metatuple.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastcore.utils import *\n",
    "from fastcore.dispatch import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m   \u001b[0mtypedispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mType:\u001b[0m        DispatchReg\n",
       "\u001b[0;31mString form:\u001b[0m <fastcore.dispatch.DispatchReg object at 0x7fbb36a08290>\n",
       "\u001b[0;31mFile:\u001b[0m        ~/dev/env37/lib/python3.7/site-packages/fastcore/dispatch.py\n",
       "\u001b[0;31mSource:\u001b[0m     \n",
       "\u001b[0;32mclass\u001b[0m \u001b[0mDispatchReg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m\"A global registry for `TypeDispatch` objects keyed by function name\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTypeDispatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mnm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf'{f.__qualname__}'\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "typedispatch??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(float,str) -> hello\n",
      "(int,str) -> hello\n"
     ]
    }
   ],
   "source": [
    "@typedispatch\n",
    "def hello(x:int,y:str):\n",
    "    print('intx',y)\n",
    "    \n",
    "@typedispatch\n",
    "def hello(x:float,y:str):\n",
    "    print('floatx',y)\n",
    "    \n",
    "print(hello)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intx y\n"
     ]
    }
   ],
   "source": [
    "hello(1,'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "floatx y\n"
     ]
    }
   ],
   "source": [
    "hello(1.,'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(float,str) -> hello\n",
      "(int,str) -> hello\n"
     ]
    }
   ],
   "source": [
    "@typedispatch\n",
    "def hello(x:(float,int),y:str):\n",
    "    print('float,int x',y)\n",
    "@typedispatch\n",
    "def hello(x:(int,float),y:str):\n",
    "    print('int, float x',y)\n",
    "\n",
    "print(hello)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int, float x y\n"
     ]
    }
   ],
   "source": [
    "hello(1,'y')#bug???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int, float x y\n"
     ]
    }
   ],
   "source": [
    "hello(1.,'y')#bug???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hello((1.,1),'y') # it does not reconize within the tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hello((1,1.),'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hello(tuple(''),'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "@typedispatch\n",
    "def hello(x,y):\n",
    "    print('unknown',y)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int, float x y\n"
     ]
    }
   ],
   "source": [
    "hello(1,'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int, float x y\n"
     ]
    }
   ],
   "source": [
    "hello(1.,'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(int,str) -> hello\n",
      "(float,str) -> hello\n",
      "(object,object) -> hello\n"
     ]
    }
   ],
   "source": [
    "print(hello)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastcore.dispatch import _p2_anno, _TypeDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{}\n",
      "(<class 'int'>, <class 'float'>)\n",
      "{<class 'float'>: {<class 'str'>: <function f at 0x7fbb36980440>}, <class 'int'>: {<class 'str'>: <function f at 0x7fbb36980440>}}\n"
     ]
    }
   ],
   "source": [
    "# @typedispatch\n",
    "def f(x:(int,float),y:str,*args):\n",
    "    print([type(o) for o in x] , 'wo dispatch')\n",
    "a0,a1 = _p2_anno(f)\n",
    "funcs = _TypeDict()\n",
    "print(funcs)\n",
    "t = None\n",
    "if t is None:\n",
    "    t = _TypeDict()\n",
    "    print(a0)\n",
    "    funcs.add(a0, t)\n",
    "t.add(a1, f)\n",
    "print(funcs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{<class 'int'>: {<class 'str'>: <function f at 0x7fbb36980830>}}\n"
     ]
    }
   ],
   "source": [
    "print(funcs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<class 'object'>, <class 'object'>]\n"
     ]
    }
   ],
   "source": [
    "@typedispatch\n",
    "def tuple_inside(x:(int,float),y:str):\n",
    "    if type(x) is tuple:\n",
    "        print([type(o) for o in x] , 'w dispatch')\n",
    "    print(type(x), 'w distpatch')\n",
    "print(_p2_anno(tuple_inside))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1.0)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple_inside((1,1.),'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'> w distpatch\n"
     ]
    }
   ],
   "source": [
    "tuple_inside(1,'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'float'> w distpatch\n"
     ]
    }
   ],
   "source": [
    "tuple_inside(1.,'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So it just adds both types in the tuple???\n",
    "# is this intented?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# yes this is (semi) intented!\n",
    "# _TypeDict.add :\n",
    "def add(self, t, f):\n",
    "    \"Add type `t` and function `f`\"\n",
    "    if not isinstance(t,tuple): t=tuple(L(t))\n",
    "    for t_ in t: self.d[t_] = f\n",
    "    self._reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tako/dev/env37/lib/python3.7/site-packages/pandas/compat/__init__.py:117: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "from fastai2.basics import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorBase([1]), {'meta': '1'})"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o = TensorBase([1], meta='1')\n",
    "o, o._meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MetaTuple(Tuple):\n",
    "    def __new__(cls, x, *rest, **kwargs):\n",
    "        r = Tuple.__new__(cls,x, *rest)\n",
    "        r._meta = {i:a._meta for i,a in enumerate(L(r)) if hasattr(a,'_meta')}\n",
    "        r._types = [type(a) for a in L(r)]\n",
    "        return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1), {}, [int])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o = MetaTuple(1)\n",
    "o, o._meta, o._types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'label': 'x'}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((TensorBase(1), TensorBase(1)),\n",
       " {0: {'label': 'x'}, 1: {'label': 'y'}},\n",
       " [fastai2.torch_core.TensorBase, fastai2.torch_core.TensorBase])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = TensorBase(tensor(1), label='x')\n",
    "b = TensorBase(tensor(1), label='y')\n",
    "print(a._meta)\n",
    "o = MetaTuple((a,b))\n",
    "o, getattr(o,'_meta',None), o._types"
   ]
  }
 ],
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