{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = 21888242871839275222246405745257275088548364400416034343698204186575808495617"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Input, AveragePooling2D\n",
    "from tensorflow.keras import Model\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = Input(shape=(5,5,3))\n",
    "x = AveragePooling2D(pool_size=2)(inputs)\n",
    "model = Model(inputs, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 5, 5, 3)]         0         \n",
      "                                                                 \n",
      " average_pooling2d (AverageP  (None, 2, 2, 3)          0         \n",
      " ooling2D)                                                       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 0\n",
      "Trainable params: 0\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[0.4697424 , 0.26405168, 0.50556795],\n",
       "         [0.31879275, 0.10661064, 0.89969558],\n",
       "         [0.51674454, 0.44716256, 0.84376492],\n",
       "         [0.70730021, 0.23034467, 0.21298659],\n",
       "         [0.39270991, 0.26666702, 0.20998017]],\n",
       "\n",
       "        [[0.06338963, 0.40992356, 0.65531956],\n",
       "         [0.65641348, 0.61781921, 0.71094249],\n",
       "         [0.66084759, 0.01212395, 0.96300368],\n",
       "         [0.05580187, 0.96245161, 0.20798007],\n",
       "         [0.94943203, 0.05702934, 0.37272236]],\n",
       "\n",
       "        [[0.35969427, 0.24114588, 0.40254835],\n",
       "         [0.87560584, 0.29480004, 0.71005672],\n",
       "         [0.89799237, 0.03862642, 0.41636709],\n",
       "         [0.60385376, 0.00368971, 0.73375191],\n",
       "         [0.39912794, 0.02359739, 0.58151196]],\n",
       "\n",
       "        [[0.75162628, 0.57732706, 0.51714415],\n",
       "         [0.42886868, 0.4817775 , 0.55461973],\n",
       "         [0.96844644, 0.72937866, 0.96170145],\n",
       "         [0.09311228, 0.70769801, 0.37766384],\n",
       "         [0.22594407, 0.64493633, 0.84161072]],\n",
       "\n",
       "        [[0.94736999, 0.95913254, 0.66551587],\n",
       "         [0.12700579, 0.80182768, 0.24940166],\n",
       "         [0.26694546, 0.07422724, 0.87879383],\n",
       "         [0.26784968, 0.33568869, 0.71071351],\n",
       "         [0.36125259, 0.89039872, 0.57815661]]]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.random.rand(1,5,5,3)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 28ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-23 19:58:12.306880: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[[0.37708455, 0.34960127, 0.6928814 ],\n",
       "         [0.48517358, 0.41302067, 0.5569338 ]],\n",
       "\n",
       "        [[0.6039488 , 0.39876258, 0.5460923 ],\n",
       "         [0.6408512 , 0.3698482 , 0.6223711 ]]]], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = model.predict(X)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def AveragePooling2DInt (nRows, nCols, nChannels, poolSize, strides, input):\n",
    "    Input = [[[str(input[i][j][k] % p) for k in range(nChannels)] for j in range(nCols)] for i in range(nRows)]\n",
    "    out = [[[0 for _ in range(nChannels)] for _ in range((nCols-poolSize)//strides + 1)] for _ in range((nRows-poolSize)//strides + 1)]\n",
    "    remainder = [[[None for _ in range(nChannels)] for _ in range((nCols-poolSize)//strides + 1)] for _ in range((nRows-poolSize)//strides + 1)]\n",
    "    for i in range((nRows-poolSize)//strides + 1):\n",
    "        for j in range((nCols-poolSize)//strides + 1):\n",
    "            for k in range(nChannels):\n",
    "                for x in range(poolSize):\n",
    "                    for y in range(poolSize):\n",
    "                        out[i][j][k] += input[i*strides+x][j*strides+y][k]\n",
    "                remainder[i][j][k] = str(out[i][j][k] % poolSize**2 % p)\n",
    "                out[i][j][k] = str(out[i][j][k] // poolSize**2 % p)\n",
    "    return Input, out, remainder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_in = [[[int(X[0][i][j][k]*1e36) for k in range(3)] for j in range(5)] for i in range(5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([[['377084566964233814727207747512696832',\n",
       "    '349601274166158063422211165597466624',\n",
       "    '692881395085479491591287820028739584'],\n",
       "   ['485173550880783982864257598131011584',\n",
       "    '413020696901266553234304779809718272',\n",
       "    '556933813298750165384079226175488000']],\n",
       "  [['603948769893606922513439699094208512',\n",
       "    '398762621026204510661452706879635456',\n",
       "    '546092235924829818623026255726903296'],\n",
       "   ['640851212450790063364758949182046208',\n",
       "    '369848200094279040953444891822653440',\n",
       "    '622371069389430669959468896315506688']]],\n",
       " [[['0', '0', '0'], ['0', '0', '0']], [['0', '0', '0'], ['0', '0', '0']]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_in, out, remainder = AveragePooling2DInt(5, 5, 3, 2, 2, X_in)\n",
    "out, remainder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_json = {\n",
    "    \"in\": X_in,\n",
    "    \"out\": out,\n",
    "    \"remainder\": remainder \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"averagePooling2D_input.json\", \"w\") as f:\n",
    "    json.dump(in_json, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = Input(shape=(10,10,3))\n",
