{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The data block API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "from fastai.gen_doc.nbdoc import *\n",
    "from fastai.vision import * \n",
    "from fastai import *\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data block API lets you customize how to create 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 set?\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",
    "For each of those questions, you can have multiple possible blocks: your inputs might be in a folder, a csv file, a dataframe. You may want to split them randomly, by certain indexes 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 or may not have data augmentation to deal with. Or a test set. 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 you total flexibility to create your customized [`DataBunch`](/basic_data.html#DataBunch) for training. 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",
    "<img src=\"imgs/mix_match.png\" alt=\"Mix and match\" width=\"200\">\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 by our traditional MNIST example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/jhoward/.fastai/data/mnist_tiny/history.csv'),\n",
       " PosixPath('/home/jhoward/.fastai/data/mnist_tiny/valid'),\n",
       " PosixPath('/home/jhoward/.fastai/data/mnist_tiny/models'),\n",
       " PosixPath('/home/jhoward/.fastai/data/mnist_tiny/train'),\n",
       " PosixPath('/home/jhoward/.fastai/data/mnist_tiny/test'),\n",
       " PosixPath('/home/jhoward/.fastai/data/mnist_tiny/labels.csv')]"
      ]
     },
     "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/jhoward/.fastai/data/mnist_tiny/train/3'),\n",
       " PosixPath('/home/jhoward/.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 create an easy [`DataBunch`](/basic_data.html#DataBunch) suitable for classification by simply typing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ImageDataBunch.from_folder(path, ds_tfms=tfms, size=24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is aimed at data that is in folders following an ImageNet style, with a train and valid directory containing each one subdirectory per class, where all the pictures are. There is also a test set containing unlabelled pictures. With the data block API, we can group everything together like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (ImageItemList.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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5PPPMM+xUzArjSCRiqwjWsouZdaQdltfr5dA3lUpx1lNnZyfLbjdu3MDY2BiPQUsg+TaC3aZY7CUYDHKB5tmzZ7lIr7m52ab164lmbW2NCwBjsRh3Drh79y4AYHx8/JGOx7yuqanhhc+RI0e4MDATx1Noe4hOsxn9mVxYWOC9svv377NjMI8+MVOVl5eXs3L45j7RxsYGF6jquen06dMsr3V1dXHatNfr5QWw2VVlYmIC77//PgDg9ddf57lnYmIiLx0KdiKTrDYC8H0A3UqpPzJ+9GMAX9+8/jqAH+3+8IRCQ+xFyBaxmdIjk4jneQAvA/iIiK5tPvYHAL4L4K+I6BsABgF8dW+GuDf4fD6uuThx4gSvJKqrq20Fd3rVsry8zCHxdl1hXS4XryhMCa6hoYEjoePHj/OmYn9/P2cgTUxM8IqlCCgae/H7/ZxQ8JnPfIazm2pra3mj32x5sry8zPf3zp07fEigruMaHh7edkNaf7+V1tZWtsUDBw7Yai5MG9RR9OXLl7ko2TyiowBwrM2sra1x7d1OmK2Ntt7XnZ6v5w0zM7ampganT58G8CDiOXbsGP+8trbWlliibWB8fBy3b98GAFy/fp3t7tKlSxwNOynaATLLansHwE7pOZ/e3eHkjng8zhLIxYsXWVOtqqriD7tSisNm8/wcs5Gffm4oFGLNNRqN8h5PdXU1p9ia2u7bb7+NX/ziFwCAK1euFI3jKSZ7SaVSfK8TiQRP5qlUiot/Y7EYT0zDw8Msrw0NDXGGmT6/aXl52eZ4NFsnKj2JpdNptsvm5maWfYEH6bZmJtvVq1e5sr0AUqiZQreZTJwNYD9GQWehdXZ2clr0M888w9KqXhSbZRxut5sdyOrqKsu6ly5dwttvvw0AuH37Nu7duwfgwfEtTkR6tQmCIAg5pWRb5qyurnKnV6/Xy1FJeXk5RzFmS/L5+XnOjzdbWOjnBoNBW4GhTlaoqqpiGa+yspIfb2xsxGc+8xkA1orJPA/daWFxqbKyssI2cvHiRZa0/H4/r0L7+/u5PuL+/ft8HxcWFrgX29ZW9x+Hx+Ph1w6Hw5yksm/fPo54lFIs+w4ODnKUMzw8zCfZmplxQv4wWyHppKXDhw+zhHvy5Enu/9bZ2WlLZgIeTj4wkwh0Qejrr7+On//85wCsDLtCaMFVso4nmUxyBlA6neaMNZ/PZzszRU8Wq6ur7BBMx6NxuVzbNgcsKytjh9TR0cHpkG1tbZytkkwmeb/HPJY2nU4XXcZbIZFIJFiuuHbtGnemMO/vyMgI37vJyUnbCaCPc++i0ShPSufPn2d7aW5u5n0lpRSP68MPP+Ssq8nJSZZyCyWFutgw+zcSETubUCjEe4Rf/OIXuc9aNBrlso5QKLRjU1HA3olgfHycC0/v3bvHi6J0Ol0QjYZFahMEQRBySslGPKlUiuWKeDxua29idpTVK8fHjT78fj93rV5YWODXCAaDnO32zDPPsCxz7do1TnoYHh7moi/9cyF3pFIp3sA1o1LTDlZWVjjpYHV19YlbkdTU1ODpp58GYLVIOXLkCABLvtXR1NzcHNdnXLlyhaW+xcVFiXTyjNfrZZksGo1yltrx48dx8OBBAJbUpltxBYPBh+oDdyKRSPCcNTw8bOs2reXZRCLBNuBktaRkHY9SKic9rNbX13limpiY4Kyn2tpa7vd16tQpDslPnz7Np0h+8MEHnIqr9xqE3KGUYk19L7IOzSOz9eJk//793OL+4MGDvPeolOLsuP7+fly+fBmAtVDRleqyN5g7iIgz0yorK1lOr62t5QLftrY2ltSeeeYZfk4wGORiYDO1+lF4PB7eIz5w4ADf77W1NXY2w8PDnHHp5AJikdoEQRCEnFKyEU+u2NjY4MhqdnaWCwyDwSC3x2hpaeFVT2trK65evQrAkgBFOile9D0vKyvjuo329nYcPXoUgBXx6M3mjY0Nrte5fPkyJxSMjo4WWrFowbFdREJEnBRw8uRJTgI5duwYS+jRaHTb+kCzQF0pZZNndRRsHvqnCQQCXExaWVnJ88dTTz3FPdl+/vOf4+LFiwCs+SPfB77thDieHJJIJFguWVlZYXnNNKjGxkYOkRcWFjg10uVyiRMqIkypprm5Gc899xwA4MKFC1xECDwoBDWbVb7zzjucQl0IqbOFjM/nszkP3buvoaGB92k6OjrY2bS1tfH9+7iTX83OE9o5mJmz+msymeTPfWNjI0uvFRUVvJfU2NjI+0SxWIy7GJgF0E5zPCK1CYIgCDlFIp4cYm5Wx+NxXL9+HYAltejOx+app36/n1csT3qomOAMzLYpWnJpb2/HF77wBQDAuXPn+P67XC6W0QYHB3kle/v2ba7bcPIGcqFSUVHBNVPRaJSLeNvb2zkyPXHiBCcLRCIRjjj8fj9HOkop22mkOjM1mUzaeqiZx2foCFcrI5OTk/x6L774IsuwHo+HZTjz+IWKigruE5lOpzkidlpBsTiePKGU4irz4eFhNu6Ojg4O4c329y6Xy3HhspA95gRRX2+da3bs2DEcOHAAgJU2rZ1TIpHgVOmf//znuHnzJgArw267ImbhydDS1blz5zgNur29nT+PNTU1/JmMRqOcmeb3+20lGOYkrz+zMzMznJk6ODho6+mnHZJ5DpB+LB6P817O8ePHbSnSZoNi7fgqKyt5v3BmZobLMZyGSG2CIAhCTpGIJ4+YYbVeDXV1dXHRYE1NjaxoiwBTXtOr6qNHj+KFF14AADz77LPcz89MIlFKcTHxu+++y0Wjku24N2jV4fz589wxur29nZMFdkrwMXstxmIxvp6fn2epdGZmhruYDwwMcCTb09PDUptSihNOzAPndDSTTCb55xsbGyzBra+vczJCIpGwyfJOlWIl4hEEQRByikQ8eUSvnsrLy1njNzcNw+GwrY5DKEz0/fT7/Vz78dRTT+FTn/oUAGtVrTeqE4kER789PT348MMPAVjHWuvVs+z17Q16L+fQoUO8T1JeXs6fwWQyuW3qcyKR4HZKt2/f5hqr3t5eTgIhIo4+1tbWtj2gzazpMet3tG2EQqFta4qSyaTt6G1zXE6NeMTx5AkiYiMOh8PckbiyspKPXwiFQpxdox8TCgOzFYo+FPDQoUPcAv/ZZ59FW1sbACuhQG9ULy4ucjHgT37yE858jMViUrOzx5hHEmhJy+PxsDOIx+Mso/X29nKW4fDwMPf0Gx8f5+tYLMa91TY2NjJaPGqJTdcOlZeX49ixYwCApqYmHpd56mkikWBHtri4yIkJiUTCsf3aRGoTBEEQcopEPHnC4/FwNBMOh3lzuby8nJv8eb1erul41LnvgrNwuVy8OtURz3PPPYff/M3fBADU1dXx4y6Xi1epk5OT6O7uBmDJNrqeIxcNbUsZv9/Pkcq1a9e41KG5uZnv49TUFN+PW7dusQw6MjLC0WgymeRI1zwSO9PIQ3eZ1h0KOjs7WQ2pqanZthvC2toaJyrNz8/zWHTSghMRx5MnIpEIa8oNDQ0c5ldXV9ty74PBYF7GJzwZoVCI93N0O5UDBw6grq4OgCXn6AnKPOn01q1bnL02Njb20Km3wt6hF3dvv/02S5zV1dW8tzY+Ps5S2+rqKjunZDLJ98fcp3kcdAab3tdpamqyHRSnMR3Z/Pw8jz0Wi7ETcrLjEalNEARByCkZRzxE5AZwCcCoUurXiOgAgB8CiAK4DOBlpZToARkSCAS4tcX+/ft5M7GyspJXKvPz847dHHwUpW4v5eXlfPDXU089BcCSTbS85vV6+d4uLS1xXce7777LRxovLS1xFXyh2kE25NNmUqkUS9zJZJKVhlAoxFHn9PT0niZ4+Hw+7mahm5G2tbXx3BAIBGzSnZZfx8bGuKtFT08P/z+cfHhkNlLbNwF0Ayjf/P4PAfwnpdQPiej/BvANAN/b5fEVFW63mzVcs635vn372EimpqZYdunr62Oj93q9juu39AhKzl48Hg9npzU1NeHEiRMAwJlsLS0tnImUSqV4gTExMYFf/vKXAIDXXnsNU1NTACztvhQcjkHebCadTtsO/dP3yVwg7IXcuTX78dy5cwCAT37ykwCsTEg9T3g8HluqtJb9+vr6uHP54OAgj9PJ80VGUhsRtQD4IoA/3fyeAHwKwN9sPuUHAH5jLwYoFB5iL0K2iM2UFplGPP8ZwL8DENn8PgpgXimllwAjAJq3+0XhAT6fj4+ura+v55VMV1cXr35HR0e56GxoaIizaAqsaLCk7MU8AllLJV1dXTh//jwA69hjwF6MmEgkWLYZHx/nmpB79+4V2r3eLRxlMzrK2atsQh1RmZ2lOzs7ceHCBQDglj2hUIhlv3Q6zQrIxMQEy7PXrl1jlSSZTNoOlnMqj3Q8RPRrAGJKqctE9FK2b0BErwB45THGVnS4XC5Ok6yvr+ceULW1tTZ5TZ80uba2VnCTUCnai5ZKqqurcfjwYQDWqZT6WjujdDrNE9ng4CCfFPmLX/yCuxWXIqVoM3oBEolEuGSiq6uLMyB1eYXb7eaFTTKZZHnN7Gpx69YtTgV3srxmkknE8zyALxHRFwAEYOmvfwygkog8myuSFgCj2/2yUupVAK8CABE51wULu4XYi5AtYjMlxiMdj1Lq9wH8PgBsrkb+rVLqnxPRXwP4Z7CyTr4O4Ed7OM6CxSwUra+v5/YXZ8+eZdltbm6OJbXbt29zHcfS0lLBnbtSKvaioxyPx8M1F62trSyvnTp1ynaeEmBtTuskkqGhIbz55psAgH/8x3/kje1Cuc+7SanYjImu22tubua+cMeOHePaPl3PAzyQ5dbW1rh2aGRkhGt35ubmCq7A+EkKSL8F4IdE9H8AuArg+7szpOLC5/Nx0WBnZydnOp0/f55Ta9PpNBd9DQ4Oore3F4BlaEU0ERWVvWipJBgM8iTS1tbGWUknTpx4qPh3YWGBm0neuHEDAwMDAKwsKicX++WRorIZE50u/eKLL7LNHDly5KHFysbGBu/VLC4u8gJ1cHCQr/WipZDIyvEopd4E8Obm9T0A53d/SEKxIPYiZIvYTGkgLXP2GJfLxZJaZ2cnFxXW19ezjGZ2up2enuYNwiKKdooOLYW0tLSwfHry5EmOboPBIG8Ka2KxGC5fvgwAuHLlCieRLC8vOzoDSXhyPB4PZ68BD4qKf/VXf5Wvw+EwwuEwgAdZrGtraxwN9/b24tKlSwCAS5cucb1X0Uc8Tsc8eS+fH2Sv18vyS2NjIw4dOgTAcjza+O7fv8+ZKFevXsXVq1cBWNqtNrpCy2grdrR9ERHLaAcPHsQXv/hFANZkortRBAIBXljoXl8DAwPcA+z27dus1wu7BxHB5/PZzrVKp9O2z5Re0OVyYVdeXs7Zja2trXj22WcBWH38dGcCr9fLEq62mfn5ee5EcP36dbz//vsALPsxz/cpNKRXmyAIgpBTCibiMc+t1xKG2SJ8Y2ODVzXmdS4jH/PkUF0cevz4cQ6lDx8+zC1VRkdHWV67fPky5+QnEgmJdByKtrdQKISmpiYA1obwkSNHAFjRrW6JlEqlbBvBgFXop0+nvHv3Lq9URWbbPdxuN8rLyxEIBPh+pdNpjg7M6GdtbY2zwVKp1K7dB61q+P1+nqs6OjpY+Thx4gTPCU1NTbYMNh2FaTWkr6+Pj8m4fPky7t69C8CS1wrZbgrG8ehJPRAIsA5q9i9bX1+3XesbmKubY54I6PP5WHI5f/48urq6AFgFhroAbGhoiJtBaiPL5XiF7NFFfR0dHZyJdPr