{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ezkl==7.0.0 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from -r ../../requirements.txt (line 1)) (7.0.0)\n",
      "Requirement already satisfied: torch in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from -r ../../requirements.txt (line 2)) (2.2.0)\n",
      "Requirement already satisfied: requests in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from -r ../../requirements.txt (line 3)) (2.31.0)\n",
      "Requirement already satisfied: scipy in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from -r ../../requirements.txt (line 4)) (1.12.0)\n",
      "Requirement already satisfied: numpy in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from -r ../../requirements.txt (line 5)) (1.26.3)\n",
      "Requirement already satisfied: matplotlib in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from -r ../../requirements.txt (line 6)) (3.8.2)\n",
      "Requirement already satisfied: statistics in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from -r ../../requirements.txt (line 7)) (1.0.3.5)\n",
      "Requirement already satisfied: onnx in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from -r ../../requirements.txt (line 8)) (1.15.0)\n",
      "Requirement already satisfied: filelock in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from torch->-r ../../requirements.txt (line 2)) (3.13.1)\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from torch->-r ../../requirements.txt (line 2)) (4.9.0)\n",
      "Requirement already satisfied: sympy in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from torch->-r ../../requirements.txt (line 2)) (1.12)\n",
      "Requirement already satisfied: networkx in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from torch->-r ../../requirements.txt (line 2)) (3.2.1)\n",
      "Requirement already satisfied: jinja2 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from torch->-r ../../requirements.txt (line 2)) (3.1.3)\n",
      "Requirement already satisfied: fsspec in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from torch->-r ../../requirements.txt (line 2)) (2023.12.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from requests->-r ../../requirements.txt (line 3)) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from requests->-r ../../requirements.txt (line 3)) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from requests->-r ../../requirements.txt (line 3)) (2.2.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from requests->-r ../../requirements.txt (line 3)) (2024.2.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (4.47.2)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (23.2)\n",
      "Requirement already satisfied: pillow>=8 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (10.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (3.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (2.8.2)\n",
      "Requirement already satisfied: docutils>=0.3 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from statistics->-r ../../requirements.txt (line 7)) (0.20.1)\n",
      "Requirement already satisfied: protobuf>=3.20.2 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from onnx->-r ../../requirements.txt (line 8)) (4.25.2)\n",
      "Requirement already satisfied: six>=1.5 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib->-r ../../requirements.txt (line 6)) (1.16.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from jinja2->torch->-r ../../requirements.txt (line 2)) (2.1.4)\n",
      "Requirement already satisfied: mpmath>=0.19 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from sympy->torch->-r ../../requirements.txt (line 2)) (1.3.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install -r ../../requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ezkl\n",
    "import torch\n",
    "from torch import nn\n",
    "import json\n",
    "import os\n",
    "import time\n",
    "import scipy\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run -i ../../zkstats/core.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# init path\n",
    "os.makedirs(os.path.dirname('shared/'), exist_ok=True)\n",
