clip_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "qiZxw1FmZRuB"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: transformers in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (4.30.2)\n",
      "Requirement already satisfied: torch in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (1.11.0)\n",
      "Requirement already satisfied: filelock in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from transformers) (3.12.2)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from transformers) (0.16.4)\n",
      "Requirement already satisfied: numpy>=1.17 in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from transformers) (1.23.5)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from transformers) (22.0)\n",
      "Requirement already satisfied: pyyaml>=5.1 in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from transformers) (6.0)\n",
      "Requirement already satisfied: regex!=2019.12.17 in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from transformers) (2023.6.3)\n",
      "Requirement already satisfied: requests in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from transformers) (2.31.0)\n",
      "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from transformers) (0.13.3)\n",
      "Requirement already satisfied: safetensors>=0.3.1 in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from transformers) (0.3.1)\n",
      "Requirement already satisfied: tqdm>=4.27 in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from transformers) (4.65.0)\n",
      "Requirement already satisfied: typing-extensions in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from torch) (4.8.0)\n",
      "Requirement already satisfied: fsspec in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.12.1)\n",
      "Requirement already satisfied: colorama in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tqdm>=4.27->transformers) (0.4.6)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->transformers) (3.1.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->transformers) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->transformers) (1.26.16)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->transformers) (2022.12.7)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages)\n",
      "\n",
      "[notice] A new release of pip is available: 23.3.1 -> 23.3.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "teILNF2YZW-8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pydantic\\_internal\\_fields.py:149: UserWarning: Field \"model_server_url\" has conflict with protected namespace \"model_\".\n",
      "\n",
      "You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.\n",
      "  warnings.warn(\n",
      "c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pydantic\\_internal\\_config.py:321: UserWarning: Valid config keys have changed in V2:\n",
      "* 'schema_extra' has been renamed to 'json_schema_extra'\n",
      "  warnings.warn(message, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "from transformers import CLIPProcessor, CLIPModel\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "FtySma1zZOCE"
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from PIL import Image\n",
    "import shutil\n",
    "import os\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams.update({'pdf.fonttype': 'truetype'})\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "9626d5793c4945d490bb8d404cf9135b",
      "b116aabfa0434162b88ca76fd130143c",
      "0f407b11fe7c4088bc4b74f5b1c3cf98",
      "549534e4209747f8a46c3d74c13c4fd0",
      "a4b79101a6264969ae76aaf3a5c601c3",
      "5dff26e881ab4a238571f53ce552457c",
      "b02b1a2c2511453a8bd747110a3e8751",
      "909d1de5cb0d4c9290a4f07b133602ab",
      "20e5fb6c1d304d3784ca9ce3029d6475",
      "e2de6858f8ff4185b6c3e2341fdb5342",
      "31e50a9e9b0b41038aaf61b6b2545eae",
      "910d142a4051466ebbc7489f0bc70b18",
      "2d491944d6d842c49642bda3b71f41c1",
      "fc342a521465480781fde984ac5addac",
      "b77e6c39b94247a1b6b8f4977969249c",
      "7d618bcf3cd246149bcd6ade71fb2134",
      "c197bca1cf494167ae2811f9d27b83a0",
      "a97841dcef32419eacded75f1c29fb3a",
      "0a9b07ae0494464fbdd64604f9285bba",
      "9b4207ce4268455eb6d9e6903e62d508",
      "8c3936cd3c2b4b51879f197e2c3a4a23",
      "6c8633070d3946e0a474ae5923494bbb",
      "423c5757d8504cf7bf012602a5402a65",
      "79d4a6fff8dc481d9ad5fcd7a40d4401",
      "4432bf5237264a5f9a86addc732757c4",
      "562b81b168874220a3e3df28baefaf1f",
      "1375ead1b20642dab07347917fc243f3",
      "a79d6ea298464884987d46e728f83547",
      "f9c3e9753edc4761926aa05d146c23c2",
      "1d4d9939f5364d1ba478e703fa058d49",
      "0cdafbda19384d5095a66f9bcc689224",
      "185b7cddcce048f8816cab4d7802927a",
      "0c6416dd3aa247689d3be44a7ddcb0bf",
      "481badd6e8814883a72e13576c96d360",
      "66432a1ec191454ea590fc845820ebfc",
      "88cd7d33a37a4b0caf8cd731bf0e0ea0",
      "6fd2c302d2d041578a8e89dc41344d45",
      "bb06c8556b9840fab73cfca87ed30293",
      "35816dbcf41e4ae8874f20bc0d9a53a9",
      "5a780a1e435e49c6b683defdf332e1bd",
      "310a5b7f438d49a385e527651817dbe1",
      "b1ba2edd7a0b49c2884efbb064e8e0db",
      "a2bb3c3cd786492bbe67466d5ed2037b",
      "6e369f81de2344b397c2e12407fd9c80",
      "628665a2ee534b10a97edca1e422222a",
      "173a8ad645fa473992d9fe34f0240dde",
      "c2f5c46882ae44b29e0eef57200c309e",
      "6b26c07e4e5249a2a95eb2ce097c689f",
      "d0855d3098ee4b34a3343cbb8d620caa",
      "7e47a501ab324eef879a5b170c140180",
      "0c8132f9c02a4a38bb4968185d4b4823",
      "76e1654f77354b70bb5e7999733244c1",
      "e7e43dbfd90947178665741e09087df8",
      "a833ed19b40f4f8bbdc4c81bddc35d14",
      "2b27967a8e1b4bb5a890c62d5fa56e9c",
      "f4e61479e89b49b599c04f53a7ddcd99",
      "42d1c341c71948928681151575c63c70",
      "6a459f78c07241ee91168210dac47159",
      "d281157460a14a35980444d1bddefd9c",
      "18369f4b9def452094a0301cd440c6e6",
      "85c471edbf68497faf73bb6d2d99b5d3",
      "430dc21cfb624a2581678fbbc2c8f54e",
      "426d55dc50854faaa55b47df79e04758",
      "86a6ee1b58b54d179099c690e9e09348",
      "68241006363a4dc8b7e8daca3dc88193",
      "3c031da1ec3a43e8ab992ce931b4f873",
      "a7fa2f3e15d143728b756f5138f783e7",
      "7755479a4e63438e93c5bd8212bf4fcd",
      "37f0add343f14fc19a0bb017ba5d5085",
      "8f3dbb4682be4c6695bcaa31f75a3e6d",
      "9b46bda89b8640c286a98d8ab36dd834",
      "83536728637644208dd5351c34a69016",
      "821cf3c8abff4e2294ac6b6ea21a1e58",
      "53deda350140485481d9499c85cf663d",
      "0104e07a3b2d4e1698443ff15746ebef",
      "44b5e736d7b2490da03a3d59928b0f67",
      "874db23882294d94aab8e5c7f7b81b53",
      "4f5025d3eb804e8486d38539b4b6fa84",
      "9a2b34e4b21947cd8a6ebd101952343f",
      "e046b0189d6349a2827ea994231150be",
      "f8c22041d23d4b43b7a53ae8acc19ed5",
      "6a3278a2520948438f7356259da1327d",
      "2cddf874788141cba26c1dee82703442",
      "a32357d77c4e4591b4661a67d87720d2",
      "31800f95310b4154aef30bfba659cd58",
      "8f0024b9a26c4d6d8d8437ae17369f10",
      "d0993928ea0b43e2b8bab4f43965ac83",
      "afc004423fb743739cd0955b30d06193"
     ]
    },
    "id": "gMKGcIXEZffL",
    "outputId": "d2b1ecc1-4461-486d-dff2-2b1163cfa12d"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a816ac0a4604478a905f34776475664",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)rocessor_config.json:   0%|          | 0.00/316 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\huggingface_hub\\file_download.py:133: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\aditb\\.cache\\huggingface\\hub. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to see activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
