{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e76b6794",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/Qwen-Image-Edit-Angles\n"
     ]
    }
   ],
   "source": [
    "%cd /home/ubuntu/Qwen-Image-Edit-Angles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0f4ce28",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n",
      "/home/ubuntu/.local/lib/python3.10/site-packages/tqdm/auto.py:21: 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",
      "Skipping import of cpp extensions due to incompatible torch version 2.9.1+cu128 for torchao version 0.14.1             Please see https://github.com/pytorch/ao/issues/2919 for more info\n",
      "TMA benchmarks will be running without grid constant TMA descriptor.\n",
      "2025-11-19 19:57:04.903119: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2025-11-19 19:57:04.917867: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "E0000 00:00:1763582224.935911 1197953 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "E0000 00:00:1763582224.941339 1197953 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "W0000 00:00:1763582224.954269 1197953 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "W0000 00:00:1763582224.954284 1197953 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "W0000 00:00:1763582224.954287 1197953 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "W0000 00:00:1763582224.954288 1197953 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "2025-11-19 19:57:04.958719: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "/usr/lib/python3/dist-packages/sklearn/utils/fixes.py:25: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
      "  from pkg_resources import parse_version  # type: ignore\n",
      "Fetching 7 files: 100%|██████████| 7/7 [00:00<00:00, 65977.82it/s]\n"
     ]
    }
   ],
   "source": [
    "from qwenimage.reporting.datamodels import ExperimentSet\n",
    "from qwenimage.reporting.visualize_barplot import compare_sets_with_timing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "226af1b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model from: /home/ubuntu/.local/lib/python3.10/site-packages/lpips/weights/v0.1/alex.pth\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment</th>\n",
       "      <th>lpips_mean</th>\n",
       "      <th>lpips_std</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qwen_base</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.752080</td>\n",
       "      <td>0.038048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>qwen_fp8_weightonly</td>\n",
       "      <td>0.250386</td>\n",
       "      <td>0.098014</td>\n",
       "      <td>2.162742</td>\n",
       "      <td>0.025480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qwen_int8_weightonly</td>\n",
       "      <td>0.194428</td>\n",
       "      <td>0.087902</td>\n",
       "      <td>1.989593</td>\n",
       "      <td>0.028399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>qwen_fp8</td>\n",
       "      <td>0.384760</td>\n",
       "      <td>0.092095</td>\n",
       "      <td>2.305696</td>\n",
       "      <td>0.038131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>qwen_int8</td>\n",
       "      <td>0.609537</td>\n",
       "      <td>0.062481</td>\n",
       "      <td>9.517996</td>\n",
       "      <td>0.055190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             experiment  lpips_mean  lpips_std  time_mean  time_std\n",
       "0             qwen_base    0.000000   0.000000   1.752080  0.038048\n",
       "1   qwen_fp8_weightonly    0.250386   0.098014   2.162742  0.025480\n",
       "2  qwen_int8_weightonly    0.194428   0.087902   1.989593  0.028399\n",
       "3              qwen_fp8    0.384760   0.092095   2.305696  0.038131\n",
       "4             qwen_int8    0.609537   0.062481   9.517996  0.055190"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = compare_sets_with_timing(\n",
    "    ExperimentSet.create(\n",
    "        \"qwen_base\",\n",
    "        \"qwen_fp8_weightonly\",\n",
    "        \"qwen_int8_weightonly\",\n",
    "        \"qwen_fp8\",\n",
    "        \"qwen_int8\",\n",
    "    ),\n",
    "    profile_target=\"loop\",\n",
    "    sort_by=None\n",
    ")\n",
    "\n",
    "df_all\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "477d7613",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model from: /home/ubuntu/.local/lib/python3.10/site-packages/lpips/weights/v0.1/alex.pth\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment</th>\n",
       "      <th>lpips_mean</th>\n",
       "      <th>lpips_std</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qwen_fa3_aot</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.141877</td>\n",
       "      <td>0.020095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>qwen_fa3_aot_fp8wo</td>\n",
       "      <td>0.250019</td>\n",
       "      <td>0.097427</td>\n",
       "      <td>1.218563</td>\n",
       "      <td>0.021344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qwen_fa3_aot_int8wo</td>\n",
       "      <td>0.189330</td>\n",
       "      <td>0.080866</td>\n",
       "      <td>1.122642</td>\n",
       "      <td>0.021759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>qwen_fa3_aot_fp8da</td>\n",
       "      <td>0.405543</td>\n",
       "      <td>0.095514</td>\n",
       "      <td>1.066381</td>\n",
       "      <td>0.022632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>qwen_fa3_aot_int8da</td>\n",
       "      <td>0.606926</td>\n",
       "      <td>0.063545</td>\n",
       "      <td>0.995964</td>\n",
       "      <td>0.019301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>qwen_fa3_aot_fp8darow</td>\n",
       "      <td>0.415502</td>\n",
       "      <td>0.086364</td>\n",
       "      <td>0.903224</td>\n",
       "      <td>0.092046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>qwen_fa3_aot_qkvint4oint8</td>\n",
       "      <td>0.262008</td>\n",
       "      <td>0.097016</td>\n",
       "      <td>1.103839</td>\n",
       "      <td>0.021350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>qwen_fa3_aot_qkvfp8oint8</td>\n",
       "      <td>0.222709</td>\n",
       "      <td>0.088010</td>\n",
       "      <td>1.096008</td>\n",
       "      <td>0.021663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>qwen_fa3_aot_fp8darow_nolast</td>\n",
