{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2590bea3-bcfc-4630-8393-2c4762cb4168",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/dell/anaconda3/envs/torch1.8/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import joblib\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c433f59-b8bf-47c3-9bf0-f3ca8f7a0e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('torch_weights.txt', 'r') as f:\n",
    "    lines = f.readlines()\n",
    "tkeys = [iline.strip() for iline in lines]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ab6977f5-2593-4dd2-9a3c-d72124ce7060",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('jax_weights.txt', 'r') as f:\n",
    "    lines = f.readlines()\n",
    "jkeys = [iline.strip() for iline in lines]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "67989df7-409e-43e9-8af9-6a21f0aef483",
   "metadata": {},
   "outputs": [],
   "source": [
    "# directory for jax and pytorch weights\n",
    "weight_dir = '/data/dell/Desktop/GitHub/ProteinMPNN_jax/weights/torch'\n",
    "dest_dir = '/data/dell/Desktop/GitHub/ProteinMPNN_jax/weights/jax'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c786705d-240b-4bb6-a767-adee16c501f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_list = [\"v_48_010\", \"v_48_020\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b264f1d9-dab7-4f69-9486-9bffaf02adf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "for iweight in weight_list:\n",
    "    ck = torch.load(weight_dir + '/' + iweight + '.pt', map_location='cpu')\n",
    "    ck_new = {}\n",
    "    ck_new['num_edges'] = ck['num_edges']\n",
    "    ck_new['noise_level'] = ck['noise_level']\n",
    "    ck_new['model_state_dict'] = {}\n",
    "    for ith, ijkey in enumerate(jkeys):\n",
    "        itkey = tkeys[ith]\n",
    "        if 'edge_embedding' in ijkey:\n",
    "            ck_new['model_state_dict'][ijkey] = {'w': np.array(ck['model_state_dict'][itkey]).T}\n",
    "        elif 'embed_token' in ijkey:\n",
    "            ck_new['model_state_dict'][ijkey] = {'W_s': np.array(ck['model_state_dict'][itkey])}\n",
    "        elif 'norm' in ijkey:\n",
    "            ck_new['model_state_dict'][ijkey] = {'scale': np.array(ck['model_state_dict'][itkey + '.weight']),\n",
    "                                                 'offset': np.array(ck['model_state_dict'][itkey + '.bias'])}\n",
    "        else:\n",
    "            ck_new['model_state_dict'][ijkey] = {'w': np.array(ck['model_state_dict'][itkey + '.weight']).T,\n",
    "                                                 'b': np.array(ck['model_state_dict'][itkey + '.bias']).T}\n",
    "    \n",
    "    joblib.dump(ck_new, dest_dir + '/' + iweight + '.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d72eca08-24b8-4e8c-942d-93ebc4e35ee3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6de4720-0c61-474d-9503-498c694c35f1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c84397b9-2857-43be-90cf-2be3a6525543",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86ff0de3-2f1f-4319-a565-fa9a5eb97fdc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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