      "  warnings.warn(message)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f5efa982ba14a85a993d4dfab3bd17d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading tokenizer_config.json:   0%|          | 0.00/568 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a7d13e4e3d9642aea713d0933cf38455",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading vocab.json:   0%|          | 0.00/862k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a8f2ac1c6a2f49548d3d0ab883f363a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading merges.txt:   0%|          | 0.00/525k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0900925d5d7491eba0bbcc49ebee477",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading tokenizer.json:   0%|          | 0.00/2.22M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41aa579d4eec4a64ac4bebb269064e01",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/389 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "376e36b7bca34d15be0b875b02474012",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading config.json:   0%|          | 0.00/4.19k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "712f62a895a14233b02a5b4d0d8f7fe5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/605M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "CLIPModel(\n",
       "  (text_model): CLIPTextTransformer(\n",
       "    (embeddings): CLIPTextEmbeddings(\n",
       "      (token_embedding): Embedding(49408, 512)\n",
       "      (position_embedding): Embedding(77, 512)\n",
       "    )\n",
       "    (encoder): CLIPEncoder(\n",
       "      (layers): ModuleList(\n",
       "        (0): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (1): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (2): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (3): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (4): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (5): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (6): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (7): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (8): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (9): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (10): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (11): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (vision_model): CLIPVisionTransformer(\n",
       "    (embeddings): CLIPVisionEmbeddings(\n",
       "      (patch_embedding): Conv2d(3, 768, kernel_size=(32, 32), stride=(32, 32), bias=False)\n",
       "      (position_embedding): Embedding(50, 768)\n",
       "    )\n",
       "    (pre_layrnorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "    (encoder): CLIPEncoder(\n",
       "      (layers): ModuleList(\n",
       "        (0): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (1): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (2): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (3): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (4): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (5): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (6): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (7): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (8): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (9): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (10): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (11): CLIPEncoderLayer(\n",
       "          (self_attn): CLIPAttention(\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (mlp): CLIPMLP(\n",
       "            (activation_fn): QuickGELUActivation()\n",
       "            (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (post_layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (visual_projection): Linear(in_features=768, out_features=512, bias=False)\n",
       "  (text_projection): Linear(in_features=512, out_features=512, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_id = \"openai/clip-vit-base-patch32\"\n",
    "\n",
    "processor = CLIPProcessor.from_pretrained(model_id)\n",
    "model = CLIPModel.from_pretrained(model_id)\n",
    "\n",
    "# move model to device if possible\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sIqH41QKajHN",
    "outputId": "83236fd5-e8a3-4af9-dfa0-6e9152c9db4d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n",
      "Cloning into '/content/images'...\n",
      "remote: Enumerating objects: 128, done.\u001b[K\n",
      "remote: Counting objects: 100% (66/66), done.\u001b[K\n",
      "remote: Compressing objects: 100% (55/55), done.\u001b[K\n",
      "remote: Total 128 (delta 7), reused 64 (delta 6), pack-reused 62\u001b[K\n",
      "Receiving objects: 100% (128/128), 47.56 MiB | 28.77 MiB/s, done.\n",
      "Resolving deltas: 100% (12/12), done.\n"
     ]
    }
   ],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')\n",
    "# !git clone https://github.com/ikatsov/tensor-house-data.git  /content/images\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LX-aidrE41zu"
   },
   "outputs": [],
   "source": [
    "# zip_path = '/content/drive/MyDrive/fashion_images.zip'\n",
    "# !unzip -q '/content/drive/MyDrive/fashion_images.zip' -d '/content/extracted_images_folder/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(Path('../Data/images/fashion-dataset/images/').exists())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 825
    },
    "id": "Yf8fIw5tZ_6V",
    "outputId": "b405ed21-19dc-42e1-c89a-f6fe4e851712"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Images found: 44441\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Set the path to the images\n",
    "image_paths = glob.glob('../Data/images/fashion-dataset/images/*.jpg')\n",
    "\n",
    "# List all JPGs in the folder\n",
    "images_files = image_paths\n",
    "\n",
    "# Print some statistics\n",
    "print(f'Images found: {len(images_files)}')\n",
    "\n",
    "# Display images\n",
    "rows, columns = (4, 4)\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "grid = ImageGrid(fig, 111,\n",
    "                 nrows_ncols=(rows, columns),\n",
    "                 axes_pad=0.1,\n",
    "                 )\n",
    "\n",
    "for ax, im in zip(grid, images_files[:rows*columns]):\n",
    "    ax.imshow(Image.open(im))\n",
    "    ax.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U-uJMC3icCat",
    "outputId": "499f0400-1bc0-414e-f32d-aa9a71d6657c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing batch 1/2778\n",
      "Processing batch 2/2778\n",
      "Processing batch 3/2778\n",
      "Processing batch 4/2778\n",
      "Processing batch 5/2778\n",
      "Processing batch 6/2778\n",
      "Processing batch 7/2778\n",
      "Processing batch 8/2778\n",
      "Processing batch 9/2778\n",
      "Processing batch 10/2778\n",
      "Processing batch 11/2778\n",
      "Processing batch 12/2778\n",
      "Processing batch 13/2778\n",
      "Processing batch 14/2778\n",
      "Processing batch 15/2778\n",
      "Processing batch 16/2778\n",
      "Processing batch 17/2778\n",
      "Processing batch 18/2778\n",
      "Processing batch 19/2778\n",
      "Processing batch 20/2778\n",
      "Processing batch 21/2778\n",
      "Processing batch 22/2778\n",
      "Processing batch 23/2778\n",
      "Processing batch 24/2778\n",
      "Processing batch 25/2778\n",
      "Processing batch 26/2778\n",
      "Processing batch 27/2778\n",
      "Processing batch 28/2778\n",
      "Processing batch 29/2778\n",
      "Processing batch 30/2778\n",
      "Processing batch 31/2778\n",
      "Processing batch 32/2778\n",
      "Processing batch 33/2778\n",
      "Processing batch 34/2778\n",
      "Processing batch 35/2778\n",
      "Processing batch 36/2778\n",
      "Processing batch 37/2778\n",
      "Processing batch 38/2778\n",
      "Processing batch 39/2778\n",
      "Processing batch 40/2778\n",
      "Processing batch 41/2778\n",
      "Processing batch 42/2778\n",
      "Processing batch 43/2778\n",