       "      <td>0.306802</td>\n",
       "      <td>0.120262</td>\n",
       "      <td>0.892194</td>\n",
       "      <td>0.032847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>qwen_fa3_aot_fp8darow_nolast_cint8</td>\n",
       "      <td>0.308823</td>\n",
       "      <td>0.115862</td>\n",
       "      <td>0.892351</td>\n",
       "      <td>0.032397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>qwen_fa3_aot_fp8darow_nofirstlast</td>\n",
       "      <td>0.285035</td>\n",
       "      <td>0.114618</td>\n",
       "      <td>0.898829</td>\n",
       "      <td>0.033120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            experiment  lpips_mean  lpips_std  time_mean  \\\n",
       "0                         qwen_fa3_aot    0.000000   0.000000   1.141877   \n",
       "1                   qwen_fa3_aot_fp8wo    0.250019   0.097427   1.218563   \n",
       "2                  qwen_fa3_aot_int8wo    0.189330   0.080866   1.122642   \n",
       "3                   qwen_fa3_aot_fp8da    0.405543   0.095514   1.066381   \n",
       "4                  qwen_fa3_aot_int8da    0.606926   0.063545   0.995964   \n",
       "5                qwen_fa3_aot_fp8darow    0.415502   0.086364   0.903224   \n",
       "6            qwen_fa3_aot_qkvint4oint8    0.262008   0.097016   1.103839   \n",
       "7             qwen_fa3_aot_qkvfp8oint8    0.222709   0.088010   1.096008   \n",
       "8         qwen_fa3_aot_fp8darow_nolast    0.306802   0.120262   0.892194   \n",
       "9   qwen_fa3_aot_fp8darow_nolast_cint8    0.308823   0.115862   0.892351   \n",
       "10   qwen_fa3_aot_fp8darow_nofirstlast    0.285035   0.114618   0.898829   \n",
       "\n",
       "    time_std  \n",
       "0   0.020095  \n",
       "1   0.021344  \n",
       "2   0.021759  \n",
       "3   0.022632  \n",
       "4   0.019301  \n",
       "5   0.092046  \n",
       "6   0.021350  \n",
       "7   0.021663  \n",
       "8   0.032847  \n",
       "9   0.032397  \n",
       "10  0.033120  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = compare_sets_with_timing(\n",
    "    ExperimentSet.create(\n",
    "        \"qwen_fa3_aot\",\n",
    "        \"qwen_fa3_aot_fp8wo\",\n",
    "        \"qwen_fa3_aot_int8wo\",\n",
    "        \"qwen_fa3_aot_fp8da\",\n",
    "        \"qwen_fa3_aot_int8da\",\n",
    "        \"qwen_fa3_aot_fp8darow\",\n",
    "\n",
    "        \"qwen_fa3_aot_qkvint4oint8\",\n",
    "        \"qwen_fa3_aot_qkvfp8oint8\",\n",
    "\n",
    "        \"qwen_fa3_aot_fp8darow_nolast\",\n",
    "        \"qwen_fa3_aot_fp8darow_nolast_cint8\",\n",
    "        \"qwen_fa3_aot_fp8darow_nofirstlast\",\n",
    "    ),\n",
    "    profile_target=\"loop\",\n",
    "    sort_by=None,\n",
    ")\n",
    "\n",
    "df_all\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e99efc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model from: /home/ubuntu/.local/lib/python3.10/site-packages/lpips/weights/v0.1/alex.pth\n"
     ]
    },
    {
     "data": {
      "image/png": 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8f94e+F1ENJeUuw74BlkN/TNkLQ0/BNweWZ/5V4CHSsp3Ntd/osw4JElaLBN0SVKlrZ/XoM4EjiFLPI9LKXX2954GvCultFZK6d0ppXWB3+fbDs+fVwFG5q+3Tyltm1JaC9gYuC1f/w2yRPJpYL2U0jbAJ/Ntn4uIdZcy/p+nlDYpiWVFoLPGt9xztgNP5Y/ZiznX54AVyD6jXVNKWwBf7qbcLfnzphGxOvMT2GklrzsT9XFLGGunmcDmZDdL7svXvXcxsQOQUvoccHHJ8s4ppc4uBD8HVkspbZpSGgtsma9fEWhmQZsCOwDr58s3AR/r6fy5/wesk1JqSiltS9YXfkK+7XCAiNib7IYPwGkppc2BTYA3yG4cfDul1EbW3L7TGfn7+VyZcUiStFgm6JKkSptFVut4P1ltbADn5DW6kNWIfzwins6bIyegsynyOgAppVeBO/N1T0fEYxFxJbA3MDlfv1P+vAkwLT9OZ6I/hPlJ9ZL6Xf5cWmu65pKcM6V0TUpps/xRWrvc1Vb5839TSg/kr//UTbm7yBJoyGrPdyOrAf4nsEtEbAiMyrd3JvNL+vn8O6X0YkppLtmNhdL3vbQagb/mTcnnAv8t2bZOacGU0kHAUGA7slrwfYHzyjzPXODHETE5IjrIfu86f986z7NDSfk/5Od8mfmf1/ZIktTHHCROklRpL3XWoEbE5mSJ7irAZ4CvkzWb/npe9jngZWA0WYJZemP5vcCRZM26twAOAT5KVgv7BbLEH7Lm0xNY2NIOSPdG/txRsi66PPf2ORcrpfRORNxJdoNib7Lk+s9kzbEPBU7Mi74BPLyUsb5R8rrzvQdLKSJWAP5BVos/k2zgwNnMv3FQ13WflNJs4IGIuIisGf4nIuL7KaWnezjd78lqxxNZU/43yX5nVuzuPHk5SZIqzhp0SVI1lSZ4DflzZ/Pnp1NKTWQ1wg8vsFNE5OsvSyl9Ok/4f5Nv3id/vid/ng4cUNK0el/ggpTSDb36TpbgnBFxcD7A3JMRMWpRBwMezZ83ivnzhn90EWXH5c9HA8uT9c/ubO5/fP78n7wGvOxY+9CmZMk5wKdTStsBp3YtlA+St2vJ8nLA+0uKjCjjXJ2/U79KKb2LbKyCt7qUubfk9VH5udYiu+EB85v1z1jCc0uSVDYTdElSpa0dEXdFxH3MT3rmAn/LXz+SP28SEc+SDR7XddqzOuBm4PWIeDwiHiVLTEv3/yFZX+/1gOfyEdAnktUYX9a7b2mecs/ZSJagbsr8GxPdOY8skRwC3BERjwM/XUTZzqbYjfnz/yOrQX+LrB87zE/ilyTWvjKR+XOh/zoiHgGu7abcdmSDtL0aEQ8CL5EN3AZZrfvD3ezTVefvxLH5Z/gMMKy0QErpFrLfKYCzImI8WVP+lclq+M/It71C9hkBtEbE3RHx+TJikCSpRybokqRKG0rWjHk7sqbSdwKHpZT+k2//IVlt+BvASsAVwPldjjEHuJAsyVuHrB/1pHzdSQB5s+ed8v3fJJuLezmyJPXUPnhfvX7OlNIUstreh8luSnQAByyi+N3Mb5b+KvBkSmkO8/vqQ0mCXo3Pp1RK6XWy1gBPkH0fmUX37+1+sr70s/IYh+X7nAnsU9IiYHGOJruBMZOsdcGpzE/aS32Y7PdvItlI7nOB68kG6HsojzsBnyXrFjCcrDvB+t0cS5KkJRbZ/xlJktRfdM4jDnw3pVSoZiySJKn3OEicJElaJhFx16K2lUyp1pfn/zYLT8vW6Yx8ejRJkmqeCbokSVpWO/VcpE9tuJgYVq9kIJIkLQubuEuSJEmSVAMcJE6SJEmSpBpggi5JkiRJUg0wQZckSZIkqQaYoEuSJEmSVANM0CVJkiRJqgEm6JIkSZIk1QATdEmSJEmSaoAJuiRJkiRJNcAEXZIkSZKkGmCCLkmSJElSDTBBlyRJkiSpBpigS5IkSZJUA0zQJUmSJEmqASbokiRJkiTVABN0SZIkSZJqgAm6JEmSJEk1oL7aAfSVIUOGpOHDh1c7DEmSJElShc2YMSOllPpdhfSATdCHDx/O9OnTqx2GJEmSJKnCIuLtasewNPrdHQVJkiRJkgYiE3RJkiRJkmrAgG3iLg14vzsYXnoYdjoR9jxtwW23nwPjr4Mh9bD2NrD/mRAB9/wK7roA0lz4wkPzy993CTz4e6gbCnu1wAZ7VfCNSJIkSQKIlFK1Y+gTI0aMSF37oM+ePZtJkyYxc+bMKkU18A0bNozRo0fT0NBQ7VAGvvYXYeI4mDZ54QT91WdgtQ2z11d+CrY/Jku635oCw1eBX+wwP0F/6xX4/SHw2X9Dx0y47EPZ6yF1FXwzkiRJUu+JiBkppRHVjmNJDaoa9EmTJrHiiivS1NRERFQ7nJr12vRZvDZ9FgCjVh7G8KHzf02mvDmTaW93ADB8aB3rNA4jInjhtRm8PauD6dOncePdj/Hh3d4NwLinpnDuv/4LwKnv24Q9N1kdgPNumcC/n5zC0LohnPmRrVl31eUr+RYHhsZRi97WmZxDVis+JP8ZrrDGwmXfeB5W3wzqGrLH0BHw2rPw5kvwz9Oz5ZXXg4PO7934JUmSJC1gUCXoM2fONDnvQcecubz61jtsuMYKdMyZywuvvc2Ga6wwb3vjsAbWWHEYAM+9Op3p73SwwrCstnzUKsuz/JorUjfrTQDmzE203vgkfzr+PQAc9ss72W2jkTw7dTp3PDOVq07chbsnvsqP/v4kvzhy2wq/00Hi2dvgrZdh/V0XXWbVMfDyozBzGsx6C15+DN5+Hcb/Dfb5Fmz0Xpg7t3IxS5IkSYPUoBskzuR88d6ePYcRy9UzJIKh9XXMSYm5Jd0glmuY3+w5IrJ+zbmX2t9m4ivTmTFrDgDPTp3O6FWWp3F4A43DGxi9yvI89+p07pr4KntvmtXk7rTBaox/aVqF3t0g8/Jj8K/vwkcuW+DntJDlV4W9vw5/PBz+/nVYa0tYcS3Y9RR46ka46lh46PcVC1uSJEkarAZdgl5tK6ywwkLrCoUCo0aNYuzYsWy55ZZcd91189afffbZABx99NGMGTOGsWPHsu2223LnnXcCcNddd7HTTjsxduxYNt98cwqFwkLHHzduHI2NjYwdO3be4+abb+42vjlzE3VD5idzdUOCOXMXHqfgrZkddMyZy4ihWcK+duMwNlpjRdZfbXnemjmb51+dQfvbs2gcPr8v+krD63l9xmza3