    "os.makedirs(os.path.dirname('prover/'), exist_ok=True)\n",
    "verifier_model_path = os.path.join('shared/verifier.onnx')\n",
    "prover_model_path = os.path.join('prover/prover.onnx')\n",
    "verifier_compiled_model_path = os.path.join('shared/verifier.compiled')\n",
    "prover_compiled_model_path = os.path.join('prover/prover.compiled')\n",
    "pk_path = os.path.join('shared/test.pk')\n",
    "vk_path = os.path.join('shared/test.vk')\n",
    "proof_path = os.path.join('shared/test.pf')\n",
    "settings_path = os.path.join('shared/settings.json')\n",
    "srs_path = os.path.join('shared/kzg.srs')\n",
    "witness_path = os.path.join('prover/witness.json')\n",
    "# this is private to prover since it contains actual data\n",
    "sel_data_path = os.path.join('prover/sel_data.json')\n",
    "# this is just dummy random value\n",
    "sel_dummy_data_path = os.path.join('shared/sel_dummy_data.json')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "=======================  ZK-STATS FLOW ======================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cov:  3214.7648484848482\n",
      "x mean:  49.5\n",
      "y mean:  227.703\n",
      "dummy_cov:  4.143981818181821\n",
      "dummy x mean:  16.269\n",
      "dummy y mean:  14.982\n"
     ]
    }
   ],
   "source": [
    "data_path = os.path.join('data.json')\n",
    "dummy_data_path = os.path.join('shared/dummy_data.json')\n",
    "\n",
    "data = json.loads(open(data_path, \"r\").read())\n",
    "x_vals = data['x']\n",
    "y_vals = data['y']\n",
    "\n",
    "real_cov = statistics.covariance(x_vals, y_vals)\n",
    "x_mean = statistics.mean(x_vals)\n",
    "y_mean = statistics.mean(y_vals)\n",
    "print(\"cov: \",real_cov )\n",
    "print(\"x mean: \", x_mean)\n",
    "print(\"y mean: \", y_mean)\n",
    "\n",
    "\n",
    "create_dummy(data_path, dummy_data_path)\n",
    "dummy_data = json.loads(open(dummy_data_path, \"r\").read())\n",
    "dummy_x_vals = dummy_data['x']\n",
    "dummy_y_vals = dummy_data['y']\n",
    "\n",
    "dummy_cov = statistics.covariance(dummy_x_vals, dummy_y_vals)\n",
    "dummy_x_mean = statistics.mean(dummy_x_vals)\n",
    "dummy_y_mean = statistics.mean(dummy_y_vals)\n",
    "print('dummy_cov: ', dummy_cov)\n",
    "print(\"dummy x mean: \", dummy_x_mean)\n",
    "print(\"dummy y mean: \", dummy_y_mean)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "scales = [0]\n",
    "selected_columns = ['x', 'y']\n",
    "commitment_maps = get_data_commitment_maps(data_path, scales)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages/torch/onnx/symbolic_opset9.py:2174: FutureWarning: 'torch.onnx.symbolic_opset9._cast_Bool' is deprecated in version 2.0 and will be removed in the future. Please Avoid using this function and create a Cast node instead.\n",
      "  return fn(g, to_cast_func(g, input, False), to_cast_func(g, other, False))\n"
     ]
    }
   ],
   "source": [
    "# Verifier/ data consumer side:\n",
    "class verifier_model(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(verifier_model, self).__init__()\n",
    "        self.cov = nn.Parameter(data = torch.tensor(dummy_cov), requires_grad = False)\n",
    "        self.x_mean = nn.Parameter(data = torch.tensor(dummy_x_mean), requires_grad = False)\n",
    "        self.y_mean = nn.Parameter(data = torch.tensor(dummy_y_mean), requires_grad = False)\n",
    "    def forward(self,X,Y):\n",
    "        # print(\"x size: \", X.size()[1])\n",
    "        #  need to enforce same length, not yet\n",
    "        x_mean_cons = torch.abs(torch.sum(X)-X.size()[1]*(self.x_mean))<=torch.abs(0.01*X.size()[1]*(self.x_mean))\n",
    "        y_mean_cons = torch.abs(torch.sum(Y)-Y.size()[1]*(self.y_mean))<=torch.abs(0.01*Y.size()[1]*(self.y_mean))\n",
    "        return (torch.logical_and(torch.logical_and(x_mean_cons,y_mean_cons), torch.abs(torch.sum((X-self.x_mean)*(Y-self.y_mean))-(X.size()[1]-1)*(self.cov))<0.01*(X.size()[1]-1)*(self.cov)), self.cov)\n",
    "\n",
    "\n",
    "\n",
    "verifier_define_calculation(dummy_data_path, selected_columns, sel_dummy_data_path,verifier_model, verifier_model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Theory output:  tensor(3214.7649)\n",