0aS4Erqqq4okmnU7z8Rba2dy6dYu7UugTI4XdpaqqCl/5ylfQ2Nho63+o78XU1BR/Bufn5zmb1DwhNJPPoHkEtSnZEREXiTc0NPA8cPbsWc5obW1t5cfD4bDNKer9HL1Yef/991mGv3//PstuhT5PiNQmCIIg5JSCiXh02BwIBLjFRCAQYLlidXXVFvHo6COZTPLqYC83E4PBIEdiNTU1vDpeW1vDnTt3eCxafrlz5w63SZmeni64QtFSxGxtorsHHz58mFe4Ho+H7186ncboqFVor1esPT09BZmBVEhEo1G8/PLLaG9v56hyYWEB8/PzAICZmRnOBrt79y4Xaw8PD2NiYoKfs1NdlY50ystmWuHTAAAgAElEQVTLOYEoEolwsonb7eaC0KamJm6Bc/bsWZZkQ6EQS+5ExFHXzMwMZzrqdkoffPAB7t27BwCYnZ0tyESC7SgIx6N1W8Cq9NVhaiQSYY02kUiwoc3OzrJxLSws8KSeSqWymtiJiKUTr9fL2q3WZCsqKrjgKxKJsL4fDAb58bW1NQ7th4eHWaMdHx9niW11dbXgQ+diRU80LpeL73s0GuUJJRqNso2Yp0+aezy6IHh4eHhPz3MRLAnz0qVL8Hg8XMawf/9+lrvNiT4Wi/HiYHJykh3P5OQkzx+xWIyvp6eneb6JRCLsYNrb27njQG1tLX/2zQVoNBrlk0TNOWh5eZk7EPT09LAsq9Omr127xhlu5hZCoSNSmyAIgpBTHBvxBINBXi00NTXxisKMeMrKyngFuby8zHUR9+/ft+W4a9nN3EA0V7DBYJCvA4EARzZExM/3er0cHmtJbd++fbaVr14dExGvfBcXF3ljs6+vjyOe1dVVDucl2nEu+p76fD5bxKNbnpiFoqlUilfN9+/ftxUFA5aNFkr34EIlFovhT/7kT/CTn/yEkz6ampo4Oqmvr+d5paysDA0NDQCseiwtly0uLvLxA3Nzc7yhPzMzwxGPaQ+VlZXc/qq2tpYjm4qKCttnW89Dq6urPG9NTExwUfEvf/lLltX0Vx01FxuOdTyRSATt7e0ArMwh3WLeNKJQKMRhczwe5w89EbEWamafmNKZWSUcCoVsjkeztrbGBmKeHqqbfnZ1deHMmTP8ejpkHh4eZq12dHSUw/m5uTmeeHYzfVPYG4iI73VNTQ2nyNfV1bFdeb1evl5bW+NspPfee+8hxyP92PaeRCKBnp4ePqtGo51NQ0MDd5eora21OQx9XVNTw86jurqaF73l5eU2WdU8htq0Bz1PuN3ubTPWpqen2ZnduHEDr7/+OgDgrbfe4uc4+djq3UCkNkEQBCGnODbiKS8vR2dnJwDghRdewOnTpwFYkZAZleiN+1AoxKuRRCLBGSezs7OcEed2u1kuKysr43b2oVCIVyler5dXKXNzc7yxmEwmbRvN+qs+wE0pxVlq9+7d43qN6elpzqiZn5/nSEwiHudj1mR0dHTwBnVLSwvLsW63m+XYZDLJke6dO3fYBiTSyT9ahp+bm+OCTJNwOMzJCLW1tRwV1dXVcauburo6llhbWlr4OZWVlTyX+Hw+WxatVkzi8TgrH93d3Zzp2t/fzwkFWjEpBRzreMzU1FgsxjeqqqqKs0a0cwEsw9GTgdfrZX03kUjYHtcOJhKJsLEQkU0C01kkY2NjbETbZSMlEgmeXOLxOIf3PT09LPstLS0VfdhcbJhdMvRkdOTIEZZVW1pabFXxejGxsrJi61agbUDuv/NZXl7mrNjh4WHbz/ScUV1dzU6opaWF55jGxkbeK6qrq+P5KRgM8p7Q8vIybt68CQB44403uFNJOp3m+aaUEKlNEARByCmOjXiWl5dZtggEArwZt2/fPs4Ya2xs5JVnOBzmDcSamhpetZqbeul0mh93uVy2nm9mx1q9oby+vs4rloWFhYcKUVdWVnjjeHR0lDNRxsbGWF4rlrz7UsHlcvEKNxwOcyJLV1cXLly4AMCSgbVku76+zhFNPB5nSWdxcZGjZOm9V9jo+WN8fJzlsGvXrvHPPR4PJyPU19ezzVRWVvK2ABGx1Nbb28vRMFCaWa2OdTwrKys2jVw7nkQiwY4hHA7b9lvMbgV6wjebivr9fs5k83g8PHmY52QQEe/xJJNJW8qzdiY61XJycpLHGIvF+Ofr6+slaUzFgJlmX1dXx+nynZ2dD50OCVgLCy3RTE9P8/Xa2hpLcLL4KG7MYuHJyUncuHHjoeeYc1UikSj5+UGkNkEQBCGnODri0Rlly8vLLHmZufFDQ0O2iEfLbj6fj6OiyspKbrdTXV3NUY7P52NJxe/3sxyysLDA8llvb6+t3Ynus6Vrh8xDmhYXF/nna2trssotIIiIbaesrIxrxi5cuIAXXngBANDW1sar1K0HDpptm/TrmKeP6ueX+iq3lJFu5HYc63jS6TR/oAH70QE6hToYDLLjcbvd/KGvqqriVMfm5mZ2Kj6fj59jFoeaXQ/m5+f5emBggKW0qakpNh7zq36NRCIhVekFitvtZnktEong6NGjAIBf//Vf5zR+vUjZitfr5ezIuro6zm6KRCI2BwWI4xEEjUhtgiAIQk4piIjHPEFweXmZoxyzVYXL5eJeS01NTZwAsLCwwEWeoVCIEwlWV1c5f35paYmfPz8/z9czMzOcMLC0tCSbxUWKx+OxSbM6Q8mUY03pzMTtdrNNmZHzysqKraWKIAgPyMjxEFElgD8FcByAAvAvAfQA+EsAbQAGAHxVKTW3WwPb2Nhg6SqZTG5bwElEth5r2jnFYjF2ErFYzDZpaKexvLxsczxmzzdT4hOyJx/28iSYx26YTSRNKddsm2+m1U5MTPDCZmxsjHu1jYyMyAIlCwrNZoQnI1Op7Y8B/FQpdQTASQDdAL4N4HWl1EEAr29+LwiA2IuQPWIzJQQ9SgYgogoA1wC0K+PJRNQD4CWl1DgRNQJ4Uyl1+BGvtauag9lt2uPx2Op19LXL5bLV5ehIqFhO8ssWpRQ9+lmPj5PtZSfC4bCtY3FTUxNf64jaPBBsZmbGloyio+UildQuK6XO7eUb7KbNeL1eVVlZaUtGEnJLJnNMJlLbAQBTAP47EZ0EcBnANwHUK6V0V7sJAPXb/TIRvQLglYxGnCXmiY8bGxsska2srEi1eP5wrL3sxNraGp+ZNDs7y8chp1Ip7kqgFy/CnrBrNlNVVYXvfOc7GBkZ4Z5rQ0NDfD05OSm98xxAJlKbB8AZAN9TSp0GsIwtIe/mKmXb5Z5S6lWl1Lm9XjUJjkHsRciWXbMZs3Gw4FwyiXhGAIwopX65+f3fwDKKSSJqNMLg2F4N8uOQDVzH4Wh72Y50Oi0Ffvll12ympqYGv/M7v4P5+XmW28bGxjAwMADAqs3TPdPGxsY4SSQWi7F8KnPK3vNIx6OUmiCiYSI6rJTqAfBpALc3/30dwHc3v/5oT0cqFARiL0K27KbNrK2tobu7G1VVVWhpaQFgnaX03HPPAbCkNu2QJiYmuMfaxMSELWtRX8diMX5+KR5fsFdkWsfzrwH8BRH5ANwD8DuwZLq/IqJvABgE8NW9GaJQgIi9CNkiNlNCPDKrbVffjGgKln7rtJSTGjhvTMDuj6tVKfVwi2WH4mB7AZxpM3sxpkKzmTis+h+n4UR7AfI0x+TU8QAAEV1y2saxE8cEOHdcucSpfwMnjsuJY8o1Tv0byLjsSK82QRAEIaeI4xEEQRBySj4cz6t5eM9H4cQxAc4dVy5x6t/AieNy4phyjVP/BjIug5zv8QiCIAiljUhtgiAIQk4RxyMIgiDklJw5HiL6PBH1EFE/EeWtvTkR7SOiN4joNhHdIqJvbj7+HSIaJaJrm/++kIexDRDRR5vvf2nzsWoi+hkR9W1+rcr1uPKB2EtGYxN7MXCCzYi9ZDiWXOzxEJEbQC+Az8Lqy/QhgK8ppW7v+Zs/PJZGAI1KqStEFIHVCfc3YFVFLyml/kOux2SMbQDAOaXUtPHYvwcwq5T67uaHqUop9a18jTEXiL1kPLYBiL0AcI7NiL1kRq4invMA+pVS95RSSQA/BPDlHL23DaXUuFLqyuZ1HNaBU835GEuGfBnADzavfwDLiIsdsZfHpxTtBXCIzYi9ZEauHE8zgGHj+xE44GYQURuA0wB0V9zfJaIbRPRneZIoFIB/JKLLm2eMABmeSVJkiL1khtjLAxxnM2IvO1OyyQVEVAbgbwH8nlJqEcD3AHQAOAVgHMB/zMOwPqGUOgPgnwD4V0T0SfOHH3cmibC3iL0I2SD28vHkyvGMAthnfN+y+VheICIvLKP4C6XU3wGAUmpSKZVWSm0A+G+wQvecopQa3fwaA/A/Nscwuakba/3YMefY7CFiLxkg9mLDMTYj9vJocuV4PgRwkIgOkNX2/LcA/DhH722DiAjA9wF0K6X+yHi80Xja/wTgZo7HFd7cjAQRhQH86uYYfgzrLBKgdM6xEXt59LjEXuw4wmbEXjIj0/N4ngilVIqIfhfAawDcAP5MKXUrF++9Dc8DeBnAR0R0bfOxPwDwNSI6BSvUHADwv+Z4XPUA/odlt/AA+P+UUj8log9RYmeSiL1khNiLgYNsRuwlA6RljiAIgpBTSja5QBAEQcgP4ngEQRCEnCKORxAEQcgp4ngEQRCEnCKORxAEQcgp4ngEQRCEnCKORxAEQcgp4ngEQRCEnCKORxAEQcgp4ngEQRCEnCKORxAEQcgp4ngEQRCEnCKORxAEQcgp4ngeAREtbfmXJqL/mu9xCc5FbEbIhlK0l5ycx1PIKKXK9PXmcbYTAP46fyMSnI7YjJANpWgvEvFkxz+FdTTs2/keiFAwiM0I2VAS9iKOJzu+DuDPlZyeJ2SO2IyQDSVhL3ICaYYQUSuAewA6lVL38z0ewfmIzQjZUEr2IhFP5rwM4J1iNwhhVxGbEbKhZOxFHE/m/AsAP8j3IISCQmxGyIaSsReR2jKAiJ4D8DMADUqpeL7HIzgfsRkhG0rNXiTiyYyvA/i7UjAIYdcQmxGyoaTsRSIeQRAEIadIxCMIgiDklCdyPET0eSLqIaJ+Ivr2bg1KKF7EZoRsEHspTh5baiMiN4BeAJ8FMALgQwBfU0rd3r3hCcWE2IyQDWIvxcuTRDznAfQrpe4ppZIAfgjgy7szLKFIEZsRskHspUh5kiahzQCGje9HADzzcb9ARJLJkGeUUpTHt8/KZsReHMG0Uqo2T+8tc0wBkskcs+fdqYnoFQCv7PX7CMWB2IvjGMz3AB6F2Ezh8SSOZxTAPuP7ls3HbCilXgXwKiCrEeHRNiP2IhjIHFOkPMkez4cADhLRASLyAfgtAD/enWEJRYrYjJANYi9FymNHPEqpFBH9LoDXALgB/JlS6taujUwoOsRmhGwQeylectq5QMLg/JPn5IKsEHtxBJeVUufyPYhMEZvJP5nMMdK5QBAEQcgp4ngEQRCEnCKORxAEQcgp4ngEQRCEnCKORxAEQcgp4ngEQRCEnCKORxAEQcgpe96rrRRxuVzw+XwAgLKyMpSVlQEAIpEIIpEIX5eXlz/0u9PT05iamgIATE1NYWZmBgCQSqVyMXRBEIQ9RxzPLkJk1U25XC74/X4AQG1tLVpaWgAA+/fvR1tbGwCgvb0dHR0dAACPx8O/e+vWLVy6dAkAcOXKFayurgIAFhcXc/b/EARB2EtEahMEQRByikQ8j4EZ2WhJLRAIIBwOAwAqKioQjUYB2KOcffv2Yf/+/QCA1tZWtLa2AgD8fj+/ZkVFBUKhEAAgkUhgcNDqSi8RT3FBRHC5XHy9sbEBAPxVEIoZcTyPgdvtBgB4vV5UVFQAAOrq6tDc3AwA6OzsRGdnJwCgqakJTU1NAB7e49GvAwC6Z15dXR3Onj0LAJibm8PFixcBAMPD5nlYQqHj9/tttlBZWQkAvB8IAOl02ia1rqys8ON6z29lZQWJRAKAOK29wOVy8QLB7XbD43kwZerP7Mf1u8y2F6ZegOq5wXz/rYsV/RxzHgHANrOyssJ2kk6nsxrHXiNSmyAIgpBTSi7i0SsKIuJr83FzhbF1taHRUlg4HEZdXR0AS1LTyQInT57EU089BQCorq5GVVUVgIdXJtvh8/k4impubuaVsFBcBAIBm+3oCLmjo4PlW7fbjYWFBQDA2NgYZmdn+Xf1cwYGBjAwMAAAiMVi/HwdBQlPhs/nYwndzFD1eDxIJpMAgPX19V17P6/Xy+8FWHOMTlTy+/22a50VW15ezvOUUgo9PT0AgBs3biAWiwGwRz9OoKQcj8fj4Q+sz+ezORhzr0Y7lrKyMjY60/HoCWPfvn1oaGgAYDmYmpoaAJa8pvd4gsHgtg4OsIfh+vFEIsETydDQ0K4atZA7fD4fgsEgAPDXUCjEE0dNTQ1Ls+3t7bxQOXPmDC88Kioq2Nn09vZyan1NTQ0vZt566y288847ACwb0g5HHM/u4PP5ePFXV1eH+vp6ANbEv7S0BABYW1vbtffTtqLfp7a2liXZ8vJyW2mGtp+mpibbova1114DAMTjcZbdEomEoxyPSG2CIAhCTimpiMftdvOKIhwO80ah2+3mlURFRQWqq6sBWKsOHdGY0lx7ezsA4PTp05yZtpNEB9gjG329NQrS329sbGBsbAwAMDg4uKurKSF3+P1+1NbWAgDb0P79+7mmq6amhn9eV1eHxsZGAJb9aRt1u90ccTc3N/Pza2treRU+OTnJiScTExNsO8Lj4/V6WfLav38/urq6AFgKh1Y1vF4vJ3vsZnQZCAQAgN+nurqabSAUCtmutT1Eo1FbxKMjoerqapstOYmidzyBQMAmb+zbtw+AFZ5qec3r9fINNTsKRKNRlsxM56BveHV1NRvo2toaG+L4+DhPAPF4nB+vqqpiLb+xsZGNQo8PsJzW/Pw8AGB0dJRDZcG5aLvw+Xy8mKmpqeHU+UOHDgGwZLQzZ84AsMsm5u/5/X7bvqK20erqas5aC4VCLMF6PB6W5gKBgGS27QKBQIDngKNHj+Jzn/scAKCtrY1leJfLxfdgNzPGtB2YMq2eY0yH6PF4eCzmIlY/D7AW19qROc3xiNQmCIIg5JSijXj0KiAcDnPU0tnZyTUyZ86c4dWk3++3JR3oCCQYDPKKwYx49M/Lysp4hbm8vIy5uTkAVtuba9euAbCiHx3BNDQ04FOf+hQAa1VlrpQ1Xq+XNy1HRkawvLwMwFphyWrWmejVZDQa5cSTzs5OHDt2DABw8OBBAFbRsJbdQqEQr1jNlklbMe3SREfRgUCApWGfz+e4eo1CQt+DYDDIUldXVxc+/elPAwBaWlq2vU873bvdYGsdkFk7ZGbommhbMZNZTOnfCRSt49GEQiGeDDo6OtjxfPrTn+awdmtRmP7wbmxs8GSfSCRY9orH4wCAmZkZfmx0dJTltVu3buHOnTsArMI//RrBYJBTML1ery0FUr9mPB7H9PQ0AKthqHZCe2ncwpOh7ae+vh7Hjx8HAJw6dQqnT58GAN4HDIVCtnRc7bCe5N6WlZXxwsrn80kW5BOg70M0GsXhw4f5Wn82Z2dnbfK8KXvtFUopvqfJZJIXoktLS5zZWFlZaZPStisZcdr84Sw3KAiCIBQ9j3TVRLQPwJ8DqAegALyqlPpjIqoG8JcA2gAMAPiqUmpu74aaHTokDQQCHDbX1tbaVpzb9cpKJpMclayurnJW2dTUFCYmJgCAI5KlpSWW0QYHB7mv2vT0NMtuPp+Pc/LLy8t509JMTHC5XNyLbWhoiIu+Zmdn+fFCkdkK1V4ywVzh6qyympoajqgPHjyIo0ePArA2pY8cOQLgQd0X8GDlaUolj4O23YqKCluBsrbdQsIpNqPvR01NDWeymRGPx+PhJKRwOMz3YC8jHgB8T5eWlnhumJyc5Eh6a/stM7vWqRFPJn+xFIB/o5S6QkQRAJeJ6GcAfhvA60qp7xLRtwF8G8C39m6oj0cgEGApoq6ujh2P2+22OR7tqNbX11k+W1hY4Il/YGCAK4J1+urs7Cw7mMHBQYyMjACA7cMfjUZt6Y1mRbIu6EokEhgfHwcAfPTRR/w6KysrhTiRFLS97AQRseMJBoM4cOAAACulXjuYxsZGvtfmIsd8DW1n6XSaJd3l5WWWUNLptE0C1vtAfr+fH3e5XHxdXl7OBYZEVKh7PI6wme0m6bm5Of6bmvty4XCY728oFLLdM3PiN9mpb9t2NqHltUQiwXL74uIiL34nJib49VtaWmx7gDt1Z3ESj5TalFLjSqkrm9dxAN0AmgF8GcAPNp/2AwC/sVeDFAoHsRchW8RmSo+sYkQiagNwGsAvAdQrpcY3fzQBK0ze7ndeAfDK4w/xyVhdXeUTPQcHB3nF4vP5bJ1/dTi9tLTEK4z5+XnufTU1NcWvo9uYrK6ucnbRwsLCtnIYEbEs09TUxKG6UsqWrNDX1wcAeO+999Db2wsAhRjt2ChEezHxeDwc5QQCAZa0qqqqOIngxRdf5NqciooK26my2/X506vbVCrFhYexWIwTU1ZXV221HFqmi0ajbLvBYNCW3WTabqGTT5vRn9/h4WG8/fbbAOwRpdvt5r+72+22ZY+ZfdN0JpnOiAU+vku1jm7MWkAt4S8uLvI8kEgkbPdaR1xnzpzheQXAthGX0yKfjB0PEZUB+FsAv6eUWtzyYVJEtO1fVin1KoBXN18jux7hu8D8/Dzu3r0L4OGsEC2TjY6O8r7NysoKPycej7PUZraf14aQSqU4NDavt6KNsrW1lYv9zIlndnYWQ0NDAICbN2/i3r17AAq731ah2ouJ3+9nZ1NXV8eFoIcOHeLrzs5Ovr+BQMDmbPT/We8Tmra1sLBgS5vX9z+RSLCzq6ysZJsyi0wDgYBtIisWx5Nvm9GOx5wPzEzEVCplO5rCLK8wu0qYTUV3wrx/ej6Jx+PscCYnJwFYUp+5yDDPbdI2uLXIvGiy2ojIC8sg/kIp9XebD08SUePmzxsBxPZmiEKhIfYiZIvYTGmRSVYbAfg+gG6l1B8ZP/oxgK8D+O7m1x/tyQifkHg8zpv4y8vLvPpcWlpiyWxkZIRXODtltWWzaev1enkV3NzczP259u3bx2F7KpXijcKrV69ylGO2L8/2ECknUOj2YsoTwWCQV7KHDh3CCy+8AAB4/vnnWT4tLy/n9iY7ZRHpiGRkZIQzH/v7+znKmZub4+jbzJxqa2vjQwSDwaAtuUCvcpeXl9l29fsUGk6zGXMOMOv3UqkUzwemrO7xeFidiMfjD3Ul3w7zs60/76urqxy1aolfR8ga81A6/XtbJf7tstqcRiZS2/MAXgbwERFd23zsD2AZw18R0TcADAL46t4M8clIp9NsFAsLC/xhX1pasmWv6Ru8VT57nMnf5/NxhXpnZydnQDU3N9v2eLTju337NsuBi4uLrPkWouNBgdqL2X1AZ0G2tLRwQ9hDhw5x9lprayvr+F6v15YirW0nnU7z/dN7g319fbh69SoA4NKlS1xknEqleKILh8Msx3q9Xp5UwuEw7ykQEUs+09PTGB0dBfBgsipAHGsz6XTa5my2+0xubGzw/UilUtsWpu+EKZ+lUilbseh2mD39tCS7le0WP05zQI/8yyil3gGw06g/vbvDEQodsRchW8RmSo+ib5mTTqd5RWHW6ExOTtpWGnqlut0RBplgnoEeDoe5nuPIkSPcETsSifBrzs3NsdQ2PDxsy2oq0EinoNEryYaGBi4CPXToELdOOXDgAEexgUDA1u7GLD42C451RHvz5k0AwPXr1zljcXBwkH+eSqVYNnG73SzTmqdfmu+ZTqc56WViYoLtqFClNidj1tbsxMbGBs8re91N3sx43Nq/T2OepuzU5IKidzxKKZ7INzY29uwUvq1HVuvjD06dOsVnrRARTw7Dw8NciDo2NsYVyXL+zt5insFknjqr792hQ4c4Pfrw4cPc4LOpqYnlNZ/PZztXSU9MiUSC92ru3r3LKfK6Yez169c5W2lpaYllFVOjj0aj7Pi6urrY2bndbtsptdpe+vr6WGrTco9QvOgUbbMYfatT2S6z0mlIrzZBEAQhpxR9xJMrvF4v13y0tLRwjv2pU6d4s9o8tXBkZIQjnunp6ULeGC4YzLY3Ziul+vp6lkNPnTrFHczr6+ttnZ+11GUmhszNzXFW2dTUFEc0AwMDuH//PgBw5HP//n3OWtq6Ua1fu6GhgbtaHz16lN/flE1WVlZYmu3u7mapTaLl4seMeHSG7NbWPCK1FTl+v581+Pr6et4POHHiBGeymc0Ek8mkTYrRKdSFXvhXKBARy2WRSIRTlY8cOcJn55w8eRJPPfUUAHt/NFPq2tjY4Ey13t5e3rcxZa+5uTl2TroocHV1lWW1rft4pnav93jMEyTNiWN1dZXtaGpqivd79kpGFpyD7l5RX1/PKf1bTxfdrmOG0xyPSG2CIAhCTpGI5wkIhUKcvXbw4EGWSM6fP8/STVlZGa9AzE6zQ0ND6O/vBwBesQp7CxHxhmxDQwPX6HR1dXEbfLPWCngQmaysrLCUtby8zDU4165dw61btwBYspeWwNbW1rJqebRd369AILBtrUYikeCxJJNJOfythNDyWktLCx/3srVeaDupzWmI43kCgsEgdyXo6urCyZMnAVhSmw6JvV6v7cye27dvA7Cy2rQUU8g92QoJl8vFH9ympibOWDtx4gRnkgWDQZYukskk35u5uTk+umJ4eBiXL18GYDke7Wymp6f5+ZnIXnpSMFOoa2tr+fymSCTy0Om4gCW16bTd9fV1kdhKCL1w2r9//46OZ7usNqc5IJHaBEEQhJwiEc8TEAgE0NbWBgA4e/Ys9u/fz4+bNRd6Rfz+++/jgw8+AGBFP1p2E6kkNxARrw7D4TBnITY0NNhOCdWsrq5ytDo6OsoHAd6+fZsjno8++ohlr50KkXdiu+MPDhw4wCdLVlVV8XPMLLiFhQXOpFtYWJBsthJCR+N+v9/WQslEzyfz8/OYmZkB4DxVRRzPE+Dz+djZXLhwgZsC+v1+7rW0trbGGVA3btzAhx9+CMA6CmGvq5yFh8mmZfzw8DCuX78OwMpYGxgYAGDtz+lTYpeXl9nZZHM8ORFxxlplZSXvCba3t7N86/P5bJl0+n3m5+fZ8SwuLjpuUhH2HrMwfusiR889Znq/04qLRWoTBEEQcopEPFlgFh8ClhSiC/xqampsKw9dZ9Hb28sdiUdHRzmDTVapuWfrQVqPingGBgbw1ltvAQDu3LnDbWpmZ2dtSQTZ9NYz29pree3IkSM4d+4cAKvztZZQ3G43j9c8RG5gYICjr7m5ObGlEmInW9vuYLmZmRmW2pyGRDyCILmvm5gAACAASURBVAhCTpGIJwt0KqNu+tna2sppsMCDlbNSijelb926xRHPyMgIt8bJ5mA5YXfY2Njgv//g4CCfgXTgwAGOYtfX13lztqenh9vdDAwM2DoQPC56c9jj8fBezvnz57lNT1NTEycUEBGnSi8sLHA6d19fH9eAzc3NSXJKCaHtNBqN8txjRsbr6+scATu5y704nizQtTl6I7izsxPV1dUA7HUbiUSCEwrMLsUzMzMySeQR0/EMDw9zy6L79++zQ1haWmI59M6dOzzZm92kHxciYhktHA5zYorZYqmiosLWe0tPIlNTU+ju7gZg2ZTOlHTy5CLsPrrlU3V1NTsej8fDC1mzcDmbZJdcI1KbIAiCkFMk4nkEeoUaDAZtx1kD1qZwbW0tAGt1oRMKBgcHuY3KwMAAdw9eXl62yXFCblFKcc3L3NwcBgcHAVhp7vreLS0tsaTW39/PacsrKytPvIJ0uVyccl9dXc01YMeOHeNuBVpK0ZgJBTq1e3x8XOynBDFT8Kuqqlj6d7vdnFCwsLDAtuxkdUUczyPQfbui0ShLbPrIgxMnTnBmksvl4gmrr6+PZZGhoSHOpXdy6FsqmJKoPpYilUqxbJFKpViqiMVi7Ki2HmPwOHi9Xu4o3NbWxo5n//79LKEA9kXJdjalnaFQGug9P6/Xy3J/KBRim3G5XFynMzAwwHuXTj6RVqQ2QRAEIadIxPMItDTS0NDAK9SOjg4A4O7GgLUJrFei9+7d40PAFhYWJNJxEPpeJJNJjkQnJye37ehr1v3shrQVCAS4m/mJEydsHQpM9Ebx+vo6JxHcvXuXuyU4eSUr7D464gkEAjtGPKYkWwjnfGXseIjIDeASgFGl1K8R0QEAPwQQBXAZwMtKqeTeDDN3uN1u1lFDoRB3MD5z5gynvOoJw+12c4i7tLTEE9ng4CDvHywuLpbkvk6h2Yu+N7uZ5m46M8DKWNPZa8ePH+e0/J16ba2srPDBcv39/WxfxdpqqdBsJldoZ1NTU8P9Bf1+v+0AOLM/m+56r/d9nEg2Uts3AXQb3/8hgP+klOoEMAfgG7s5MKHgEXsRskVspkTIKOIhohYAXwTwfwL438laon0KwP+8+ZQfAPgOgO/twRhzitvt5jNb6urqOJHg2WefxdNPPw0AvOrwer22lYau3RkdHWVZJJFIlFSkA5SWvXwcOnLWiQvt7e18xPbBgwe5BmwrptSmbWpycrKou5mLzWwPEXFCSmdnJx/XvvWAQD3HmIcEOrlIPVOp7T8D+HcAIpvfRwHMK6V0itAIgObtfpGIXgHwypMMMpe43W6+0fv27ePU6UOHDnG7eo1Sivt3Xbp0iVvlT05O8uRQovs7JWMvH4fOiNSp0ocOHcLx48cBWN0S9ALHRCnFE0YymWTZZHZ2lqWTIrUpsZkd0AuUQ4cOsePZui9o7l0WguN5pNRGRL8GIKaUuvw4b6CUelUpdU4pde5xfl8oLMRehGwRmyk9Mol4ngfwJSL6AoAAgHIAfwygkog8myuSFgCjezfMvcXj8dgKs3T22smTJzmDzVydmtKZLg69fPkyLl26BMAq8NOr01KT2VAC9pIp+mjiEydO8FddCxaJRGxyibaT9fV1LgAcHR3lrLYiz44Um/kY9NxUXV3NUbTZVgl4EN3E43Fu+eTk5IJHOh6l1O8D+H0AIKKXAPxbpdQ/J6K/BvDPYGWdfB3Aj/ZwnHuK1+vlKuCamhrOPDp16hQOHz4MwDqsa6sTUUpxCrXZuDGZTBbzJPGxlIK9ZIrOWtN7g2bBsc/n48nDTNteWVnhdNjLly9zWn4xH30gNvPxmCfV6hTqrZmQujA6Ho9z0bGTHc+TFJB+C9YmYD8sPfb7uzMkoUgRexGyRWymSMmqgFQp9SaANzev7wE4v/tDyj0+n48z1VpaWjjiaWtr415sPp+PVxC6tmJwcBA3b94EYG9PX6rRzlaK1V62olefbrebN30DgQAnoxw5cgQA0NzcbOuvpUkmk1wAODIywjZ18eLFkutCXSo2kylKKY54QqEQ29fWiEdLbUtLS9yB3ckRT8l2LnC5XPzhr6ys5C4Ep0+f5n2dqqoq1lddLheHs7o49P333+dJwjzyoFQmCcGaALQd+f1+XsDU19fzXqHuVhCJRHgSMVlYWEBvby8A4MqVK3j77bcBANevX5e+bALPJ5nMK8lkkmXZgs5qEwRBEITdpGQjHrfbzVlFFRUVHPE8++yzfEBXTU0N92pLp9Mcuuri0Nu3b/Pmbzwel0inBHG73WwjVVVVHC0fOXKEJTZdxxMKhR7KRgKsaPnGjRsAgHfffRcXL14E8MDOBAGwIp6doh8zK7IQIp6SczxaG/V4PNwDyTzyoKOjAxUVFQCsfR0tr83Ozto6EwBWKvXMzAyA4u2fJXw85v7gwYMH8dJLLwEALly4wDalU2A9Hs9D2jxg7/M3MzMjtiTY0Dbj9Xptx6LrvWTzKI9kMmk7+sOpiNQmCIIg5JSSjXgqKiq4/URbWxufLlpWVsbSidvt5vYTs7OznFSgM40mJyc54inG/lnCowkGg5z5ePToUXzyk58EAHziE5/I+DUWFha4EHlubq6oa3aE7NHF6/v372db83q9trlJz0mFEi2XrOMpLy/ndNeOjg4u9isvL7cdP6z3dSYnJznzSJ9cubCw4OiURWHvCYfDvCfY3t7OstujWF1d5YljaGiIFzVzc3NiU4IN7Xj27dvH3TB8Ph+nTU9MTGB8fBwA+JgWpyNSmyAIgpBTSiriISIuwKqtreVMto6ODg5hPR4Pr0QXFhY4s+ijjz7Chx9+CADc0kQX/QmlhynZajs6cOAAJ6bshNkaR8u0g4ODGBoaAmDZXCFsDgu5QyswlZWVXIAMgOepyclJkdqciHnjdK+sw4cPc7prR0cHh7Aul4tv3tTUFAYGBgAA3d3duH79OoAHWW36xgulRTAY5IzIAwcO4OjRowCA1tZW28Sg0c5mfX2dj62+du0arl69CsDqyWaeLiqdLwSTnVKo9V5gLBYTqU0QBEEQPo6SiHh0llpDQwN3mz527BifBnn48GGOijweD0c84+Pj3HF6YGCA63gKZVUh7A3hcJil2Y6ODnR1dQGw+vzpSMjElNe0dPvmm2/itddeA2BtDutISKIdYSvZRDwitTkI7VQaGhpYFuns7ORMtlAoxOnQq6urLKXdvHmTTxUdGxuTlOkSRxfvtba24vx5q3fl2bNnOS0/HA5v24tNTwZjY2Pc2+/GjRu4e/cu/1z2dQQTvYfocrm27XYBPFikFMpx1yYitQmCIAg5pWgjHr3y9Hg8nGnU3NzMEc/+/fu5lcnGxgaHrfF4nIv5ent7ceXKFQDA/Py8JBOUMETEh3AdPXoUX/rSlwBYxcfRaBSAVVuxXUscLaP19fVxQsHQ0BBnRRbKKlXIHXr+8vl8tpNqTbStEZHtuhAoSsdDRLZT+7TjaWlp4X2dpqYmmx6vHc/i4iLv5YyNjbETSqVSor+XINrZ1NXV8TEHp0+fZjuKRCL8HFMSSafTLJ9pG7p16xZu374NwNLlRV4TtmL2kgSsbYJMHE+hIVKbIAiCkFOKNuLRqwQzA6mlpYUTCsxCv1QqxbLH1NQU5ubmAFhRkF5NmC3JhdKAiLgu5/jx43j++ecBAGfOnOG6L4/Hs+3mbzKZZIlNFxxfv36dsyT1zwTBRM832u7q6+tRXV0NwH5qLfBAol1eXmZ7KpQEqKJxPOb5OqFQiPtndXR04MyZM3ytn0NE7EiIiFOkp6enMTs7C8DKNtLPEZmtdDAXLbqf3+nTp3HhwgUAVtGo2c9vO2ZmZtjJXLt2DQDQ09PDMq40AhW2Qzse7WwOHjzIi+WtkpuWauPxOObn5wEUjl2J1CYIgiDklKKJeHw+H4en0WiUC0UvXLiAp59+GoCVyaZXquaGnMvl4ohncnISsVgMgPRiK1V00sm+fftw4sQJAMC5c+f4ersi0a309/fjH/7hHwAAly5dAmAlq+iaHomghe3Qsq2OeA4dOrRjxKOltqWlJe5UXSgRT0aOh4gqAfwpgOMAFIB/CaAHwF8CaAMwAOCrSqm5PRllBvh8PpSXlwOwMpB01tELL7yAU6dO8fO0w0kkEryXMzU1xYV93d3dfJz1/Py8TBCPQSHYSyZsbGywoxgdHWUbcblctgK/7a7feust/OIXvwDw4AjrhYUFSZ3egWKxmd1CZ0pWVFTwYtnlcrH9pFIpts2VlZWCOO7aJFOp7Y8B/FQpdQTASQDdAL4N4HWl1EEAr29+LwiA2IuQPWIzJcQjIx4iqgDwSQC/DQBKqSSAJBF9GcBLm0/7AYA3AXxrLwaZCX6/nzONDhw4wOFpIBCwJRHo6+XlZV6J9vX1cX1Fb28vH8olEU/2FIq9fBxadh0eHmYJo6enhyPqrQV7Wh4x5VuzBkyvTAtlNZprisFmdhszUUpHP0TENpRIJNhO19bWCi7iyURqOwBgCsB/J6KTAC4D+CaAeqXU+OZzJgDUb/fLRPQKgFd2YawfSygUQn29NQRTFzX1eDMdenV1lRvrdXd38+miQ0NDmJ6eBmClxIrjyZqCsJePQ6ekrq+vY3FxEcADuUzYEwreZnYbXUAaCoX4DDHT8aysrGBpaQmANZcVShq1JhOpzQPgDIDvKaVOA1jGlpBXWTP6tkUuSqlXlVLnlFLnnnSwQkEg9iJki9hMiZFJxDMCYEQp9cvN7/8GllFMElGjUmqciBoBxPZqkJkQCAQ44jl69Ciam5sBWPU9OkttcXGRV7D37t3j+oo7d+7YNoD16qFQwlaHURD2IjgKsZlNtFyrj3KJRqOIRCIArLlMZ9oODg5yElQhFiM/0vEopSaIaJiIDiulegB8GsDtzX9fB/Ddza8/2tORPoJUKsVZah999BF6enoAWDdLS2zxeJxvUiwWY2czOTnJBVhLS0vseKRbQfYUir0IzkFs5mFMx6PLRNxuN8trpuPRjxUSmdbx/GsAf0FEPgD3APwOLJnur4joGwAGAXx1b4YoFCBiL0K2iM2UEJTLFT0RTcHSb6dz9qaZUQPnjQnY/XG1KqVqd/H19hQH2wvgTJvZizEVms3EYdX/OA0n2guQpzkmp44HAIjoktM2AZ04JsC548olTv0bOHFcThxTrnHq30DGZUd6tQmCIAg5RRyPIAiCkFPy4XhezcN7Pgonjglw7rhyiVP/Bk4clxPHlGuc+jeQcRnkfI9HEARBKG1EahMEQRByijgeQRAEIafkzPEQ0eeJqIeI+okob+3NiWgfEb1BRLeJ6BYRfXPz8e8Q0SgRXdv894U8jG2AiD7afP9Lm49VE9HPiKhv82tVrseVD8ReMhqb2IuBE2xG7CXDseRij4eI3AB6AXwWVl+mDwF8TSl1e8/f/OGxNAJoVEpdIaIIrE64vwGrKnpJKfUfcj0mY2wDAM4ppaaNx/49gFml1Hc3P0xVSqmibg0v9pLx2AYg9gLAOTYj9pIZuYp4zgPoV0rd2zxr44cAvpyj97ahlBpXSl3ZvI7DOnCqOR9jyZAvwzqLBJtffyOPY8kVYi+PTynaC+AQmxF7yYxcOZ5mAMPG9yNwwM0gojYApwHorri/S0Q3iOjP8iRRKAD/SESXN88YATI8k6TIEHvJDLGXBzjOZsRedqZkkwuIqAzA3wL4PaXUIoDvAegAcArAOID/mIdhfUIpdQbAPwHwr4jok+YPP+5MEmFvEXsRskHs5ePJleMZBbDP+L5l87G8QERe/P/tnXts3NeV37+H8x4Oh8PXcIYPkXrSskTJsiwltpxYip10m900WaAxEgRB2qbwomiKLNCiyeav/tE/skWbdvtHt3AejYF44abZBJGBII4T27FjxXpaT4sSKfH9fpPDIefF2z9+c4/u0FI4Y5HzPB/A4HA0M79r/s7cc8/3nnuOZRQvK6V+DgBKqUmlVEoptQ7g+7BC97yilBpN/5wC8Iv0GCbTurHWj8u+JwnEXrJC7CWDorEZsZfNyZfjOQ9gLxHtJKvs+ZcAnM7TtTMgIgLwQwA3lVLfM54PGy/7SwDX8zyu6vRmJIioGsBn0mM4DasXCVA5PUnEXjYfl9hLJkVhM2Iv2ZFtP56HQimVJKJvAHgNgA3Aj5RSN/Jx7ftwAsBXAVwjosvp574D4MtE9BisUHMAwF/leVzNAH5h2S3sAP5BKfVrIjqPCutJIvaSFWIvBkVkM2IvWSAlcwRBEIS8UrHJBYIgCEJhEMcjCIIg5BVxPIIgCEJeEccjCIIg5BVxPIIgCEJeEccjCIIg5BVxPIIgCEJeEccjCIIg5BVxPIIgCEJeEccjCIIg5BVxPIIgCEJeEccjCIIg5BVxPIIgCEJeEceTBUT0EyIaJ6IlIrpNRP+60GMSihuxGSEXKs1epC1CFhDRAQB9SqkYET0C4C0Af66UuljYkQnFitiMkAuVZi8S8WSBUuqGUiqmf03/t7uAQxKKHLEZIRcqzV7E8WQJEf0vIooC6AEwDuBXBR6SUOSIzQi5UEn2IlJbDhCRDcCTAE4C+FulVKKwIxKKHbEZIRcqxV4k4skBpVRKKfUHAG0A/k2hxyMUP2IzQi5Uir2I4/lo2FHG+quwLYjNCLlQ1vYijmcTiChIRF8iIh8R2YjonwD4MoDfFXpsQnEiNiPkQiXai+zxbAIRNQH4GYDDsBz1IID/qZT6fkEHJhQtYjNCLlSivYjjEQRBEPKKSG2CIAhCXhHHIwiCIOSVh3I8RPRnRHSLiPqI6NtbNSihfBGbEXJB7KU8+ch7POmDTrcBfBrACIDzAL6slPpg64YnlBNiM0IuiL2UL/aHeO9xWEXt7gIAEb0C4PMAHmgURCSZDAVGKUUFvHxONiP2UhTMKKWaCnRtmWNKkGzmmIeR2loBDBu/j6Sfy4CIXiCiC0R04SGuJZQHm9qM2EvRMVjAa8scU6Y8TMSTFUqpFwG8CMhqRNgcsRchV8RmSo+HiXhGAbQbv7elnxOEByE2I+SC2EuZ8jCO5zyAvUS0k4icAL4E4PTWDEsoU8RmhFwQeylTPrLUppRKEtE3ALwGwAbgR0qpG1s2soektrYWANDU1ASfzwcAsNlsILL2vZRSSCaTAIDl5WVEo1EAwOrqKmIxqx9TMplEKpXi1wsPR7HbjFBciL2UL3ktmZNP/XXnzp0AgMOHD/PjQCCAmpoaAIDL5cL6+joAYGBgACMjIwCAyclJTE9PAwAWFxextLQEwHI8+m9VVVXFP7WTWl1dzcf/1kNT4Ky2nBC9vii4qJR6otCDyBaxmcKz3VltgiAIgpAz257VVii0pGaz2VBfXw8AOHjwIPbu3QvAkuCcTicA4MqVK+jp6QEA9Pb2oq+vDwAwMjLCUZEZ8ej3ORwOLC4uAgBisRi/VhAEQXgwZet41tbWAABTU1OYnZ0FYDmKxsZGAIDH42HJrLW1FTabDQDQ0NCAzs5OAMDExAQmJiYAAIlEAomE1YVWv89ut2NychIAMD4+zrLcysoKVlZWAADRaFT2hwRBEAxEahMEQRDyStlGPHqzf2pqipMFYrEYRzbAPTmutbUVO3bsAGBFNjpaMhMNYrEY4vE4ACvS0T91UsLQ0BCGhoYAWBJdf38/ACtxQb9PEARBqADHMzMzg7GxMQDW/o1Os66treW9mmg0ymnTwWCQ5bimpiYEAgEAQDweZwdSXV3NnzEzMwPAkuV6e3v5Oprp6WlO25Y9oPKgqqqKFzB68bLxMXAvBd/8eb89Q0GoNERqEwRBEPJK2UY8Wi5bW1vD+Pg4AKC/vx9+vx+AFc243W4AwNzcHJ/HicVi/LzH4+HXLyws8CFTHfnEYjGOflpbW/l9wWCQXzM7O8sS3NzcHEc/QmlBRJxU4vP5UFdXB8CKel0uFwAr29Hr9QIAvF4vR9RmJGRGPPd7vLi4iOXlZQCWzS0sLAAAIpEI246OzgWhVClbx2Oiv7xXr17F1NQUAKCmpoYnhng8zrLHwMAAS2W1tbUstUWjUXY8+hBqIBBAU5NVMb6pqQn79u0DABw4cACRSASAtfejZT9z8hBKC9Px1NTUoKOjAwDQ2dnJ8m0gEEBzczMAoKWlhdP4tSxns9nYwayvr2c81nbR29vL+4O3b99mW0wmk7w4Wl9fF5lOKGlEahMEQRDySkVEPDoBIBKJ4Pbt2wAsWURnpwH3MtXq6+vR0NAAwEoi0HXeYrEYy3cejweAtfLdtWsXACvK0aV5duzYwWeBnn76ab7GysoKn/uRA6fFi7YFl8vF8mkgEGB5rbW1FXv27AFgRTzaRmpqajgxJRQK8esdDgeAewePNVqCIyKWzxoaGhAMBgFYkp5+r8PhYDuenZ3lM2US/QilSEU4Hi1jRCIRlsA2omWU6elp1umdTidPPKurqyy16cnA7Xbj0Ucf5WuYh1C1QwoEArzfMzg4yNdPJpPieIoUfX/9fj8vQvbu3cv3uqOjA21tbQCA5uZmdiBVVVX8XrvdzgsVff/175qNWXD6mrq6Rk1NDVpbW/n6Fy5Yfc6uX7/Oh5VjsZjIt0LJIVKbIAiCkFcqIuLJBi1XxONxjkTsdjtHOclkkuUN86c+I3Tt2jXOcNu1axdvLAeDQd5wNhMabDYbf45QeHS0arfbOVmgpaWFJdPDhw/j4x//OAAgHA5ztqPL5eIodmVlhR/Pzc2xfGZWMzcf62uakVJ9fT1aWloAAF1dXRzNDA4O8uOxsTG2nWQyKRGPUHKI40ljOp7NKg2Y2rzeD1BKsb6/sLCQMalo6c7tdmc8LxQPet/O5/Nxxlp3dzcOHToEAGhvb+e9l3g8zplnc3NzGB4eBgCMjo6y44lGox+q7WdmxpmPq6qqWNI9ePAgHn/8cQDWPpFezLjdbr5+e3s7O5t4PM7ZboJQKsjsJwiCIOQViXg+AjrKcTqdnLnU0tLCG9F2u53lulQqlbGyNaMloTgwo9LGxkbs3r0bAPDUU0/h5MmTAKx7quWwgYEBDA4OArDO2ty8eRMAcOfOHW6TEYlEOHI277kZ8ZhJCfr6CwsLGR1z9TU9Hg9CoRAAS8rVkZWuvC4IpYQ4no+A3qfx+XycdXTs2DEcOXIEgKXTaykkGo3yBJRKpVh+kRTYwmK321neqq6u5vToRx55hB/X1tbygePp6WnMzc0BsBzPwMAAAKsgrG6dMTU1ldFCPZe9Fy31jY2NceHZxsZG3iv0er289xQMBvkQs3kkQChvzMWK2+1mGdbj8fCc5HQ62ZbcbndGOv6D5hyzEou25YmJiYyU/a1GpDZBEAQhr8hyKUu8Xi+vkHUSQXNzMx555BEAwKFDh/icRyAQ4DMbs7OzXHvLTFyQMzyFxeFwcNQQDAZx4MABAMCpU6f4/q6vr2N0dBQAcPPmTe5MOzw8zNmMZp2/tbU1zmTLtZ6aXo2urKxkRFDt7e0ArKhMr2rNx5KkUjlUVVVlHHTXrVyam5s5Gq6vr2dJtqmpiWVbn893X3mfiFgenp+fx+uvvw4AeOONN7iZ5XYkr4jjyRK/389p0VqKOXDgANdn2717N+/3uFwu7uPT09PD9bbm5+fZIUmhx8LicDj4yxoOh/nLGg6HWWIYHBzk7LW+vj7cvXsXgCW76b0V/eV8WPSkkEgk+HDo8vJyxj6RWVHB7CsllDfaNlwuFzuS9vZ2dHd3A7D2/HTNyFAolHG4Wb9ey28as1WHXhgvLi5ifn4egFVQWS+uZmdnt7y1y6bLJSJqJ6I3iegDIrpBRN9MP19PRK8TUW/6Z92WjEgoacRehFwRm6k8sol4kgD+vVLqEhHVALhIRK8D+BcAfqeU+i4RfRvAtwF8a/uGujWYBwXNMzV6VZFKpXg14HQ6eXMuHA7z+Q59tuPUqVOcAeXz+bg8PhHxJnN/fz/u3LkDwIp49PNlnFxQEvayMSNRrwgXFxc5ceDChQscrZotCsyEkYdBy2Q2my2jJpxOWGlubuYN5Hg8ztefmJjgqKhMDiGXhM3kEz0f2Ww2nld2797Ncv7+/ftZ5g+FQhmSmo7kvV5vRvKJOefoCCaRSPA8WFNTg+PHj/P1z5w5A8Cq6q9rTOr562HZ1PEopcYBjKcfLxPRTQCtAD4P4GT6ZS8BeAslYBRaG/d4PBnZH/oGJRIJvkGhUIglmJaWFg5htbNpbGxkyWxsbIy10tnZWZZlLl++zI5naWmpnB0OgNKxF7vdzl9W3foCsArK6kKy77//Pj9OpVJbLo/qycVut7MtbtTuTcejZZCJiQmWR8qhakGp2Ew+0bbhcDjYBh599FF89rOfBWBlX+oFSk1NTcYi+n4LanPeWV9fz8hk07bn9XrxxBNPAADa2trY3mdnZzmjc6vIaY+HiDoBHAFwFkBz2mAAYAJA8wPe8wKAFz76EIVSRexFyBWxmcoga8dDRD4A/wjgr5VSS2aGhFJKEdF9l/JKqRcBvJj+jIIs93U0U1VVxauH2tpazmqqqalhSW11dZVXBwcPHuSzOaFQiGto6YOi9fX1LHlMTU3xocKBgQHOgLp+/TpvUOuGcJVAsdtLLBbjBJA7d+5wy4Hx8XGW2paXl3nVtx1ZiFpCCQQCbFutra0c8QSDQV6NRiIRltomJyfLKuLRFLvNbAc2m43nJ7fbzQeJdTReV1fHiQPHjx/nhIKWlhaW1My6j/F4nBOYotEoz0+Li4v8mlgsxtFzLBbjGoR79+7NqK5uHoLf6mg/K8dDRA5YBvGyUurn6acniSislBonojCAqS0d2RZhs9lYy6+rq+Mv9c6dO/l5t9vNenssFmPH09HRwRlsdXV1/HptKJFIhNtaX716leW1kZERPgQ4MTHBN7lSKAV7icfjnJm2vr7OCxKfz8eHRk3Hsx1op9LU1MTFSDs7O/lxY2Mj7/2Yk8XU1FTZOZ5SsJmtRMth1dXVvABuaWnhnl56P9lc8O7Zs4flZ2dN3wAAHnZJREFUterqav6MZDLJlSwWFxd5QTU+Ps57lAMDAxkOSS+07HY7O7Y9e/ZkLNLNowFb/T3IJquNAPwQwE2l1PeMfzoN4Gvpx18D8MstHZlQkoi9CLkiNlN5ZBPxnADwVQDXiOhy+rnvAPgugJ8S0dcBDAJ4fnuG+HA4HA4uO9LR0YGjR48CAE6cOIFwOAzA2sjTUY7ZoM3n83H5e5vNxisMc3WhEweuXLnCG9Gzs7O8OtWhbgVREvaSSCQ4akilUvzY6XTy/d3YuG2r0dFMMBjM6F6rE1pcLhevNJeWltimZmZm+PxQmUQ8JWEzW4VZs6+6upqrjh84cABPPvkkgHuZs6FQiA80OxwOjkjMBIHFxUU+czM0NMRqS39/P9cRHBoaypDjdHZaU1NThp1reXN9fZ1tTymVf6lNKfUHAA+qaPnslo5mm9B/tEQiwRpmc3MzZ6eZadMAMjJBzHYJ+gSv/rxIJMKTweTkJN/w2dlZznCrNErFXpLJJH/54vE4Lyo26uXbBRGx1BYMBllaaWhoyJBydTbR0NAQS4ALCwtsi+VQAaNUbOZh0XNMTU0N7xN3dXXxvo1ZJ1AvPsz9Z9NmFxYW2B76+vpw5coVANZ+pd4LnJ+fZznZlI2VUuz4PB4P7yuZJBKJDNvKu9QmCIIgCFtJ2ZfMSaVSLHeNjY3xKmFmZoY31Wpra3mVacpuZu77/c7fEBGvFpqamjhkXl1drdiIp1QwpYrtjGw2YmZqadsJhUIstTU2NnL0tby8zHXbRkZGMlavejVaDhFPpaCzGMPhMJfaeuqpp/DUU08BsDIadaKBlmHNg+7mvNLf348bN24AAM6ePYt3330XgFVH8EHRiY5yXC4Xy3e7du3i6MuUnPv7+zm70+ymu1VUhOPRf8xEIsHZHDMzMyxjuFwulj02Fl3czPno9zU3N/Pn6WsI5Y/pSMyy9cC9icY8oGdmWO7fvx+A1elUZyv5/X52JoODg7hw4QIA6yCyXjSVyb5ORaDnE7vdzhP8nj17uMvs0aNHuUCtz+fjCV4vhubm5tjZjIyM8JGN3t5ePrLR29vLi5IHOQjz+jt37mRnd/LkSc6ijEQiuHr1KgDgt7/9Lc6ePQsAvPjZSkRqEwRBEPJK2Uc8ZkZGIpHgHPfr169nNGvT5ef9fj8f3lJKZdRMMhvA6c8za2Xpla3f7+cV7NraGmeNxONxfn0kEuHsKfP5ci+pU25s7CRqdhvVUppeaYZCIXR1dQGwDifrlebOnTs5w9Jut7O9DgwMcMRz48YNiaRLACLiZACn05lRZkvf+3379nESwY4dO3heMZMHdARz+/ZtXL9+HUBmlDM1NcVJBMvLy5vWUDPHcuLECXzlK18BYNV801JeX18f/vCHPwAAzpw5w1LbdtQDrAjHY0oTZrsCs9y4dhp2u511Vv1+wHI8OmzWKdZOp5OdUDAY5Cy5gwcPsuFMTU3xhLG8vMzOZnh4GMPDwwCsdEgtr4iMUpyYKbB+v59twOPx8P6gw+HIcEL6NVpPb21tZVnl8ccf5z3B2tpa1vSTySTbwOLiYsae5FYVaBS2j6qqKr6XPp+Pi3p+5jOf4aMcjY2NGYfXtf3EYjHej9aS2nvvvYd33nmHnxsftyoIxePxB+7vmV1KdSuXrq4uLgD65JNP8kJHKcVzUk9PD95++20AVnbcdhagFalNEARByCtlH/EAyIgmdHg6NDTEISYR8YG81tbWjKqvOqIxV6L6p9lCwayz1NnZyZ89MjLCq5TZ2VmOhHw+H7/XbrfzKiUajWbUBxPprbCYUqtemba0tHBWUjgczihJb/a412V4dARdV1fHK9BQKMT/bkZKpnTn9Xq5Hpff7+ezO9Fo9L6Zl0LhMSOeuro6lvD37NnDmYtut5vltZWVFZ4ThoaG8MEHHwAAy2u3bt3iWo8LCwschfypaEfPPfX19Xwg9eTJkzh27BgAa37SNmuWYrp69SrOnz8PAJyQtV1UhOMx0WElEfFNnJmZ4ZpG4XCYayN1dHRg165dAKwboZ2W/ozV1dWMQ6VaimlsbGRd3+VycVjtdDpZfvF6vXxIbGxsjJ3T5OTkfQ8KlknflZJD31On08kTSltbG5eP379/P6flNzY28uLD6/Xy5GIWXtyYBad/agdiSnoej4dbNvj9fpZhNhTP3Or/ZeEh0bK9KanV1NSwbdjtdr7HCwsLnBZ9/vx5vPfeewCAa9euAbDmms26Fpvdaaurq3nuOXz4ME6dOgUAOHbsGB9S9ng8vNAeGBhgZ3Pu3Lm89QsTqU0QBEHIKxUV8ayvr3MIubKywpGFzWbjSCQcDnN4vLi4yKvV6elpzmfXoeni4iKvEGKxGK9i2tvbM6oN7927F4AVemuZrrOzk1cwo6OjXOetp6eHw22zbpxEPIVBryR9Ph/X/GtpaeH7u2PHDrYdMyklmUzyWYwHyWJ6ZVxdXc12ZkolnZ2dLI/YbDa2hUgkktHIS6Ke4kLf14aGBpZKXS4Xzw/xeJzvX39/Py5evAjAijh6enoA3EuCMst2PQiv18vbA11dXZzE8Pjjj3MmXTAY5IgrFovx3Pf+++/j1VdfBWBJbflKbqooxwPc+/Kbpb4TiURGN0jzRmtZzeyHop+LRqMcBpufEYlEWLcdGxvjtES/38+6fjAYZAdXXV3Nsozb7eZJyOPx8HvHx8fZKOS0ev7Qjqempob3Z9ra2jiDsbOzkw+K2u12ljBM2zDT6U2ZTMswDoeD77l5gHnv3r2cEed0Ojk7cmxsjG0wkUhsa+sGITfMA+sjIyO8oGxpaWGpdm5ujh3LxYsXucV0T08PL2pz+Y7X1NRwG+znnnsOn/rUpwBYiyLtBImI54/R0VG88cYbAIC33nqL95P0/JYPRGoTBEEQ8krFRTwPQktZS0tLGYc89VmbZDL5oY1+M9PNXKEsLi5idHQUgHUAzDxIqM9u7Nu3j6OctrY23vjz+/28yg2Hw7xCXlhY4JWzHoew/ej7G4lEOOLo6+vDH//4RwDW5qxeyVZVVXF5k6WlJe44ayagmOd8dFJCKBTiRJNQKMQ2EggEWN4bHR3NOPdjHn4WigdTzjcbsTU3N3P00d/fn9FORScXzM7OZi2b+nw+lteOHj2KkydPAgCOHDnCkbnL5WJ7SyaTPK5Lly7hxz/+MQDrUKqOnvN5hlAiHkEQBCGvSMSTxlzZ6hWA1mG3iubmZi5bkUqleMXS3t7Oj83SGnv27OEGT7du3eKoSiKe/KGj28XFRf67V1VVsY0EAgHe4wHAhWLn5+cz9v+AD0c8OrINhUJcQuWJJ57gZAWHw8FRsdPp5I3q+vp6jnQ2FrUVCo/ZWE3v0bpcLt73HR0dxeTkJABrv+6jpDAHg0E8/fTTAKx9nRMnTgCwbEknp6RSKd5znJycZPXG3NfRUXm+EceTR6LRKDuzoaEhrrvU1NTEWVJtbW08kdXX13OmlMvlYoM2z30I24t29mtra+x4iIgnC4/Hw4kBSineHJ6fn/+QNLtxw1gnFzQ1NfH7AoEANwbzer2c3OByufhMT2NjIzs4M1lBKA70InZ1dZUzYSORCGerrqysZMiwfypj1TyQ6vV6eVHy1FNPsbzW3d3Nkuz6+npG2Z1z584BsBIXdKPKq1evFnzxKsslQRAEIa9IxJNHVldXOfXV6/VyGN7S0sIrmfb2do54Ghsb+fnq6uoMeUVSaAvH2toaRxwOhyND7jITQMxWww/6HMCKjkzpZWhoCIAlp2iprba2lqtoLC4u8vWHh4c/dF5IKCxmkz4zUSkXtE2Fw2E+B2jK8N3d3fy4oaGB5wxTVTlz5gxefvllAJbkp+eP1dXVgh/JEMezzZg1vpxOZ4Zmr5/XmqxGG4X5Xv0TEHmlUOiJfXV1laWSjc3fcqmhZkpx+kDfxMQEZ0Q6nU4ufxIIBLg+XCKR4Gwp0/FJbb/SxyyjBFgL0WeeeQaAlb2mmwc2NDTwmcD19XXOpuzp6cHvfvc7AMDbb7+Nu3fvArBKfm2sNVlIRGoTBEEQ8krWEQ8R2QBcADCqlPoLItoJ4BUADQAuAviqUip/zetLBKfTySuT2tpa3lDu6OjgEHrfvn2c4QTck2AikUhGRlQplc2pFHvJpqTJgzAlGS2DTE1NcTTj9XqxY8cOAFYSgz6fsbS0xBluZuScSCRKOuKpFJv5U5gVVABLatPJJrt27eII2Ov1slKysrLC8uybb76Jl156CYAlrxVaUnsQuUht3wRwE4A//fvfAvjvSqlXiOh/A/g6gL/f4vGVPA6Hg0uQNzU1cZmcffv2cZOoxx57jOUSImIJZmN4XGJdSsVeckDv00xNTXGZlWAwyLbg9/s5w9Hv9/OhZJfLxVl1qVSqaCeaLKl4m9HORP8Mh8PcPLCtrY0PoZoN3G7fvs2dQ9966y2udF/MtpCV1EZEbQD+HMAP0r8TgE8B+Fn6JS8B+MJ2DFAoPcRehFwRm6ksso14/geA/whAl99tALCglNK7VCMAWu/3RiJ6AcALDzPIYkZHKi6XizNLzNVpY2MjlzoJBoNcGqW9vZ1LpjidzoxNab2SmZiY4E3s9fV1jn5KIOIRe8kRHfHMzs5y2+Ouri6+53a7PaNgqV75ulwufr7ED5NWvM0QEUevOpu1oaGBHzudTpbiZmZmuGfPG2+8gV//+tcArHJO5gHWYmVTx0NEfwFgSil1kYhO5noBpdSLAF5Mf1bRz5i5or/sZrfI9vZ2rr3W3t7O8lowGGSHFAgEWLM39wmUUnzaeHp6mh+XitQm9vLRMCtn6FTpSCRyX7lkfX09Y39IU8x28acQm7mH2UQOsA6Rm51qNXNzc1zj7cqVK7wvmGvadqHIJuI5AeCfEdFnAbhh6a9/ByBARPb0iqQNwOj2DVMoIcRehFwRm6kwNnU8Sqm/AfA3AJBejfwHpdRXiOj/AfjnsLJOvgbgl9s4zqLCrLelz+X4/X6Wznbv3o0jR44AAB555BE+fxEOh3mFaq5OzcrXsViMD4CNjIxwKZVS6bsi9vLRMCMefc9XVlYeGPHkcl6o2BGbuYdZTRqw5PZbt24BsJILdCRkzj3mGb9S4WFG/C0ArxDRfwbwPoAfbs2QipPq6mrOTjNrbJml6vXzO3bswM6dO/k1ZiaK2TlSTyqpVIqLgY6NjXEBv2vXrqG/vx9A4Yr5bSEVZS+5orOYPB4Py7Eej4cnlY0LFb0IKfNDoxVlM+b+rp4nLl++zCnUx48f5/0em82Wsc9XauTkeJRSbwF4K/34LoDjWz8koVwQexFyRWymMii9GK1AeL1eltJ0NHPo0CHOsfd6vXy2IhAIcCVhs901cG8lE41GOZxOJBJcsrynp4cjnuvXr3NFWWn4Vd7cT7Ktra3N2FDWEfLGiEcoHzY2exweHsbNmzcBWEqKnjNcLldGw8BSK6MljgeWXmru25iHOTWBQAAtLS0AwP3NT5w4gU9/+tP8Wv0+pVTGXo5Z40vv30xOTmYYmT40eOvWLS4eOjk5yXq/8PCY99O8z0qpTQt6bjdmKwzdm6mhoYEdUjKZzFi06IkpmUzed99QKG30vVxeXuZF6fT0NNup2SZDS26lREkn/guCIAilR0VFPETEm7h2u52lMa/Xyxu65uE88+zMjh07sHv3bgD3Ip5gMJixyrxflGOuVOfm5jiy6enp4YSBaDTKktrg4CA3jyqFg2ClhFlJ2jyQacpVharcq22xtbWV7aulpYUjnmg0ytHv2NhYRuabjn5Edis/7HY7S6+hUIil1/X1dZ7LqqqqSi7arSjHU1VVxZON2+3mLLSGhgauKNDa2srd/FZWVnhvpbOzk2urmRlr97vh5nOpVIonhqWlJZbRrl+/juXlZQBWCq2W4CYmJrCwsACgOMqXlzpExJN3dXU1T/A2m42/uKurq1xWvhB/cyLifcD29nYcPHgQgGWLeqJZXl6+r42srKwUXCYUtg5T8gesxe3Ro0cBWEVCtS2vrq5yK+3x8fGSmytEahMEQRDySsVFPHoF6Xa7ObJpb29nGa2rqwvhcBiA1RlSryx37tzJGWz631OpFK80zOSCtbU1jpRmZ2f5jM7g4CB6enr4sc7ZX11d5T7py8vLBe+HXg6YVX71fd69ezd38TQZHh7mQ3pbmT24cfVqRllOp5Mj7vr6em7wdeDAAS6x5Pf72b76+/tx6dIlAMD777/PjeNSqZREOmWEVmR0+a2uri4cPnwYgCW96n8fGRnBL39pnae9cOECy/mlQkU5HpvNxqGqz+djB/Loo4+yfNbe3s4TVXV1NU8Ozc3NLNNoLT0ej/MNN7uFzs3NcWnyu3fvck2l3t5efn5ycjKjA6Xezym1kLlY0ZO93W7nA3hHjhzhLMREIsH7d2fOnMloPb3VY9A/nU4n3G43AGsvUfdj2r9/PzueQ4cOseMxW2T09/fjwoULAKyDxdrxiNMpL/R+jq588vTTT/OCt6mpiRczAwMDeO+99wBYUlup7e+J1CYIgiDklYqNeGpqatDW1gbAWl3oatJ1dXWc1RaPx3lV7PP5eANYrzLj8ThLM2Zrg4WFBYyOWvUMb926xSvVy5cv8wo2Ho9nVBjW7y21lUuxoqMMh8PBkWpjYyNHE5FIhKVOM+nA4/HwPd8Yfd4vI87MlLPZbCzlOp1OtiMze9I8ZKyz17q7u9HZ2QnASijQnz0xMcGRzQcffMAZkcPDw3KguEzRHWefeeYZANbcpGW3eDzO2YwDAwOcXFCK0nzFOR49MVRXV3MmW3d3Nzshu93Ok5Ypx6ytrXFWkd6PmZ+f58dVVVWs34+OjnKNtd7eXnZCMzMz2/7/KFiYafN6wdDQ0MD3eXJykrPB3G53hnPQae4b90/M3kvaqZj7Nh6PhyeJQCDA9qUl3fr6+oy2GPrkeSgU4jHabDZOp79w4QL3XLl27Ro/H41GS6oNuvBgqqqqWNrftWsXS8EnT54EYO1L6sPFCwsLvBd57do1nntKoXjwRkRqEwRBEPJKRUU86+vrvDqIx+MstUxOTvKqora2lh+bcszIyAhHLnojenZ2lkNfswTL/Pw8r04nJiak7E0BMbMNHQ5HRsSrE0Oam5tZ6lpbW+PzVcvLyxlnZLQEZlYqt9vtHPH4fL6MKEZn0GlJLRQKcZkTHfnoMWrpLBKJ8GHiK1eu4N133wVgbSDriLsMKpVXPKZNannt1KlTLLHpxBOXy8Xzx7lz5/CTn/wEAPD2229zk8hSpKIcj1KKdft4PJ7R6VNLHU6nkycSsxvkwMAAF+vTrYmnp6dZZzXrvZk9VZaWlkpSgy11zD0zfc9XVlY4Pd5ms3EG0aFDh1hq279/P0sYkUgkoxindjxerzdDGtOTiMfj4bL1fr+fnZNeyMzOzrJTWVtby9jv03Y0PT3NmXV9fX2cBbm8vCzyWomj5we32831+Lq7u/HJT34SAHDs2DFerGh7NKuavPnmm9ziutQXsyK1CYIgCHmloiIec/Ubj8d5ZTs+Ps4STCqV4rMWU1NTLG/cunUrI6sIsM7r6JWHufKNxWIcTUm9tcJgNtnTkcXi4iLLpE1NTRzxNDY28iE9U4LdGPHoSNjtdnNygSnlbaxQrm1Dl+MZHR3laHlycpJtJBqNcmQzNjbG0c/KygqPxTysLJQeRMQya0tLC5577jkAwPPPP8/ndHw+H9ubnpuuXr2KX/3qVwCA3/zmN2wPpU5FOZ5UKsW6/vLyMk8CVVVVXFGguro6Q2rTk8P4+Djv2+gJJRKJsGMxJx2zX4pQGPTfPxaL8aT+7rvvcnpyfX09txE2nVBdXR0vPFwu131bXUSjUd4HWl1d5T0X01GsrKxkVKMALKlNL2Tm5+cz2mJoZzMzM5PhbMSOiguz8oSWw8xjFX/qfum9nCeffBLPPvssAGsvx1zEaJvR2YyvvvoqXnnlFQDWYqVcDgyL1CYIgiDklYqOeIaGhgBY+fFaarPb7RkHRPXrzUrVZqkbveFrNhlbX1+Xg6AFxpTItLy2vLzMcmldXR2fn+jo6MC+ffsAWGcpzMw0jVKK7//8/DxHvdPT03w+a3Jykq81Nzf3oW6SsViMnzMPEJty4NraGktq5bK6LXX0d9vhcHCiSF1dHdvJ/Pw8R9LJZJIPqbvdbrahXbt24ROf+AQAqwyOrg3p9/v58+fm5nD27FkAwOuvvw4AeOeddzgKKid7qDjHY0owOsNJKD/0l1QplTHZm6nSWt4y0+anpqZYgmtubuZJgYj4c8zisTMzM/w5U1NTGan2GysgyIKkNDGzFvUB5H379nGblEgkwu1O5ubmMmrz6ZprH/vYx7jdRUdHB0t2qVSKD5ufPn2as9Z0fceZmZmytBmR2gRBEIS8klXEQ0QBAD8AcBCAAvCvANwC8H8BdAIYAPC8Uqq0k8uFLaFY7cVcOZplZxKJBEtnvb29vNnrdrszMtbMw8dagl1bW+NIKBqNcjJKLBbLqMUHlJdUstUUq80A4DNbBw8e5FI2Bw8e5Gy0aDSK69evA7AyF7Uc5/P5uOp4R0cHJ7DY7faMCve6luPp06f5c7QdlWO0AwCUzZeBiF4C8I5S6gdE5ATgBfAdAHNKqe8S0bcB1CmlvrXJ58g3r8AopWjzVz0clWYv5v6epoyczEWl1BPbfZFithktvX7uc5/DF7/4RQBWhprZl0sfNE8kEnxw2O/3c+0+ImLJNRKJoK+vDwDw+9//ntOlr1y5whJuKdtPNnPMplIbEdUC+CSAH6Y/NK6UWgDweQAvpV/2EoAvfPShCuWC2IuQK2IzlUc2UttOANMA/g8RHQZwEcA3ATQrpcbTr5kA0Lw9QxRKjIqzl1JenRYJRW0zWlZNJBIc3bpcLs6EDQQCnCF5v+hXo+WzO3fu4LXXXgMAvPLKK7hz5w5fp1JsKRvHYwfwOIB/p5Q6S0R/B+Db5guUUupBIS4RvQDghYceqVAqiL0IuVLUNmPu4elD58lkkvcFm5qauOir7li88b1jY2O8l3PmzBmcO3cOgLUnpB1bpTgdILusthEAI0qps+nffwbLSCaJKAwA6Z9T93uzUupFpdQT+dCJhaJA7EXIFbGZCmPTiEcpNUFEw0TUpZS6BeBZAB+k//sagO+mf/5yW0cqlARiL0KuFLvN6Ijk0qVLXDm8tbUV3d3dAIDHHnuMIx2Xy8VR0d27d3H37l1+rBMKxsbGKr7SeLZZbY/BSnV0ArgL4F/CipZ+CmAHgEFYqY5zm3xO5cSSRUqestrEXsqHfGW1Fb3NmN1mGxoa0NHRAcDa49HOaX5+ntsY6Lp8lUY2c0xW53iUUpcB3M/4ns11UEL5I/Yi5IrYTGWRVcSzZRcjmgawAmAmbxfNjkYU35iArR9Xh1KqaQs/b1spYnsBitNmtmNMpWYzy7AOnhYbxWgvQIHmmLw6HgAgogvFtglYjGMCindc+aRY/wbFOK5iHFO+Kda/gYwrE6nVJgiCIOQVcTyCIAhCXimE43mxANfcjGIcE1C848onxfo3KMZxFeOY8k2x/g1kXAZ53+MRBEEQKhuR2gRBEIS8Io5HEARByCt5czxE9GdEdIuI+tK9NQoCEbUT0ZtE9AER3SCib6af/09ENEpEl9P/fbYAYxsgomvp619IP1dPRK8TUW/6Z91mn1MOiL1kNTaxF4NisBmxlyzHko89HiKyAbgN4NOwCgKeB/BlpdQH237xD48lDCCslLpERDWwSrB/AcDzACJKqf+a7zEZYxsA8IRSasZ47r8gx2ZYpY7YS9ZjG4DYC4DisRmxl+zIV8RzHECfUuquUioO4BVYTZ7yjlJqXCl1Kf14GcBNAK2FGEuWVGIzLLGXj04l2gtQJDYj9pId+XI8rQCGjd9HUAQ3g4g6ARwBoMuxf4OIrhLRjwokUSgAvyGii+keI0CRNMPKM2Iv2SH2co+isxmxlwdTsckFROQD8I8A/loptQTg7wHsBvAYgHEA/60Aw3paKfU4gH8K4N8S0SfNf1SWLir57wVA7EXIBbGXP02+HM8ogHbj97b0cwWBiBywjOJlpdTPAUApNamUSiml1gF8H1bonleUUqPpn1MAfpEeQ1bNsMoMsZcsEHvJoGhsRuxlc/LleM4D2EtEO4nICeBLAE7n6doZEBEB+CGAm0qp7xnPh42X/SWA63keV3V6MxJEVA3gM+kxnIbVBAuonAZqYi+bj0vsJZOisBmxl+zIqh/Pw6KUShLRNwC8BsAG4EdKqRv5uPZ9OAHgqwCuEdHl9HPfAfBlsppRKQADAP4qz+NqBvALy25hB/APSqlfE9F5AD8loq8j3Qwrz+PKO2IvWSH2YlBENiP2kgVSMkcQBEHIKxWbXCAIgiAUBnE8giAIQl4RxyMIgiDkFXE8giAIQl4RxyMIgiDkFXE8giAIQl4RxyMIgiDklf8Pw2k1Z524U7UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch(3, figsize=(6,6), hide_axis=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((Image (3, 64, 64), Category 7), ['7', '3'])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.train_ds[0], data.test_ds.classes"
   ]
  },
  {
   "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": [],
   "source": [
    "data = ImageDataBunch.from_csv(planet, folder='train', size=128, suffix='.jpg', sep = ' ', 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": [],
