", "\n", "Create an [`ItemList`](/data_block.html#ItemList) in `path` from the inputs in the `cols` of `df`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.from_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dataframe has 2 columns. The first column is the path to the image and the second column contains label id for that image. In case you have multi-labels (i.e more than one label for a single image), you will have a space (as determined by `label_delim` argument of `label_from_df`) seperated string in the labels column.\n", "\n", "`from_df` and `from_csv` can be used in a more general way. In cases you are not able to figure out how to get your ImageList, it is very easy to make a csv file with the above format.\n", "\n", "How to set `path`? `path` refers to your root data directory. So the paths in your csv file should be relative to `path` and not absolute paths. In the below example, in _labels.csv_ the paths to the images are __path + train/3/7463.png__" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/home/ubuntu/.fastai/data/mnist_sample/labels.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/export.pkl'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/item_list.txt'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/train'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/history.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/models'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/cleaned.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/trained_model.pkl'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/valid')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = untar_data(URLs.MNIST_SAMPLE)\n", "path.ls()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namelabel
0train/3/7463.png0
1train/3/21102.png0
2train/3/31559.png0
3train/3/46882.png0
4train/3/26209.png0
\n", "
" ], "text/plain": [ " name label\n", "0 train/3/7463.png 0\n", "1 train/3/21102.png 0\n", "2 train/3/31559.png 0\n", "3 train/3/46882.png 0\n", "4 train/3/26209.png 0" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(path/'labels.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ImageList (14434 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /home/ubuntu/.fastai/data/mnist_sample" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ImageList.from_df(df, path)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

from_csv[source][test]

\n", "\n", "> from_csv(**`path`**:`PathOrStr`, **`csv_name`**:`str`, **`cols`**:`IntsOrStrs`=***`0`***, **`delimiter`**:`str`=***`None`***, **`header`**:`str`=***`'infer'`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n", "\n", "
×

No tests found for from_csv. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create an [`ItemList`](/data_block.html#ItemList) in `path` from the inputs in the `cols` of `path/csv_name` " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.from_csv)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/home/ubuntu/.fastai/data/mnist_sample/labels.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/export.pkl'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/item_list.txt'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/train'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/history.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/models'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/cleaned.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/trained_model.pkl'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_sample/valid')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = untar_data(URLs.MNIST_SAMPLE)\n", "path.ls()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ImageList (14434 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /home/ubuntu/.fastai/data/mnist_sample" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ImageList.from_csv(path, 'labels.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optional step: filter your data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The factory method may have grabbed too many items. For instance, if you were searching sub folders with the `from_folder` method, you may have gotten files you don't want. To remove those, you can use one of the following methods." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

filter_by_func[source][test]

\n", "\n", "> filter_by_func(**`func`**:`Callable`) → `ItemList`\n", "\n", "
×

No tests found for filter_by_func. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Only keep elements for which `func` returns `True`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.filter_by_func)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namelabel
0train/3/7463.png0
1train/3/21102.png0
2train/3/31559.png0
3train/3/46882.png0
4train/3/26209.png0
\n", "
" ], "text/plain": [ " name label\n", "0 train/3/7463.png 0\n", "1 train/3/21102.png 0\n", "2 train/3/31559.png 0\n", "3 train/3/46882.png 0\n", "4 train/3/26209.png 0" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = untar_data(URLs.MNIST_SAMPLE)\n", "df = pd.read_csv(path/'labels.csv')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose that you only want to keep images with a suffix \".png\". Well, this method will do magic for you." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'.png'" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Path(df.name[0]).suffix" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ImageList (14434 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /home/ubuntu/.fastai/data/mnist_sample" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ImageList.from_df(df, path).filter_by_func(lambda fname: Path(fname).suffix == '.png')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

filter_by_folder[source][test]

\n", "\n", "> filter_by_folder(**`include`**=***`None`***, **`exclude`**=***`None`***)\n", "\n", "
×

Tests found for filter_by_folder:

  • pytest -sv tests/test_data_block.py::test_filter_by_folder [source]

To run tests please refer to this guide.

\n", "\n", "Only keep filenames in `include` folder or reject the ones in `exclude`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.filter_by_folder)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

filter_by_rand[source][test]

\n", "\n", "> filter_by_rand(**`p`**:`float`, **`seed`**:`int`=***`None`***)\n", "\n", "
×

Tests found for filter_by_rand:

  • pytest -sv tests/test_data_block.py::test_filter_by_rand [source]

To run tests please refer to this guide.

\n", "\n", "Keep random sample of `items` with probability `p` and an optional `seed`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.filter_by_rand)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ImageList (7267 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /home/ubuntu/.fastai/data/mnist_sample" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = untar_data(URLs.MNIST_SAMPLE)\n", "ImageList.from_folder(path).filter_by_rand(0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Contrast the number of items with the list created without the filter." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ImageList (14434 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /home/ubuntu/.fastai/data/mnist_sample" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ImageList.from_folder(path)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

to_text[source][test]

\n", "\n", "> to_text(**`fn`**:`str`)\n", "\n", "
×

No tests found for to_text. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Save `self.items` to `fn` in `self.path`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.to_text)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namelabel
0train/3/7463.png0
1train/3/21102.png0
2train/3/31559.png0
3train/3/46882.png0
4train/3/26209.png0
\n", "
" ], "text/plain": [ " name label\n", "0 train/3/7463.png 0\n", "1 train/3/21102.png 0\n", "2 train/3/31559.png 0\n", "3 train/3/46882.png 0\n", "4 train/3/26209.png 0" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = untar_data(URLs.MNIST_SAMPLE)\n", "pd.read_csv(path/'labels.csv').head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "file_name = \"item_list.txt\"\n", "ImageList.from_folder(path).to_text(file_name)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "train/3/5736.png\r\n", "train/3/35272.png\r\n", "train/3/26596.png\r\n", "train/3/42120.png\r\n", "train/3/39675.png\r\n", "train/3/47881.png\r\n", "train/3/38241.png\r\n", "train/3/59054.png\r\n", "train/3/9932.png\r\n", "train/3/50184.png\r\n", "cat: write error: Broken pipe\r\n" ] } ], "source": [ "! cat {path/file_name} | head" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

use_partial_data[source][test]

\n", "\n", "> use_partial_data(**`sample_pct`**:`float`=***`0.01`***, **`seed`**:`int`=***`None`***) → `ItemList`\n", "\n", "
×

No tests found for use_partial_data. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Use only a sample of `sample_pct`of the full dataset and an optional `seed`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.use_partial_data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ImageList (7217 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /home/ubuntu/.fastai/data/mnist_sample" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = untar_data(URLs.MNIST_SAMPLE)\n", "ImageList.from_folder(path).use_partial_data(0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Contrast the number of items with the list created without the filter." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ImageList (14434 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /home/ubuntu/.fastai/data/mnist_sample" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ImageList.from_folder(path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Writing your own [`ItemList`](/data_block.html#ItemList)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First check if you can't easily customize one of the existing subclass by:\n", "- subclassing an existing one and replacing the `get` method (or the `open` method if you're dealing with images)\n", "- applying a custom `processor` (see step 4)\n", "- changing the default `label_cls` for the label creation\n", "- adding a default [`PreProcessor`](/data_block.html#PreProcessor) with the `_processor` class variable\n", "\n", "If this isn't the case and you really need to write your own class, there is a [full tutorial](/tutorial.itemlist) that explains how to proceed." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

analyze_pred[source][test]

\n", "\n", "> analyze_pred(**`pred`**:`Tensor`)\n", "\n", "
×

No tests found for analyze_pred. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Called on `pred` before `reconstruct` for additional preprocessing. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.analyze_pred)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

get[source][test]