      "Processing batch 44/2778\n",
      "Processing batch 45/2778\n",
      "Processing batch 46/2778\n",
      "Processing batch 47/2778\n",
      "Processing batch 48/2778\n",
      "Processing batch 49/2778\n",
      "Processing batch 50/2778\n",
      "Processing batch 51/2778\n",
      "Processing batch 52/2778\n",
      "Processing batch 53/2778\n",
      "Processing batch 54/2778\n",
      "Processing batch 55/2778\n",
      "Processing batch 56/2778\n",
      "Processing batch 57/2778\n",
      "Processing batch 58/2778\n",
      "Processing batch 59/2778\n",
      "Processing batch 60/2778\n",
      "Processing batch 61/2778\n",
      "Processing batch 62/2778\n",
      "Processing batch 63/2778\n",
      "Processing batch 64/2778\n",
      "Processing batch 65/2778\n",
      "Processing batch 66/2778\n",
      "Processing batch 67/2778\n",
      "Processing batch 68/2778\n",
      "Processing batch 69/2778\n",
      "Processing batch 70/2778\n",
      "Processing batch 71/2778\n",
      "Processing batch 72/2778\n",
      "Processing batch 73/2778\n",
      "Processing batch 74/2778\n",
      "Processing batch 75/2778\n",
      "Processing batch 76/2778\n",
      "Processing batch 77/2778\n",
      "Processing batch 78/2778\n",
      "Processing batch 79/2778\n",
      "Processing batch 80/2778\n",
      "Processing batch 81/2778\n",
      "Processing batch 82/2778\n",
      "Processing batch 83/2778\n",
      "Processing batch 84/2778\n",
      "Processing batch 85/2778\n",
      "Processing batch 86/2778\n",
      "Processing batch 87/2778\n",
      "Processing batch 88/2778\n",
      "Processing batch 89/2778\n",
      "Processing batch 90/2778\n",
      "Processing batch 91/2778\n",
      "Processing batch 92/2778\n",
      "Processing batch 93/2778\n",
      "Processing batch 94/2778\n",
      "Processing batch 95/2778\n",
      "Processing batch 96/2778\n",
      "Processing batch 97/2778\n",
      "Processing batch 98/2778\n",
      "Processing batch 99/2778\n",
      "Processing batch 100/2778\n",
      "Processing batch 101/2778\n",
      "Processing batch 102/2778\n",
      "Processing batch 103/2778\n",
      "Processing batch 104/2778\n",
      "Processing batch 105/2778\n",
      "Processing batch 106/2778\n",
      "Processing batch 107/2778\n",
      "Processing batch 108/2778\n",
      "Processing batch 109/2778\n",
      "Processing batch 110/2778\n",
      "Processing batch 111/2778\n",
      "Processing batch 112/2778\n",
      "Processing batch 113/2778\n",
      "Processing batch 114/2778\n",
      "Processing batch 115/2778\n",
      "Processing batch 116/2778\n",
      "Processing batch 117/2778\n",
      "Processing batch 118/2778\n",
      "Processing batch 119/2778\n",
      "Processing batch 120/2778\n",
      "Processing batch 121/2778\n",
      "Processing batch 122/2778\n",
      "Processing batch 123/2778\n",
      "Processing batch 124/2778\n",
      "Processing batch 125/2778\n",
      "Processing batch 126/2778\n",
      "Processing batch 127/2778\n",
      "Processing batch 128/2778\n",
      "Processing batch 129/2778\n",
      "Processing batch 130/2778\n",
      "Processing batch 131/2778\n",
      "Processing batch 132/2778\n",
      "Processing batch 133/2778\n",
      "Processing batch 134/2778\n",
      "Processing batch 135/2778\n",
      "Processing batch 136/2778\n",
      "Processing batch 137/2778\n",
      "Processing batch 138/2778\n",
      "Processing batch 139/2778\n",
      "Processing batch 140/2778\n",
      "Processing batch 141/2778\n",
      "Processing batch 142/2778\n",
      "Processing batch 143/2778\n",
      "Processing batch 144/2778\n",
      "Processing batch 145/2778\n",
      "Processing batch 146/2778\n",
      "Processing batch 147/2778\n",
      "Processing batch 148/2778\n",
      "Processing batch 149/2778\n",
      "Processing batch 150/2778\n",
      "Processing batch 151/2778\n",
      "Processing batch 152/2778\n",
      "Processing batch 153/2778\n",
      "Processing batch 154/2778\n",
      "Processing batch 155/2778\n",
      "Processing batch 156/2778\n",
      "Processing batch 157/2778\n",
      "Processing batch 158/2778\n",
      "Processing batch 159/2778\n",
      "Processing batch 160/2778\n",
      "Processing batch 161/2778\n",
      "Processing batch 162/2778\n",
      "Processing batch 163/2778\n",
      "Processing batch 164/2778\n",
      "Processing batch 165/2778\n",
      "Processing batch 166/2778\n",
      "Processing batch 167/2778\n",
      "Processing batch 168/2778\n",
      "Processing batch 169/2778\n",
      "Processing batch 170/2778\n",
      "Processing batch 171/2778\n",
      "Processing batch 172/2778\n",
      "Processing batch 173/2778\n",
      "Processing batch 174/2778\n",
      "Processing batch 175/2778\n",
      "Processing batch 176/2778\n",
      "Processing batch 177/2778\n",
      "Processing batch 178/2778\n",
      "Processing batch 179/2778\n",
      "Processing batch 180/2778\n",
      "Processing batch 181/2778\n",
      "Processing batch 182/2778\n",
      "Processing batch 183/2778\n",
      "Processing batch 184/2778\n",
      "Processing batch 185/2778\n",
      "Processing batch 186/2778\n",
      "Processing batch 187/2778\n",
      "Processing batch 188/2778\n",
      "Processing batch 189/2778\n",
      "Processing batch 190/2778\n",
      "Processing batch 191/2778\n",
      "Processing batch 192/2778\n",
      "Processing batch 193/2778\n",
      "Processing batch 194/2778\n",
      "Processing batch 195/2778\n",
      "Processing batch 196/2778\n",
      "Processing batch 197/2778\n",
      "Processing batch 198/2778\n",
      "Processing batch 199/2778\n",
      "Processing batch 200/2778\n",
      "Processing batch 201/2778\n",
      "Processing batch 202/2778\n",
      "Processing batch 203/2778\n",
      "Processing batch 204/2778\n",
      "Processing batch 205/2778\n",
      "Processing batch 206/2778\n",
      "Processing batch 207/2778\n",
      "Processing batch 208/2778\n",
      "Processing batch 209/2778\n",
      "Processing batch 210/2778\n",
      "Processing batch 211/2778\n",
      "Processing batch 212/2778\n",
      "Processing batch 213/2778\n",
      "Processing batch 214/2778\n",
      "Processing batch 215/2778\n",
      "Processing batch 216/2778\n",
      "Processing batch 217/2778\n",
      "Processing batch 218/2778\n",
      "Processing batch 219/2778\n",
      "Processing batch 220/2778\n",
      "Processing batch 221/2778\n",
      "Processing batch 222/2778\n",
      "Processing batch 223/2778\n",
      "Processing batch 224/2778\n",
      "Processing batch 225/2778\n",
      "Processing batch 226/2778\n",
      "Processing batch 227/2778\n",
      "Processing batch 228/2778\n",
      "Processing batch 229/2778\n",
      "Processing batch 230/2778\n",
      "Processing batch 231/2778\n",
      "Processing batch 232/2778\n",
      "Processing batch 233/2778\n",
      "Processing batch 234/2778\n",
      "Processing batch 235/2778\n",
      "Processing batch 236/2778\n",
      "Processing batch 237/2778\n",
      "Processing batch 238/2778\n",
      "Processing batch 239/2778\n",
      "Processing batch 240/2778\n",
      "Processing batch 241/2778\n",
      "Processing batch 242/2778\n",
      "Processing batch 243/2778\n",
      "Processing batch 244/2778\n",
      "Processing batch 245/2778\n",
      "Processing batch 246/2778\n",
      "Processing batch 247/2778\n",
      "Processing batch 248/2778\n",
      "Processing batch 249/2778\n",
      "Processing batch 250/2778\n",
      "Processing batch 251/2778\n",
      "Processing batch 252/2778\n",
      "Processing batch 253/2778\n",
      "Processing batch 254/2778\n",
      "Processing batch 255/2778\n",
      "Processing batch 256/2778\n",