   "source": [
    "data = (ImageItemList.from_csv(planet, 'labels.csv', folder='train', suffix='.jpg')\n",
    "        #Where to find the data? -> in planet 'train' folder\n",
    "        .random_split_by_pct()\n",
    "        #How to split in train/valid? -> randomly with the default 20% in valid\n",
    "        .label_from_df(sep=' ')\n",
    "        #How to label? -> use the csv file\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",
      "text/plain": [
       "<Figure size 648x504 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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='<U17')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codes = np.loadtxt(camvid/'codes.txt', dtype=str); codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we define the following function that infers the mask filename from the image filename."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_y_fn = lambda x: path_lbl/f'{x.stem}_P{x.suffix}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can easily define a [`DataBunch`](/basic_data.html#DataBunch) using the data block API. Here we need to use `tfm_y=True` in the transform call because we need the same transforms to be applied to the target mask as were applied to the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (SegmentationItemList.from_folder(path_img)\n",
    "        .random_split_by_pct()\n",
    "        .label_from_func(get_y_fn, classes=codes)\n",
    "        .transform(get_transforms(), tfm_y=True, size=128)\n",
    "        .databunch())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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 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\n",
    "        .random_split_by_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\n",
    "        .transform(get_transforms(), tfm_y=True)\n",
    "        #Data augmentation? -> Standard transforms with tfm_y=True\n",
    "        .databunch(bs=16, collate_fn=bb_pad_collate))   \n",
    "        #Finally we convert to a DataBunch and we use bb_pad_collate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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 or tabular data. With ouy 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": null,
   "metadata": {
    "hide_input": true
   },
   "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.from_csv(imdb, 'texts.csv', cols='text')\n",
    "           #Where are the inputs? Column 'text' of this csv\n",
    "                   .random_split_by_pct()\n",
    "           #How to split it? Randomly with the default 20%\n",
    "                   .label_for_lm()\n",
    "           #Label it for a language model\n",
    "                   .databunch())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>  <col width='5%'>  <col width='95%'>  <tr>\n",
       "    <th>idx</th>\n",
       "    <th>text</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>0</th>\n",
       "    <th>xxfld 1 old jane 's mannered tale seems very popular these days . i have lost count of the number of versions going around . probably the reason is that her \" xxunk \" are our \" xxunk \" even at this late date . this tv mini - series gives it a mannered telling suitable to the novel . xxunk , xxunk emma is a pretty \" modern \" girl when you think about it , even though the xxunk of jane austen 's world may seem a xxunk artificial to us today .</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>country - road music score from xxunk jones , amazing performances in two principal roles from robert blake and scott wilson and first time in a movie a sad comment about xxunk punishment at the last moments before their deaths . jones , hall and brooks ( as director and as writer for adapted screenplay ) are academy award xxunk . gripping , superbly directed and frightening , one of the best films of this decade xxfld 1 there were a lot of truly great horror movies produced in the seventies - but this film</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>sister xxunk , who pretty much steals the show . with absolutely beautiful xxunk , she sings several songs throughout the film , though i actually would have liked to have seen them feature her even more in this . the plot in this film is a bit silly , but nevertheless , i found the film to be entertaining and fun . xxfld 1 there 's something compelling and strangely believable about this episode . from the very beginning , an atmosphere of tension is created by the knowledge that a certain planet is</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>\" xxunk \" plot , this one has a xxunk mess of a story , with too many dull characters xxunk each other in the back so many times the potential for any sympathy or xxunk is xxunk . gone is the effective xxunk between the lead characters ; azumi and her xxunk are often reduced to a bunch of xxunk teenagers xxunk in a forest . xxunk is non existent ; if anyone watching actually cares who lives and who dies , i 'll be shocked . the same xxunk to the villains here</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>gary cooper as wild bill xxunk , with jean arthur as xxunk jane . james xxunk was buffalo bill , john xxunk ( not a villain as usual ) was general george a. xxunk , and anthony quinn was one of the indians who fought at little big xxunk . the villains were led by charles xxunk ( xxunk arms to the indians ) and porter hall as jack xxunk ( who killed wild bill xxunk ) . \\n\\n basically the film takes up the history of the u.s . after the civil war .</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>xxunk . where it all comes xxunk is in the script , which did n't do any better when it was called missing in action and starred xxunk xxunk . what little semblance of logic there was in the original is now gone , as the filmmakers decide to paint a big s on rambo 's massive chest . \\n\\n the film picks up a little while after the end of first blood . the film , that is - the novel did n't allow for the possibility of sequels . in this mediocre follow</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>the xxunk and the xxunk ' ( xxunk ) , ' 28 days later ' ( 2002 ) and its sequel , as well as many , many , others too numerous to mention . \\n\\n this one is not really a zombie film . judging this movie on its own terms , it 's more of a semi - gothic romance . as such it ranks a little below some of universal 's bottom billed b horror movies of the late 30s and early xxunk . so i 'll give it a 5 .</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>of xxup the xxup demon ) \\n\\n * spoiler * \\n\\n this was a drive - in feature , co - billed with xxup the xxup xxunk xxup vampire . a spanish - italian co - production where a series of women in a village are being murdered around the same time a local count named yanos xxunk is seen on xxunk , riding off with his ' man - eating ' dog behind him . \\n\\n the xxunk already suspect he is the one behind it all and want his castle burned down .</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>the visual than in the message . \\n\\n thus , you will find some funny scenes ( the first xxunk of the town , a \" xxunk \" xxunk xxunk ) and the casting is xxunk , with special mentions to \" doc \" , who xxunk in a \" xxunk fly \" character , and to xxunk , who seems open to xxunk - xxunk . \\n\\n ice on the cake : the main title is xxunk by danny xxunk , and like every other great xxunk , you recognize his \" voice \"</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>xxunk only very slightly by a little inept gore , a gratuitous rape scene , and loads of nudity . \\n\\n gorgeous blonde xxunk xxunk plays movie star laura xxunk who is abducted by a gang of ruthless xxunk and taken to a remote xxunk island inhabited by a savage xxunk who worship the ' devil god ' that xxunk in the jungle ( a big , naked , xxunk - xxunk native who likes to eat the hearts of xxunk female sacrifices ) . \\n\\n employed by laura 's agent to deliver a $</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "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 labelling is done. Here we use the column 'label' of our csv."