\n", "\n", "> get(**`i`**) → `Any`\n", "\n", "
×

No tests found for get. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Subclass if you want to customize how to create item `i` from `self.items`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.get)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will have a glimpse of how `get` work with the following demo. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/export.pkl'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/test'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/history.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/models'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/cleaned.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/valid')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_data = untar_data(URLs.MNIST_TINY); path_data.ls()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemList (20 items)\n", "/home/ubuntu/.fastai/data/mnist_tiny/test/1503.png,/home/ubuntu/.fastai/data/mnist_tiny/test/5071.png,/home/ubuntu/.fastai/data/mnist_tiny/test/617.png,/home/ubuntu/.fastai/data/mnist_tiny/test/585.png,/home/ubuntu/.fastai/data/mnist_tiny/test/2032.png\n", "Path: /home/ubuntu/.fastai/data/mnist_tiny" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "il_data_base = ItemList.from_folder(path=path_data, extensions=['.png'], include=['test'])\n", "il_data_base" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`get` is used inexplicitly within `il_data_base[15]`. `il_data_base.get(15)` gives the same result here, because its defulat it's to return that." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/test/6736.png')" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "il_data_base[15]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While creating your custom [`ItemList`](/data_block.html#ItemList) however, you can override this function to do some things to your item (like opening an image)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ImageList (20 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /home/ubuntu/.fastai/data/mnist_tiny" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "il_data_image = ImageList.from_folder(path=path_data, extensions=['.png'], include=['test'])\n", "il_data_image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, normally `get` is used inexplicitly within `il_data_image[15]`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/jpeg": "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\n", "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAYAAAByDd+UAAAABHNCSVQICAgIfAhkiAAAAOlJREFUSIntlksOwyAMRE3Vgw0ny3Ay52Z0UVGlfE3Ssqg6EpuE5MEzjuJEJMrC3FbC/sCv5G6dCEAAvF0jKQBk3/cpaLQMVY2WkIwAeu/6LDClBTXX0Hsvzjnx3ksIYagx1z+ttDUAVHeoqteU1kAtzR3YHLAHMcLsQJJXIPPAVq1Gp/KSUpLdnRqh509oDW6AngemcQzJ7tyi8QEIyfxyM5e/pbOHIG+TKaWpJiMtAKr1Gz1XAI+rnYVZrbhElafPl+cQQuF+27ZmXZxzzXt53lY/G6PGdlv0GjtFVc+ASqUr8vt/bcuBD5ipIJ8bKsRaAAAAAElFTkSuQmCC\n", "text/plain": [ "Image (3, 28, 28)" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "il_data_image[15]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The reason why an image is printed out instead of a FilePath object, is [`ImageList.get`](/vision.data.html#ImageList.get) overwrites [`ItemList.get`](/data_block.html#ItemList.get) and use [`ImageList.open`](/vision.data.html#ImageList.open) to print an image." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

new[source][test]

\n", "\n", "> new(**`items`**:`Iterator`\\[`T_co`\\], **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n", "\n", "
×

No tests found for new. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create a new [`ItemList`](/data_block.html#ItemList) from `items`, keeping the same attributes. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.new)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You'll never need to subclass this normally, just don't forget to add to `self.copy_new` the names of the arguments that needs to be copied each time `new` is called in `__init__`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will get a feel of how `new` works with the following examples." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/export.pkl'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/test'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/history.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/models'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/cleaned.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/valid')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_data = untar_data(URLs.MNIST_TINY); path_data.ls()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemList (699 items)\n", "/home/ubuntu/.fastai/data/mnist_tiny/valid/3/7692.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/7484.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/9157.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/8703.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/9182.png\n", "Path: /home/ubuntu/.fastai/data/mnist_tiny/valid" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itemlist1 = ItemList.from_folder(path=path_data/'valid', extensions=['.png'])\n", "itemlist1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you will see below, `copy_new` allows us to borrow any argument and its value from `itemlist1`, and `itemlist1.new(itemlist1.items)` allows us to use `items` and arguments inside `copy_new` to create another [`ItemList`](/data_block.html#ItemList) by calling [`ItemList.__init__`](/data_block.html#ItemList.__init__)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itemlist1.copy_new == ['x', 'label_cls', 'path']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "((itemlist1.x == itemlist1.label_cls == itemlist1.inner_df == None) \n", " and (itemlist1.path == Path('/Users/Natsume/.fastai/data/mnist_tiny/valid')))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can select any argument from [`ItemList.__init__`](/data_block.html#ItemList.__init__)'s signature and change their values. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "itemlist1.copy_new = ['x', 'label_cls', 'path', 'inner_df']\n", "itemlist1.x = itemlist1.label_cls = itemlist1.path = itemlist1.inner_df = 'test'\n", "itemlist2 = itemlist1.new(items=itemlist1.items)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(itemlist2.inner_df == itemlist2.x == itemlist2.label_cls == 'test' \n", "and itemlist2.path == Path('test'))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

reconstruct[source][test]

\n", "\n", "> reconstruct(**`t`**:`Tensor`, **`x`**:`Tensor`=***`None`***)\n", "\n", "
×

No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Reconstruct one of the underlying item for its data `t`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.reconstruct)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Split the data between the training and the validation set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This step is normally straightforward, you just have to pick one of the following functions depending on what you need." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_none[source][test]

\n", "\n", "> split_none()\n", "\n", "
×

No tests found for split_none. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Don't split the data and create an empty validation set. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_none)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_rand_pct[source][test]

\n", "\n", "> split_by_rand_pct(**`valid_pct`**:`float`=***`0.2`***, **`seed`**:`int`=***`None`***) → `ItemLists`\n", "\n", "
×

Tests found for split_by_rand_pct:

  • pytest -sv tests/test_data_block.py::test_splitdata_datasets [source]

Some other tests where split_by_rand_pct is used:

  • pytest -sv tests/test_data_block.py::test_regression [source]

To run tests please refer to this guide.

\n", "\n", "Split the items randomly by putting `valid_pct` in the validation set, optional `seed` can be passed. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_rand_pct)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_subsets[source][test]

\n", "\n", "> split_subsets(**`train_size`**:`float`, **`valid_size`**:`float`, **`seed`**=***`None`***) → `ItemLists`\n", "\n", "
×

Tests found for split_subsets:

  • pytest -sv tests/test_data_block.py::test_split_subsets [source]

To run tests please refer to this guide.

\n", "\n", "Split the items into train set with size `train_size * n` and valid set with size `valid_size * n`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_subsets)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function is handy if you want to work with subsets of specific sizes, e.g., you want to use 20% of the data for the validation dataset, but you only want to train on a small subset of the rest of the data: `split_subsets(train_size=0.08, valid_size=0.2)`." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_files[source][test]

\n", "\n", "> split_by_files(**`valid_names`**:`ItemList`) → `ItemLists`\n", "\n", "
×

No tests found for split_by_files. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Split the data by using the names in `valid_names` for validation. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_files)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_fname_file[source][test]

\n", "\n", "> split_by_fname_file(**`fname`**:`PathOrStr`, **`path`**:`PathOrStr`=***`None`***) → `ItemLists`\n", "\n", "
×

No tests found for split_by_fname_file. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Split the data by using the names in `fname` for the validation set. `path` will override `self.path`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_fname_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Internally makes a call to `split_by_files`. `fname` contains your image file names like 0001.png." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_folder[source][test]

\n", "\n", "> split_by_folder(**`train`**:`str`=***`'train'`***, **`valid`**:`str`=***`'valid'`***) → `ItemLists`\n", "\n", "
×

Tests found for split_by_folder:

Some other tests where split_by_folder is used:

  • pytest -sv tests/test_data_block.py::test_wrong_order [source]

To run tests please refer to this guide.

\n", "\n", "Split the data depending on the folder (`train` or `valid`) in which the filenames are. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_folder)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "
Note: This method looks at the folder immediately after `self.path` for `valid` and `train`.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jekyll_note(\"This method looks at the folder immediately after `self.path` for `valid` and `train`.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Basically, `split_by_folder` takes in two folder names ('train' and 'valid' in the following example), to split `il` the large [`ImageList`](/vision.data.html#ImageList) into two smaller [`ImageList`](/vision.data.html#ImageList)s, one for training set and the other for validation set. Both [`ImageList`](/vision.data.html#ImageList)s are attached to a large [`ItemLists`](/data_block.html#ItemLists) which is the final output of `split_by_folder`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/export.pkl'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/test'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/train'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/history.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/models'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/cleaned.csv'),\n", " PosixPath('/home/ubuntu/.fastai/data/mnist_tiny/valid')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_data = untar_data(URLs.MNIST_TINY); path_data.ls()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemList (1439 items)\n", "/home/ubuntu/.fastai/data/mnist_tiny/labels.csv,/home/ubuntu/.fastai/data/mnist_tiny/export.pkl,/home/ubuntu/.fastai/data/mnist_tiny/history.csv,/home/ubuntu/.fastai/data/mnist_tiny/cleaned.csv,/home/ubuntu/.fastai/data/mnist_tiny/test/1503.png\n", "Path: /home/ubuntu/.fastai/data/mnist_tiny" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "il = ItemList.from_folder(path=path_data); il" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemLists;\n", "\n", "Train: ItemList (713 items)\n", "/home/ubuntu/.fastai/data/mnist_tiny/train/export.pkl,/home/ubuntu/.fastai/data/mnist_tiny/train/3/9932.png,/home/ubuntu/.fastai/data/mnist_tiny/train/3/7189.png,/home/ubuntu/.fastai/data/mnist_tiny/train/3/8498.png,/home/ubuntu/.fastai/data/mnist_tiny/train/3/8888.png\n", "Path: /home/ubuntu/.fastai/data/mnist_tiny;\n", "\n", "Valid: ItemList (699 items)\n", "/home/ubuntu/.fastai/data/mnist_tiny/valid/3/7692.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/7484.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/9157.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/8703.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/9182.png\n", "Path: /home/ubuntu/.fastai/data/mnist_tiny;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sd = il.split_by_folder(train='train', valid='valid'); sd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Behind the scenes, `split_by_folder` uses `_get_by_folder(name)`, to turn both 'train' and 'valid' folders into two list of indexes, and pass them onto `split_by_idxs` to split `il` into two [`ImageList`](/vision.data.html#ImageList)s, and finally attached to a [`ItemLists`](/data_block.html#ItemLists). " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "([24, 25, 26, 27, 28], [732, 733, 734, 735, 736], 713)" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_idx = il._get_by_folder(name='train')\n", "train_idx[:5], train_idx[-5:], len(train_idx)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "([740, 741, 742, 743, 744], [1434, 1435, 1436, 1437, 1438], 699)" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "valid_idx = il._get_by_folder(name='valid') \n", "valid_idx[:5], valid_idx[-5:],len(valid_idx)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By the way, `_get_by_folder(name)` works in the following way, first, index the entire `il.items`, loop every item and if an item belongs to the named folder, e.g., 'train', then put it into a list. The folder `name` is the only input, and output is the list." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_idx[source][test]

\n", "\n", "> split_by_idx(**`valid_idx`**:`Collection`\\[`int`\\]) → `ItemLists`\n", "\n", "
×

Tests found for split_by_idx:

Some other tests where split_by_idx is used:

  • pytest -sv tests/test_data_block.py::test_category [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_multi_category [source]

To run tests please refer to this guide.