569wPpuDq9l9eoz8NeT4COXwIjVei6/xYFwzA2w/4+gYTisvC4MXxWaz4ZDfgW3/zSrYZckSZLUZyrWxL2ppW0/4FygDri42Nrc2mX7gcAZwFygAzi12Np8e76tCLwJzAE6iq3N2/dGTIce+Qmef/Hl3jgUAOuNWour/vC7pdr3i1/8Il/5ylcYP348u+++O1OmTFmozFlnncVHPvIRbrrpJo4//ngeeeQRPvWpT3HllVeyzTbbMGfOHJ566qluj7/77rtz/fXXL/L8KSVSSvMS8s7lrgk7wNuz5vDytJk0rbY8c+fOpa6ujvq67F5Pfd0Qlmuo44mXprHRGiswbebsefu9ObODlZfPatOnzeyYt36IjRqWznWfhxfugY53YPKD2ejrE2+BXb+Q1YTPbIdrTszK7noKbPIBePwauO9SePNl+M2HYe9vwno7wdXHw7QXs+R8/zOzfe48D575dzbi+wZ7w7CVqvdeJUmSpEGgIgl6U0tbHXAe8H5gEnBvU0vbdcXW5idKiv0LuK7Y2pyaWtq2Bq4ENivZvnextXlqb8b1/Isvs/UxP+y14z1y6TeW+Ribb7459fX1TJ266Le6xx57MGHCBACmTJnC2muvDUBdXR1bbLFF2ecqFovsv//+7L333tx5552cc845nHDCCWy706488fD9XPmXqznrJ+dw53/+RUTwrW99i4MO+QhXXf8PLv7Zmayzzjo89NBDPPHEE3TMnUv9kCHMTYlZHXPZYNQIxowcwQuvzeDNPEl/4bUZNK02gpQS3/3bE3xmtzHc/9xrbL62id9S+fDPF1639tbZ81FXdr/Puw7OHl0d8suF1+152sKjw0uSJEnqM5WqQd8RmFBsbZ4I0NTSdgVwIDAvQS+2Nr9VUn4EMCgbPt99990MGTKE1VdffZFl/va3v7HVVlsBWc37pptuyl577cV+++3Hpz71KYYNG7bQPrfddhtjx46dt3zVVVdRV1fHU089xaWXXsr5559PsVjkqaee4pzzL2LDd53D36//K888+Th33Xs/z01+mf333p0x79qeuSTuufdebvjP3Wz7rk0BeP7VGXlT9cTwoXVssuaKAHxtv8345CX3zHtdNyTYaI0V2aFpVQ694A4a6oIzD92mdz48SZIkSerHKpWgjwJeKFmeBOzUtVBTS9vBwP8BawDNJZsScFNTS1sCfllsbb6ou5NExHHAcQANDQ0L1ULPmTOH2bPnN7lOcxNzU++NTp3mpgWOvyhdy8yZM4ef/vSn/O53v2PFFVfk8ssvp6Ojgzlz5syLee7cuZx22mmcccYZrL766lx44YXMnj2br3/963zsYx/j5ptv5vLLL+cPf/jDQv3LOzo62G233bj22msXWF8sFll//fXZbrvtmD17NrNnz2b99ddnn912BuC/D9/DEYcfxnL1Q9hkvXXYfffdmfT0w6yz0krsuMMO7LrNpvPez7orLzfvuDOnxLzPfquRQ/jVxzadt61z/ZHbrMKR26ySf3AzmDp1RhmfsCRJkiQNXJVK0LvrZbxQDXmxtfka4JqmlrY9yPqjvy/ftGuxtXlyU0vbGsA/m1raniy2Nt+60AFTugi4CGDEiBFp5MiRC2x/5ZVXaGiYPzhZDAmGRO+NkxdDYoHjL0rXMnV1dfP6oHddX1dXR0NDA0OGDJnXB72rzTbbjM0224wTTjiB1VdfnWnTprHaavMHBquvrydi4dgaGhoYMWLEvPVdlyNi3vkBhgwZQn19PfX19aywwgqLfK91dXV0/ewlSZIkSYtXqVHcJwHrliyPBiYvqnCefG/Y1NI2Ml+enD9PAa4hazIvoK2tjZRPg/bf//6Xuro6Vl555V459h577MGf/vQn5syZwyuvvMKtt97KjjvO/+gnT57Mfffdt9DjueeeIyLmPbobWV6VUygUFvh5LOrhz0mSJEmqrkrVoN8LbNzU0jYGeBE4HDiytEBTS9tGwDP5IHHbAkOBV5ta2kYAQ4qtzW/mr/cFvlehuHvdjBkzGD169LzlL33pS8t0vN/97nd88YtfZPnll6e+vp7LL7+curq6hcp17YP+rW99i+23X/xg+AcffDB33nkn22yzDRHBmWeeyVprrcWTTz4JwDrrrMM666wzr3znCPIjRoyYd9NA1VcoFBZIvvfaay8gm35PkiRJUu2ISiVSTS1tHwTOIZtm7ZJia/MPmlraTgAotjZf2NTS9jXgk8Bs4G3gtGJr8+1NLW0bkNWaQ3ZD4Q/F1uYf9HS+ESNGpOnTpy+wbvz48Wy++ebzlmtpmrWBoDNBnzt37gKfs2qLCbokSZIGuoiYkVIaUe04llTFEvRKKydBV+8yQe8fTNAlSZI00PXXBL1SfdAlSZIkSdJimKBLkiRJklQDKjVInNRvfebIQ5jy4nPVDqPXPPrw0wAcsOd2VY6kd60xan1+/Yerqx2GJEmStNRM0KUeTHnxOf52/EbVDqPX7PX9FwAG1HsCOOCXE6odgiRJkvqLQuMlwIeAKRTat+xm+1HA1/Klt4ATKbQ/3Ndh2cRdkiRJkjTYXAbst5jtzwJ7UmjfGjgDuKgSQZmgV9gKK6zQZ8c++OCDGTt2LBtttBGNjY2MHTuWsWPHcscdd7DLLrv02XklSSrb7w6GMzeA/5y18Lbn74bz3wNnrAHtLy68/dIPwl9Pzl63vwiXNsMl+8Gv94UXH+jbuCVJA0uh/VbgtcVsv4NC++v50l3A6EqENaibuPd23+Jq94G95ppsuvhx48Zx9tlnc/3118/bdscdd1QrLEmS5vvwL2DiOJg2eeFta2wGn/kn/OGwhbc99XdYbsX5y8utAB+9DFZYHaY8CdefCp/+ex8FLUnqh+oj4r6S5YtSSktbC/4Z4MZeiKlHgzpB7+2+xUvSBzalxFe/+lVuvPFGIoJvfetbHHbYYYtcP27cOE4//XRWW201nnrqKfbYYw/OP/98hgwprxHECiuswFtvvcW4ceP4zne+w5prrslDDz3EIYccwlZbbcW5557L22+/zbXXXsuGG27IK6+8wgknnMDzzz8PwDnnnMOuu+66VJ+Lqqtw1WN89+onFlofR125wPJ3DtmCwqELd7+RpF7VOGrR24Y1dr9+7ly491ew04nwxLULl61rgCH5V5pnb4N/ng5DR8DK68FB5/dK2JKkfqcjpbT9Mh+l0Lg3WYK+2zIfqwyDOkGvpquvvpqHHnqIhx9+mKlTp7LDDjuwxx57cMcdd3S7HuCee+7hiSeeYP3112e//fbj6quv5iMf+cgSn/vhhx9m/PjxrLrqqmywwQYce+yx3HPPPZx77rn8/Oc/55xzzuELX/gCX/ziF9ltt914/vnn+cAHPsD48eN7+2NQBRQO3dLEW1L/9vAfYPMDoH65hbfNnQM3nAa7fzlbHv832OdbsNF7s8RekqSlVWjcGrgY2J9C+6uVOKUJepXcfvvtHHHEEdTV1bHmmmuy5557cu+99y5y/UorrcSOO+7IBhtsAMARRxzB7bffvlQJ+g477MDaa68NwIYbbsi+++4LwFZbbcUtt9wCwM0338wTT8yvdZ02bRpvvvkmK6644sIHlCSpr8yeCY9cCR+/Gp6/c+Htf/sCbPIB2HDvbHnXU+D2c+DhP8KYPWDbT1Y0XEkD1O8Ohpcezlry7HnagtuevzvrZvPqM3DKg/NbCl35qaw7T5oD238G3n1Utv43H85uLs56C3b5PGy15N/nVQGFxvWAq4FPUGh/ulKnNUGvkpTSEq0HiIjFLpdrueXm10AMGTJk3vKQIUPo6OgAYO7cudx5550MHz58qc4hSVKveOM5mNkOf/gYvP06vPU/uP83sN2n4KZvwYprwU7Hzy8/fFVoPhtSgp9vC1scBMNWqlr4kgaIpRk/472nw2obZjcaz98ZtjwUGobBUX+B+qEwcxpcuKsJerUUGv8I7AWMpNA4CfgO0JBta78QOB1YDTifQiNAB4X2ZW8y3wMT9CrZY489+OUvf8mnPvUpXnvtNW699VbOOussOjo6ul3/5JNPcs899/Dss8+y/vrr86c//Ynjjjuuz+Lbd999+cUvfsFpp2V3CB966CHGjh3bZ+eTJA0S130eXrgHOt6ByQ/CXi0w8RbY9QswdQK0fQn+9yhc9ZnsS+sOx8Lx/8n2ffY2eORPWXL+4gNw1wWw7s7ZaO4jVoOP/RbuPA+e+TekubDB3ibnknrH0oyfsdqG2XNdA8QQ6Kxcqx+aPc+eAatvnr12/IzKK7Qf0cP2Y4FjKxPMfCboVXLwwQdz5513ss022xARnHnmmay11lqLXP/kk0/ynve8h5aWFh599FH22GMPDj744D6L72c/+xknnXQSW2+9NR0dHeyxxx5ceOGFfXY+SdIg8eGfL7xu7a2z55EbwaeuW/S+Y3bPHgCjtoXTu+kOuOdpCzc/laRquu3H2Q3HznE05s6B3xwAU8bD+76TrXP8DOVicU2q+7MRI0ak6dOnL7Bu/PjxbL755vOW+9M0a91NnVZrnnrqKSBrHl/6Ofd3B+y5Xa+O9q++ccAvJ/C3/9xf7TAkSdJA9eDlWRP3Rd0EvLQZDrlowdr2h/4I//0HHHoJdJ19acZrcNFecMJt8M6b2fgZM99w/IxeEhEzUkojqh3HkhrUNejVnLNckiRJ0gD2ZBs8+mc44or5yfmc2RB12XLD8lA/LHsMaXD8DAGDPEHvT/baay/22muvaochSZIkDT5LM37GVZ+FkRtnI8ADHPqr7Pkvn4Ehddmx9vxq1vT9P2c5foYAE3RJklSDCoUC3/3ud3ss953vfIdCodD3AUka3JZm/IxvdjPiO8Cnb1x4neNnKDfoEvSU0lJPT6aeDdQxDSRJlVUoFBZIvDtbkY0bN64q8UiSVAlDei4ycAwbNoxXX33VJLKPpJR45513GDZsWLVDkSRJkqR+Z1DVoI8ePZpJkybxyiuvVDuUAemll17ipZde4mMf+1i1Q5EkSZKkfmdQJegNDQ2MGTOm2mEMWCeeeCIARx11VJUjkSRJkirP8TO0rAZVgi5J0mDxmSMPYcqLz1U7jF7z6MNPA3DAnttVOZLetcao9Z32VRpAHD9Dy8oEXZKkAWjKi8/xt+M3qnYYvWav778AMKDeE8ABv5xQ7RAkSTVkUA0SJ0mSJElSrTJBlyRJkiSpBpigS5IkSZJUA+yDLkmSak7hqsf47tVPLLQ+jrpygeXvHLIFhUO3rFRYkvqYA1z2Dw5w2XdM0CVJUs0pHLqlibc0CDnAZf/gAJd9xybukiRJkiTVABN0SZIkSZJqgAm6JEmSJEk1wD7okiRJktQLHOBSy8oEXZIkSZJ6gQNcalnZxF2SJEmSpBpggi5JkiRJUg0wQZckSZIkqQZUrA96U0vbfsC5QB1wcbG1ubXL9gOBM4C5QAdwarG1+fZy9pUkSZIkqb+rSA16U0tbHXAesD+wBXBEU0vbFl2K/QvYptjaPBb4NHDxEuwrSZIkSVK/Vqka9B2BCcXW5okATS1tVwAHAvPmICi2Nr9VUn4EkMrdV5IkSZKk/q5SCfoo4IWS5UnATl0LNbW0HQz8H7AG0Lwk+wJExHHAcQANDQ1MnTp1mQNX+WbPng0w4D73jo45zJo1q9phqAcdHXMG3O+etCy8dvUPXrukBXnt6h+8dvWdSiXo0c261HVFsbX5GuCappa2Pcj6o7+v3H0BUkoXARcBjBgxIo0cOXKpA9aSa2hoAGCgfe719XUMHTq02mGoB/X1dQPud09aFl67+gevXdKCvHb1D167+k6lRnGfBKxbsjwamLyowsXW5luBDZta2kYu6b6SJEmSJPVHlapBvxfYuKmlbQzwInA4cGRpgaaWto2AZ4qtzamppW1bYCjwKvBGT/tKkiRJktTfVaQGvdja3AGcDPwDGA9cWWxtfryppe2Eppa2E/JihwKPNbW0PUQ2avthxdbmtKh9KxG3JEmSJEmVUrF50IutzTcAN3RZd2HJ6x8BPyp3X0mSJEmSBpJK9UGXJEmSJEmLYYIuSZIkSVINMEGXJEmSJKkGmKBLkiRJklQDTNAlSZIkSaoBJuiSJEmSJNUAE3RJkiRJkmqACbokSZIkSTWgvtoBSJIkSZJUUYXGS4APAVMotG/ZzfYAzgU+CMwAjqbQ/kBfh2UNuiRJkiRpsLkM2G8x2/cHNs4fxwEXVCAmE3RJkiRJ0iBTaL8VeG0xJQ4EfkuhPVFovwtYmULj2n0dlk3cJUmSpCr5830v8Md7nici+O6H38WWoxrnbbv/udf4xtWP8eyr0/nPaXuxduNwAG55ago//efTLFc/hHVWHs6PP7oN9XVDuPzu57jyvkkMrQtOfd8m7LrRSCa