{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mnt/c/Users/rzimm/Workspace/data/zero-to-hero\n"
     ]
    }
   ],
   "source": [
    "%cd ..\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from datasets import downloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "from glob import glob, escape\n",
    "import imageio.v2 as imageio"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# download.download(\"cityscapes\", \"datasets/downloaded\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "def load_dataset(name=\"gtFine\", path=\"datasets/downloads/\"):\n",
    "    src = path+name\n",
    "    test, train, val = [f\"{src}/{subpath}\" for subpath in [\"test\", \"train\", \"val\"]]\n",
    "\n",
    "    dataset = {\"test\": glob(test + \"/*/*\"), \"train\": glob(train + \"/*/*\"), \"val\": glob(val + \"/*/*\")}\n",
    "\n",
    "    return dataset"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "list"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(load_dataset()[\"train\"])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "a = [1, 2, 3]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "outputs": [],
   "source": [
    "class DataLoader:\n",
    "    def __init__(self, data):\n",
    "        self.data = np.array(data)\n",
    "        self.total = len(self.data)\n",
    "        self.__items = self.data\n",
    "        self.__remaining = len(self.data)\n",
    "    def __next__(self, n=1):\n",
    "        if n > self.total:\n",
    "            raise ValueError(f\"Dataset doesn't have enough elements to suffice request of {n} elements.\")\n",
    "        if self.__remaining > 0:\n",
    "            indices = random.sample(range(self.__remaining), n)\n",
    "            sampled = self.__items[indices]\n",
    "            self.__items = np.delete(self.__items, indices)\n",
    "            self.__remaining -= n\n",
    "            return sampled\n",
    "        else:\n",
    "            self.__items = self.data\n",
    "            self.__remaining = len(self.data)\n",
    "            return self.__next__(n)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "outputs": [],
   "source": [
    "loader = DataLoader(a)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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