\n", "\n", "Split the data according to the indexes in `valid_idx`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_idx)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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4train/3/26209.png0
\n", "
" ], "text/plain": [ " name label\n", "0 train/3/7463.png 0\n", "1 train/3/21102.png 0\n", "2 train/3/31559.png 0\n", "3 train/3/46882.png 0\n", "4 train/3/26209.png 0" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = untar_data(URLs.MNIST_SAMPLE)\n", "df = pd.read_csv(path/'labels.csv')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can pass a list of indices that you want to put in the validation set like [1, 3, 10]. Or you can pass a contiguous list like `list(range(1000))`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemLists;\n", "\n", "Train: ImageList (13434 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /home/ubuntu/.fastai/data/mnist_sample;\n", "\n", "Valid: ImageList (1000 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /home/ubuntu/.fastai/data/mnist_sample;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = (ImageList.from_df(df, path)\n", " .split_by_idx(list(range(1000))))\n", "data" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_idxs[source][test]

\n", "\n", "> split_by_idxs(**`train_idx`**, **`valid_idx`**)\n", "\n", "
×

No tests found for split_by_idxs. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Split the data between `train_idx` and `valid_idx`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_idxs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Behind the scenes, `split_by_idxs` turns two index lists (`train_idx` and `valid_idx`) into two [`ImageList`](/vision.data.html#ImageList)s, and then pass onto `split_by_list` to split `il` into two [`ImageList`](/vision.data.html#ImageList)s and attach to a [`ItemLists`](/data_block.html#ItemLists)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemLists;\n", "\n", "Train: ItemList (713 items)\n", "/home/ubuntu/.fastai/data/mnist_tiny/train/export.pkl,/home/ubuntu/.fastai/data/mnist_tiny/train/3/9932.png,/home/ubuntu/.fastai/data/mnist_tiny/train/3/7189.png,/home/ubuntu/.fastai/data/mnist_tiny/train/3/8498.png,/home/ubuntu/.fastai/data/mnist_tiny/train/3/8888.png\n", "Path: /home/ubuntu/.fastai/data/mnist_tiny;\n", "\n", "Valid: ItemList (699 items)\n", "/home/ubuntu/.fastai/data/mnist_tiny/valid/3/7692.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/7484.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/9157.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/8703.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/9182.png\n", "Path: /home/ubuntu/.fastai/data/mnist_tiny;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sd = il.split_by_idxs(train_idx=train_idx, valid_idx=valid_idx); sd" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_list[source][test]

\n", "\n", "> split_by_list(**`train`**, **`valid`**)\n", "\n", "
×

No tests found for split_by_list. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Split the data between `train` and `valid`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`split_by_list` takes in two [`ImageList`](/vision.data.html#ImageList)s which in the case below are `il[train_idx]` and `il[valid_idx]`, and pass them onto `_split` ([`ItemLists`](/data_block.html#ItemLists)) to initialize an [`ItemLists`](/data_block.html#ItemLists) object, which basically takes in the training, valiation and testing (optionally) [`ImageList`](/vision.data.html#ImageList)s as its properties." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemLists;\n", "\n", "Train: ItemList (713 items)\n", "/home/ubuntu/.fastai/data/mnist_tiny/train/export.pkl,/home/ubuntu/.fastai/data/mnist_tiny/train/3/9932.png,/home/ubuntu/.fastai/data/mnist_tiny/train/3/7189.png,/home/ubuntu/.fastai/data/mnist_tiny/train/3/8498.png,/home/ubuntu/.fastai/data/mnist_tiny/train/3/8888.png\n", "Path: /home/ubuntu/.fastai/data/mnist_tiny;\n", "\n", "Valid: ItemList (699 items)\n", "/home/ubuntu/.fastai/data/mnist_tiny/valid/3/7692.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/7484.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/9157.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/8703.png,/home/ubuntu/.fastai/data/mnist_tiny/valid/3/9182.png\n", "Path: /home/ubuntu/.fastai/data/mnist_tiny;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sd = il.split_by_list(train=il[train_idx], valid=il[valid_idx]); sd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is more of an internal method, you should be using `split_by_files` if you want to pass a list of filenames for the validation set." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_by_valid_func[source][test]

\n", "\n", "> split_by_valid_func(**`func`**:`Callable`) → `ItemLists`\n", "\n", "
×

No tests found for split_by_valid_func. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Split the data by result of `func` (which returns `True` for validation set). " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_by_valid_func)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

split_from_df[source][test]

\n", "\n", "> split_from_df(**`col`**:`IntsOrStrs`=***`2`***)\n", "\n", "
×

No tests found for split_from_df. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Split the data from the `col` in the dataframe in `self.inner_df`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.split_from_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use this function, you need a boolean column (default to the third column of the dataframe). The examples put in the validation set correspond to the indices with `True` value in that column." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(14434, 3)\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
namelabelis_valid
2071train/3/28571.png0True
9382train/7/24434.png1False
6399train/7/56604.png1True
130train/3/4740.png0True
9226train/7/18876.png1False
\n", "
" ], "text/plain": [ " name label is_valid\n", "2071 train/3/28571.png 0 True\n", "9382 train/7/24434.png 1 False\n", "6399 train/7/56604.png 1 True\n", "130 train/3/4740.png 0 True\n", "9226 train/7/18876.png 1 False" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path = untar_data(URLs.MNIST_SAMPLE)\n", "df = pd.read_csv(path/'labels.csv')\n", "\n", "# Create a new column for is_valid\n", "df['is_valid'] = [True]*(df.shape[0]//2) + [False]*(df.shape[0]//2)\n", "\n", "# Randomly shuffle dataframe\n", "df = df.reindex(np.random.permutation(df.index))\n", "print(df.shape)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemLists;\n", "\n", "Train: ImageList (7217 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /home/ubuntu/.fastai/data/mnist_sample;\n", "\n", "Valid: ImageList (7217 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /home/ubuntu/.fastai/data/mnist_sample;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = (ImageList.from_df(df, path)\n", " .split_from_df())\n", "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "
Warning: This method assumes the data has been created from a csv file or a dataframe.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jekyll_warn(\"This method assumes the data has been created from a csv file or a dataframe.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3: Label the inputs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To label your inputs, use one of the following functions. Note that even if it's not in the documented arguments, you can always pass a `label_cls` that will be used to create those labels (the default is the one from your input [`ItemList`](/data_block.html#ItemList), and if there is none, it will go to [`CategoryList`](/data_block.html#CategoryList), [`MultiCategoryList`](/data_block.html#MultiCategoryList) or [`FloatList`](/data_block.html#FloatList) depending on the type of the labels). This is implemented in the following function:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

get_label_cls[source][test]

