{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from pprint import pprint\n",
    "\n",
    "from huggingface_hub import snapshot_download\n",
    "from hydra import compose, initialize\n",
    "from hydra.core.global_hydra import GlobalHydra\n",
    "from IPython.display import Video\n",
    "from omegaconf import OmegaConf\n",
    "from omegaconf.dictconfig import DictConfig\n",
    "\n",
    "from examples.notebook_utils import config_notebook\n",
    "from examples.pretrained_script import download_eval_pretrained\n",
    "from lerobot.scripts.eval import eval\n",
    "\n",
    "# Select policy and env\n",
    "POLICY = \"diffusion\"  # \"tdmpc\" | \"diffusion\"\n",
    "ENV = \"pusht\"  # \"pusht\" | \"simxarm\"\n",
    "\n",
    "# Select device\n",
    "DEVICE = \"mps\"  # \"cuda\" | \"mps\"\n",
    "\n",
    "# Generated videos will be written here\n",
    "OUT_DIR = Path(\"./outputs\")\n",
    "OUT_EXAMPLE = OUT_DIR / \"eval\" / \"eval_episode_0.mp4\"\n",
    "\n",
    "PRETRAINED_REPO = \"lerobot/diffusion_policy_pusht_image\"\n",
    "pretrained_folder = Path(snapshot_download(repo_id=PRETRAINED_REPO, repo_type=\"model\", revision=\"v1.0\"))\n",
    "pretrained_model_path = pretrained_folder / \"model.pt\"\n",
    "\n",
    "cfg_path = pretrained_folder / \"config.yaml\"\n",
    "GlobalHydra.instance().clear()\n",
    "\n",
    "print(pretrained_folder)\n",
    "\n",
    "initialize(config_path=\"../../../.cache/huggingface/hub/models--lerobot--diffusion_policy_pusht_image/snapshots/163d168f5c193c356b82e3bf6bbf5b4eeaa780d7\")\n",
    "overrides = [\n",
    "    f\"env={ENV}\",\n",
    "    f\"policy={POLICY}\",\n",
    "    f\"device={DEVICE}\",\n",
    "    f\"+policy.pretrained_model_path={pretrained_model_path}\",\n",
    "    f\"eval_episodes=1\",\n",
    "    f\"+env.episode_length=200\",\n",
    "]\n",
    "cfg = compose(config_name=\"config\", overrides=overrides)\n",
    "pprint(OmegaConf.to_container(cfg))\n",
    "# Setup config\n",
    "#cfg = config_notebook(cfg_path, policy=POLICY, env=ENV, device=DEVICE, print_config=False, pretrained_model_path=pretrained_model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# eval(cfg, out_dir=OUT_DIR)\n",
    "download_eval_pretrained(OUT_DIR, cfg)\n",
    "Video(OUT_EXAMPLE, embed=True)"
   ]
  }
 ],
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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