   ]
  },
  {
   "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": [
       "<table>  <col width='90%'>  <col width='10%'>  <tr>\n",
       "    <th>text</th>\n",
       "    <th>label</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>xxfld 1 raising victor vargas : a review \\n\\n you know , raising victor vargas is like sticking your hands into a big , xxunk bowl of xxunk . it 's warm and gooey , but you 're not sure if it feels right . try as i might ,</th>\n",
       "    <th>negative</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>xxfld 1 xxup the xxup shop xxup around xxup the xxup corner is one of the xxunk and most feel - good romantic comedies ever made . there 's just no getting around that , and it 's hard to actually put one 's feeling for this film into words</th>\n",
       "    <th>positive</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>xxfld 1 now that che(2008 ) has finished its relatively short australian cinema run ( extremely limited xxunk screen in xxunk , after xxunk ) , i can xxunk join both xxunk of \" at the movies \" in taking steven soderbergh to task . \\n\\n it 's usually satisfying</th>\n",
       "    <th>negative</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>xxfld 1 many neglect that this is n't just a classic due to the fact that it 's the first 3d game , or even the first xxunk - up . it 's also one of the first xxunk games , one of the xxunk definitely the first ) truly</th>\n",
       "    <th>positive</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>xxfld 1 i really wanted to love this show . i truly , honestly did . \\n\\n for the first time , gay viewers get their own version of the \" the bachelor \" . with the help of his obligatory \" hag \" xxunk , james , a good</th>\n",
       "    <th>negative</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_clas.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "from fastai.tabular import *"
   ]
  },
  {
   "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 [`PreProcessor`](/data_block.html#PreProcessor) that are going to be applied to our data once the splitting and the labelling is done."
   ]
  },
  {
   "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 = '>=50k'\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": [
       "<table>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <tr>\n",
       "    <th>workclass</th>\n",
       "    <th>education</th>\n",
       "    <th>marital-status</th>\n",
       "    <th>occupation</th>\n",
       "    <th>relationship</th>\n",
       "    <th>race</th>\n",
       "    <th>sex</th>\n",
       "    <th>native-country</th>\n",
       "    <th>education-num_na</th>\n",
       "    <th>education-num</th>\n",
       "    <th>hours-per-week</th>\n",
       "    <th>age</th>\n",
       "    <th>capital-loss</th>\n",
       "    <th>fnlwgt</th>\n",
       "    <th>capital-gain</th>\n",
       "    <th>target</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th> Self-emp-inc</th>\n",
       "    <th> HS-grad</th>\n",
       "    <th> Married-civ-spouse</th>\n",
       "    <th> Protective-serv</th>\n",
       "    <th> Husband</th>\n",
       "    <th> White</th>\n",
       "    <th> Male</th>\n",
       "    <th> United-States</th>\n",
       "    <th>False</th>\n",
       "    <th>-0.4224</th>\n",
       "    <th>2.3943</th>\n",
       "    <th>0.5434</th>\n",
       "    <th>-0.2164</th>\n",
       "    <th>-0.6319</th>\n",
       "    <th>-0.1459</th>\n",
       "    <th>0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th> Local-gov</th>\n",
       "    <th> Masters</th>\n",
       "    <th> Married-civ-spouse</th>\n",
       "    <th> Prof-specialty</th>\n",
       "    <th> Husband</th>\n",
       "    <th> White</th>\n",
       "    <th> Male</th>\n",
       "    <th> United-States</th>\n",
       "    <th>False</th>\n",
       "    <th>1.5334</th>\n",
       "    <th>-0.0356</th>\n",
       "    <th>1.5695</th>\n",
       "    <th>-0.2164</th>\n",
       "    <th>-0.4559</th>\n",
       "    <th>-0.1459</th>\n",
       "    <th>1</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th> Private</th>\n",
       "    <th> 10th</th>\n",
       "    <th> Separated</th>\n",
       "    <th> Other-service</th>\n",
       "    <th> Own-child</th>\n",
       "    <th> Black</th>\n",
       "    <th> Female</th>\n",
       "    <th> United-States</th>\n",
       "    <th>False</th>\n",
       "    <th>-1.5958</th>\n",
       "    <th>-2.3036</th>\n",
       "    <th>0.0303</th>\n",
       "    <th>-0.2164</th>\n",
       "    <th>1.2202</th>\n",
       "    <th>-0.1459</th>\n",
       "    <th>0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th> Private</th>\n",
       "    <th> Bachelors</th>\n",
       "    <th> Never-married</th>\n",
       "    <th> Exec-managerial</th>\n",
       "    <th> Not-in-family</th>\n",
       "    <th> White</th>\n",
       "    <th> Male</th>\n",
       "    <th> United-States</th>\n",
       "    <th>False</th>\n",
       "    <th>1.1422</th>\n",
       "    <th>-0.0356</th>\n",
       "    <th>-1.0692</th>\n",
       "    <th>-0.2164</th>\n",
       "    <th>-0.4714</th>\n",
       "    <th>-0.1459</th>\n",
       "    <th>0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th> Private</th>\n",
       "    <th> Bachelors</th>\n",
       "    <th> Never-married</th>\n",
       "    <th> Machine-op-inspct</th>\n",
       "    <th> Not-in-family</th>\n",
       "    <th> White</th>\n",
       "    <th> Female</th>\n",
       "    <th> United-States</th>\n",
       "    <th>False</th>\n",
       "    <th>1.1422</th>\n",
       "    <th>-0.1166</th>\n",
       "    <th>-0.3362</th>\n",
       "    <th>-0.2164</th>\n",
       "    <th>2.3204</th>\n",
       "    <th>-0.1459</th>\n",
       "    <th>0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th> Local-gov</th>\n",
       "    <th> Assoc-voc</th>\n",
       "    <th> Never-married</th>\n",
       "    <th> Other-service</th>\n",
       "    <th> Unmarried</th>\n",
       "    <th> Black</th>\n",
       "    <th> Female</th>\n",
       "    <th> United-States</th>\n",
       "    <th>False</th>\n",
       "    <th>0.3599</th>\n",
       "    <th>-0.6836</th>\n",
       "    <th>-0.1163</th>\n",
       "    <th>-0.2164</th>\n",
       "    <th>0.3157</th>\n",
       "    <th>-0.1459</th>\n",
       "    <th>0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th> Federal-gov</th>\n",
       "    <th> HS-grad</th>\n",
       "    <th> Married-civ-spouse</th>\n",
       "    <th> Adm-clerical</th>\n",
       "    <th> Husband</th>\n",
       "    <th> White</th>\n",
       "    <th> Male</th>\n",
       "    <th> United-States</th>\n",
       "    <th>False</th>\n",
       "    <th>-0.4224</th>\n",
       "    <th>-0.0356</th>\n",
       "    <th>0.3968</th>\n",
       "    <th>-0.2164</th>\n",
       "    <th>-0.0533</th>\n",
       "    <th>-0.1459</th>\n",
       "    <th>1</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th> Private</th>\n",
       "    <th> 11th</th>\n",
       "    <th> Married-civ-spouse</th>\n",
       "    <th> Craft-repair</th>\n",
       "    <th> Husband</th>\n",
       "    <th> White</th>\n",
       "    <th> Male</th>\n",
       "    <th> United-States</th>\n",
       "    <th>False</th>\n",
       "    <th>-1.2046</th>\n",
       "    <th>-0.0356</th>\n",
       "    <th>0.8365</th>\n",
       "    <th>-0.2164</th>\n",
       "    <th>0.7282</th>\n",
       "    <th>-0.1459</th>\n",
       "    <th>0</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "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 into 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": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"ItemList\"><code>class</code> <code>ItemList</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L23\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>ItemList</code>(`items`:`Iterator`, `create_func`:`Callable`=`None`, `path`:`PathOrStr`=`'.'`, `label_cls`:`Callable`=`None`, `xtra`:`Any`=`None`, `processor`:[`PreProcessor`](/data_block.html#PreProcessor)=`None`, `kwargs`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList, title_level=3, doc_string=False)"
   ]
  },
  {
   "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...) `create_func` is applied to `items` to get the final output. `label_cls` will be called to create the labels from the result of the label function, `xtra` contains additional information (usually an underlying dataframe) and `processor` is to be applied to the inputs after the splitting and labelling."