\n", "\n", "> get_label_cls(**`labels`**, **`label_cls`**:`Callable`=***`None`***, **`label_delim`**:`str`=***`None`***, **\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for get_label_cls. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Return `label_cls` or guess one from the first element of `labels`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.get_label_cls)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Behind the scenes, [`ItemList.get_label_cls`](/data_block.html#ItemList.get_label_cls) basically select a label class according to the item type of `labels`, whereas `labels` can be any of `Collection`, `pandas.core.frame.DataFrame`, `pandas.core.series.Series`. If the list elements are of type string or integer, `get_label_cls` will output [`CategoryList`](/data_block.html#CategoryList); if they are of type float, then it will output [`FloatList`](/data_block.html#FloatList); if they are of type Collection, then it will output [`MultiCategoryList`](/data_block.html#MultiCategoryList)." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "from fastai.vision import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemLists;\n", "\n", "Train: ImageList (709 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny;\n", "\n", "Valid: ImageList (699 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_data = untar_data(URLs.MNIST_TINY)\n", "sd = ImageList.from_folder(path_data).split_by_folder('train', 'valid'); sd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "fastai.data_block.CategoryList" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels = ['7', '3']\n", "label_cls = sd.train.get_label_cls(labels); label_cls" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "fastai.data_block.CategoryList" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels = [7, 3]\n", "label_cls = sd.train.get_label_cls(labels); label_cls" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "fastai.data_block.FloatList" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels = [7.0, 3.0]\n", "label_cls = sd.train.get_label_cls(labels); label_cls" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "fastai.data_block.MultiCategoryList" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels = [[7, 3],]\n", "label_cls = sd.train.get_label_cls(labels); label_cls" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "fastai.data_block.MultiCategoryList" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels = [['7', '3'],]\n", "label_cls = sd.train.get_label_cls(labels); label_cls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If no `label_cls` argument is passed, the correct labeling type can usually be inferred based on the data (for classification or regression). If you have multiple regression targets (e.g. predict 5 different numbers from a single image/text), be aware that arrays of floats are by default considered to be targets for one-hot encoded classification. If your task is regression, be sure the pass `label_cls = FloatList` so that learners created from your databunch initialize correctly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first example in these docs created labels as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "path = untar_data(URLs.MNIST_TINY)\n", "ll = ImageList.from_folder(path).split_by_folder().label_from_folder().train" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to save the data necessary to recreate your [`LabelList`](/data_block.html#LabelList) (not including saving the actual image/text/etc files), you can use `to_df` or `to_csv`:\n", "\n", "```python\n", "ll.train.to_csv('tmp.csv')\n", "```\n", "\n", "Or just grab a `pd.DataFrame` directly:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
xy
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" ], "text/plain": [ " x y\n", "0 train/7/9243.png 7\n", "1 train/7/9519.png 7\n", "2 train/7/7534.png 7\n", "3 train/7/9082.png 7\n", "4 train/7/8377.png 7" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ll.to_df().head()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

label_empty[source][test]

\n", "\n", "> label_empty(**\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for label_empty. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Label every item with an [`EmptyLabel`](/core.html#EmptyLabel). " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.label_empty)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

label_from_df[source][test]

\n", "\n", "> label_from_df(**`cols`**:`IntsOrStrs`=***`1`***, **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**)\n", "\n", "
×

Tests found for label_from_df:

Some other tests where label_from_df is used:

  • pytest -sv tests/test_data_block.py::test_category [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_multi_category [source]
  • pytest -sv tests/test_data_block.py::test_regression [source]

To run tests please refer to this guide.

\n", "\n", "Label `self.items` from the values in `cols` in `self.inner_df`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.label_from_df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "
Warning: This method only works with data objects created with either `from_csv` or `from_df` methods.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jekyll_warn(\"This method only works with data objects created with either `from_csv` or `from_df` methods.\")" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

label_const[source][test]

\n", "\n", "> label_const(**`const`**:`Any`=***`0`***, **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n", "\n", "
×

Tests found for label_const:

Some other tests where label_const is used:

  • pytest -sv tests/test_data_block.py::test_split_subsets [source]
  • pytest -sv tests/test_data_block.py::test_splitdata_datasets [source]

To run tests please refer to this guide.

\n", "\n", "Label every item with `const`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.label_const)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

label_from_folder[source][test]

\n", "\n", "> label_from_folder(**`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n", "\n", "
×

Tests found for label_from_folder:

  • pytest -sv tests/test_text_data.py::test_filter_classes [source]
  • pytest -sv tests/test_text_data.py::test_from_folder [source]

Some other tests where label_from_folder is used:

  • pytest -sv tests/test_data_block.py::test_wrong_order [source]

To run tests please refer to this guide.

\n", "\n", "Give a label to each filename depending on its folder. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.label_from_folder)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "
Note: This method looks at the last subfolder in the path to determine the classes.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jekyll_note(\"This method looks at the last subfolder in the path to determine the classes.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Behind the scenes, when an [`ItemList`](/data_block.html#ItemList) calls `label_from_folder`, it creates a lambda function which outputs a foldername which a file Path object immediately or directly belongs to, and then calls `label_from_func` with the lambda function as input. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On the practical and high level, `label_from_folder` is mostly used with [`ItemLists`](/data_block.html#ItemLists) rather than [`ItemList`](/data_block.html#ItemList) for simplicity and efficiency, for details see the `label_from_folder` example on [ItemLists](). Even when you just want a training set [`ItemList`](/data_block.html#ItemList), you still need to do `split_none` to create an [`ItemLists`](/data_block.html#ItemLists) and then do labeling with `label_from_folder`, as the example shown below." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "from fastai.vision import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/valid'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/test'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/history.csv'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/models'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/train')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_data = untar_data(URLs.MNIST_TINY); path_data.ls()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LabelLists;\n", "\n", "Train: LabelList (709 items)\n", "x: ImageList\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "y: CategoryList\n", "7,7,7,7,7\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny/train;\n", "\n", "Valid: LabelList (0 items)\n", "x: ImageList\n", "\n", "y: CategoryList\n", "\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny/train;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sd_train = ImageList.from_folder(path_data/'train').split_none()\n", "ll_train = sd_train.label_from_folder(); ll_train" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

label_from_func[source][test]

\n", "\n", "> label_from_func(**`func`**:`Callable`, **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n", "\n", "
×

No tests found for label_from_func. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Apply `func` to every input to get its label. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.label_from_func)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inside `label_from_func`, it applies the input `func` to every item of an [`ItemList`](/data_block.html#ItemList) and puts all the function outputs into a list, and then passes the list onto [`ItemList._label_from_list`](/data_block.html#ItemList._label_from_list). Below is a simple example of using `label_from_func`." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "from fastai.vision import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemLists;\n", "\n", "Train: ImageList (709 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny;\n", "\n", "Valid: ImageList (699 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_data = untar_data(URLs.MNIST_TINY)\n", "sd = ImageList.from_folder(path_data).split_by_folder('train', 'valid');sd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "func=lambda o: (o.parts if isinstance(o, Path) else o.split(os.path.sep))[-2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The lambda function above is to access the immediate foldername for a file Path object." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LabelLists;\n", "\n", "Train: LabelList (709 items)\n", "x: ImageList\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "y: CategoryList\n", "7,7,7,7,7\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny;\n", "\n", "Valid: LabelList (699 items)\n", "x: ImageList\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "y: CategoryList\n", "7,7,7,7,7\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ll = sd.label_from_func(func); ll" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

label_from_re[source][test]

\n", "\n", "> label_from_re(**`pat`**:`str`, **`full_path`**:`bool`=***`False`***, **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n", "\n", "
×

No tests found for label_from_re. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Apply the re in `pat` to determine the label of every filename. If `full_path`, search in the full name. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.label_from_re)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class CategoryList[source][test]

\n", "\n", "> CategoryList(**`items`**:`Iterator`\\[`T_co`\\], **`classes`**:`Collection`\\[`T_co`\\]=***`None`***, **`label_delim`**:`str`=***`None`***, **\\*\\*`kwargs`**) :: [`CategoryListBase`](/data_block.html#CategoryListBase)\n", "\n", "
×

No tests found for CategoryList. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Basic [`ItemList`](/data_block.html#ItemList) for single classification labels. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryList, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[`ItemList`](/data_block.html#ItemList) suitable for storing labels in `items` belonging to `classes`. If `None` are passed, `classes` will be determined by the unique different labels. `processor` will default to [`CategoryProcessor`](/data_block.html#CategoryProcessor)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[`CategoryList`](/data_block.html#CategoryList) uses `labels` to create an [`ItemList`](/data_block.html#ItemList) for dealing with categorical labels. Behind the scenes, [`CategoryList`](/data_block.html#CategoryList) is a subclass of [`CategoryListBase`](/data_block.html#CategoryListBase) which is a subclass of [`ItemList`](/data_block.html#ItemList). [`CategoryList`](/data_block.html#CategoryList) inherits from [`CategoryListBase`](/data_block.html#CategoryListBase) the properties such as `classes` (default as `None`), `filter_missing_y` (default as `True`), and has its own unique property `loss_func` (default as `CrossEntropyFlat()`), and its own class attribute `_processor` (default as [`CategoryProcessor`](/data_block.html#CategoryProcessor)). " ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "from fastai.vision import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([1, 1, 1, 1, ..., 0, 0, 0, 0]), ['3', '7'], Category 7)" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_data = untar_data(URLs.MNIST_TINY)\n", "ll = ImageList.from_folder(path_data).split_by_folder('train', 'valid').label_from_folder()\n", "ll.train.y.items, ll.train.y.classes, ll.train.y[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CategoryList (709 items)\n", "7,7,7,7,7\n", "Path: ." ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cl = CategoryList(ll.train.y.items, ll.train.y.classes); cl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the behavior of printing out [`CategoryList`](/data_block.html#CategoryList) object or access an element using index, please see [`CategoryList.get`](/data_block.html#CategoryList.get) below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Behind the scenes, [`CategoryList.get`](/data_block.html#CategoryList.get) is used inexplicitly when printing out the [`CategoryList`](/data_block.html#CategoryList) object or `cl[idx]`. According to the source of [`CategoryList.get`](/data_block.html#CategoryList.get), each `item` is used to get its own `class`. When 'classes' is a list of strings, then elements of `items` are used as index of a list, therefore they must be integers in the range from 0 to `len(classes)-1`; if `classes` is a dictionary, then elements of `items` are used as keys, therefore they can be strings too. See examples below for details." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "from fastai.vision import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CategoryList (5 items)\n", "3,7,9,7,3\n", "Path: ." ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "items = np.array([0, 1, 2, 1, 0])\n", "cl = CategoryList(items, classes=['3', '7', '9']); cl" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CategoryList (5 items)\n", "3,7,9,7,3\n", "Path: ." ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "items = np.array(['3', '7', '9', '7', '3'])\n", "classes = {'3':3, '7':7, '9':9}\n", "cl = CategoryList(items, classes); cl" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class MultiCategoryList[source][test]