+8hZf+fPDNNQNoWNu4owDt2SLdVaq1tuVKqk+Iu4rWb4opXTREuw/CnihZHlSvu6l3ghuUUzQJUmSpCponzGby+4ocs3nduV/02byxT89xF9O3GXe9o3XXJGrP7cLn77s3gX2+8lNT3PBx7dl9CrL8+UrH+a2CVPZalQjf7j7ea49aVfe6ZjLERfdxbUn7cp6qy7PVSfuQkRwx4Sp/OKW/3L+UdtV+q1K1dCRUtp+GfaPbtalZTheWWziLkmSJFXBgy+8zg5NqzK0fgjrrro802fN4Z2OOfO2rzSsgRHLLVyftvGaKzDt7Q5SSrw5czarjRjKpNffZuM1VqChbggrLFfP8KF1PPfqdOrrhhCR5RlvvtPBZmtZey6VaRKwbsnyaGByX5/UGnRJkiSpCtrfnk3j8IZ5yysOq6d9xmzWWKlusfsduu1oPnXpPay4XD2br70SW49emTdmzOKJl6bx5szZTH9nDuNfmsYbb88G4NFJ7Zx+3WNMfuNtLvy4tedSma4DTqbQeAWwE9BOob1Pm7eDCbokSZJUFY3DG5g2c/a85TdndtC4fMNi9sh845pH+etJu7LOysP5xjWP0vbISzRvvTZffN8mfOY397H6Csux+dorseZKwwDYanQj13xuVx564Q2+89fH+OvJu/XZe5L6jULjH4G9gJEUGicB3wGyP8BC+4XADWRTrE0gm2btmEqEZYIuSZIkVcG7112FH9/0NLPnzGXKm+8wYmgdy9UvvvYcoC5iXs37aiOG8sbbswDYf6u12X+rtZkybSan/eURRq08nJmz5zCsITvmSsPq572WBr1C+xE9bE/ASZUJZj4TdEmSJKkKGpdv4BM7r89hv7yTiOA7B2zB45Pbuf2/Uzl+zw2Z+MpbfPuvjzH+pWmc8scH+fDYUXxi5/X58r6bcuSv7mK5+jpWGl7PCXtuCMCX/vQQk9vfZnhDHd/98LsAuOOZqVw4biJD8pGnTj9gi2q9XUllMEGXJEmSquRjO6zLx3ZYd4F171onm2ptg9VX4PJjd15on+at16Z564WnY/7JYWMXWrfPZmuyz2Zr9k6wkvqco7hLkiRJklQDTNAlSZKkGlcoFIiIHh+FQqHaoUpaBjZxlyRJkmpcoVBYIPnea6+9ABg3blxV4pHUN6xBlyRJkiSpBpigS5IkSZJUA0zQJUmSJEmqASbokiRJkiTVABN0SZIkSZJqgAm6JEmSJEk1wARdkiRJkqQaYIIuSZIkSVINMEGXJEmSJKkGmKBLkiRJklQDTNAlSZIkSaoBJuiSJEmSJNUAE3RJkiRJkmpAfaVO1NTSth9wLlAHXFxsbW7tsv0o4Gv54lvAicXW5ofzbUXgTWAO0FFsbd6+UnFLkiRJklQJFalBb2ppqwPOA/YHtgCOaGpp26JLsWeBPYutzVsDZwAXddm+d7G1eazJuSRJkiRpIKpUDfqOwIRia/NEgKaWtiuAA4EnOgsUW5vvKCl/FzC6QrFJkiRJklR1leqDPgp4oWR5Ur5uUT4D3FiynICbmlra7m9qaTuuD+KTJEmSJKmqKlWDHt2sS90VbGpp25ssQd+tZPWuxdbmyU0tbWsA/2xqaXuy2Np860IniTgOOA6goaGBqVOnLnvkfeTbZ/yAKa+8Vu0wetXj458E4NAjP1XlSHrXC5NeZNas9aodhnrQ0TGnpv/mpUrr6JjDrFmzqh2GeuC1S0tr9uzZAAPu98drV//gtavvVCpBnwSsW7I8GpjctVBTS9vWwMXA/sXW5lc71xdbmyfnz1OaWtquIWsyv1CCnlK6iLzv+ogRI9LIkSN78z30qvseeoytj/lhtcPoVfUPPAbAyvueXOVIetf0cdcydOjQaoehHtTX11HLf/NSpdXX13nt6ge8dmlpNTQ0AAy43x+vXf2D166+U6kE/V5g46aWtjHAi8DhwJGlBZpa2tYDrgY+UWxtfrpk/QhgSLG1+c389b7A9yoUtyRJkiRJFVGRPujF1uYO4GTgH8B44Mpia/PjTS1tJzS1tJ2QFzsdWA04v6ml7aGmlrb78vVrArc3tbQ9DNwDtBVbm/9eibglSZIkSaqUis2DXmxtvgG4ocu6C0teHwsc281+E4Ft+jxASZIkSZKqqFKjuEuSJEmSpMUwQZckSZIkqQaYoEuSJEmSVANM0CVJkiRJqgEm6JIkSZIk1QATdEmSJEmSaoAJuiRJkiRJNcAEXZIkSZKkGlBf7QAkSZIkSRowCo3LA6OAFym0z1iSXU3QJUmSJElaVoXGVYCLgENK1l0NHE+h/bVyDmGCLkmSJEnSsvs1cFCXdYcAdZQm7YthH3RJkiRJkpbd+4BngbHAMODdwHP5+rJYgy5JkiRJ0rIrAvdQaH8kX36YQuMtZIl6WUzQJUmSJEladr8Hvkmh8SHgSWBz4FCgQKFxj3mlCu23LuoAJuiSJEmSJC27ViAB53ZZ/+OS14nF5OEm6JIkSZKkwaXQuB9ZIl0HXEyhvbXL9kayGvH1yPLmsym0X9rDUZ8nS8CXmgm6JEmSJGnwKDTWAecB7wcmAfdSaLyOQvsTJaVOAp6g0H4AhcbVgacoNF5OoX3Woo/b3rSsoZmgS5IkSZIGkx2BCRTaJwJQaLwCOBAoTdATsCKFxgBWAF4DOno8clbz/lFgfbJB466i0P5GuYE5zZokSZIkaaCpj4j7Sh7HlWwbBbxQsjwpX1fqF2SDvE0GHgW+QKF97mLPWGjchCzJ/yXwDeAi4HEKjZuWHXRPBZpa2uqAmcBVxdbmw8s9sCRJkiRJVdKRUtp+Eduim3Vd+45/AHgI2AfYEPgnhcbbKLRPW8w5zwbWzvd7EtiUbIq1M8lq6HvUYw16sbV5DvA0sPi7BZIkSZIk1b5JwLoly6PJaspLHQNcTaE9UWifADwLbNbDcXcGbqLQvi2F9iMptG8H/AN4T7mBldsH/XKg0NTS9hRwB/BO54Zia/Mi53CTJEmSJKnG3AtsTKFxDPAicDhwZJcyzwPvBW6j0LgmWW34xB6OG0DXQeRm0X2NfbfKTdC/T1blf3qX9Yudw02SJEmSpJpSaO+g0HgyWe12HXAJhfbHKTSekG+/EDgDuIxC46NkCfbXKLRP7eHI9wPNFBr/BYwnq3HfG7ip3NDKTa6XeT43SZIkSZJqQqH9BuCGLusuLHk9Gdh3CY/6VeA/ZEn53vm6dqCl3AOUlaAXW5ubljAwSZIkSZIGj0L7IxQaNwc+ATSRTbP2ewrtL5V7iLKbpze1tC00n1uxtfmN8qOVJEmSJGkAK7S/TKHxGrK8+bklSc6hzAS9qaVtE+AWYK2S1d9ramnbp9ja/NSSnFCSJEmSpAGn0NgI/JFsirbOdTcBh1Noby/nED1Os5brnM/tYeBPZPO6rU02n5skSZIkSYPdD4D9gDnA//LnfYEflnuAcpu47wzcVGxt3q9zRVNL240swXxukiRJkiQNYB8CHgP2ptD+KoXG1chaojcDJ5VzgHJr0Jd5PjdJkiRJkgawVYH7KbS/CpA/35+vL0u5Nej3A81NLW1LPZ+bJEmSJEkD2FPA4RQanwGeJMubDwMeL/cA5SbopfO57UVWc75E87lJkiRJkjSA/RC4CvhuyboAWss9QFlN3IutzY8AmwNfAy7Mn7cotjY/XHaokiRJkiQNVIX2a8j6m/+DrAb978CHKLRfVe4heqxBb2ppq89PcEextfnbSxmqJEmSJEkDW6H9RuDGpd29xwS92Nrc0dTSthkwcWlPIkmSJEnSgFNovKSMUolC+2fKOVy5fdC/B3y/qaXt92Q16bPL3E+SJEmSpIHqaCCx+BnOEtCrCfoF+UH/DdDU0jbvRMXW5rKO0dTSth9wLlAHXFxsbW7tsv0osr7tAG8BJ3b2ce9pX0mSJEmSquC7PRcpX7kJOnR/R6CsedCbWtrqgPOA9wOTgHubWtquK7Y2P1FS7Flgz2Jr8+tNLW37AxcBO5W5ryRJkiRJlVVor0qCPmYZz7MjMKHY2jwRoKml7QrgQGBekl1sbb6jpPxdwOhy95UkSZIkqb8rdxT3S1i2UdxHAS+ULE8CdlpM+c8wf+S7sveNiOOA4wAaGhqYOnXqUobb99YbtQ6r1r9T7TB6VX3MBRhw72toQwOzZs2qdhjqQUfHnJr+m5cqraNjjteufsBrl5bW7NnZkFAD7ffHa1f/4LWr71RqFPfumsKn7go2tbTtTZag77ak+6aULiJrGs+IESPSyJEjlzzSCnn+xcms3LFctcPoVR1pCACvDbD3NWv2bIYOHVrtMNSD+vo6avlvXqq0+vo6r139gNcuLa2GhgaAAff747Wrf/Da1XeGlFnue8BBTS1teza1tDUsxXkmAeuWLI8GJnct1NTStjVwMXBgsbX51SXZV5IkSZKk/qxSo7jfC2zc1NI2BngROBw4srRAU0vbesDVwCeKrc1PL8m+kiRJkiTVhELjScChFNr3WdJdy61Bh6ypeddHWfsXW5s7gJOBfwDjgSuLrc2PN7W0ndDU0nZCXux0YDXg/KaWtoeaWtruW9y+SxC3JEmSJEmVshGw59LsWKlR3Cm2Nt8A3NBl3YUlr48Fji13X0mSJEmSBpKyEvRia/NzfR2IJEmSJEkDwOvA80uz42KbqDe1tD3Q1NL2pfz1EU0tbZeUbPthU0vbq4veW5IkSZKkQabQ/j0K7UvVCr2nPuRjyUZNB9gR+FTJtuHAyktzUkmSJEmStKAlGSROkiRJkiT1kXL6oKc+j0KSJEnqQ4ce+Qmef/HlaofRa558+GEAdtjz/VWOpHfNKD5HNgC2NDiVk6B/rqml7RiyJu00tbS9lq8f3mdRSZIkSb3o+RdfZutjfljtMHrNpNZspuKB9J4Abv3G+6odglRV5SToy+WPTiuXvLZ2XZIkSZKkQuOaZOO4rQK8ATxEoX2Jmu70lKDvvVSBSZIkSZI00BUa64CjgeOB7brZ/gBwAfAbCu1zejrcYhP0Ymvzf5YqSEmSJEmSBr4nmD9wwjPAeGAasBKwObAtcDHwNWDTng5WThN3SZIkSZK0sKHAacAVFNonL7S10LgOcARwcjkHM0GXJEmSJGnpbLTYputZ0v5jCo3nlHMw50GXJEmSJGlpLDY5b9yVQuOWPZYrYQ26JEmSJEnLqtB4IbAz8G7gd2RN26HQ+HkK7eeXc4glStCbWtpWAj4NrAj8q9jafMeS7C9JkiRJ0gC1L9mgccsDhwGPAk3AKcCyJ+hNLW1XAAcAo4B24N9kdwMAvtPU0nZUsbX5T0sTuSRJkiRJA8jawI3AFmTdyT9BNjjcJ8o9QE990LcD7i62Nr8B7Eo2RPxs4DpgLtlodZIkSZIkDXZvAWPJmrbPBp4kG+V9ZrkH6ClBXwt4On/9vvz50mJr88HAVcDGSxCsJEmSJEkD1TjgPcAXgH9TaJ8NbAU8Ve4BeuqDPpesvznALkAC/pUvTwdiCYKVJEmSJGmg+ixZMl4PnEOhcShZ6/MHyj1ATwn6Y8BHmlralgf2AeYwP0HfDHhpSSOWJEmSJGnAKbS/AXyry9rvLckhemrifjpZ2/kD87I/LbY2v97U0rYB2fDxty/JySRJkiRJGjAKjd+l0DiyhzIjKTSWlagvtga92Nr8r6aWtk2AnYCXiq3Nd+WbOpP2x8s5iSRJkiRJNaPQuB9wLlAHXEyhvbWbMnsB5wANwFQK7Xt2c6RvAy0UGv8F3AqMB94k6yq+ObAnWWv0erIK8MUqZx70KcAEYGrnimJr8wvAC2XsK0mSJElS7Sg01gHnAe8HJgH3Umi8jkL7EyVlViabu3w/Cu3PU2hcYxFHey/wQ2A/4ANdtnWO2XYX8M1yQutpHvSNgL8DY/LlXxRbm79QzoElSZIkSapBOwITKLRPBKDQeAVZC/EnSsocCVxNof35rEz7lG6PVGi/BXgPhcZtgA8BWwOrAG8AjwDXU2h/qNzAeqpBbwU2KFk+uaml7U/F1uY7yj2BJEmSJEkVVh8R95UsX5RSuih/PYoFW4RPIuvWXWoToIFC4ziy5urnUmj/7SLPVmh/GHh4WYPuaZC4XYFngO2BL5NV0e+2rCeVJEmSJKkPdaSUti95XFSyrbvpwlOX5XpgO6CZrOn6tyk0btJHsc7TU4K+JnBjsbX5AeBn+bpFtb2XJEmSJKnWTQLWLVkeDUzupszfKbRPp9A+lWwAuG36OrByBolbpamlbSvm32VYs6mlbevOjcXW5kf6JDJJkiRJknrfvcDGFBrHAC8Ch5P1OS/1V+AXFBrrgaFkTeB/2teB9VSDDlmgDwEPklX7H5m/fhB4oM8ikyRJkiSptxXaO4CTgX+QTYt2JYX2xyk0nkCh8YS8zHiyAdMfAe4hm4rtsb4OrZwa9O7a55ezTZIkSZKk2lNovwG4ocu6C7ssnwWctWTHbVwV+AiwEfADsmbxT1Fo/185uy82QS+2NpdTwy5JkiRJ0uBWaHwX8G9gZL7mB8CNwG+BE8s5hAm4JEmSJEnL7ifAykA+v3p7O/Af4H3lHmCxNehNLW099TFPxdbm7co9mSRJkiRJA9SOwLVkI8Kfkq97Dtiz3AP01Ad9bA/bu84VJ0mSJEnSYDQdWKHLuq2BV8s9QE8J+jFLGpEkSZIkSYPQ7WQDxG0FQKHxHmA74I/lHqCnQeJ+swzBSZIkSZI0WJwGbA9skC9vT9Yf/RvlHqCnPugrABcAHwReAb5dbG3+81KFKkmSJEnSQFVof4FC41bAh4D1gSJwA4X2GeUeoqcm7mcAR+WvVwEub2ppu6/Y2vzsksba1NK2H3AuUAdcXGxtbu2yfTPgUmBb4JvF1uazS7YVgTeBOUBHsbV5+yU9vyRJkiRJfarQ/jaw1JXaPSXoBwHTgO8BOwOHAgcAP1uSkzS1tNUB5wHvByYB9za1tF1XbG1+oqTYa2Qj3R20iMPsXWxtnrok51XfeuDai3jorxcvtP6SY3ZcYHnsgcey7UHHVSosSZIkSaq8QuNYslx5LDCiZEui0N5T7g30nKCPBi4ttjb/JG/u/hFg1JJHyo7AhGJr80SAppa2K4ADgXkJerG1eQowpamlrXkpjq8q2Pag40y8JUmSJCnzO+Bd3ayPcg/QU4JeB7wFUGxtfquppQ2godyDlxgFvFCyPAnYaQn2T8BNTS1tCfhlsbX5ou4KRcRxwHEADQ0NTJ1auxXu641ah1Xr36l2GCrD0IYGZs2aVe0w1IOOjjk1/TcvVVpHxxyvXf2A167KGWjfvepjLsCAek/g967+wmvXIjUBdwAnkHXRXmLlVLOf2NTSdnT+OnVdLrY2r1bGMbq7Y7Akc6jvWmxtntzU0rYG8M+mlrYni63Nty50wJQuAi4CGDFiRBo5cuQSnKKynn9xMit3LFftMFSGWbNnM3To0GqHoR7U19dRy3/zUqXV19d57eoHvHZVzkD77tWRhgDw2gB6T+D3rv7Ca9ci/RHYBihSaH9raQ5QToK+XP7obrncJHsSsG7J8mhgcpn7Umxtnpw/T2lqabuGrMn8Qgm6JEmSJElV8iPgPmAqhcb/kQ1yDlkf9A3LOUBPCfreyxBcqXuBjZta2sYALwKHA0eWs2NTS9sIYEixtfnN/PW+ZIPWSZIkSZJUKy4HGvPXpRXUZbceX2yCXmxt/s+itjW1tO0GrFrOSYqtzR1NLW0nA/8g69d+SbG1+fGmlrYT8u0XNrW0rUV2t2ElYG5TS9upwBbASOCavP97PfCHYmvz38s5ryRJkiRJFbIV8CRZTfobS3OAsoZ6X4SzgB3KPUaxtfkG4IYu6y4sef0yWdP3rqaRteOXJEmSJKlWXQ2sSqH9N0t7gGVJ0GEJhouXJEmSJGkAWw3Yl0LjU2Q16aV90A8t5wDLmqBLkiRJkiTYL3/eOH906p0+6E0tbR9ezOZVyj2JJEmSJEkD3PdYsunEF9JTDfq1izlBLOvJJUmSJEkaEArthWU9RE8J+vOYhEuSJEmStLBC4+nAXRTab8pfdydRaD+jnMP1NM1a0xKGJ0mSJEnSYFEAzgFuyl93reDubHm+7Am6JEmSJElapN8A9+Svf0sf90GXJEmSJEndKbQfQ6FxDwqNG1BoP3pZDzekF0KSJEmSJGmwugU4uTcOZIIuSZIkSdLSi946kE3cJUmSJElaNqMpNO6xyK2F9lvLOYgJuiRJkiRJy+bQ/NGdRJm5twm6JEmSJEnLZjbw9rIexARdkiRJkqRlcz6F9i8t60EcJE6SJEmSpBpggi5JkiRJ0tJ7DnitNw5kE3dJkiRJkpZWoX1Mbx3KGnRJkiRJkmqACbokSZIkSTXABF2SJEmSpBpggi5JkiRJUg0wQZckSZIkqQaYoEuSJEmSVANM0CVJkiRJqgEm6JIkSZIk1YD6agcgSZIkSVJFFRr3A84F6oCLKbS3LqLcDsBdwGEU2v/S12FZgy5JkiRJGjwKjXXAecD+wBbAERQat1hEuR8B/6hUaCbokiRJkqTBZEdgAoX2iRTaZwFXAAd2U+7zwFXAlEoFZoIuSZIkSRpo6iPivpLHcSXbRgEvlCxPytfNV2gcBRwMXNjnkZawD7okSZIkaaDpSCltv4ht0c261GX5HOBrFNrnUGjs1cAWxxp0SZIkSdJgMglYt2R5NDC5S5ntgSsoNBaBjwDnU2g8qK8DswZdkiRJkjSY3AtsTKFxDPAicDhw5AIlCu1j5r9uvAy4nkL7tX0dmAm6JEmSVOMeuPYiHvrrxQutv+SYHRdYHnvgsWx70HELlZNUotDeQaHxZLLR2euASyi0P06h8YR8e0X7nZcyQZckSZJq3LYHHWfiLfWmQvsNwA1d1nWfmBfaj+77gDL2QZckSZIkqQaYoEuSJEmSVAMq1sS9qaVtP+Bcsjb+Fxdbm1u7bN8MuBTYFvhmsbX57HL3lSRJkiSpv6tIDXpTS1sdcB6wP7AFcERTS9sWXYq9BpwCnL0U+0qSJEmS1K9Vqon7jsCEYmvzxGJr8yzgCuDA0gLF1uYpxdbme4HZS7qvJEmSJEn9XaWauI8CXihZngTs1Nv7RsRxwHEADQ0NTJ06dckjrZD1Rq3DqvXvVDsMlWFoQwOzZs2qdhjqQUfHnJr+m5cqraNjjteufsBrV+X43at/8HtX/+C1q+9UKkGPbtal3t43pXQRcBHAiBEj0siRI8s8ReU9/+JkVu5YrtphqAyzZs9m6NCh1Q5DPaivr6OW/+alSquvr/Pa1Q947aocv3v1D37v6h+8dvWdSjVxnwSsW7I8GphcgX0lSZIkSeoXKlWDfi+wcVNL2xjgReBw4MgK7CtJkiRJUr9QkQS92Nrc0dTSdjLwD7Kp0i4ptjY/3tTSdkK+/cKmlra1gPuAlYC5TS1tpwJbFFubp3W3byXiliRJkiSpUio2D3qxtfkG4IYu6y4sef0yWfP1svaVJEmSJGkgqVQfdEmSJEmStBgm6JIkSZIk1QATdEmSJEmSaoAJuiRJkiRJNcAEXZIkSZKkGmCCLkmSJElSDTBBlyRJkiSpBpigS5IkSZJUA0zQJUmSJEmqASbokiRJkiTVABN0SZIkSZJqgAm6JEmSJEk1wARdkiRJkqQaYIIuSZIkSVINMEGXJEmSJKkGmKBLkiRJklQDTNAlSZIkSaoBJuiSJEmSJNUAE3RJkiRJkmqACbokSZIkSTXABF2SJEmSpBpggi5JkiRJUg0wQZckSZIkqQaYoEuSJEmSVANM0CVJkiRJqgEm6JIkSZIk1QATdEmSJEmSaoAJuiRJkiRJNcAEXZIkSZKkGmCCLkmSJElSDaivdgCSJEmSJFVUoXE/4FygDriYQntrl+1HAV/Ll94CTqTQ/nBfh2UNuiRJkiRp8Cg01gHnAfsDWwBHUGjcokupZ4E9KbRvDZwBXFSJ0KxBlyRJkiQNJjsCEyi0TwSg0HgFcCDwxLwShfY7SsrfBYyuRGDWoEuSJEmSBpNRwAsly5PydYvyGeDGPo0oZw26JEmSJGmgqY+I+0qWL0opdTZTj27Kp26PUmjcmyxB3613w+ueCbokSZIkaaDpSCltv4htk4B1S5ZHA5MXKlVo3Bq4GNifQvurvR5hNyqWoDe1tC0wSl6xtbm1y/bIt38QmAEcXWxtfiDfVgTeBOYAHcXW5kV90JIkSZIkLc69wMYUGscALwKHA0cuUKLQuB5wNfAJCu1PVyqwivRBb2ppW2iUvKaWtq6j5O0PbJw/jgMu6LJ972Jr81iTc0mSJEnSUiu0dwAnA/8AxgNXUmh/nELjCRQaT8hLnQ6sBpxPofEhCo33LeJovapSNeg7AhOKrc0TAZpa2hYeJS9b/m2xtTkBdzW1tK3c1NK2drG1+aUKxShJkiRJGgwK7TcAN3RZd2HJ62OBYysbVOUS9O5GydupjDKjgJfIOuzf1NTSloBfFlubu52DLiKOI6t9p6GhgalTp/ZO9H1gvVHrsGr9O9UOQ2UY2tDArFmzqh2GetDRMaem/+alSuvomOO1qx/w2lU5fvfqH/ze1T947eo7lUrQyxklb3Fldi22Nk9uamlbA/hnU0vbk8XW5lsXKpyNyncRwIgRI9LIkSOXJeY+9fyLk1m5Y7lqh6EyzJo9m6FDh1Y7DPWgvr6OWv6blyqtvr7Oa1c/4LWrcvzu1T/4vat/8NrVdyo1D3o5o+QtskyxtbnzeQpwDVmTeUmSJEmSBoxK1aDfC2zc1NK26FHy4Drg5Lx/+k5Ae7G1+aWmlrYRwJBia/Ob+et9ge9VKG5JkiRJkiqiIjXoxdbmhUbJK7Y2P97U0nZCU0tb5yh5NwATgQnAr4DP5evXBG5vaml7GLgHaCu2Nv+9EnFLkiRJklQpFZsHvdjavNAoecXW5gtLXifgpG72mwhs0+cBSpIkSZJURZXqgy5JkiRJkhbDBF2SJEmSpBpggi5JkiRJUg0wQZckSZIkqQaYoEuSJEmSVANM0CVJkiRJqgEm6JIkSZIk1QATdEmSJEmSaoAJuiRJkiRJNcAEXZIkSZKkGmCCLkmSJElSDTBBlyRJkiSpBpigS5IkSZJUA0zQJUmSJEmqASbokiRJkiTVABN0SZIkSZJqgAm6JEmSJEk1wARdkiRJkqQaYIIuSZIkSVINMEGXJEmSJKkGmKBLkiRJklQDTNAlSZIkSaoBJuiSJEmSJNUAE3RJkiRJkmqACbokSZIkSTXABF2SJEmSpBpggi5JkiRJUg0wQZckSZIkqQaYoEuSJEmSVANM0CVJkiRJqgH11Q5AkqTe8Of7XuCP9zxPRPDdD7+LLUc1zts2c/YcvnbVI0x+423WWXk4Pzp0a4Y11PHCazP46l8eYdacueyz2RqctPdGAIx7agrn/uu/AJz6vk3Yc5PVATjvlgn8+8kpDK0bwpkf2Zp1V12+8m9UkiQNWNagS5L6vfYZs7nsjiJXHPcezjlsLIXrHl9g+1/un8SGq6/An0/YhQ1GrsBf7p8EwI/+/iRffP8mXHXiLtzxzFQmTHmLOXMTrTc+yWXH7Mhlx+zI/90wnjlzExOmvMUdz0zlqhN34dT3bcyP/v5kNd6qJEkawEzQJUn93oMvvM4OTasytH4I6666PNNnzeGdjjnztt818VX22WwNAN67+Rrc8+xrADzx0jR2HLMqAHtvmq1/dup0Rq+yPI3DG2gc3sDoVZbnuVenc9fEV9l70+wYO22wGuNfmlbhdylJkgY6E3RJUr/X/vZsGoc3zFtecVg97TNmd7u9cXgDr8+YBUBK84+xUr6+/e1ZCxxrpeH1vD5j9kLnmFuyryRJUm8wQZck9XuNwxuYNnN+Qv7mzA4al2/odvu0mbNZefmhAESwwD4rL99A4/ChCx0rW9/AtJkd89YPKdlXkiSpN5igS5L6vXevuwr3FV9n9py5vPjG24wYWsdy9XXztu+0wWqMe+oVAMY99Qo75c3aN197Je5/7rV8/RR2GrMqY0aO4IXXZvDmzNm8OXM2L7w2g6bVRrDzBqsy7qkpANz/3GtsvvZKFX6XkiRpoKvYKO5NLW37AecCdcDFxdbm1i7bI9/+QWAGcHSxtfmBcvaVJA1ujcs38Imd1+ewX95JRPCdA7bg8cnt3P7fqRy/54Z8dLvRnPaXR/johXewVuNwzvrI1gB87QOb8dWrHmb2nMRem6zORmusmK3fbzM+eck9817XDQk2WmNFdmhalUMvuIOGuuDMQ7ep2vuVJEnLqNC4QI5Job21y/aF8lMK7Q/0dVgVSdCbWtrqgPOA9wOTgHubWtquK7Y2P1FSbH9g4/yxE3ABsFOZ+0qSBrmP7bAuH9th3QXWvWudbKq1YQ11/PyIdy+0z3qrLc8Vx71nofV7b7YGe+eDypU65b0bc8p7N+6liCVJUlUUGhfKMSk0Xkehvcf8tK9Dq1QT9x2BCcXW5onF1uZZwBXAgV3KHAj8ttjanIqtzXcBKze1tK1d5r6SJEmSJJVjR2AChfaJFNoXm59SaE8U2u8CVqbQuHZfB1apJu6jgBdKliex8N2H7sqMKnNfACLiOOC4fDFFxNvLEHOfu+/Wm6sdgspTH0dd2dFzMVVbhKN2qSzDKO8G9VxgZh/H0pfq49YHvHb1A167KsfvXv2C37v6iX5w7RoeEfeVLF+UUroof70s+elLvR1oqUol6N399LpOULOoMuXsm63MPvCLutsmLa2IuC+ltH2145CkJeG1S1J/5LVLFbIs+WmfqlSCPgko7Rg4