      "Processing batch 257/2778\n",
      "Processing batch 258/2778\n",
      "Processing batch 259/2778\n",
      "Processing batch 260/2778\n",
      "Processing batch 261/2778\n",
      "Processing batch 262/2778\n",
      "Processing batch 263/2778\n",
      "Processing batch 264/2778\n",
      "Processing batch 265/2778\n",
      "Processing batch 266/2778\n",
      "Processing batch 267/2778\n",
      "Processing batch 268/2778\n",
      "Processing batch 269/2778\n",
      "Processing batch 270/2778\n",
      "Processing batch 271/2778\n",
      "Processing batch 272/2778\n",
      "Processing batch 273/2778\n",
      "Processing batch 274/2778\n",
      "Processing batch 275/2778\n",
      "Processing batch 276/2778\n",
      "Processing batch 277/2778\n",
      "Processing batch 278/2778\n",
      "Processing batch 279/2778\n",
      "Processing batch 280/2778\n",
      "Processing batch 281/2778\n",
      "Processing batch 282/2778\n",
      "Processing batch 283/2778\n",
      "Processing batch 284/2778\n",
      "Processing batch 285/2778\n",
      "Processing batch 286/2778\n",
      "Processing batch 287/2778\n",
      "Processing batch 288/2778\n",
      "Processing batch 289/2778\n",
      "Processing batch 290/2778\n",
      "Processing batch 291/2778\n",
      "Processing batch 292/2778\n",
      "Processing batch 293/2778\n",
      "Processing batch 294/2778\n",
      "Processing batch 295/2778\n",
      "Processing batch 296/2778\n",
      "Processing batch 297/2778\n",
      "Processing batch 298/2778\n",
      "Processing batch 299/2778\n",
      "Processing batch 300/2778\n",
      "Processing batch 301/2778\n",
      "Processing batch 302/2778\n",
      "Processing batch 303/2778\n",
      "Processing batch 304/2778\n",
      "Processing batch 305/2778\n",
      "Processing batch 306/2778\n",
      "Processing batch 307/2778\n",
      "Processing batch 308/2778\n",
      "Processing batch 309/2778\n",
      "Processing batch 310/2778\n",
      "Processing batch 311/2778\n",
      "Processing batch 312/2778\n",
      "Processing batch 313/2778\n",
      "Processing batch 314/2778\n",
      "Processing batch 315/2778\n",
      "Processing batch 316/2778\n",
      "Processing batch 317/2778\n",
      "Processing batch 318/2778\n",
      "Processing batch 319/2778\n",
      "Processing batch 320/2778\n",
      "Processing batch 321/2778\n",
      "Processing batch 322/2778\n",
      "Processing batch 323/2778\n",
      "Processing batch 324/2778\n",
      "Processing batch 325/2778\n",
      "Processing batch 326/2778\n",
      "Processing batch 327/2778\n",
      "Processing batch 328/2778\n",
      "Processing batch 329/2778\n",
      "Processing batch 330/2778\n",
      "Processing batch 331/2778\n",
      "Processing batch 332/2778\n",
      "Processing batch 333/2778\n",
      "Processing batch 334/2778\n",
      "Processing batch 335/2778\n",
      "Processing batch 336/2778\n",
      "Processing batch 337/2778\n",
      "Processing batch 338/2778\n",
      "Processing batch 339/2778\n",
      "Processing batch 340/2778\n",
      "Processing batch 341/2778\n",
      "Processing batch 342/2778\n",
      "Processing batch 343/2778\n",
      "Processing batch 344/2778\n",
      "Processing batch 345/2778\n",
      "Processing batch 346/2778\n",
      "Processing batch 347/2778\n",
      "Processing batch 348/2778\n",
      "Processing batch 349/2778\n",
      "Processing batch 350/2778\n",
      "Processing batch 351/2778\n",
      "Processing batch 352/2778\n",
      "Processing batch 353/2778\n",
      "Processing batch 354/2778\n",
      "Processing batch 355/2778\n",
      "Processing batch 356/2778\n",
      "Processing batch 357/2778\n",
      "Processing batch 358/2778\n",
      "Processing batch 359/2778\n",
      "Processing batch 360/2778\n",
      "Processing batch 361/2778\n",
      "Processing batch 362/2778\n",
      "Processing batch 363/2778\n",
      "Processing batch 364/2778\n",
      "Processing batch 365/2778\n",
      "Processing batch 366/2778\n",
      "Processing batch 367/2778\n",
      "Processing batch 368/2778\n",
      "Processing batch 369/2778\n",
      "Processing batch 370/2778\n",
      "Processing batch 371/2778\n",
      "Processing batch 372/2778\n",
      "Processing batch 373/2778\n",
      "Processing batch 374/2778\n",
      "Processing batch 375/2778\n",
      "Processing batch 376/2778\n",
      "Processing batch 377/2778\n",
      "Processing batch 378/2778\n",
      "Processing batch 379/2778\n",
      "Processing batch 380/2778\n",
      "Processing batch 381/2778\n",
      "Processing batch 382/2778\n",
      "Processing batch 383/2778\n",
      "Processing batch 384/2778\n",
      "Processing batch 385/2778\n",
      "Processing batch 386/2778\n",
      "Processing batch 387/2778\n",
      "Processing batch 388/2778\n",
      "Processing batch 389/2778\n",
      "Processing batch 390/2778\n",
      "Processing batch 391/2778\n",
      "Processing batch 392/2778\n",
      "Processing batch 393/2778\n",
      "Processing batch 394/2778\n",
      "Processing batch 395/2778\n",
      "Processing batch 396/2778\n",
      "Processing batch 397/2778\n",
      "Processing batch 398/2778\n",
      "Processing batch 399/2778\n",
      "Processing batch 400/2778\n",
      "Processing batch 401/2778\n",
      "Processing batch 402/2778\n",
      "Processing batch 403/2778\n",
      "Processing batch 404/2778\n",
      "Processing batch 405/2778\n",
      "Processing batch 406/2778\n",
      "Processing batch 407/2778\n",
      "Processing batch 408/2778\n",
      "Processing batch 409/2778\n",
      "Processing batch 410/2778\n",
      "Processing batch 411/2778\n",
      "Processing batch 412/2778\n",
      "Processing batch 413/2778\n",
      "Processing batch 414/2778\n",
      "Processing batch 415/2778\n",
      "Processing batch 416/2778\n",
      "Processing batch 417/2778\n",
      "Processing batch 418/2778\n",
      "Processing batch 419/2778\n",
      "Processing batch 420/2778\n",
      "Processing batch 421/2778\n",
      "Processing batch 422/2778\n",
      "Processing batch 423/2778\n",
      "Processing batch 424/2778\n",
      "Processing batch 425/2778\n",
      "Processing batch 426/2778\n",
      "Processing batch 427/2778\n",
      "Processing batch 428/2778\n",
      "Processing batch 429/2778\n",
      "Processing batch 430/2778\n",
      "Processing batch 431/2778\n",
      "Processing batch 432/2778\n",
      "Processing batch 433/2778\n",
      "Processing batch 434/2778\n",
      "Processing batch 435/2778\n",
      "Processing batch 436/2778\n",
      "Processing batch 437/2778\n",
      "Processing batch 438/2778\n",
      "Processing batch 439/2778\n",
      "Processing batch 440/2778\n",
      "Processing batch 441/2778\n",
      "Processing batch 442/2778\n",
      "Processing batch 443/2778\n",
      "Processing batch 444/2778\n",
      "Processing batch 445/2778\n",
      "Processing batch 446/2778\n",
      "Processing batch 447/2778\n",
      "Processing batch 448/2778\n",
      "Processing batch 449/2778\n",
      "Processing batch 450/2778\n",
      "Processing batch 451/2778\n",
      "Processing batch 452/2778\n",
      "Processing batch 453/2778\n",
      "Processing batch 454/2778\n",
      "Processing batch 455/2778\n",
      "Processing batch 456/2778\n",
      "Processing batch 457/2778\n",
      "Processing batch 458/2778\n",
      "Processing batch 459/2778\n",
      "Processing batch 460/2778\n",
      "Processing batch 461/2778\n",
      "Processing batch 462/2778\n",
      "Processing batch 463/2778\n",
      "Processing batch 464/2778\n",
      "Processing batch 465/2778\n",
      "Processing batch 466/2778\n",
      "Processing batch 467/2778\n",