\n", "\n", "> MultiCategoryList(**`items`**:`Iterator`\\[`T_co`\\], **`classes`**:`Collection`\\[`T_co`\\]=***`None`***, **`label_delim`**:`str`=***`None`***, **`one_hot`**:`bool`=***`False`***, **\\*\\*`kwargs`**) :: [`CategoryListBase`](/data_block.html#CategoryListBase)\n", "\n", "
×

No tests found for MultiCategoryList. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Basic [`ItemList`](/data_block.html#ItemList) for multi-classification labels. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(MultiCategoryList, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It will store a list of labels in `items` belonging to `classes`. If `None` are passed, `classes` will be determined by the different unique labels. `sep` is used to split the content of `items` in a list of tags.\n", "\n", "If `one_hot=True`, the items contain the labels one-hot encoded. In this case, it is mandatory to pass a list of `classes` (as we can't use the different labels)." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class FloatList[source][test]

\n", "\n", "> FloatList(**`items`**:`Iterator`\\[`T_co`\\], **`log`**:`bool`=***`False`***, **`classes`**:`Collection`\\[`T_co`\\]=***`None`***, **\\*\\*`kwargs`**) :: [`ItemList`](/data_block.html#ItemList)\n", "\n", "
×

No tests found for FloatList. To contribute a test please refer to this guide and this discussion.

\n", "\n", "[`ItemList`](/data_block.html#ItemList) suitable for storing the floats in items for regression. Will add a `log` if this flag is `True`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(FloatList, title_level=3)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class EmptyLabelList[source][test]

\n", "\n", "> EmptyLabelList(**`items`**:`Iterator`\\[`T_co`\\], **`path`**:`PathOrStr`=***`'.'`***, **`label_cls`**:`Callable`=***`None`***, **`inner_df`**:`Any`=***`None`***, **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **`x`**:`ItemList`=***`None`***, **`ignore_empty`**:`bool`=***`False`***) :: [`ItemList`](/data_block.html#ItemList)\n", "\n", "
×

No tests found for EmptyLabelList. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Basic [`ItemList`](/data_block.html#ItemList) for dummy labels. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(EmptyLabelList, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Invisible step: preprocessing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This isn't seen here in the API, but if you passed a `processor` (or a list of them) in your initial [`ItemList`](/data_block.html#ItemList) during step 1, it will be applied here. If you didn't pass any processor, a list of them might still be created depending on what is in the `_processor` variable of your class of items (this can be a list of [`PreProcessor`](/data_block.html#PreProcessor) classes).\n", "\n", "A processor is a transformation that is applied to all the inputs once at initialization, with a state computed on the training set that is then applied without modification on the validation set (and maybe the test set). For instance, it can be processing texts to tokenize then numericalize them. In that case we want the validation set to be numericalized with exactly the same vocabulary as the training set.\n", "\n", "Another example is in tabular data, where we fill missing values with (for instance) the median computed on the training set. That statistic is stored in the inner state of the [`PreProcessor`](/data_block.html#PreProcessor) and applied on the validation set.\n", "\n", "This is the generic class for all processors." ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class PreProcessor[source][test]

\n", "\n", "> PreProcessor(**`ds`**:`Collection`\\[`T_co`\\]=***`None`***)\n", "\n", "
×

No tests found for PreProcessor. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Basic class for a processor that will be applied to items at the end of the data block API. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(PreProcessor, title_level=3)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

process_one[source][test]

\n", "\n", "> process_one(**`item`**:`Any`)\n", "\n", "
×

Tests found for process_one:

Some other tests where process_one is used:

  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]

To run tests please refer to this guide.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(PreProcessor.process_one)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Process one `item`. This method needs to be written in any subclass." ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

process[source][test]

\n", "\n", "> process(**`ds`**:`Collection`\\[`T_co`\\])\n", "\n", "
×

No tests found for process. To contribute a test please refer to this guide and this discussion.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(PreProcessor.process)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`ds`: an object of [`ItemList`](/data_block.html#ItemList) \n", "Process a dataset. This default to apply `process_one` on every `item` of `ds`." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class CategoryProcessor[source][test]

\n", "\n", "> CategoryProcessor(**`ds`**:[`ItemList`](/data_block.html#ItemList)) :: [`PreProcessor`](/data_block.html#PreProcessor)\n", "\n", "
×

No tests found for CategoryProcessor. To contribute a test please refer to this guide and this discussion.

\n", "\n", "[`PreProcessor`](/data_block.html#PreProcessor) that create `classes` from `ds.items` and handle the mapping. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryProcessor, title_level=3)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

generate_classes[source][test]

\n", "\n", "> generate_classes(**`items`**)\n", "\n", "
×

No tests found for generate_classes. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Generate classes from `items` by taking the sorted unique values. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryProcessor.generate_classes)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

process[source][test]

\n", "\n", "> process(**`ds`**)\n", "\n", "
×

No tests found for process. To contribute a test please refer to this guide and this discussion.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryProcessor.process)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`ds` is an object of [`CategoryList`](/data_block.html#CategoryList). \n", "It basically generates a list of unique labels (assigned to `ds.classes`) and a dictionary mapping `classes` to indexes (assigned to `ds.c2i`)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is an internal function only called to apply processors to training, validation and testing datasets after the labeling step." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class MultiCategoryProcessor[source][test]

\n", "\n", "> MultiCategoryProcessor(**`ds`**:[`ItemList`](/data_block.html#ItemList), **`one_hot`**:`bool`=***`False`***) :: [`CategoryProcessor`](/data_block.html#CategoryProcessor)\n", "\n", "
×

No tests found for MultiCategoryProcessor. To contribute a test please refer to this guide and this discussion.

\n", "\n", "[`PreProcessor`](/data_block.html#PreProcessor) that create `classes` from `ds.items` and handle the mapping. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(MultiCategoryProcessor, title_level=3)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

generate_classes[source][test]

\n", "\n", "> generate_classes(**`items`**)\n", "\n", "
×

No tests found for generate_classes. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Generate classes from `items` by taking the sorted unique values. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(MultiCategoryProcessor.generate_classes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optional steps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add transforms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Transforms differ from processors in the sense they are applied on the fly when we grab one item. They also may change each time we ask for the same item in the case of random transforms." ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

transform[source][test]

\n", "\n", "> transform(**`tfms`**:`Optional`\\[`Tuple`\\[`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]\\]\\]=***`(None, None)`***, **\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for transform. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Set `tfms` to be applied to the xs of the train and validation set. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.transform)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is primary for the vision application. The `kwargs` arguments are the ones expected by the type of transforms you pass. `tfm_y` is among them and if set to `True`, the transforms will be applied to input and target.\n", "\n", "For examples see: [vision.transforms](vision.transform.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add a test set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To add a test set, you can use one of the two following methods." ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

add_test[source][test]

\n", "\n", "> add_test(**`items`**:`Iterator`\\[`T_co`\\], **`label`**:`Any`=***`None`***, **`tfms`**=***`None`***, **`tfm_y`**=***`None`***)\n", "\n", "
×

Tests found for add_test:

  • pytest -sv tests/test_data_block.py::test_add_test [source]

To run tests please refer to this guide.

