{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# Load the checkpoint\n",
    "input_state_dict = torch.load(\"vodka_v5_4_768-ep60-gs146640.ckpt\")\n",
    "\n",
    "root_is_state_dict = False\n",
    "if \"state_dict\" in input_state_dict:\n",
    "    root_is_state_dict = True\n",
    "    state_dict = input_state_dict[\"state_dict\"]\n",
    "else:\n",
    "    state_dict = input_state_dict\n",
    "\n",
    "mappings = {\n",
    "    \"attn_1.to_out.0\": \"attn_1.proj_out\",\n",
    "    \"attn_1.to_k\": \"attn_1.k\",\n",
    "    \"attn_1.to_q\": \"attn_1.q\",\n",
    "    \"attn_1.to_v\": \"attn_1.v\"\n",
    "}\n",
    "\n",
    "def replace_all(key):\n",
    "    for mapping in mappings:\n",
    "        key = key.replace(mapping, mappings[mapping])\n",
    "    return key\n",
    "\n",
    "# First, create a new state_dict with renamed keys\n",
    "renamed_state_dict = dict()\n",
    "for key in state_dict:\n",
    "    renamed_state_dict[replace_all(key)] = state_dict[key]\n",
    "\n",
    "# Then, reshape the tensors in the renamed state_dict\n",
    "def reshape_tensors(sd):\n",
    "    for key in sd:\n",
    "        if \"attn_1\" in key and sd[key].shape == torch.Size([512, 512]):\n",
    "            sd[key] = sd[key].unsqueeze(2).unsqueeze(3)\n",
    "    return sd\n",
    "\n",
    "output_state_dict = reshape_tensors(renamed_state_dict)\n",
    "\n",
    "# Finally, save the updated state_dict\n",
    "if root_is_state_dict:\n",
    "    input_state_dict[\"state_dict\"] = output_state_dict\n",
    "    torch.save(input_state_dict, \"6_vodka_v5_768_adamw8bit_ep60.ckpt\")\n",
    "else:\n",
    "    torch.save(output_state_dict, \"6_vodka_v5_768_adamw8bit_ep60.ckpt\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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