      "Processing batch 468/2778\n",
      "Processing batch 469/2778\n",
      "Processing batch 470/2778\n",
      "Processing batch 471/2778\n",
      "Processing batch 472/2778\n",
      "Processing batch 473/2778\n",
      "Processing batch 474/2778\n",
      "Processing batch 475/2778\n",
      "Processing batch 476/2778\n",
      "Processing batch 477/2778\n",
      "Processing batch 478/2778\n",
      "Processing batch 479/2778\n",
      "Processing batch 480/2778\n",
      "Processing batch 481/2778\n",
      "Processing batch 482/2778\n",
      "Processing batch 483/2778\n",
      "Processing batch 484/2778\n",
      "Processing batch 485/2778\n",
      "Processing batch 486/2778\n",
      "Processing batch 487/2778\n",
      "Processing batch 488/2778\n",
      "Processing batch 489/2778\n",
      "Processing batch 490/2778\n",
      "Processing batch 491/2778\n",
      "Processing batch 492/2778\n",
      "Processing batch 493/2778\n",
      "Processing batch 494/2778\n",
      "Processing batch 495/2778\n",
      "Processing batch 496/2778\n",
      "Processing batch 497/2778\n",
      "Processing batch 498/2778\n",
      "Processing batch 499/2778\n",
      "Processing batch 500/2778\n",
      "Processing batch 501/2778\n",
      "Processing batch 502/2778\n",
      "Processing batch 503/2778\n",
      "Processing batch 504/2778\n",
      "Processing batch 505/2778\n",
      "Processing batch 506/2778\n",
      "Processing batch 507/2778\n",
      "Processing batch 508/2778\n",
      "Processing batch 509/2778\n",
      "Processing batch 510/2778\n",
      "Processing batch 511/2778\n",
      "Processing batch 512/2778\n",
      "Processing batch 513/2778\n",
      "Processing batch 514/2778\n",
      "Processing batch 515/2778\n",
      "Processing batch 516/2778\n",
      "Processing batch 517/2778\n",
      "Processing batch 518/2778\n",
      "Processing batch 519/2778\n",
      "Processing batch 520/2778\n",
      "Processing batch 521/2778\n",
      "Processing batch 522/2778\n",
      "Processing batch 523/2778\n",
      "Processing batch 524/2778\n",
      "Processing batch 525/2778\n",
      "Processing batch 526/2778\n",
      "Processing batch 527/2778\n",
      "Processing batch 528/2778\n",
      "Processing batch 529/2778\n",
      "Processing batch 530/2778\n",
      "Processing batch 531/2778\n",
      "Processing batch 532/2778\n",
      "Processing batch 533/2778\n",
      "Processing batch 534/2778\n",
      "Processing batch 535/2778\n",
      "Processing batch 536/2778\n",
      "Processing batch 537/2778\n",
      "Processing batch 538/2778\n",
      "Processing batch 539/2778\n",
      "Processing batch 540/2778\n",
      "Processing batch 541/2778\n",
      "Processing batch 542/2778\n",
      "Processing batch 543/2778\n",
      "Processing batch 544/2778\n",
      "Processing batch 545/2778\n",
      "Processing batch 546/2778\n",
      "Processing batch 547/2778\n",
      "Processing batch 548/2778\n",
      "Processing batch 549/2778\n",
      "Processing batch 550/2778\n",
      "Processing batch 551/2778\n",
      "Processing batch 552/2778\n",
      "Processing batch 553/2778\n",
      "Processing batch 554/2778\n",
      "Processing batch 555/2778\n",
      "Processing batch 556/2778\n",
      "Processing batch 557/2778\n",
      "Processing batch 558/2778\n",
      "Processing batch 559/2778\n",
      "Processing batch 560/2778\n",
      "Processing batch 561/2778\n",
      "Processing batch 562/2778\n",
      "Processing batch 563/2778\n",
      "Processing batch 564/2778\n",
      "Processing batch 565/2778\n",
      "Processing batch 566/2778\n",
      "Processing batch 567/2778\n",
      "Processing batch 568/2778\n",
      "Processing batch 569/2778\n",
      "Processing batch 570/2778\n",
      "Processing batch 571/2778\n",
      "Processing batch 572/2778\n",
      "Processing batch 573/2778\n",
      "Processing batch 574/2778\n",
      "Processing batch 575/2778\n",
      "Processing batch 576/2778\n",
      "Processing batch 577/2778\n",
      "Processing batch 578/2778\n",
      "Processing batch 579/2778\n",
      "Processing batch 580/2778\n",
      "Processing batch 581/2778\n",
      "Processing batch 582/2778\n",
      "Processing batch 583/2778\n",
      "Processing batch 584/2778\n",
      "Processing batch 585/2778\n",
      "Processing batch 586/2778\n",
      "Processing batch 587/2778\n",
      "Processing batch 588/2778\n",
      "Processing batch 589/2778\n",
      "Processing batch 590/2778\n",
      "Processing batch 591/2778\n",
      "Processing batch 592/2778\n",
      "Processing batch 593/2778\n",
      "Processing batch 594/2778\n",
      "Processing batch 595/2778\n",
      "Processing batch 596/2778\n",
      "Processing batch 597/2778\n",
      "Processing batch 598/2778\n",
      "Processing batch 599/2778\n",
      "Processing batch 600/2778\n",
      "Processing batch 601/2778\n",
      "Processing batch 602/2778\n",
      "Processing batch 603/2778\n",
      "Processing batch 604/2778\n",
      "Processing batch 605/2778\n",
      "Processing batch 606/2778\n",
      "Processing batch 607/2778\n",
      "Processing batch 608/2778\n",
      "Processing batch 609/2778\n",
      "Processing batch 610/2778\n",
      "Processing batch 611/2778\n",
      "Processing batch 612/2778\n",
      "Processing batch 613/2778\n",
      "Processing batch 614/2778\n",
      "Processing batch 615/2778\n",
      "Processing batch 616/2778\n",
      "Processing batch 617/2778\n",
      "Processing batch 618/2778\n",
      "Processing batch 619/2778\n",
      "Processing batch 620/2778\n",
      "Processing batch 621/2778\n",
      "Processing batch 622/2778\n",
      "Processing batch 623/2778\n",
      "Processing batch 624/2778\n",
      "Processing batch 625/2778\n",
      "Processing batch 626/2778\n",
      "Processing batch 627/2778\n",
      "Processing batch 628/2778\n",
      "Processing batch 629/2778\n",
      "Processing batch 630/2778\n",
      "Processing batch 631/2778\n",
      "Processing batch 632/2778\n",
      "Processing batch 633/2778\n",
      "Processing batch 634/2778\n",
      "Processing batch 635/2778\n",
      "Processing batch 636/2778\n",
      "Processing batch 637/2778\n",
      "Processing batch 638/2778\n",
      "Processing batch 639/2778\n",
      "Processing batch 640/2778\n",
      "Processing batch 641/2778\n",
      "Processing batch 642/2778\n",
      "Processing batch 643/2778\n",
      "Processing batch 644/2778\n",
      "Processing batch 645/2778\n",
      "Processing batch 646/2778\n",
      "Processing batch 647/2778\n",
      "Processing batch 648/2778\n",
      "Processing batch 649/2778\n",
      "Processing batch 650/2778\n",
      "Processing batch 651/2778\n",
      "Processing batch 652/2778\n",
      "Processing batch 653/2778\n",
      "Processing batch 654/2778\n",
      "Processing batch 655/2778\n",
      "Processing batch 656/2778\n",
      "Processing batch 657/2778\n",
      "Processing batch 658/2778\n",
      "Processing batch 659/2778\n",
      "Processing batch 660/2778\n",
      "Processing batch 661/2778\n",
      "Processing batch 662/2778\n",
      "Processing batch 663/2778\n",
      "Processing batch 664/2778\n",
      "Processing batch 665/2778\n",
      "Processing batch 666/2778\n",
      "Processing batch 667/2778\n",
      "Processing batch 668/2778\n",
      "Processing batch 669/2778\n",
      "Processing batch 670/2778\n",
      "Processing batch 671/2778\n",
      "Processing batch 672/2778\n",
      "Processing batch 673/2778\n",
      "Processing batch 674/2778\n",
      "Processing batch 675/2778\n",
      "Processing batch 676/2778\n",
      "Processing batch 677/2778\n",
      "Processing batch 678/2778\n",
      "Processing batch 679/2778\n",
