{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "root = \"model_assets/jvnv-F1\"\n",
    "pth_files = [os.path.join(root, f) for f in os.listdir(root) if f.endswith(\".pth\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "model_name = \"G_0.pth\"\n",
    "model = torch.load(f\"pretrained/{model_name}\", map_location=torch.device(\"cpu\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enc_p.emo_proj.bias\n",
      "enc_p.emo_q_proj.weight\n",
      "enc_p.emo_q_proj.bias\n"
     ]
    }
   ],
   "source": [
    "from safetensors.torch import save_file\n",
    "state_dict = model[\"model\"]\n",
    "new_dict = {}\n",
    "for k in state_dict.keys():\n",
    "    if k.startswith(\"enc_p.emo\"):\n",
    "        print(k)\n",
    "    else:\n",
    "        new_dict[k] = state_dict[k]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_file(new_dict, f\"pretrained/{model_name.replace('.pth', '.safetensors')}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}