   ]
  },
  {
   "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",
    "  - [`ImageItemList`](/vision.data.html#ImageItemList) for data that are images\n",
    "  - [`SegmentationItemList`](/vision.data.html#SegmentationItemList) like [`ImageItemList`](/vision.data.html#ImageItemList) 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 [`ImageItemList`](/vision.data.html#ImageItemList) 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",
    "  - [`TextList`](/text.data.html#TextList) for text data\n",
    "  - [`TextFilesList`](/text.data.html#TextFilesList) for text data stored in files\n",
    "  - [`TabularList`](/tabular.data.html#TabularList) for tabular data"
   ]
  },
  {
   "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": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.from_folder\"><code>from_folder</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L67\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>from_folder</code>(`path`:`PathOrStr`, `extensions`:`StrList`=`None`, `recurse`=`True`, `kwargs`) → `ItemList`\n",
       "\n",
       "Get the list of files in `path` that have a suffix in `extensions`. `recurse` determines if we search subfolders.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.from_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.from_df\"><code>from_df</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L72\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>from_df</code>(`df`:`DataFrame`, `path`:`PathOrStr`=`'.'`, `cols`:`Union`\\[`int`, `Collection`\\[`int`\\], `str`, `StrList`\\]=`0`, `kwargs`) → `ItemList`\n",
       "\n",
       "Create an [`ItemList`](/data_block.html#ItemList) in `path` from the inputs in the `cols` of `df`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.from_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.from_csv\"><code>from_csv</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L79\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>from_csv</code>(`path`:`PathOrStr`, `csv_name`:`str`, `cols`:`Union`\\[`int`, `Collection`\\[`int`\\], `str`, `StrList`\\]=`0`, `header`:`str`=`'infer'`, `kwargs`) → `ItemList`\n",
       "\n",
       "Create an [`ItemList`](/data_block.html#ItemList) in `path` from the inputs in the `cols` of `path/csv_name` opened with `header`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.from_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": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.filter_by_func\"><code>filter_by_func</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L85\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>filter_by_func</code>(`func`:`Callable`) → `ItemList`\n",
       "\n",
       "Only keeps elements for which `func` returns `True`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.filter_by_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.filter_by_folder\"><code>filter_by_folder</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L90\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>filter_by_folder</code>(`include`=`None`, `exclude`=`None`)\n",
       "\n",
       "Only keep filenames in `include` folder or reject the ones in `exclude`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.filter_by_folder)"
   ]
  },
  {
   "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",
    "- changing the `create_func` (example: opening images with your custom function and not [`open_image`](/vision.image.html#open_image))\n",
    "- applying a custom `processor` (see step 4)\n",
    "- changing the default `label_cls` for the label creation.\n",
    "\n",
    "If this isn't the case and you really need to write your own class, here is what you should code:\n",
    "\n",
    "```\n",
    "class MyCustomItemList():\n",
    "    #If you need custom arguments you will have to overwrite __init__ and new like this.\n",
    "    def __init__(self, items:Iterator, my_args, **kwargs):\n",
    "        super().__init__(items, **kwargs)\n",
    "        #store my args, initialize what is needed.\n",
    "\n",
    "    def new(self, items:Iterator, **kwargs)->'NumericalizedTextList':\n",
    "        #Retrive your custom args stored and send them to new like this\n",
    "        return super().new(items=items,  my_args, **kwargs)\n",
    "\n",
    "    #This is how to get your data stored at index i\n",
    "    def get(self, i):\n",
    "        o = super().get(i)\n",
    "        return what you need from o\n",
    "```\n",
    "\n",
    "You can add custom splitting or labelling methods if you need them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.predict\"><code>predict</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L54\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>predict</code>(`res`)\n",
       "\n",
       "Called at the end of `Learn.predict`; override for optional post-processing  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.predict)"
   ]
  },
  {
   "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 oe of the following functions depending on what you need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.random_split_by_pct\"><code>random_split_by_pct</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L122\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>random_split_by_pct</code>(`valid_pct`:`float`=`0.2`, `seed`:`int`=`None`) → `ItemLists`\n",
       "\n",
       "Split the items randomly by putting `valid_pct` in the validation set. Set the `seed` in numpy if passed.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.random_split_by_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_by_files\"><code>split_by_files</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L134\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_by_files</code>(`valid_names`:`ItemList`) → `ItemLists`\n",
       "\n",
       "Split the data by using the names in `valid_names` for validation.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_by_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_by_fname_file\"><code>split_by_fname_file</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L139\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_by_fname_file</code>(`fname`:`PathOrStr`, `path`:`PathOrStr`=`None`) → `ItemLists`\n",
       "\n",
       "Split the data by using the file names in `fname` for the validation set. `path` will override `self.path`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_by_fname_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_by_folder\"><code>split_by_folder</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L118\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_by_folder</code>(`train`:`str`=`'train'`, `valid`:`str`=`'valid'`) → `ItemLists`\n",
       "\n",
       "Split the data depending on the folder (`train` or `valid`) in which the filenames are.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "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": [
       "<div markdown=\"span\" class=\"alert alert-info\" role=\"alert\"><i class=\"fa fa-info-circle\"></i> <b>Note: </b>This method looks at the folder immediately after `self.path` for `valid` and `train`.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jekyll_note(\"This method looks at the folder immediately after `self.path` for `valid` and `train`.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_by_idx\"><code>split_by_idx</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L108\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_by_idx</code>(`valid_idx`:`Collection`\\[`int`\\]) → `ItemLists`\n",
       "\n",
       "Split the data according to the indexes in `valid_idx`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_by_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_by_idxs\"><code>split_by_idxs</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L104\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_by_idxs</code>(`train_idx`, `valid_idx`)\n",
       "\n",
       "Split the data between `train_idx` and `valid_idx`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_by_idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_by_list\"><code>split_by_list</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L100\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_by_list</code>(`train`, `valid`)\n",
       "\n",
       "Split the data between `train` and `valid`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_by_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_by_valid_func\"><code>split_by_valid_func</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L129\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_by_valid_func</code>(`func`:`Callable`) → `ItemLists`\n",
       "\n",
       "Split the data by result of `func` (which returns `True` for validation set)  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_by_valid_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.split_from_df\"><code>split_from_df</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L145\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>split_from_df</code>(`col`:`Union`\\[`int`, `Collection`\\[`int`\\], `str`, `StrList`\\]=`2`)\n",
       "\n",
       "Split the data from the `col` in the dataframe in `self.xtra`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.split_from_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<div markdown=\"span\" class=\"alert alert-danger\" role=\"alert\"><i class=\"fa fa-danger-circle\"></i> <b>Warning: </b>This method assumes the data has been created from a csv file or a dataframe.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "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)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.label_from_list\"><code>label_from_list</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L160\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_from_list</code>(`labels`:`Iterator`, `kwargs`) → `LabelList`\n",
       "\n",
       "Label `self.items` with `labels` using `label_cls`  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.label_from_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.label_from_df\"><code>label_from_df</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L168\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_from_df</code>(`cols`:`Union`\\[`int`, `Collection`\\[`int`\\], `str`, `StrList`\\]=`1`, `kwargs`)\n",
       "\n",
       "Label `self.items` from the values in `cols` in `self.xtra`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "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": [
       "<div markdown=\"span\" class=\"alert alert-danger\" role=\"alert\"><i class=\"fa fa-danger-circle\"></i> <b>Warning: </b>This method assumes the data has been created from a csv file or a dataframe.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "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": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.label_const\"><code>label_const</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L173\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_const</code>(`const`:`Any`=`0`, `kwargs`) → `LabelList`\n",
       "\n",
       "Label every item with `const`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.label_const)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.label_from_folder\"><code>label_from_folder</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L181\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_from_folder</code>(`kwargs`) → `LabelList`\n",
       "\n",
       "Give a label to each filename depending on its folder.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "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": [
       "<div markdown=\"span\" class=\"alert alert-info\" role=\"alert\"><i class=\"fa fa-info-circle\"></i> <b>Note: </b>This method looks at the last subfolder in the path to determine the classes.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jekyll_note(\"This method looks at the last subfolder in the path to determine the classes.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.label_from_func\"><code>label_from_func</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L177\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_from_func</code>(`func`:`Callable`, `kwargs`) → `LabelList`\n",
       "\n",
       "Apply `func` to every input to get its label.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.label_from_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.label_from_re\"><code>label_from_re</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L185\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_from_re</code>(`pat`:`str`, `full_path`:`bool`=`False`, `kwargs`) → `LabelList`\n",
       "\n",
       "Apply the re in `pat` to determine the label of every filename.  If `full_path`, search in the full name.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.label_from_re)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"CategoryList\"><code>class</code> <code>CategoryList</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L222\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>CategoryList</code>(`items`:`Iterator`, `classes`:`Collection`=`None`, `processor`:[`PreProcessor`](/data_block.html#PreProcessor)=`None`, `kwargs`) :: [`CategoryListBase`](/data_block.html#CategoryListBase)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "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": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"MultiCategoryList\"><code>class</code> <code>MultiCategoryList</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L245\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>MultiCategoryList</code>(`items`:`Iterator`, `classes`:`Collection`=`None`, `processor`:[`PreProcessor`](/data_block.html#PreProcessor)=`None`, `sep`:`str`=`None`, `kwargs`) :: [`CategoryListBase`](/data_block.html#CategoryListBase)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(MultiCategoryList, title_level=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[`ItemList`](/data_block.html#ItemList) suitable for storing list of labels in `items` belonging to `classes`. If `None` are passed, `classes` will be determined by the unique different labels. `sep` is used to split the content of `items` in a list of labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"FloatList\"><code>class</code> <code>FloatList</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L257\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>FloatList</code>(`items`:`Iterator`, `log`:`bool`=`False`, `kwargs`) :: [`ItemList`](/data_block.html#ItemList)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(FloatList, title_level=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[`ItemList`](/data_block.html#ItemList) suitable for storing the floats in items for regression. Will add a `log` if this flag is `True`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Invisible step: preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This isn't seen tehre 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. A processor is a transformation that is applied to all the inputs once and for all, 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": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"PreProcessor\"><code>class</code> <code>PreProcessor</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L19\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>PreProcessor</code>()"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(PreProcessor, title_level=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"PreProcessor.process_one\"><code>process_one</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L20\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process_one</code>(`item`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "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": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"PreProcessor.process\"><code>process</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L21\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process</code>(`ds`:`Collection`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(PreProcessor.process)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Process a dataset. This default to apply `process_one` on every `item` of `ds`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"CategoryProcessor\"><code>class</code> <code>CategoryProcessor</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L195\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>CategoryProcessor</code>(`classes`:`Collection`=`None`) :: [`PreProcessor`](/data_block.html#PreProcessor)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryProcessor, title_level=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[`PreProcessor`](/data_block.html#PreProcessor) that will convert labels to codes usings `classes` (if passed) in a single classificatio problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"MultiCategoryProcessor\"><code>class</code> <code>MultiCategoryProcessor</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L237\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>MultiCategoryProcessor</code>(`classes`:`Collection`=`None`) :: [`CategoryProcessor`](/data_block.html#CategoryProcessor)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(MultiCategoryProcessor, title_level=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[`PreProcessor`](/data_block.html#PreProcessor) that will convert labels to codes usings `classes` (if passed) in a single multi-classificatio problem."
   ]
  },
  {
   "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": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemLists.transform\"><code>transform</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L310\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>transform</code>(`tfms`:`Optional`\\[`Tuple`\\[`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]\\]\\]=`(None, None)`, `kwargs`)\n",
       "\n",
       "Set `tfms` to be applied to the train and validation set.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelLists.transform)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is primary for the vision application. The `kwargs` are the one 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."
   ]
  },
  {
   "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": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelLists.add_test\"><code>add_test</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L334\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>add_test</code>(`items`:`Iterator`, `label`:`Any`=`None`)\n",
       "\n",
       "Add test set containing items from `items` and an arbitrary `label`  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "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": [
       "<div markdown=\"span\" class=\"alert alert-info\" role=\"alert\"><i class=\"fa fa-info-circle\"></i> <b>Note: </b>Here `items` can be an `ItemList` or a collection.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jekyll_note(\"Here `items` can be an `ItemList` or a collection.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelLists.add_test_folder\"><code>add_test_folder</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L343\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>add_test_folder</code>(`test_folder`:`str`=`'test'`, `label`:`Any`=`None`)\n",
       "\n",
       "Add test set containing items from folder `test_folder` and an arbitrary `label`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelLists.add_test_folder)"
   ]
  },
  {
   "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": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelLists.databunch\"><code>databunch</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L329\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>databunch</code>(`path`:`PathOrStr`=`None`, `kwargs`) → `ImageDataBunch`\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": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelLists.databunch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inner classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"LabelList\"><code>class</code> <code>LabelList</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L348\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>LabelList</code>(`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)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList, title_level=3, doc_string=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The basic dataset in fastai. Inputs are in `x`, targets in `y`. Optionally apply `tfms` to `x` and also `y` if `tfm_y` is `True`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.from_lists\"><code>from_lists</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L396\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>from_lists</code>(`path`:`PathOrStr`, `inputs`, `labels`) → `LabelList`\n",
       "\n",
       "Create a [`LabelList`](/data_block.html#LabelList) in `path` with `inputs` and `labels`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.from_lists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"ItemLists\"><code>class</code> <code>ItemLists</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L272\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>ItemLists</code>(`path`:`PathOrStr`, `train`:[`ItemList`](/data_block.html#ItemList), `valid`:[`ItemList`](/data_block.html#ItemList), `test`:[`ItemList`](/data_block.html#ItemList)=`None`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemLists, doc_string=False, title_level=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data in `path` split between several streams of inputs, [`train`](/train.html#train), `valid` and maybe `test`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemLists.label_from_lists\"><code>label_from_lists</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L301\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_from_lists</code>(`train_labels`:`Iterator`, `valid_labels`:`Iterator`, `label_cls`:`Callable`=`None`, `kwargs`) → `LabelList`\n",
       "\n",
       "Use the labels in `train_labels` and `valid_labels` to label the data. `label_cls` will overwrite the default.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemLists.label_from_lists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h3 id=\"LabelLists\"><code>class</code> <code>LabelLists</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L318\" class=\"source_link\">[source]</a></h3>\n",
       "\n",
       "> <code>LabelLists</code>(`path`:`PathOrStr`, `train`:[`ItemList`](/data_block.html#ItemList), `valid`:[`ItemList`](/data_block.html#ItemList), `test`:[`ItemList`](/data_block.html#ItemList)=`None`) :: [`ItemLists`](/data_block.html#ItemLists)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelLists, title_level=3, doc_string=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"get_files\"><code>get_files</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L13\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>get_files</code>(`c`:`PathOrStr`, `extensions`:`StrList`=`None`, `recurse`:`bool`=`False`) → `FilePathList`\n",
       "\n",
       "Return list of files in `c` that have a suffix in `extensions`. `recurse` determines if we search subfolders.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(get_files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Undocumented Methods - Methods moved below this line will intentionally be hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.get\"><code>get</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L38\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>get</code>(`i`) → `Any`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryListBase.new\"><code>new</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L219\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>new</code>(`items`, `classes`=`None`, `kwargs`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryList.new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.label_cls\"><code>label_cls</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L150\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>label_cls</code>(`labels`, `label_cls`:`Callable`=`None`, `sep`:`str`=`None`, `kwargs`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.label_cls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelLists.get_processors\"><code>get_processors</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L319\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>get_processors</code>()"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelLists.get_processors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.from_lists\"><code>from_lists</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L396\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>from_lists</code>(`path`:`PathOrStr`, `inputs`, `labels`) → `LabelList`\n",
       "\n",
       "Create a [`LabelList`](/data_block.html#LabelList) in `path` with `inputs` and `labels`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.from_lists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.set_item\"><code>set_item</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L357\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>set_item</code>(`item`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.set_item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.new\"><code>new</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L365\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>new</code>(`x`, `y`, `kwargs`) → `LabelList`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryList.get\"><code>get</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L229\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>get</code>(`i`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryList.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.predict\"><code>predict</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L360\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>predict</code>(`res`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.new\"><code>new</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L58\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>new</code>(`items`:`Iterator`, `create_func`:`Callable`=`None`, `processor`:[`PreProcessor`](/data_block.html#PreProcessor)=`None`, `kwargs`) → `ItemList`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.clear_item\"><code>clear_item</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L358\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>clear_item</code>()"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.clear_item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.process_one\"><code>process_one</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L48\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process_one</code>(`item`, `processor`=`None`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.process_one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemList.process\"><code>process</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L42\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process</code>(`processor`=`None`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemList.process)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelLists.process\"><code>process</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L324\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process</code>()"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelLists.process)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryList.predict\"><code>predict</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L233\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>predict</code>(`res`)\n",
       "\n",
       "Called at the end of `Learn.predict`; override for optional post-processing  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryList.predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"ItemLists.transform\"><code>transform</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L310\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>transform</code>(`tfms`:`Optional`\\[`Tuple`\\[`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\]\\]\\]=`(None, None)`, `kwargs`)\n",
       "\n",
       "Set `tfms` to be applied to the train and validation set.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(ItemLists.transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.process\"><code>process</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L386\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process</code>(`xp`=`None`, `yp`=`None`)\n",
       "\n",
       "Launch the preprocessing on `xp` and `yp`.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.process)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.transform\"><code>transform</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L402\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>transform</code>(`tfms`:`Union`\\[`Callable`, `Collection`\\[`Callable`\\]\\], `tfm_y`:`bool`=`None`, `kwargs`)\n",
       "\n",
       "Set the `tfms` and `` tfm_y` value to be applied to the inputs and targets.  "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.transform)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New Methods - Please document or move to the undocumented section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"MultiCategoryProcessor.process_one\"><code>process_one</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L238\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process_one</code>(`item`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(MultiCategoryProcessor.process_one)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"FloatList.get\"><code>get</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L268\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>get</code>(`i`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(FloatList.get)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryProcessor.process_one\"><code>process_one</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L203\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process_one</code>(`item`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryProcessor.process_one)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryProcessor.create_classes\"><code>create_classes</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L198\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>create_classes</code>(`classes`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryProcessor.create_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryProcessor.process\"><code>process</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L205\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>process</code>(`ds`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryProcessor.process)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"MultiCategoryList.get\"><code>get</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L253\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>get</code>(`i`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(MultiCategoryList.get)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"LabelList.filter_missing_y\"><code>filter_missing_y</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L392\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>filter_missing_y</code>()"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(LabelList.filter_missing_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"FloatList.new\"><code>new</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L265\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>new</code>(`items`, `kwargs`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(FloatList.new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"MultiCategoryProcessor.generate_classes\"><code>generate_classes</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L240\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>generate_classes</code>(`items`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(MultiCategoryProcessor.generate_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"CategoryProcessor.generate_classes\"><code>generate_classes</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L202\" class=\"source_link\">[source]</a></h4>\n",
       "\n",
       "> <code>generate_classes</code>(`items`)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(CategoryProcessor.generate_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "jekyll": {
   "keywords": "fastai",
   "summary": "The data block API",
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  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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 },
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