      "Processing batch 680/2778\n",
      "Processing batch 681/2778\n",
      "Processing batch 682/2778\n",
      "Processing batch 683/2778\n",
      "Processing batch 684/2778\n",
      "Processing batch 685/2778\n",
      "Processing batch 686/2778\n",
      "Processing batch 687/2778\n",
      "Processing batch 688/2778\n",
      "Processing batch 689/2778\n",
      "Processing batch 690/2778\n",
      "Processing batch 691/2778\n",
      "Processing batch 692/2778\n",
      "Processing batch 693/2778\n",
      "Processing batch 694/2778\n",
      "Processing batch 695/2778\n",
      "Processing batch 696/2778\n",
      "Processing batch 697/2778\n",
      "Processing batch 698/2778\n",
      "Processing batch 699/2778\n",
      "Processing batch 700/2778\n",
      "Processing batch 701/2778\n",
      "Processing batch 702/2778\n",
      "Processing batch 703/2778\n",
      "Processing batch 704/2778\n",
      "Processing batch 705/2778\n",
      "Processing batch 706/2778\n",
      "Processing batch 707/2778\n",
      "Processing batch 708/2778\n",
      "Processing batch 709/2778\n",
      "Processing batch 710/2778\n",
      "Processing batch 711/2778\n",
      "Processing batch 712/2778\n",
      "Processing batch 713/2778\n",
      "Processing batch 714/2778\n",
      "Processing batch 715/2778\n",
      "Processing batch 716/2778\n",
      "Processing batch 717/2778\n",
      "Processing batch 718/2778\n",
      "Processing batch 719/2778\n",
      "Processing batch 720/2778\n",
      "Processing batch 721/2778\n",
      "Processing batch 722/2778\n",
      "Processing batch 723/2778\n",
      "Processing batch 724/2778\n",
      "Processing batch 725/2778\n",
      "Processing batch 726/2778\n",
      "Processing batch 727/2778\n",
      "Processing batch 728/2778\n",
      "Processing batch 729/2778\n",
      "Processing batch 730/2778\n",
      "Processing batch 731/2778\n",
      "Processing batch 732/2778\n",
      "Processing batch 733/2778\n",
      "Processing batch 734/2778\n",
      "Processing batch 735/2778\n",
      "Processing batch 736/2778\n",
      "Processing batch 737/2778\n",
      "Processing batch 738/2778\n",
      "Processing batch 739/2778\n",
      "Processing batch 740/2778\n",
      "Processing batch 741/2778\n",
      "Processing batch 742/2778\n",
      "Processing batch 743/2778\n",
      "Processing batch 744/2778\n",
      "Processing batch 745/2778\n",
      "Processing batch 746/2778\n",
      "Processing batch 747/2778\n",
      "Processing batch 748/2778\n",
      "Processing batch 749/2778\n",
      "Processing batch 750/2778\n",
      "Processing batch 751/2778\n",
      "Processing batch 752/2778\n",
      "Processing batch 753/2778\n",
      "Processing batch 754/2778\n",
      "Processing batch 755/2778\n",
      "Processing batch 756/2778\n",
      "Processing batch 757/2778\n",
      "Processing batch 758/2778\n",
      "Processing batch 759/2778\n",
      "Processing batch 760/2778\n",
      "Processing batch 761/2778\n",
      "Processing batch 762/2778\n",
      "Processing batch 763/2778\n",
      "Processing batch 764/2778\n",
      "Processing batch 765/2778\n",
      "Processing batch 766/2778\n",
      "Processing batch 767/2778\n",
      "Processing batch 768/2778\n",
      "Processing batch 769/2778\n",
      "Processing batch 770/2778\n",
      "Processing batch 771/2778\n",
      "Processing batch 772/2778\n",
      "Processing batch 773/2778\n",
      "Processing batch 774/2778\n",
      "Processing batch 775/2778\n",
      "Processing batch 776/2778\n",
      "Processing batch 777/2778\n",
      "Processing batch 778/2778\n",
      "Processing batch 779/2778\n",
      "Processing batch 780/2778\n",
      "Processing batch 781/2778\n",
      "Processing batch 782/2778\n",
      "Processing batch 783/2778\n",
      "Processing batch 784/2778\n",
      "Processing batch 785/2778\n",
      "Processing batch 786/2778\n",
      "Processing batch 787/2778\n",
      "Processing batch 788/2778\n",
      "Processing batch 789/2778\n",
      "Processing batch 790/2778\n",
      "Processing batch 791/2778\n",
      "Processing batch 792/2778\n",
      "Processing batch 793/2778\n",
      "Processing batch 794/2778\n",
      "Processing batch 795/2778\n",
      "Processing batch 796/2778\n",
      "Processing batch 797/2778\n",
      "Processing batch 798/2778\n",
      "Processing batch 799/2778\n",
      "Processing batch 800/2778\n",
      "Processing batch 801/2778\n",
      "Processing batch 802/2778\n",
      "Processing batch 803/2778\n",
      "Processing batch 804/2778\n",
      "Processing batch 805/2778\n",
      "Processing batch 806/2778\n",
      "Processing batch 807/2778\n",
      "Processing batch 808/2778\n",
      "Processing batch 809/2778\n",
      "Processing batch 810/2778\n",
      "Processing batch 811/2778\n",
      "Processing batch 812/2778\n",
      "Processing batch 813/2778\n",
      "Processing batch 814/2778\n",
      "Processing batch 815/2778\n",
      "Processing batch 816/2778\n",
      "Processing batch 817/2778\n",
      "Processing batch 818/2778\n",
      "Processing batch 819/2778\n",
      "Processing batch 820/2778\n",
      "Processing batch 821/2778\n",
      "Processing batch 822/2778\n",
      "Processing batch 823/2778\n",
      "Processing batch 824/2778\n",
      "Processing batch 825/2778\n",
      "Processing batch 826/2778\n",
      "Processing batch 827/2778\n",
      "Processing batch 828/2778\n",
      "Processing batch 829/2778\n",
      "Processing batch 830/2778\n",
      "Processing batch 831/2778\n",
      "Processing batch 832/2778\n",
      "Processing batch 833/2778\n",
      "Processing batch 834/2778\n",
      "Processing batch 835/2778\n",
      "Processing batch 836/2778\n",
      "Processing batch 837/2778\n",
      "Processing batch 838/2778\n",
      "Processing batch 839/2778\n",
      "Processing batch 840/2778\n",
      "Processing batch 841/2778\n",
      "Processing batch 842/2778\n",
      "Processing batch 843/2778\n",
      "Processing batch 844/2778\n",
      "Processing batch 845/2778\n",
      "Processing batch 846/2778\n",
      "Processing batch 847/2778\n",
      "Processing batch 848/2778\n",
      "Processing batch 849/2778\n",
      "Processing batch 850/2778\n",
      "Processing batch 851/2778\n",
      "Processing batch 852/2778\n",
      "Processing batch 853/2778\n",
      "Processing batch 854/2778\n",
      "Processing batch 855/2778\n",
      "Processing batch 856/2778\n",
      "Processing batch 857/2778\n",
      "Processing batch 858/2778\n",
      "Processing batch 859/2778\n",
      "Processing batch 860/2778\n",
      "Processing batch 861/2778\n",
      "Processing batch 862/2778\n",
      "Processing batch 863/2778\n",
      "Processing batch 864/2778\n",
      "Processing batch 865/2778\n",
      "Processing batch 866/2778\n",
      "Processing batch 867/2778\n",
      "Processing batch 868/2778\n",
      "Processing batch 869/2778\n",
      "Processing batch 870/2778\n",
      "Processing batch 871/2778\n",
      "Processing batch 872/2778\n",
      "Processing batch 873/2778\n",
      "Processing batch 874/2778\n",
      "Processing batch 875/2778\n",
      "Processing batch 876/2778\n",
      "Processing batch 877/2778\n",
      "Processing batch 878/2778\n",
      "Processing batch 879/2778\n",
      "Processing batch 880/2778\n",
      "Processing batch 881/2778\n",
      "Processing batch 882/2778\n",
      "Processing batch 883/2778\n",
      "Processing batch 884/2778\n",
      "Processing batch 885/2778\n",
      "Processing batch 886/2778\n",
      "Processing batch 887/2778\n",
      "Processing batch 888/2778\n",
      "Processing batch 889/2778\n",
      "Processing batch 890/2778\n",