GphcZpmhZewrSZIkSVI5liU/7VOVStDvBTZuamkbA7wIHA4c2aXMdcDJTS1tV5A1L2gvtja/1NTS9koZ+0qSJEmSVI57gY0pNPaYn1JonJefUmjv0+btUKFB4oqtzR3AycA/gPHAlcXW5sebWtpOaGppOyEvdgMwEZgA/Ar43OL2rUTcUs5uE5L6I69dkvojr13qe4X2hXJMCu2PU2g8gULjYvPTvhYp9XkzekmSJEmS1INKTbMmSZIkSZIWwwRdkiRJkqQaYIIuSZIkSVINMEGXJKmfiYjo7rUkSerfTNAlSeonImJERIxIKaWI2AIgOdqrJEkDhgm6BqXOGqeI2DgiRkXE6HzZvwlJtWwH4IKIOBK4IiI2qnZAkiSp9zjNmgatiNgPOAu4B9gROCWldEtEDEkpza1udJLUvYi4CjgA+GhK6a8RUZdSmlPtuCRpUSIi8pY/uwFrA/UppT9WOy6pFllbqEEnMqOAHwAnpZQ+A3wP+HNE7GRyLqnGXQ78DvhyRIwyOZdU6/Lk/H3Ab4Am4LsRUYiI1aobmVR7TNA16OT9NV8GxgP35+v+DJwJfLiKoUnSQkq65OwYEYcA/8tvLN4CXBMRwyJil4j4UlUDlaRFiIh64Gjg2ymls4C9gLHAF6oXlVSbTNA1qETEuyPiyrzGaTXgjJLNrwLDqxOZJHUvr3k6APgVWdP2z0XEr4HvA/8Crs+3PVu9KCWpexGxNpCACcAaEbF8SmkycBrw3ohorGqAUo2pr3YAUl/r7PcEkFJ6MCJWiojPAh8B7o2IlYHHgE8BX6tepJK0sIioAw4h65Jze0SsDxwHnJZS+npE7Ai8kVJ6uvR6J0nVFhEjgZ+Q3VC8B/gEcFtEPAgMA94B7FoolbAGXQNeXvu0b0T8Ku97/m1gfaAR2Bl4Ii/65ZTS351TWFKNqQPWIWsOCvAicDewMUBK6Z6U0tP5a5NzSTUhIlZJKU0lS8wvAG7KX38F+DNwKfDzlNKb1YtSqj3WoGvA6lKTtDxwMPA6sArwFrBPSun3wNml+/kFV1I1lYx2PBp4J6X0SkT8H/CNiJiUUro2Il4D1skHWHrN65akWhIR2wJ/iYiLgF+Q5RxfSSm1RsSmwErArJTSw7b8kRbkNGsa0CJiJ2BoSum2iDgX+DuwJtmo7asAh6SU/lnNGCWpq4g4EPgS2U3Ff5HVPG0M/Cx//X6yqSHbqhakJJXommhHxF+BFchaAf0F2Ar4XkrpxSqFKPUL1qBrQImIDYGjgIfIRmgfDfw0Ik4kG7n9c8BBwEvAp4EZVQlUkhYhnyf460Bz/vwFYATwa2BPYBRwgTVPkmpBRCyXUnonb/mzPdmMOD8nu6G4HFlt+fvIxvoZChxTtWClfsAEXQNGRGxO1qfpP8DxwKUppb9ExP+Aj5KNcLwJ8NWU0v9FxO0ppel+wZVUYxqBU4D3ALsDX80fawO/SCnd1VnQa5ekasqbq58TEY8DT5HVlG8CnEg2W84qwI9TSldExHXAzKoFK/UTNnHXgJDPr3kW8HhK6eKIOBbYjKzGaTLQAWyTl1kZ2D2l9FqVwpWkxYqIocC5wO9SSndExE+AMcAXU0rFqgYnSUBEbABcC1xG1kpxz5TS8RExHNgW2JesBdAk4MiU0iP5flaMSIthDboGhJRSR0TMAZoj4nLg88DzwA+BV8iag96RNx3d1uRcUi1LKc2KiJlAISLOJhvB/esm55JqyJbAtSmln+TTP34+Ij5DVgF4MfD/IuJN4EOlO5mcS4tnDbr6vZIRj0cCvwemAXUppUPzplfHAo+llH5T1UAlqRsRMSSltNA8wBGxJtBC1vrnpymlv1U8OElahIg4BDgPOAEoAE8CNwMfB+5NKX01L7dGSmlKteKU+htr0NWvlSTny+Vzbe4XEQeRNasipfRUREwD3lXNOCWpU0RsTDbYW31K6cLuknOAlNL/gC/mcwm/HhGRr/fOuqSqyLvfDEkpzUwpXR0RK5N1v5mUUjoiL/Mg8LmSweNMzqUlMKTaAUhLKyLq8uT8A8DPIuKMiNgSuAPoiIgfRsTuwJFk06tJUlWVDGa5KvDNiDinZFt0KRv5TcjXIUvMTc4lVUtEDCGbXeJDEXFURNwGXE7WD31IRKyeFx0FbEg2grukJWSCrn4nIlYCSCnNiYidyZpV3QKsTzZ1xwHADWR3dE8FvpZS+ndVgpWkXEQ0AheRjYlxJvBB4O2I2BG6rRkfkt+EXCki9uqawEtSJeWtfe4Gvgv8GDgzryEvAg8DD0TE58kG5D0rpTStasFK/Zh90NWvRMQWwG/JmrCvSDbP5j0ppe9HxIpkteXbAKfmgyytlFKa5oihkqotIpYHNkopPZLXRD0KPEc2L/BTwDdSSu152br8JmQj8E/gCymlO6sVu6TBrfR7VEScTnaD8VLg6pTSK/n644HhZOP+3Ox3L2npmKCr38inUvs/4AXgKuADwA7549Ml03fcSfZl9p5qxSpJ3SlJvDcH9kopXZBf224Gbkkpfbdz0Lg8Ob8a+E5K6faqBi5p0IuIrYB2YCpZJcnvgH+nlFojYjtgekrpyWrGKA0ENnFXfzKHbC7NDwJ/JWtm9UOgjWwwkl0iYgywEtlI7pJUdaVN01NKc/KXE1JKF+TrOoBfATPy5bl5V56bMTmXVEWd16+I2Ae4Hvg1cDowAvgs8N6IOBf4N1lXQ0nLyBp09SsR8W6ywUgeBw7Km7FvCRxH1vf8IbLpiG61aZWkaouI+pRSR0R8ENgHmA78JqU0saSmfFfgAuDLKaV/5vutBaxnSyBJ1RYRuwBHk/U7B/gYsApZN8M3gZ2BV1JKd1clQGmAsQZd/c1Ushr0F4DzImLdlNJjwM/I7uo+D0ysYnySRESMiojGPDn/EHAGcCPZGBl/jIjN8uR8b+AXwDdLkvO6lNLLJueSqiEi1o+Ib+ev64BPAR8FXkopPUVWUfIacBrQlFK6vjM5dzBLadmZoKtfSSm9kFJ6HDiFbPqOb0TEeimlCcB1wEzglHzuTWvPJVVcRGwI3ARslA9euRNwFFmfzVWBccAVEbEBcA9wVErpbyXznM/p9sCSVBmTgbaIGJVfj74A3A78HiCl9ChZc/cpwFulO/rdS1p2NnFXzeoyYuiQfHoPSpqFNpDNv/kmcEJKaXbe3P3llNLU6kUuabDKk+xPA+sCF5M1a7+Z7IbiFWSJehF4EKgDtk0pzaxKsJLURZfvW7cDL6SUjoiI4WRjZQxPKR2ab18hpfTWYg4naSlYg66aExEjImJEPv/vFjBv7k06X+f/QGaTfdn9Wf6alNJjJueSqiW/qXgncBJZ7fg9KaXJQAfZtGqvAjuSDXR5lMm5pFpS+n0LeC+wZkRcnFJ6m2xQOCLib/n26ZWOTxoMTNBVi3YALoiII8magW7UtUCepNellGanlB6ufIiStLC8Br1INq85wOr581vASOAssmkib0spPVjxACWpDPkAl+8A+wObRMQv8yT948C3websUl+xibtqUkRcRTYq+0dTSn/tnDu4S5nO+YRXANZMKT1TlWAlqYu8C86uwKXAV1JKV0XEqsBaQF3eh1OSqm4xXQo7v2cNJWsZ9EhK6ZhqxioNBvXVDkBahMuBN4AvR8R9KaUXSzeWTF20Mln/zs9UPkRJWlDJF905KaVxEXEqcFZ+zfoT2cjHklR1ETECIKU0PSK2SCk90aVL4Zw8SZ+VT7X27qoFKw0iNnFXTegcvTgidoyIQ4D/pZQ+A9wCXBMRwyJil4j4EkCenDcCfwG+aDN3SbWgsxYq74YTKaW/Al8Hzo6INasbnSQtoJwuhZ1J+jsppbsqH6I0+NjEXTUjIg4Avg88AAwlmzLtBOB7ZP9E1iabK/javOb8eqAlpXR7dSKWpPkW0RUn8gEv10gpTalWbJLUHbsUSrXHBF01ISLqyKYk+nVK6faIWB84DpieUvphROwIvJFSejov/15gdkrp1upFLWmwKkm8tyGbUu2f+YBK3ZWtTyl1lO5XyVglaVHyVovNwMbAEeV0KbTVotS3bOKuWlEHrAOMzZdfBO4m+4dBSumekuQ8Ukr/MjmXVC15cv5+sunSTgQeiIgNu5bLa546ImLliDjI5FxSNdmlUKp9JuiqipJ/EKMjYvWU0izg/4AP519iO8gGU1onIlbrLA9O6yGp+iJiE7LBKQ9OKTWTdbn5dURsXFKms1loI9BGNvClJFVNfnPxAOBXZE3bPxcRvybrYvgvsmvZr4CJAHnNeRtQSCndVpWgpUHGJu6qmog4EPgS8DrZP4WbyGrMf5a/fj9wSkqprWpBSlKJ/GbhMKAFOAQ4O6X0m3zbD4F9gSNLWvysTFbzVHC8DEnVZpdCqfY5zZqqIiJ2IxvZuDl//gIwAvg1sCcwCrggpfSwfTYlVVvJdWj5fEqiHwIdwJYRsU9K6d8ppW9ERD2war7PCLJmo6eYnEuqEaVdCm9nfpfCgyHrUthZsLNLYRVilAY1m7irWhqBU4D3ALsDXwUOAr4FDEsp3dXZz8nkXFK1lTQL/UtEXAscA/wSeAt4f0R8IC/31S5TEX3SZqGSqsUuhVL/Y4KuqsibrT9EVoP+xZTS1cAdwHrA7CqGJkkLyVv9fIOsKeh44PP5tGmXAgnYNyJWKykfKaXpKaVHqxKwJDHv5uKBwOXAryLi88BLZN0JfxIRFwK/AX6WUnrVpFyqPvugq6oi4qfAu4Czyfp0fj2ldHd1o5KkBadEi4gPkiXiI4Avk/UzfzYiVgfmAqumlP5bvWglaWH5zcWzmd+l8CDyPuhk42mMAt62S6FUO0zQVRERMSSlNLeb9WuSJebbAD9NKf2t4sFJ0iJExHuACWTXqB+TNQX9aEppakTsD3wS+GxK6a0qhilJ3YqIZuAVYA3g28CPyLoV3g38whuLUu1xkDj1iXyqoT2B+pTShd0l5wAppf8BX4yIVVJKr3f2ffIOrqRq6VKLdCiwXUpp74i4E9gKGBYRBwE/BE4zOZdUq1JKbRExFDiXrEvhHXmt+hjsUijVJPugq9dFxObAn8lGMv5mRJxTsi26lI38y/DrkCXmJueSqinvs7ljRAxNKX0FuD0iPplSOgF4HDgdOBr4Uv7lNxZ3PEmqpnxguJlAISL2JRvBvTWlVKxmXJK6ZxN39aqIaASuB36fUvplRGwFHAlcUzp1R0n5upTSnIhYCdgW+I8JuqRqiYghwFDgv8DzwB+AAJYjG0Rpdl5meEppevUilaSF2aVQ6v9M0NWrImJ5YKOU0iP5l9hHgefIvvA+BXwjpdSel+1MzhuBfwJfSCndWa3YJSki1kopvRwRhwH7ALcBp5INDvf7lNIP8nIOpiSp6rp2KSyjvF0KpRpnE3f1qpTSDLImoACbkg1A8kFgP7LR2k+FeXd4O5Pzq8maipqcS6qa/IvuTRHxWbKbi2uSTQf5WWAKcGxErGtyLqkW2KVQGpisQVev6O4La0Q0pJRmlywfBayTUjorX14J+BfZoCW3VzRgSWL+tSsitgYOy1fvRDZn8K7A+sBHyWrQh6eUJlQnUkmazy6F0sBlgq5lFhH1KaWOfJ7gfYDpwG9SShM7+0JFxK7ABcCXU0r/zPdbC1ivu38kklQpEfEh4PPAKmRTqj0EvEE2LdH3gJNTSudXKz5J6souhdLAZRN3LbWIGBURjXly/iHgDOBGsgFI/hgRm+XJ+d7AL4BvliTndSmll03OJVVTRKxBNnDSF1JKOwL/D1idbMTjHwNnAuOrF6EkLcwuhdLAZYKupRIRGwI3ARtFxIpkTUKPAlYk6ws1DrgiIjYA7gGOSin9rWRQkjlVCVySFjQbqCOrLQe4CGgETgAOIruxeEtE1FUnPEmar7Rvecl3qQkppQvydR3Ar4AZ+fLcvFn7zcB37FIo1T6buGuJ5f8cPg2sC1xM1qz9ZrJpiK4gS9SLwINkX3y3TSnNrEqwktSDiPgSsAJwdUrpsYh4H9k85y8BzwKXeA2TVG12KZQGB2vQtcTyQUXuBE4iqx2/J6U0Gegg6wP1KrAj8FeymnO/2EqqZVcCDcCPI+IHwM+AS4AngfXIbj5KUlXYpVAaXKxB1xLLa9CHkzVx3wA4LKV0W0SsAlwKTAWagaNTSv+oXqSSVJ68q857gC2BB0uata+YUnqjqsFJGrTyLoXXAZ8Enga+SjbLxGbAl8gqTD4AHAL8D1g/pfSE00FK/ZcJupZaRDSQTUN0KfCVlNJVEbEqsBZQl1J6tKoBStJS6hz1uNpxSBq87FIoDU42cdcSKxmgZE5KaRzZSKH/FxGHpZReSyk9YXIuqT8zOZdUbXYplAYnE3Qtsc4mU3l/p0gp/RX4OnB2RKxZ3egkSZL6v7xCpEg2rzlkU0ACvAWMBM4CrgJuSyk9WPEAJfUJm7hriXTX7LOzn1NErJFSmlKt2CRJkgYauxRKg4sJuhapJPHehqz/0z9TSu8somx9PvcmDkwiSZK0bEq+h3VOoXYgWa35t1NKf6p2fJL6hk3ctUj5P4X3k/VtOhF4IB9NdAF5rXpHRKwcEQeZnEuSJC0buxRKg5MJuhYpIjYBPgMcnFJqBq4Hfh0RG5eUqUspzYmIRqANeKMqwUqSJA0Q+TSP8+SVJpFSugrYLqX0vyqFJqmPmaBrIZEZTjZ9x7uArQFSSl8D7gD+mCfv5Mn5ymSDlHwtH9VdkiRJZeicHScitomID0XEct3NJJEn6fWd4/2UzKojaQAxQdc8JRf65VNKbwM/BK4EtoyIfQBSSt8A/g2smu8zArgF+G5K6fbKRy1JktR/2aVQUikTdM2T/4M4APhLRFwLHAP8kmw6j/dHxAfycl9NKd1VsusnU0q3VTxgSZKkfs4uhZJKmaBrnojYDfgGcBwwHvh83ozqUiAB+0bEaiXlI6U03ek9JEmSloxdCiV1x2nWBrnSKdEi4oNkifgI4MvAkSmlZyNidWAusGpK6b/Vi1aSJKl/K5k+bURKaXpELAd8FVgJuDGl9O+83JnA1Smlu/IuhbcDp9hqURrYTNBFRLwHmABsA/wYeA34aEppakTsD3wS+GxK6a0qhilJkjQg5F0KPwe8A/wduJqs//kwYFxK6R9dyo8ANrDVojTw2cR9kOoy8uehwJUppZuBO4GhwLCIOIgsYf+9ybkkSdKys0uhpMWxBn0Qi4gdgYdSSrMi4gzgvyml30bERXmRNYALU0p/L20KL0mSpPLZpVBSuUzQB6GIGEJWS/5f4HngD0AAywE/SynNzssMTylNr16kkiRJA4NdCiWVwybug9MaKaWZwFeAx4B24GjgWLJBSkgpzQVmVCtASZKk/s4uhZKWlDXog0w+p+ZVwM+B/wf8EPgW0ACcA6wH7AFMskm7JEnSsrFLoaQlYYI+CJRM57E1cFi+eifgcmBXYH3go2R9oYanlCZUJ1JJkqSBwS6FkpZGfbUDUN/Lk/MPAZ8HViHr/3QTWa15Efg02QAl51ctSEmSpIFljZTSyxHxFWAfsi6Fp5JViAwDfpBSmhsRdimUNI990AeBiFgDaAG+kFLakaxp++rATLI+T2eSTfMhSZKkZZR3KbwpIj4LPAqsCTwEfBaYAhwbEevapF1SVzZxHwQiYhXgBuBrKaVbI6IBOA/Ykqwv+pUppTkRUZdSmlPNWCVJkvojuxRK6g3WoA8CKaXXgT8De0XEliml2cCVwERgW+D4iBhmci5JkrR0SroUngW8HxjDgl0K30vWpfBFk3NJi2If9MHjSuB44McRcR9wMHAy2T+PjckGLJlZvfAkSZL6r5IuhcemlJ6MiJOAJuBlsi6FK2CXQkk9sIn7IBIRKwLvIWva/mBK6ZaIqANWTCm9UdXgJEmS+jG7FErqDSbog5j/ICRJknpPRHyJrKb86pTSYxHxPuBo4CXgWeCSlJItFiUtkn3QBzGTc0mSpF51JVmf8x9HxA+AnwGXAE8C65F1KZSkRbIGXZIkSeoldimUtCxM0CVJkqQ+YpdCSUvCBF2SJEmSpBpgH3RJkiRJkmqACbokSZIkSTXABF2SJEmSpBpggi5JkiRJUg0wQZckaYCJiL0iIkVEsdqxSJKk8pmgS5K0DCKimCfDXR9jqxjWJOBc4JIqxkBEjMs/i6OrGYckSf1FfbUDkCRpgLgeeKZk+ZVqBBERDSmlCcCp1Ti/JElaetagS5LUO36dUjq18wGMiIjpEfFWRIyJiKER8Xheo3wYQElt+8kR8UxEvBERv46I4Z0HjYgPR8Q9ETEtIp6LiB9HxPL5tnlN2SOiEBGvAhd1beIeEU0l5/p8RLwcEf+LiE9ExKER8XxEvBIRLSXnrY+I0yJifP4+noiIz5ZsL+TH+0tE/DZ/nxMi4n359nHAnnnxS/Oyhb78AUiS1N+ZoEuS1Ds+ExHndD5SSk8DpwEjgIuA04EtgD+klP7UZd9vA/8BZgGfBr4PEBEfAP4KjMmfpwJfAs7rsv/6wLHAVcCjPcR5KnA3sAbwK+AXwK3AasAPI2KTvNwZwJlAAH8GViBL/j/V5XiHAusAjwEbMr9Z/V+AF/PX/yRrcn9XD7FJkjSoRUqp2jFIktRv5bXU63ddn1KKfPuNwH5AIktYt0opvZFv6/wnfFBK6a8RcSBwLTA1pbR6RLQBHyRLcJ8AhgIn5sdaAdgRuCVf3iRv2k5E7JWvfy6l1BQRTcCz+bl2J0uU3ybr6nZSSun8iLgf2Bb4GFlyPS0/x6X56w2AA4C7U0o757Xh3wEeB7YCmoCJ+TlWTylNLalFPyaldNkSfKySJA1K9kGXJKl3HJxSurab9WeSJegBXNKZnHcxPn9+Mn8eGRHLkSW9AO/PH52CLGHu9L/O5LwM41NKHRExHWgEnsrXv5k/jwBGkiXnAMd02X+jLssPpZRSRLxRsm4Fstp+SZK0BGziLklSH4mIBuDH+eI7wJciYoNuim6eP2+WP09NKb0DFPPlU1JK0fkANkwpPVay/ztLENacHpYhS66n56+3LjnvEGD7LmU78ufumuR1HtvvG5IklcEadEmSesdn8qblnX4NHAG8G/g9cAdwPvCbiNgzpTS3pOwvI+LDZE3IAX6XP/+CrIn7mRGxC1mz9K3J+ouP6aP3QV4jfh7wVeCfEfE3slrxncn6yh9d5qFeyJ+/EBFbA5emlB7u7XglSRooTNAlSeodH+qyPIUswf0f8AXgdbIB1d5LNnjcj0rKnp6vWw74DfAtgJTSjRFxMNBClqgn4GmyAdf62reAV8mS8Y+T9UN/EOg6wN3i/JjsBsUWZDcWbgdM0CVJWgQHiZMkqUpKBokbk1IqVjMWSZJUffYJkyRJkiSpBpigS5IkSZJUA2ziLkmSJElSDbAGXZIkSZKkGmCCLkmSJElSDTBBlyRJkiSpBpigS5IkSZJUA0zQJUmSJEmqAf8fG0VBpwSvv0YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment</th>\n",
       "      <th>lpips_mean</th>\n",
       "      <th>lpips_std</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>qwen_fa3_aot_fp8wo</td>\n",
       "      <td>0.250019</td>\n",
       "      <td>0.097427</td>\n",
       "      <td>1.218563</td>\n",
       "      <td>0.021344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qwen_fa3_aot</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.141877</td>\n",
       "      <td>0.020095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qwen_fa3_aot_int8wo</td>\n",
       "      <td>0.189330</td>\n",
       "      <td>0.080866</td>\n",
       "      <td>1.122642</td>\n",
       "      <td>0.021759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            experiment  lpips_mean  lpips_std  time_mean  time_std\n",
       "1   qwen_fa3_aot_fp8wo    0.250019   0.097427   1.218563  0.021344\n",
       "0         qwen_fa3_aot    0.000000   0.000000   1.141877  0.020095\n",
       "2  qwen_fa3_aot_int8wo    0.189330   0.080866   1.122642  0.021759"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = compare_sets_with_timing(\n",
    "    ExperimentSet.create(\n",
    "        \"qwen_fa3_aot\",\n",
    "        \"qwen_fa3_aot_fp8wo\",\n",
    "        \"qwen_fa3_aot_int8wo\",\n",
    "        # \"qwen_fa3_aot_fp8da\",\n",
    "        # \"qwen_fa3_aot_int8da\",\n",
    "    ),\n",
    "    profile_target=\"loop\",\n",
    "    sort_by=\"time\"\n",
    ")\n",
    "\n",
    "df_all\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "06c65a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model from: /home/ubuntu/.local/lib/python3.10/site-packages/lpips/weights/v0.1/alex.pth\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment</th>\n",
       "      <th>lpips_mean</th>\n",
       "      <th>lpips_std</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qwen_fa3_aot</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.141877</td>\n",
       "      <td>0.020095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>qwen_fa3_aot_fp8da</td>\n",
       "      <td>0.405543</td>\n",
       "      <td>0.095514</td>\n",
       "      <td>1.066381</td>\n",
       "      <td>0.022632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qwen_fa3_aot_int8da</td>\n",
       "      <td>0.606926</td>\n",
       "      <td>0.063545</td>\n",
       "      <td>0.995964</td>\n",
       "      <td>0.019301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>qwen_fa3_aot_fp8darow</td>\n",
       "      <td>0.415502</td>\n",
       "      <td>0.086364</td>\n",
       "      <td>0.903224</td>\n",
       "      <td>0.092046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              experiment  lpips_mean  lpips_std  time_mean  time_std\n",
       "0           qwen_fa3_aot    0.000000   0.000000   1.141877  0.020095\n",
       "1     qwen_fa3_aot_fp8da    0.405543   0.095514   1.066381  0.022632\n",
       "2    qwen_fa3_aot_int8da    0.606926   0.063545   0.995964  0.019301\n",
       "3  qwen_fa3_aot_fp8darow    0.415502   0.086364   0.903224  0.092046"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = compare_sets_with_timing(\n",
    "    ExperimentSet.create(\n",
    "        \"qwen_fa3_aot\",\n",
