{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e40a88b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import torch\n",
    "from torch import nn\n",
    "from importlib.util import find_spec\n",
    "if find_spec(\"text_recognizer\") is None:\n",
    "    import sys\n",
    "    sys.path.append('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ab9ac7a-a288-45bc-bfb7-8579a3a38d93",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ecab65ba-5aa0-45f0-99d7-e837464185ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function torch.nn.functional.softmax(input: torch.Tensor, dim: Optional[int] = None, _stacklevel: int = 3, dtype: Optional[int] = None) -> torch.Tensor>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.softmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3e812a1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import attr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a42a7988",
   "metadata": {},
   "outputs": [],
   "source": [
    "@attr.s\n",
    "class C(object):\n",
    "    d = {2: \"hej\"}\n",
    "    x: F.softmax = attr.ib(init=False, default=F.softmax)\n",
    "    @x.validator\n",
    "    def check(self, attribute, value):\n",
    "        print(attribute)\n",
    "        print(self.x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "660a7b1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attribute(name='x', default=<function softmax at 0x7fb624839ca0>, validator=<function C.check at 0x7fb622ce2040>, repr=True, eq=True, eq_key=None, order=True, order_key=None, hash=None, init=False, metadata=mappingproxy({}), type=<function softmax at 0x7fb624839ca0>, converter=None, kw_only=False, inherited=False, on_setattr=None)\n",
      "<function softmax at 0x7fb624839ca0>\n"
     ]
    }
   ],
   "source": [
    "c = C()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c3d1163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function torch.nn.functional.softmax(input: torch.Tensor, dim: Optional[int] = None, _stacklevel: int = 3, dtype: Optional[int] = None) -> torch.Tensor>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b3c8879c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2f5f6b75",
   "metadata": {},
   "outputs": [],
   "source": [
    "l = nn.ModuleList([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9938ec53",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = nn.Linear(10, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fc49db78",
   "metadata": {},
   "outputs": [],
   "source": [
    "for _ in range(10):\n",
    "    l.append(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e799a9dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModuleList(\n",
       "  (0): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (1): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (2): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (3): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (4): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (5): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (6): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (7): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (8): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (9): Linear(in_features=10, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "17213dfb",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Linear' object has no attribute 'copy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_31696/2302067867.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1128\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1129\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1130\u001b[0;31m         raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n\u001b[0m\u001b[1;32m   1131\u001b[0m             type(self).__name__, name))\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Linear' object has no attribute 'copy'"
     ]
    }
   ],
   "source": [
    "ff = f.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "60277c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cf86534a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ff = deepcopy(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2a260dc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140011688939472"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(ff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6dcf5f63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140011688936544"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "74958f8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140011688936544"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(l[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bcceabd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140011688936544"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(l[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "191a0b03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nn'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\".\".join(\"nn.LayerNorm\".split(\".\")[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4ff8ae08",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'LayerNorm'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_31696/162121485.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"torch.nn\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"LayerNorm\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'LayerNorm'"
     ]
    }
   ],
   "source": [
    "getattr(\"torch.nn\", \"LayerNorm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d536bf2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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