      "Processing batch 891/2778\n",
      "Processing batch 892/2778\n",
      "Processing batch 893/2778\n",
      "Processing batch 894/2778\n",
      "Processing batch 895/2778\n",
      "Processing batch 896/2778\n",
      "Processing batch 897/2778\n",
      "Processing batch 898/2778\n",
      "Processing batch 899/2778\n",
      "Processing batch 900/2778\n",
      "Processing batch 901/2778\n",
      "Processing batch 902/2778\n",
      "Processing batch 903/2778\n",
      "Processing batch 904/2778\n",
      "Processing batch 905/2778\n",
      "Processing batch 906/2778\n",
      "Processing batch 907/2778\n",
      "Processing batch 908/2778\n",
      "Processing batch 909/2778\n",
      "Processing batch 910/2778\n",
      "Processing batch 911/2778\n",
      "Processing batch 912/2778\n",
      "Processing batch 913/2778\n",
      "Processing batch 914/2778\n",
      "Processing batch 915/2778\n",
      "Processing batch 916/2778\n",
      "Processing batch 917/2778\n",
      "Processing batch 918/2778\n",
      "Processing batch 919/2778\n",
      "Processing batch 920/2778\n",
      "Processing batch 921/2778\n",
      "Processing batch 922/2778\n",
      "Processing batch 923/2778\n",
      "Processing batch 924/2778\n",
      "Processing batch 925/2778\n",
      "Processing batch 926/2778\n",
      "Processing batch 927/2778\n",
      "Processing batch 928/2778\n",
      "Processing batch 929/2778\n",
      "Processing batch 930/2778\n",
      "Processing batch 931/2778\n",
      "Processing batch 932/2778\n",
      "Processing batch 933/2778\n",
      "Processing batch 934/2778\n",
      "Processing batch 935/2778\n",
      "Processing batch 936/2778\n",
      "Processing batch 937/2778\n",
      "Processing batch 938/2778\n",
      "Processing batch 939/2778\n",
      "Processing batch 940/2778\n",
      "Processing batch 941/2778\n",
      "Processing batch 942/2778\n",
      "Processing batch 943/2778\n",
      "Processing batch 944/2778\n",
      "Processing batch 945/2778\n",
      "Processing batch 946/2778\n",
      "Processing batch 947/2778\n",
      "Processing batch 948/2778\n",
      "Processing batch 949/2778\n",
      "Processing batch 950/2778\n",
      "Processing batch 951/2778\n",
      "Processing batch 952/2778\n",
      "Processing batch 953/2778\n",
      "Processing batch 954/2778\n",
      "Processing batch 955/2778\n",
      "Processing batch 956/2778\n",
      "Processing batch 957/2778\n",
      "Processing batch 958/2778\n",
      "Processing batch 959/2778\n",
      "Processing batch 960/2778\n",
      "Processing batch 961/2778\n",
      "Processing batch 962/2778\n",
      "Processing batch 963/2778\n",
      "Processing batch 964/2778\n",
      "Processing batch 965/2778\n",
      "Processing batch 966/2778\n",
      "Processing batch 967/2778\n",
      "Processing batch 968/2778\n",
      "Processing batch 969/2778\n",
      "Processing batch 970/2778\n",
      "Processing batch 971/2778\n",
      "Processing batch 972/2778\n",
      "Processing batch 973/2778\n",
      "Processing batch 974/2778\n",
      "Processing batch 975/2778\n",
      "Processing batch 976/2778\n",
      "Processing batch 977/2778\n",
      "Processing batch 978/2778\n",
      "Processing batch 979/2778\n",
      "Processing batch 980/2778\n",
      "Processing batch 981/2778\n",
      "Processing batch 982/2778\n",
      "Processing batch 983/2778\n",
      "Processing batch 984/2778\n",
      "Processing batch 985/2778\n",
      "Processing batch 986/2778\n",
      "Processing batch 987/2778\n",
      "Processing batch 988/2778\n",
      "Processing batch 989/2778\n",
      "Processing batch 990/2778\n",
      "Processing batch 991/2778\n",
      "Processing batch 992/2778\n",
      "Processing batch 993/2778\n",
      "Processing batch 994/2778\n",
      "Processing batch 995/2778\n",
      "Processing batch 996/2778\n",
      "Processing batch 997/2778\n",
      "Processing batch 998/2778\n",
      "Processing batch 999/2778\n",
      "Processing batch 1000/2778\n",
      "Processing batch 1001/2778\n",
      "Processing batch 1002/2778\n",
      "Processing batch 1003/2778\n",
      "Processing batch 1004/2778\n",
      "Processing batch 1005/2778\n",
      "Processing batch 1006/2778\n",
      "Processing batch 1007/2778\n",
      "Processing batch 1008/2778\n",
      "Processing batch 1009/2778\n",
      "Processing batch 1010/2778\n",
      "Processing batch 1011/2778\n",
      "Processing batch 1012/2778\n",
      "Processing batch 1013/2778\n",
      "Processing batch 1014/2778\n",
      "Processing batch 1015/2778\n",
      "Processing batch 1016/2778\n",
      "Processing batch 1017/2778\n",
      "Processing batch 1018/2778\n",
      "Processing batch 1019/2778\n",
      "Processing batch 1020/2778\n",
      "Processing batch 1021/2778\n",
      "Processing batch 1022/2778\n",
      "Processing batch 1023/2778\n",
      "Processing batch 1024/2778\n",
      "Processing batch 1025/2778\n",
      "Processing batch 1026/2778\n",
      "Processing batch 1027/2778\n",
      "Processing batch 1028/2778\n",
      "Processing batch 1029/2778\n",
      "Processing batch 1030/2778\n",
      "Processing batch 1031/2778\n",
      "Processing batch 1032/2778\n",
      "Processing batch 1033/2778\n",
      "Processing batch 1034/2778\n",
      "Processing batch 1035/2778\n",
      "Processing batch 1036/2778\n",
      "Processing batch 1037/2778\n",
      "Processing batch 1038/2778\n",
      "Processing batch 1039/2778\n",
      "Processing batch 1040/2778\n",
      "Processing batch 1041/2778\n",
      "Processing batch 1042/2778\n",
      "Processing batch 1043/2778\n",
      "Processing batch 1044/2778\n",
      "Processing batch 1045/2778\n",
      "Processing batch 1046/2778\n",
      "Processing batch 1047/2778\n",
      "Processing batch 1048/2778\n",
      "Processing batch 1049/2778\n",
      "Processing batch 1050/2778\n",
      "Processing batch 1051/2778\n",
      "Processing batch 1052/2778\n",
      "Processing batch 1053/2778\n",
      "Processing batch 1054/2778\n",
      "Processing batch 1055/2778\n",
      "Processing batch 1056/2778\n",
      "Processing batch 1057/2778\n",
      "Processing batch 1058/2778\n",
      "Processing batch 1059/2778\n",
      "Processing batch 1060/2778\n",
      "Processing batch 1061/2778\n",
      "Processing batch 1062/2778\n",
      "Processing batch 1063/2778\n",
      "Processing batch 1064/2778\n",
      "Processing batch 1065/2778\n",
      "Processing batch 1066/2778\n",
      "Processing batch 1067/2778\n",
      "Processing batch 1068/2778\n",
      "Processing batch 1069/2778\n",
      "Processing batch 1070/2778\n",
      "Processing batch 1071/2778\n",
      "Processing batch 1072/2778\n",
      "Processing batch 1073/2778\n",
      "Processing batch 1074/2778\n",
      "Processing batch 1075/2778\n",
      "Processing batch 1076/2778\n",
      "Processing batch 1077/2778\n",
      "Processing batch 1078/2778\n",
      "Processing batch 1079/2778\n",
      "Processing batch 1080/2778\n",
      "Processing batch 1081/2778\n",
      "Processing batch 1082/2778\n",
      "Processing batch 1083/2778\n",
      "Processing batch 1084/2778\n",
      "Processing batch 1085/2778\n",
      "Processing batch 1086/2778\n",
      "Processing batch 1087/2778\n",
      "Processing batch 1088/2778\n",
      "Processing batch 1089/2778\n",
      "Processing batch 1090/2778\n",
      "Processing batch 1091/2778\n",
      "Processing batch 1092/2778\n",
      "Processing batch 1093/2778\n",
      "Processing batch 1094/2778\n",
      "Processing batch 1095/2778\n",
      "Processing batch 1096/2778\n",
      "Processing batch 1097/2778\n",
      "Processing batch 1098/2778\n",
      "Processing batch 1099/2778\n",