    "x = AveragePooling2D(pool_size=3, strides=2)(inputs)\n",
    "model = Model(inputs, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 10, 10, 3)]       0         \n",
      "                                                                 \n",
      " average_pooling2d_1 (Averag  (None, 4, 4, 3)          0         \n",
      " ePooling2D)                                                     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 0\n",
      "Trainable params: 0\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.random.rand(1,10,10,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 20ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[[0.49083894, 0.6503702 , 0.45121744],\n",
       "         [0.3731585 , 0.55850077, 0.5209063 ],\n",
       "         [0.392081  , 0.4962937 , 0.44958603],\n",
       "         [0.5062166 , 0.5363207 , 0.5836957 ]],\n",
       "\n",
       "        [[0.4387001 , 0.51550937, 0.4842767 ],\n",
       "         [0.36599833, 0.6358112 , 0.42103994],\n",
       "         [0.45892367, 0.48430002, 0.48299968],\n",
       "         [0.47987926, 0.51054746, 0.48522002]],\n",
       "\n",
       "        [[0.4337698 , 0.5966541 , 0.34484175],\n",
       "         [0.50603914, 0.54209197, 0.3464988 ],\n",
       "         [0.5383602 , 0.40848657, 0.5954534 ],\n",
       "         [0.39340922, 0.5384207 , 0.6712477 ]],\n",
       "\n",
       "        [[0.52239245, 0.61681217, 0.5948292 ],\n",
       "         [0.6456814 , 0.56172687, 0.5704497 ],\n",
       "         [0.5970895 , 0.65395427, 0.61463684],\n",
       "         [0.49751964, 0.57090425, 0.50965226]]]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = model.predict(X)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_in = [[[int(X[0][i][j][k]*1e36) for k in range(3)] for j in range(10)] for i in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([[['490838913202839647482031788613930552',\n",
       "    '650370206452327739765809362325238215',\n",
       "    '451217443243932157150820354890924032'],\n",
       "   ['373158499178459549901037286246696732',\n",
       "    '558500757386374812373354359379751367',\n",
       "    '520906395265463287758066336793158542'],\n",
       "   ['392081032582640205401569336636735488',\n",
       "    '496293715863929836162024945736366307',\n",
       "    '449586010677657401563374125861539384'],\n",
       "   ['506216538770442893696705511483535815',\n",
       "    '536320651604328640569669741910920760',\n",
       "    '583695794530695154314058797408758442']],\n",
       "  [['438700122269240629018040116487826090',\n",
       "    '515509373055967873846597732290585486',\n",
       "    '484276697106518139982691888834194090'],\n",
       "   ['365998311298275897695359121353838136',\n",
       "    '635811225512657877386382358476401322',\n",
       "    '421039958668517212332658528902359722'],\n",
       "   ['458923652827692860008536397550744007',\n",
       "    '484300011977437346144682467789664711',\n",
       "    '482999719051088512471698660426980465'],\n",
       "   ['479879293099700576539663622281305201',\n",
       "    '510547498500066698646970955137781304',\n",
       "    '485220023120638790352795312775961713']],\n",
       "  [['433769787149101139024853526875005838',\n",
       "    '596654066230626723871865644707566933',\n",
       "    '344841746939097107506453705665223793'],\n",
       "   ['506039134280922806908200932494569927',\n",
       "    '542091990692138506014454460431291733',\n",
       "    '346498783957661696012159449997712497'],\n",
       "   ['538360180533623713187861074538630712',\n",
       "    '408486572499003056002511420888099498',\n",
       "    '595453351874211229959940839550251463'],\n",
       "   ['393409252869458006979621409013687182',\n",
       "    '538420708502221204218833774810030990',\n",
       "    '671247738849324010704751775607772501']],\n",
       "  [['522392440772653872755969912709229226',\n",
       "    '616812156272961914033749036925481415',\n",
       "    '594829232057008563695532312541731953'],\n",
       "   ['645681406847045116741310894926637738',\n",
       "    '561726846190701362338877345898226574',\n",
       "    '570449710887660887892445378356008277'],\n",
       "   ['597089469987715047812261496899676842',\n",
       "    '653954298836488871344502997077750215',\n",
       "    '614636841404824230076303162630384298'],\n",
       "   ['497519650100367473493056256852390343',\n",
       "    '570904266316369826537673219489981326',\n",
       "    '509652208506601100112410974135488056']]],\n",
       " [[['8', '1', '0'], ['4', '1', '2'], ['0', '5', '8'], ['1', '8', '6']],\n",
       "  [['6', '2', '6'], ['8', '6', '6'], ['1', '1', '7'], ['7', '8', '7']],\n",
       "  [['2', '3', '7'], ['1', '3', '7'], ['8', '6', '1'], ['2', '2', '3']],\n",
       "  [['6', '1', '7'], ['6', '2', '3'], ['6', '1', '6'], ['1', '2', '8']]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_in, out, remainder = AveragePooling2DInt(10, 10, 3, 3, 2, X_in)\n",
    "out, remainder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_json = {\n",
    "    \"in\": X_in,\n",
    "    \"out\": out,\n",
    "    \"remainder\": remainder\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"averagePooling2D_stride_input.json\", \"w\") as f:\n",
    "    json.dump(in_json, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sklearn",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}