\n", "\n", "Add test set containing `items` with an arbitrary `label`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.add_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "
Note: Here `items` can be an `ItemList` or a collection.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jekyll_note(\"Here `items` can be an `ItemList` or a collection.\")" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

add_test_folder[source][test]

\n", "\n", "> add_test_folder(**`test_folder`**:`str`=***`'test'`***, **`label`**:`Any`=***`None`***, **`tfms`**=***`None`***, **`tfm_y`**=***`None`***)\n", "\n", "
×

No tests found for add_test_folder. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Add test set containing items from `test_folder` and an arbitrary `label`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.add_test_folder)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "
Warning: In fastai the test set is unlabeled! No labels will be collected even if they are available.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jekyll_warn(\"In fastai the test set is unlabeled! No labels will be collected even if they are available.\")" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "Instead, either the passed `label` argument or an empty label will be used for all entries of this dataset (this is required by the internal pipeline of fastai). \n", "\n", "In the `fastai` framework `test` datasets have no labels - this is the unknown data to be predicted. If you want to validate your model on a `test` dataset with labels, you probably need to use it as a validation set, as in:\n", "\n", "```\n", "data_test = (ImageList.from_folder(path)\n", " .split_by_folder(train='train', valid='test')\n", " .label_from_folder()\n", " ...)\n", "```\n", "\n", "Another approach, where you do use a normal validation set, and then when the training is over, you just want to validate the test set w/ labels as a validation set, you can do this:\n", "\n", "```\n", "tfms = []\n", "path = Path('data').resolve()\n", "data = (ImageList.from_folder(path)\n", " .split_by_pct()\n", " .label_from_folder()\n", " .transform(tfms)\n", " .databunch()\n", " .normalize() ) \n", "learn = cnn_learner(data, models.resnet50, metrics=accuracy)\n", "learn.fit_one_cycle(5,1e-2)\n", "\n", "# now replace the validation dataset entry with the test dataset as a new validation dataset: \n", "# everything is exactly the same, except replacing `split_by_pct` w/ `split_by_folder` \n", "# (or perhaps you were already using the latter, so simply switch to valid='test')\n", "data_test = (ImageList.from_folder(path)\n", " .split_by_folder(train='train', valid='test')\n", " .label_from_folder()\n", " .transform(tfms)\n", " .databunch()\n", " .normalize()\n", " ) \n", "learn.validate(data_test.valid_dl)\n", "```\n", "Of course, your data block can be totally different, this is just an example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: convert to a [`DataBunch`](/basic_data.html#DataBunch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This last step is usually pretty straightforward. You just have to include all the arguments we pass to [`DataBunch.create`](/basic_data.html#DataBunch.create) (`bs`, `num_workers`, `collate_fn`). The class called to create a [`DataBunch`](/basic_data.html#DataBunch) is set in the `_bunch` attribute of the inputs of the training set if you need to modify it. Normally, the various subclasses we showed before handle that for you." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

databunch[source][test]

\n", "\n", "> databunch(**`path`**:`PathOrStr`=***`None`***, **`bs`**:`int`=***`64`***, **`val_bs`**:`int`=***`None`***, **`num_workers`**:`int`=***`8`***, **`dl_tfms`**:`Optional`\\[`Collection`\\[`Callable`\\]\\]=***`None`***, **`device`**:[`device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch-device)=***`None`***, **`collate_fn`**:`Callable`=***`'data_collate'`***, **`no_check`**:`bool`=***`False`***, **\\*\\*`kwargs`**) → `DataBunch`\n", "\n", "
×

Tests found for databunch:

  • pytest -sv tests/test_vision_data.py::test_vision_datasets [source]

Some other tests where databunch is used:

  • pytest -sv tests/test_data_block.py::test_regression [source]

To run tests please refer to this guide.

\n", "\n", "Create an [`DataBunch`](/basic_data.html#DataBunch) from self, `path` will override `self.path`, `kwargs` are passed to [`DataBunch.create`](/basic_data.html#DataBunch.create). " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.databunch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inner classes" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class LabelList[source][test]

\n", "\n", "> LabelList(**`x`**:[`ItemList`](/data_block.html#ItemList), **`y`**:[`ItemList`](/data_block.html#ItemList), **`tfms`**:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]=***`None`***, **`tfm_y`**:`bool`=***`False`***, **\\*\\*`kwargs`**) :: [`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)\n", "\n", "
×

Tests found for LabelList:

Some other tests where LabelList is used:

  • pytest -sv tests/test_data_block.py::test_add_test [source]

To run tests please refer to this guide.

\n", "\n", "A list of inputs `x` and labels `y` with optional `tfms`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optionally apply `tfms` to `y` if `tfm_y` is `True`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Behind the scenes, it takes inputs [`ItemList`](/data_block.html#ItemList) and labels [`ItemList`](/data_block.html#ItemList) as its properties `x` and `y`, sets property `item` to `None`, and uses [`LabelList.transform`](/data_block.html#LabelList.transform) to apply a list of transforms `TfmList` to `x` and `y` if `tfm_y` is set `True`." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "from fastai.vision import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(ImageList (709 items)\n", " Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", " Path: /Users/Natsume/.fastai/data/mnist_tiny, CategoryList (709 items)\n", " 7,7,7,7,7\n", " Path: /Users/Natsume/.fastai/data/mnist_tiny)" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_data = untar_data(URLs.MNIST_TINY)\n", "ll = ImageList.from_folder(path_data).split_by_folder('train', 'valid').label_from_folder()\n", "ll.train.x, ll.train.y" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LabelList (709 items)\n", "x: ImageList\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "y: CategoryList\n", "7,7,7,7,7\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "LabelList(x=ll.train.x, y=ll.train.y)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

export[source][test]

\n", "\n", "> export(**`fn`**:`PathOrStr`, **\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for export. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Export the minimal state and save it in `fn` to load an empty version for inference. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.export)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

transform_y[source][test]

\n", "\n", "> transform_y(**`tfms`**:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]=***`None`***, **\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for transform_y. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Set `tfms` to be applied to the targets only. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.transform_y)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

get_state[source][test]

\n", "\n", "> get_state(**\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for get_state. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Return the minimal state for export. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.get_state)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

load_empty[source][test]

\n", "\n", "> load_empty(**`path`**:`PathOrStr`, **`fn`**:`PathOrStr`)\n", "\n", "
×

No tests found for load_empty. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Load the state in `fn` to create an empty [`LabelList`](/data_block.html#LabelList) for inference. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.load_empty)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

load_state[source][test]

\n", "\n", "> load_state(**`path`**:`PathOrStr`, **`state`**:`dict`) → `LabelList`\n", "\n", "
×

No tests found for load_state. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create a [`LabelList`](/data_block.html#LabelList) from `state`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.load_state)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

process[source][test]

\n", "\n", "> process(**`xp`**:[`PreProcessor`](/data_block.html#PreProcessor)=***`None`***, **`yp`**:[`PreProcessor`](/data_block.html#PreProcessor)=***`None`***, **`name`**:`str`=***`None`***, **`max_warn_items`**:`int`=***`5`***)\n", "\n", "
×

No tests found for process. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Launch the processing on `self.x` and `self.y` with `xp` and `yp`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.process)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Behind the scenes, [`LabelList.process`](/data_block.html#LabelList.process) does 3 three things: 1. ask labels `y` to be processed by `yp` with `y.process(yp)`; 2. if `y.filter_missing_y` is `True`, then removes the missing data samples from `x` and `y`; 3. ask inputs `x` to be processed by `xp` with `x.process(xp)`" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "from fastai.vision import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "path_data = untar_data(URLs.MNIST_TINY)\n", "sd = ImageList.from_folder(path_data).split_by_folder('train', 'valid')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sd.train = sd.train.label_from_folder(from_item_lists=True)\n", "sd.valid = sd.valid.label_from_folder(from_item_lists=True)\n", "sd.__class__ = LabelLists" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "([], [])" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xp,yp = sd.get_processors()\n", "xp,yp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LabelList (709 items)\n", "x: ImageList\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "y: CategoryList\n", "7,7,7,7,7\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sd.train.process(xp, yp)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

set_item[source][test]

\n", "\n", "> set_item(**`item`**)\n", "\n", "
×

No tests found for set_item. To contribute a test please refer to this guide and this discussion.

\n", "\n", "For inference, will briefly replace the dataset with one that only contains `item`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.set_item)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

to_df[source][test]

\n", "\n", "> to_df()\n", "\n", "
×

No tests found for to_df. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create `pd.DataFrame` containing `items` from `self.x` and `self.y`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.to_df)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

to_csv[source][test]

\n", "\n", "> to_csv(**`dest`**:`str`)\n", "\n", "
×

No tests found for to_csv. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Save `self.to_df()` to a CSV file in `self.path`/`dest`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.to_csv)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

transform[source][test]

\n", "\n", "> transform(**`tfms`**:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], **`tfm_y`**:`bool`=***`None`***, **\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for transform. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Set the `tfms` and `tfm_y` value to be applied to the inputs and targets. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.transform)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class ItemLists[source][test]

\n", "\n", "> ItemLists(**`path`**:`PathOrStr`, **`train`**:[`ItemList`](/data_block.html#ItemList), **`valid`**:[`ItemList`](/data_block.html#ItemList))\n", "\n", "
×

No tests found for ItemLists. To contribute a test please refer to this guide and this discussion.