      "==== Generate & Calibrate Setting ====\n",
      "scale:  [0]\n",
      "setting:  {\"run_args\":{\"tolerance\":{\"val\":0.0,\"scale\":1.0},\"input_scale\":0,\"param_scale\":0,\"scale_rebase_multiplier\":10,\"lookup_range\":[-448,6246],\"logrows\":14,\"num_inner_cols\":2,\"variables\":[[\"batch_size\",1]],\"input_visibility\":{\"Hashed\":{\"hash_is_public\":true,\"outlets\":[]}},\"output_visibility\":\"Public\",\"param_visibility\":\"Private\"},\"num_rows\":13120,\"total_assignments\":827,\"total_const_size\":8,\"model_instance_shapes\":[[1],[1]],\"model_output_scales\":[0,0],\"model_input_scales\":[0,0],\"module_sizes\":{\"kzg\":[],\"poseidon\":[13120,[2]],\"elgamal\":[0,[0]]},\"required_lookups\":[\"Abs\",{\"GreaterThan\":{\"a\":0.0}}],\"check_mode\":\"UNSAFE\",\"version\":\"7.0.0\",\"num_blinding_factors\":null}\n"
     ]
    }
   ],
   "source": [
    "# prover calculates settings, send to verifier\n",
    "\n",
    "theory_output = torch.tensor(real_cov)\n",
    "print(\"Theory output: \", theory_output)\n",
    "class prover_model(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(prover_model, self).__init__()\n",
    "        self.cov = nn.Parameter(data = torch.tensor(real_cov), requires_grad = False)\n",
    "        self.x_mean = nn.Parameter(data = torch.tensor(x_mean), requires_grad = False)\n",
    "        self.y_mean = nn.Parameter(data = torch.tensor(y_mean), requires_grad = False)\n",
    "    def forward(self,X,Y):\n",
    "        # print(\"x size: \", X.size()[1])\n",
    "        #  need to enforce same length\n",
    "        x_mean_cons = torch.abs(torch.sum(X)-X.size()[1]*(self.x_mean))<=torch.abs(0.01*X.size()[1]*(self.x_mean))\n",
    "        y_mean_cons = torch.abs(torch.sum(Y)-Y.size()[1]*(self.y_mean))<=torch.abs(0.01*Y.size()[1]*(self.y_mean))\n",
    "        return (torch.logical_and(torch.logical_and(x_mean_cons,y_mean_cons), torch.abs(torch.sum((X-self.x_mean)*(Y-self.y_mean))-(X.size()[1]-1)*(self.cov))<0.01*(X.size()[1]-1)*(self.cov)), self.cov)\n",
    "\n",
    "# note scale = 2, or 3 makes it more precise, but too big.\n",
    "\n",
    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "spawning module 0\n",
      "spawning module 2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==== setting up ezkl ====\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "spawning module 0\n",
      "spawning module 2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time setup: 3.663957118988037 seconds\n",
      "=======================================\n",
      "Theory output:  tensor(3214.7649)\n",
      "==== Generating Witness ====\n",
      "witness boolean:  1.0\n",
      "witness result 1 : 3215.0\n",
      "==== Generating Proof ====\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "spawning module 0\n",
      "spawning module 2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "proof:  {'instances': [[[14955570959218682635, 4667139652385906200, 12836539004462631467, 1774684518626433649], [4224417983558473805, 851357164555783563, 5363851773531956453, 1448631618362554917], [12436184717236109307, 3962172157175319849, 7381016538464732718, 1011752739694698287], [5743885005642251665, 3430503978676436355, 7149667244725939006, 2902673458086333540]]], 'proof': 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'transcript_type': 'EVM'}\n",
      "Time gen prf: 4.607888221740723 seconds\n"
     ]
    }
   ],
   "source": [
    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
    "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
    "setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
    "\n",
    "print(\"=======================================\")\n",
    "# Prover generates proof\n",
    "print(\"Theory output: \", theory_output)\n",
    "prover_gen_proof(prover_model_path, sel_data_path, witness_path, prover_compiled_model_path, settings_path, proof_path, pk_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3215.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifier verifies\n",
    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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