    "        # \"qwen_fa3_aot_fp8wo\",\n",
    "        # \"qwen_fa3_aot_int8wo\",\n",
    "        \"qwen_fa3_aot_fp8da\",\n",
    "        \"qwen_fa3_aot_int8da\",\n",
    "        \"qwen_fa3_aot_fp8darow\",\n",
    "\n",
    "        # \"qwen_fa3_aot_qkvint4oint8\",\n",
    "        # \"qwen_fa3_aot_qkvfp8oint8\",\n",
    "\n",
    "        # \"qwen_fa3_aot_fp8darow_nolast\",\n",
    "        # \"qwen_fa3_aot_fp8darow_nolast_cint8\",\n",
    "        # \"qwen_fa3_aot_fp8darow_nofirstlast\",\n",
    "    ),\n",
    "    profile_target=\"loop\",\n",
    "    sort_by=\"time\"\n",
    ")\n",
    "\n",
    "df_all\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "31dea8be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model from: /home/ubuntu/.local/lib/python3.10/site-packages/lpips/weights/v0.1/alex.pth\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment</th>\n",
       "      <th>lpips_mean</th>\n",
       "      <th>lpips_std</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qwen_fa3_aot</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.141877</td>\n",
       "      <td>0.020095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>qwen_fa3_aot_int8wo</td>\n",
       "      <td>0.189330</td>\n",
       "      <td>0.080866</td>\n",
       "      <td>1.122642</td>\n",
       "      <td>0.021759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qwen_fa3_aot_fp8darow</td>\n",
       "      <td>0.415502</td>\n",
       "      <td>0.086364</td>\n",
       "      <td>0.903224</td>\n",
       "      <td>0.092046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>qwen_fa3_aot_qkvint4oint8</td>\n",
       "      <td>0.262008</td>\n",
       "      <td>0.097016</td>\n",
       "      <td>1.103839</td>\n",
       "      <td>0.021350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>qwen_fa3_aot_qkvfp8oint8</td>\n",
       "      <td>0.222709</td>\n",
       "      <td>0.088010</td>\n",
       "      <td>1.096008</td>\n",
       "      <td>0.021663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  experiment  lpips_mean  lpips_std  time_mean  time_std\n",
       "0               qwen_fa3_aot    0.000000   0.000000   1.141877  0.020095\n",
       "1        qwen_fa3_aot_int8wo    0.189330   0.080866   1.122642  0.021759\n",
       "2      qwen_fa3_aot_fp8darow    0.415502   0.086364   0.903224  0.092046\n",
       "3  qwen_fa3_aot_qkvint4oint8    0.262008   0.097016   1.103839  0.021350\n",
       "4   qwen_fa3_aot_qkvfp8oint8    0.222709   0.088010   1.096008  0.021663"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = compare_sets_with_timing(\n",
    "    ExperimentSet.create(\n",
    "        \"qwen_fa3_aot\",\n",
    "        # \"qwen_fa3_aot_fp8wo\",\n",
    "        \"qwen_fa3_aot_int8wo\",\n",
    "        # \"qwen_fa3_aot_fp8da\",\n",
    "        # \"qwen_fa3_aot_int8da\",\n",
    "        \"qwen_fa3_aot_fp8darow\",\n",
    "\n",
    "        \"qwen_fa3_aot_qkvint4oint8\",\n",
    "        \"qwen_fa3_aot_qkvfp8oint8\",\n",
    "\n",
    "        # \"qwen_fa3_aot_fp8darow_nolast\",\n",
    "        # \"qwen_fa3_aot_fp8darow_nolast_cint8\",\n",
    "        # \"qwen_fa3_aot_fp8darow_nofirstlast\",\n",
    "    ),\n",
    "    profile_target=\"loop\",\n",
    "    sort_by=None\n",
    ")\n",
    "\n",
    "df_all\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4efef8a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model from: /home/ubuntu/.local/lib/python3.10/site-packages/lpips/weights/v0.1/alex.pth\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment</th>\n",
       "      <th>lpips_mean</th>\n",
       "      <th>lpips_std</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qwen_fa3_aot</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.141877</td>\n",
       "      <td>0.020095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>qwen_fa3_aot_int8wo</td>\n",
       "      <td>0.189330</td>\n",
       "      <td>0.080866</td>\n",
       "      <td>1.122642</td>\n",
       "      <td>0.021759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qwen_fa3_aot_fp8darow</td>\n",
       "      <td>0.415502</td>\n",
       "      <td>0.086364</td>\n",
       "      <td>0.903224</td>\n",
       "      <td>0.092046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>qwen_fa3_aot_fp8darow_nolast</td>\n",
       "      <td>0.306802</td>\n",
       "      <td>0.120262</td>\n",
       "      <td>0.892194</td>\n",
       "      <td>0.032847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>qwen_fa3_aot_fp8darow_nofirstlast</td>\n",
       "      <td>0.285035</td>\n",
       "      <td>0.114618</td>\n",
       "      <td>0.898829</td>\n",
       "      <td>0.033120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          experiment  lpips_mean  lpips_std  time_mean  \\\n",
       "0                       qwen_fa3_aot    0.000000   0.000000   1.141877   \n",
       "1                qwen_fa3_aot_int8wo    0.189330   0.080866   1.122642   \n",
       "2              qwen_fa3_aot_fp8darow    0.415502   0.086364   0.903224   \n",
       "3       qwen_fa3_aot_fp8darow_nolast    0.306802   0.120262   0.892194   \n",
       "4  qwen_fa3_aot_fp8darow_nofirstlast    0.285035   0.114618   0.898829   \n",
       "\n",
       "   time_std  \n",
       "0  0.020095  \n",
       "1  0.021759  \n",
       "2  0.092046  \n",
       "3  0.032847  \n",
       "4  0.033120  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = compare_sets_with_timing(\n",
    "    ExperimentSet.create(\n",
    "        \"qwen_fa3_aot\",\n",
    "        # \"qwen_fa3_aot_fp8wo\",\n",
    "        \"qwen_fa3_aot_int8wo\",\n",
    "        # \"qwen_fa3_aot_fp8da\",\n",
    "        # \"qwen_fa3_aot_int8da\",\n",
    "        \"qwen_fa3_aot_fp8darow\",\n",
    "\n",
    "        # \"qwen_fa3_aot_qkvint4oint8\",\n",
    "        # \"qwen_fa3_aot_qkvfp8oint8\",\n",
    "\n",
    "        \"qwen_fa3_aot_fp8darow_nolast\",\n",
    "        # \"qwen_fa3_aot_fp8darow_nolast_cint8\",\n",
    "        \"qwen_fa3_aot_fp8darow_nofirstlast\",\n",
    "    ),\n",
    "    profile_target=\"loop\",\n",
    "    sort_by=None\n",
    ")\n",
    "\n",
    "df_all\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15b6d974",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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