\n", "\n", "An [`ItemList`](/data_block.html#ItemList) for each of `train` and `valid` (optional `test`). " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemLists, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It initializes an [`ItemLists`](/data_block.html#ItemLists) object, which basically brings in the training, valiation and testing (optionally) [`ItemList`](/data_block.html#ItemList)s as its properties. It also offers helpful warning messages on situations when the training or validation [`ItemList`](/data_block.html#ItemList) is empty. \n", "\n", "See the following example for how to create an [`ItemLists`](/data_block.html#ItemLists) object. " ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "from fastai.vision import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/valid'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/test'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/history.csv'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/models'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/train')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_data = untar_data(URLs.MNIST_TINY); path_data.ls()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "il_train = ImageList.from_folder(path_data/'train')\n", "il_valid = ImageList.from_folder(path_data/'valid')\n", "il_test = ImageList.from_folder(path_data/'test')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemLists;\n", "\n", "Train: ImageList (709 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny/train;\n", "\n", "Valid: ImageList (699 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny/valid;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ils = ItemLists(path=path_data, train=il_train, valid=il_valid); ils" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemLists;\n", "\n", "Train: ImageList (709 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny/train;\n", "\n", "Valid: ImageList (699 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny/valid;\n", "\n", "Test: ImageList (20 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny/test" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ils.test = il_test; ils" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, we are most likely to see an [`ItemLists`](/data_block.html#ItemLists), right after a large [`ItemList`](/data_block.html#ItemList) is splitted and turned into an [`ItemLists`](/data_block.html#ItemLists) by methods like [`ItemList.split_by_folder`](/data_block.html#ItemList.split_by_folder). Then, we will add labels to all training and validation simply using `sd.label_from_folder()` (`sd` is an [`ItemLists`](/data_block.html#ItemLists), see example below). Now, some of you may be surprised because `label_from_folder` is a method of [`ItemList`](/data_block.html#ItemList) not [`ItemLists`](/data_block.html#ItemLists). Well, this is a magic of fastai data_block api.\n", "\n", "With the following example, we may understand a little better how to get labelling done by calling [`ItemLists.__getattr__`](/data_block.html#ItemLists.__getattr__) with [`ItemList.label_from_folder`](/data_block.html#ItemList.label_from_folder)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ImageList (1428 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "il = ImageList.from_folder(path_data); il" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An [`ItemList`](/data_block.html#ItemList) or its subclass object must do a split to turn itself into an [`ItemLists`](/data_block.html#ItemLists) before doing labeling to become a [`LabelLists`](/data_block.html#LabelLists) object." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemLists;\n", "\n", "Train: ImageList (709 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny;\n", "\n", "Valid: ImageList (699 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sd = il.split_by_folder(train='train', valid='valid'); sd\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LabelLists;\n", "\n", "Train: LabelList (709 items)\n", "x: ImageList\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "y: CategoryList\n", "7,7,7,7,7\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny;\n", "\n", "Valid: LabelList (699 items)\n", "x: ImageList\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "y: CategoryList\n", "7,7,7,7,7\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ll = sd.label_from_folder(); ll" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Even when there is just an [`ImageList`](/vision.data.html#ImageList) from a training set folder with no split needed, we still must do `split_none()` in order to create an [`ItemLists`](/data_block.html#ItemLists), and only then we can do `ItemLists.label_from_folder()` nicely." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ItemLists;\n", "\n", "Train: ImageList (709 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny/train;\n", "\n", "Valid: ImageList (0 items)\n", "\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny/train;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "il_train = ImageList.from_folder(path_data/'train')\n", "sd_train = il_train.split_none(); sd_train" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LabelLists;\n", "\n", "Train: LabelList (709 items)\n", "x: ImageList\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "y: CategoryList\n", "7,7,7,7,7\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny/train;\n", "\n", "Valid: LabelList (0 items)\n", "x: ImageList\n", "\n", "y: CategoryList\n", "\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny/train;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ll_valid_empty = sd_train.label_from_folder(); ll_valid_empty" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So practially, although `label_from_folder` is not an [`ItemLists`](/data_block.html#ItemLists) method, we can call `ItemLists.label_from_folder()` to label training, validation and test [`ItemList`](/data_block.html#ItemList)s once for all." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Behind the scenes, `ItemLists.label_from_folder()` actually calls `ItemLists.__getattr__('label_from_folder')`, in which all training, validation even testing [`ItemList`](/data_block.html#ItemList) get to call `label_from_folder`, and then turns the [`ItemLists`](/data_block.html#ItemLists) into a [`LabelLists`](/data_block.html#LabelLists) and calls [`LabelLists.process`](/data_block.html#LabelLists.process) at last.\n", "\n", "You can directly use `LabelLists.__getattr__` to do labelling as below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LabelLists;\n", "\n", "Train: LabelList (709 items)\n", "x: ImageList\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "y: CategoryList\n", "7,7,7,7,7\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny;\n", "\n", "Valid: LabelList (699 items)\n", "x: ImageList\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "y: CategoryList\n", "7,7,7,7,7\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny;\n", "\n", "Test: None" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ld_inner = sd.__getattr__('label_from_folder'); ld_inner()" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

label_from_lists[source][test]

\n", "\n", "> label_from_lists(**`train_labels`**:`Iterator`\\[`T_co`\\], **`valid_labels`**:`Iterator`\\[`T_co`\\], **`label_cls`**:`Callable`=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n", "\n", "
×

No tests found for label_from_lists. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Use the labels in `train_labels` and `valid_labels` to label the data. `label_cls` will overwrite the default. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemLists.label_from_lists)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

transform[source][test]

\n", "\n", "> transform(**`tfms`**:`Optional`\\[`Tuple`\\[`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]\\]\\]=***`(None, None)`***, **\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for transform. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Set `tfms` to be applied to the xs of the train and validation set. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemLists.transform)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

transform_y[source][test]

\n", "\n", "> transform_y(**`tfms`**:`Optional`\\[`Tuple`\\[`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]\\]\\]=***`(None, None)`***, **\\*\\*`kwargs`**)\n", "\n", "
×

No tests found for transform_y. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Set `tfms` to be applied to the ys of the train and validation set. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemLists.transform_y)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

class LabelLists[source][test]

\n", "\n", "> LabelLists(**`path`**:`PathOrStr`, **`train`**:[`ItemList`](/data_block.html#ItemList), **`valid`**:[`ItemList`](/data_block.html#ItemList)) :: [`ItemLists`](/data_block.html#ItemLists)\n", "\n", "
×

Tests found for LabelLists:

Some other tests where LabelLists is used:

  • pytest -sv tests/test_data_block.py::test_add_test [source]

To run tests please refer to this guide.

\n", "\n", "A [`LabelList`](/data_block.html#LabelList) for each of `train` and `valid` (optional `test`). " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists, title_level=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a [`LabelLists`](/data_block.html#LabelLists) object is exactly the same way as creating an [`ItemLists`](/data_block.html#ItemLists) object, because its base class is [`ItemLists`](/data_block.html#ItemLists) and does not overwrite [`ItemLists.__init__`](/data_block.html#ItemLists.__init__). The example below shows how to build a [`LabelLists`](/data_block.html#LabelLists) object." ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "from fastai.vision import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/valid'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/test'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/history.csv'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/models'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/train')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_data = untar_data(URLs.MNIST_TINY); path_data.ls()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "il_train = ImageList.from_folder(path_data/'train')\n", "il_valid = ImageList.from_folder(path_data/'valid')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LabelLists;\n", "\n", "Train: ImageList (709 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny/train;\n", "\n", "Valid: ImageList (699 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny/valid;\n", "\n", "Test: ImageList (20 items)\n", "Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28),Image (3, 28, 28)\n", "Path: /Users/Natsume/.fastai/data/mnist_tiny/test" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ll_test = LabelLists(path_data, il_train, il_valid); \n", "ll_test.test = il_valid = ImageList.from_folder(path_data/'test')\n", "ll_test" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

get_processors[source][test]

\n", "\n", "> get_processors()\n", "\n", "
×

No tests found for get_processors. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Read the default class processors if none have been set. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.get_processors)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Behind the scenes, `LabelLists.get_processors()` first puts `train.x._processor` classes and `train.y._processor` classes into separate lists, and then instantiates those processors and puts them into `xp` and `yp`." ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "from fastai.vision import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "path_data = untar_data(URLs.MNIST_TINY)\n", "sd = ImageList.from_folder(path_data).split_by_folder('train', 'valid')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sd.train = sd.train.label_from_folder(from_item_lists=True)\n", "sd.valid = sd.valid.label_from_folder(from_item_lists=True)\n", "sd.__class__ = LabelLists" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xp,yp = sd.get_processors()\n", "xp,yp" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

load_empty[source][test]

\n", "\n", "> load_empty(**`path`**:`PathOrStr`, **`fn`**:`PathOrStr`=***`'export.pkl'`***)\n", "\n", "
×

No tests found for load_empty. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create a [`LabelLists`](/data_block.html#LabelLists) with empty sets from the serialized file in `path/fn`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.load_empty)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

load_state[source][test]

\n", "\n", "> load_state(**`path`**:`PathOrStr`, **`state`**:`dict`)\n", "\n", "
×

No tests found for load_state. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create a [`LabelLists`](/data_block.html#LabelLists) with empty sets from the serialized `state`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.load_state)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

process[source][test]

\n", "\n", "> process()\n", "\n", "
×

No tests found for process. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Process the inner datasets. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelLists.process)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "hide_input": false }, "outputs": [ { "data": { "text/markdown": [ "

process[source][test]

\n", "\n", "> process(**`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***)\n", "\n", "
×

No tests found for process. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Apply `processor` or `self.processor` to `self`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.process)" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "`processor` is one or more `PreProcessors` objects \n", "Behind the scenes, we put all of `processor` into a list and apply them all to an object of [`ItemList`](/data_block.html#ItemList) or its subclasses." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Helper functions" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

get_files[source][test]

\n", "\n", "> get_files(**`path`**:`PathOrStr`, **`extensions`**:`StrList`=***`None`***, **`recurse`**:`bool`=***`False`***, **`exclude`**:`OptStrList`=***`None`***, **`include`**:`OptStrList`=***`None`***, **`presort`**:`bool`=***`False`***, **`followlinks`**:`bool`=***`False`***) → `FilePathList`\n", "\n", "
×

No tests found for get_files. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Return list of files in `path` that have a suffix in `extensions`; optionally [`recurse`](/core.html#recurse). " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(get_files)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To be more precise, this function returns a list of FilePath objects using files in `path` that must have a suffix in `extensions`, and hidden folders and files are ignored. If `recurse=True`, all files in subfolders will be applied; `include` is used to select particular folders to apply.\n", "\n", "Inside [`get_files`](/data_block.html#get_files), there is [`_get_files`](/data_block.html#_get_files) which turns all filenames inside `f` from directory `parent/p` into a list of FilePath objects. All filenames must have a suffix in `extensions`. All hidden files are ignored." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "path_data = untar_data(URLs.MNIST_TINY) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/valid'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/test'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/history.csv'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/models'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/train')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_data.ls()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With `recurse=False`, no subfolder files are made available." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/history.csv')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list_FilePath_noRecurse = get_files(path_data) \n", "list_FilePath_noRecurse" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With `recurse=True`, all subfolder files are made available, except hidden files." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/history.csv'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/valid/7/9294.png')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list_FilePath_recurse = get_files(path_data, recurse=True)\n", "list_FilePath_recurse[:3]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/train/3/7263.png'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/train/3/7288.png')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list_FilePath_recurse[-2:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With `extensions=['.csv']`, only files with the suffix of `.csv` are made available." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/labels.csv'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/history.csv')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list_FilePath_recurse_csv = get_files(path_data, recurse=True, extensions=['.csv'])\n", "list_FilePath_recurse_csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With `include=['test']`, only files in `path_data` and its subfolder `test` are made available." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/test/4605.png'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/test/617.png'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/test/205.png')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list_FilePath_include = get_files(path_data, recurse=True, extensions=['.png','.jpg','.jpeg'],\n", " include=['test'])\n", "list_FilePath_include[:3]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/test/1605.png'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/test/2642.png'),\n", " PosixPath('/Users/Natsume/.fastai/data/mnist_tiny/test/5071.png')]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list_FilePath_include[-3:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Undocumented Methods - Methods moved below this line will intentionally be hidden" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

new[source][test]

\n", "\n", "> new(**`items`**:`Iterator`\\[`T_co`\\], **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n", "\n", "
×

No tests found for new. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create a new [`ItemList`](/data_block.html#ItemList) from `items`, keeping the same attributes. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryList.new)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

new[source][test]

\n", "\n", "> new(**`x`**, **`y`**, **`tfms`**=***`None`***, **`tfm_y`**=***`None`***, **\\*\\*`kwargs`**) → `LabelList`\n", "\n", "
×

No tests found for new. To contribute a test please refer to this guide and this discussion.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.new)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

get[source][test]

\n", "\n", "> get(**`i`**)\n", "\n", "
×

No tests found for get. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Subclass if you want to customize how to create item `i` from `self.items`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryList.get)" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

predict[source][test]

\n", "\n", "> predict(**`res`**)\n", "\n", "
×

No tests found for predict. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Delegates predict call on `res` to `self.y`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.predict)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

new[source][test]

\n", "\n", "> new(**`items`**:`Iterator`\\[`T_co`\\], **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n", "\n", "
×

No tests found for new. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create a new [`ItemList`](/data_block.html#ItemList) from `items`, keeping the same attributes. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.new)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

process_one[source][test]

\n", "\n", "> process_one(**`item`**:[`ItemBase`](/core.html#ItemBase), **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***)\n", "\n", "
×

Tests found for process_one:

Some other tests where process_one is used:

  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]

To run tests please refer to this guide.

\n", "\n", "Apply `processor` or `self.processor` to `item`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.process_one)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

process_one[source][test]

\n", "\n", "> process_one(**`item`**)\n", "\n", "
×

Tests found for process_one:

Some other tests where process_one is used:

  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]

To run tests please refer to this guide.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(MultiCategoryProcessor.process_one)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

get[source][test]

\n", "\n", "> get(**`i`**)\n", "\n", "
×

No tests found for get. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Subclass if you want to customize how to create item `i` from `self.items`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(FloatList.get)" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

process_one[source][test]

\n", "\n", "> process_one(**`item`**)\n", "\n", "
×

Tests found for process_one:

  • pytest -sv tests/test_data_block.py::test_category_processor_existing_class [source]
  • pytest -sv tests/test_data_block.py::test_category_processor_non_existing_class [source]

To run tests please refer to this guide.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryProcessor.process_one)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It basically converts `item` which is a category name to an index." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`classes`: a list of unique and sorted labels; \n", "It creates the inner mapping from category name to index (stored in `c2i`) from the `classes`." ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

create_classes[source][test]

\n", "\n", "> create_classes(**`classes`**)\n", "\n", "
×

No tests found for create_classes. To contribute a test please refer to this guide and this discussion.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryProcessor.create_classes)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

get[source][test]

\n", "\n", "> get(**`i`**)\n", "\n", "
×

No tests found for get. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Subclass if you want to customize how to create item `i` from `self.items`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(MultiCategoryList.get)" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

new[source][test]

\n", "\n", "> new(**`items`**:`Iterator`\\[`T_co`\\], **`processor`**:`Union`\\[[`PreProcessor`](/data_block.html#PreProcessor), `Collection`\\[[`PreProcessor`](/data_block.html#PreProcessor)\\]\\]=***`None`***, **\\*\\*`kwargs`**) → `ItemList`\n", "\n", "
×

No tests found for new. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Create a new [`ItemList`](/data_block.html#ItemList) from `items`, keeping the same attributes. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(FloatList.new)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

reconstruct[source][test]

\n", "\n", "> reconstruct(**`t`**)\n", "\n", "
×

No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Reconstruct one of the underlying item for its data `t`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(FloatList.reconstruct)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

analyze_pred[source][test]

\n", "\n", "> analyze_pred(**`pred`**, **`thresh`**:`float`=***`0.5`***)\n", "\n", "
×

No tests found for analyze_pred. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Called on `pred` before `reconstruct` for additional preprocessing. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(MultiCategoryList.analyze_pred)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

reconstruct[source][test]

\n", "\n", "> reconstruct(**`t`**)\n", "\n", "
×

No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Reconstruct one of the underlying item for its data `t`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(MultiCategoryList.reconstruct)" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

reconstruct[source][test]

\n", "\n", "> reconstruct(**`t`**)\n", "\n", "
×

No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Reconstruct one of the underlying item for its data `t`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryList.reconstruct)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

analyze_pred[source][test]

\n", "\n", "> analyze_pred(**`pred`**, **`thresh`**:`float`=***`0.5`***)\n", "\n", "
×

No tests found for analyze_pred. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Called on `pred` before `reconstruct` for additional preprocessing. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(CategoryList.analyze_pred)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

reconstruct[source][test]

\n", "\n", "> reconstruct(**`t`**:`Tensor`, **`x`**:`Tensor`=***`None`***)\n", "\n", "
×

No tests found for reconstruct. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Reconstruct one of the underlying item for its data `t`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(EmptyLabelList.reconstruct)" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

get[source][test]

\n", "\n", "> get(**`i`**)\n", "\n", "
×

No tests found for get. To contribute a test please refer to this guide and this discussion.

\n", "\n", "Subclass if you want to customize how to create item `i` from `self.items`. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(EmptyLabelList.get)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "

databunch[source][test]

\n", "\n", "> databunch(**\\*\\*`kwargs`**)\n", "\n", "
×

Tests found for databunch:

Some other tests where databunch is used:

  • pytest -sv tests/test_data_block.py::test_regression [source]

To run tests please refer to this guide.

\n", "\n", "To throw a clear error message when the data wasn't split. " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(LabelList.databunch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## New Methods - Please document or move to the undocumented section" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "hide_input": true }, "outputs": [ { "data": { "text/markdown": [ "

add[source][test]

\n", "\n", "> add(**`items`**:`ItemList`)\n", "\n", "
×

No tests found for add. To contribute a test please refer to this guide and this discussion.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(ItemList.add)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "jekyll": { "keywords": "fastai", "summary": "The data block API", "title": "data_block" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 2 }