      "Processing batch 1100/2778\n",
      "Processing batch 1101/2778\n",
      "Processing batch 1102/2778\n",
      "Processing batch 1103/2778\n",
      "Processing batch 1104/2778\n",
      "Processing batch 1105/2778\n",
      "Processing batch 1106/2778\n",
      "Processing batch 1107/2778\n",
      "Processing batch 1108/2778\n",
      "Processing batch 1109/2778\n",
      "Processing batch 1110/2778\n",
      "Processing batch 1111/2778\n",
      "Processing batch 1112/2778\n",
      "Processing batch 1113/2778\n",
      "Processing batch 1114/2778\n",
      "Processing batch 1115/2778\n",
      "Processing batch 1116/2778\n",
      "Processing batch 1117/2778\n",
      "Processing batch 1118/2778\n",
      "Processing batch 1119/2778\n",
      "Processing batch 1120/2778\n",
      "Processing batch 1121/2778\n",
      "Processing batch 1122/2778\n",
      "Processing batch 1123/2778\n",
      "Processing batch 1124/2778\n",
      "Processing batch 1125/2778\n",
      "Processing batch 1126/2778\n",
      "Processing batch 1127/2778\n",
      "Processing batch 1128/2778\n",
      "Processing batch 1129/2778\n",
      "Processing batch 1130/2778\n",
      "Processing batch 1131/2778\n",
      "Processing batch 1132/2778\n",
      "Processing batch 1133/2778\n",
      "Processing batch 1134/2778\n",
      "Processing batch 1135/2778\n",
      "Processing batch 1136/2778\n",
      "Processing batch 1137/2778\n",
      "Processing batch 1138/2778\n",
      "Processing batch 1139/2778\n",
      "Processing batch 1140/2778\n",
      "Processing batch 1141/2778\n",
      "Processing batch 1142/2778\n",
      "Processing batch 1143/2778\n",
      "Processing batch 1144/2778\n",
      "Processing batch 1145/2778\n",
      "Processing batch 1146/2778\n",
      "Processing batch 1147/2778\n",
      "Processing batch 1148/2778\n",
      "Processing batch 1149/2778\n",
      "Processing batch 1150/2778\n",
      "Processing batch 1151/2778\n",
      "Processing batch 1152/2778\n",
      "Processing batch 1153/2778\n",
      "Processing batch 1154/2778\n",
      "Processing batch 1155/2778\n",
      "Processing batch 1156/2778\n",
      "Processing batch 1157/2778\n",
      "Processing batch 1158/2778\n",
      "Processing batch 1159/2778\n",
      "Processing batch 1160/2778\n",
      "Processing batch 1161/2778\n",
      "Processing batch 1162/2778\n",
      "Processing batch 1163/2778\n",
      "Processing batch 1164/2778\n",
      "Processing batch 1165/2778\n",
      "Processing batch 1166/2778\n",
      "Processing batch 1167/2778\n",
      "Processing batch 1168/2778\n",
      "Processing batch 1169/2778\n",
      "Processing batch 1170/2778\n",
      "Processing batch 1171/2778\n",
      "Processing batch 1172/2778\n",
      "Processing batch 1173/2778\n",
      "Processing batch 1174/2778\n",
      "Processing batch 1175/2778\n",
      "Processing batch 1176/2778\n",
      "Processing batch 1177/2778\n",
      "Processing batch 1178/2778\n",
      "Processing batch 1179/2778\n",
      "Processing batch 1180/2778\n",
      "Processing batch 1181/2778\n",
      "Processing batch 1182/2778\n",
      "Processing batch 1183/2778\n",
      "Processing batch 1184/2778\n",
      "Processing batch 1185/2778\n",
      "Processing batch 1186/2778\n",
      "Processing batch 1187/2778\n",
      "Processing batch 1188/2778\n",
      "Processing batch 1189/2778\n",
      "Processing batch 1190/2778\n",
      "Processing batch 1191/2778\n",
      "Processing batch 1192/2778\n",
      "Processing batch 1193/2778\n",
      "Processing batch 1194/2778\n",
      "Processing batch 1195/2778\n",
      "Processing batch 1196/2778\n",
      "Processing batch 1197/2778\n",
      "Processing batch 1198/2778\n",
      "Processing batch 1199/2778\n",
      "Processing batch 1200/2778\n",
      "Processing batch 1201/2778\n",
      "Processing batch 1202/2778\n",
      "Processing batch 1203/2778\n",
      "Processing batch 1204/2778\n",
      "Processing batch 1205/2778\n",
      "Processing batch 1206/2778\n",
      "Processing batch 1207/2778\n",
      "Processing batch 1208/2778\n",
      "Processing batch 1209/2778\n",
      "Processing batch 1210/2778\n",
      "Processing batch 1211/2778\n",
      "Processing batch 1212/2778\n",
      "Processing batch 1213/2778\n",
      "Processing batch 1214/2778\n",
      "Processing batch 1215/2778\n",
      "Processing batch 1216/2778\n",
      "Processing batch 1217/2778\n",
      "Processing batch 1218/2778\n",
      "Processing batch 1219/2778\n",
      "Processing batch 1220/2778\n",
      "Processing batch 1221/2778\n",
      "Processing batch 1222/2778\n",
      "Processing batch 1223/2778\n",
      "Processing batch 1224/2778\n",
      "Processing batch 1225/2778\n",
      "Processing batch 1226/2778\n",
      "Processing batch 1227/2778\n",
      "Processing batch 1228/2778\n",
      "Processing batch 1229/2778\n",
      "Processing batch 1230/2778\n",
      "Processing batch 1231/2778\n",
      "Processing batch 1232/2778\n",
      "Processing batch 1233/2778\n",
      "Processing batch 1234/2778\n",
      "Processing batch 1235/2778\n",
      "Processing batch 1236/2778\n",
      "Processing batch 1237/2778\n",
      "Processing batch 1238/2778\n",
      "Processing batch 1239/2778\n",
      "Processing batch 1240/2778\n",
      "Processing batch 1241/2778\n",
      "Processing batch 1242/2778\n",
      "Processing batch 1243/2778\n",
      "Processing batch 1244/2778\n",
      "Processing batch 1245/2778\n",
      "Processing batch 1246/2778\n",
      "Processing batch 1247/2778\n",
      "Processing batch 1248/2778\n",
      "Processing batch 1249/2778\n",
      "Processing batch 1250/2778\n",
      "Processing batch 1251/2778\n",
      "Processing batch 1252/2778\n",
      "Processing batch 1253/2778\n",
      "Processing batch 1254/2778\n",
      "Processing batch 1255/2778\n",
      "Processing batch 1256/2778\n",
      "Processing batch 1257/2778\n",
      "Processing batch 1258/2778\n",
      "Processing batch 1259/2778\n",
      "Processing batch 1260/2778\n",
      "Processing batch 1261/2778\n",
      "Processing batch 1262/2778\n",
      "Processing batch 1263/2778\n",
      "Processing batch 1264/2778\n",
      "Processing batch 1265/2778\n",
      "Processing batch 1266/2778\n",
      "Processing batch 1267/2778\n",
      "Processing batch 1268/2778\n",
      "Processing batch 1269/2778\n",
      "Processing batch 1270/2778\n",
      "Processing batch 1271/2778\n",
      "Processing batch 1272/2778\n",
      "Processing batch 1273/2778\n",
      "Processing batch 1274/2778\n",
      "Processing batch 1275/2778\n",
      "Processing batch 1276/2778\n",
      "Processing batch 1277/2778\n",
      "Processing batch 1278/2778\n",
      "Processing batch 1279/2778\n",
      "Processing batch 1280/2778\n",
      "Processing batch 1281/2778\n",
      "Processing batch 1282/2778\n",
      "Processing batch 1283/2778\n",
      "Processing batch 1284/2778\n",
      "Processing batch 1285/2778\n",
      "Processing batch 1286/2778\n",
      "Processing batch 1287/2778\n",
      "Processing batch 1288/2778\n",
      "Processing batch 1289/2778\n",
      "Processing batch 1290/2778\n",
      "Processing batch 1291/2778\n",
      "Processing batch 1292/2778\n",
      "Processing batch 1293/2778\n",
      "Processing batch 1294/2778\n",
      "Processing batch 1295/2778\n",
      "Processing batch 1296/2778\n",
      "Processing batch 1297/2778\n",
      "Processing batch 1298/2778\n",
      "Processing batch 1299/2778\n",
      "Processing batch 1300/2778\n",
      "Processing batch 1301/2778\n",
      "Processing batch 1302/2778\n",
      "Processing batch 1303/2778\n",
      "Processing batch 1304/2778\n",
      "Processing batch 1305/2778\n",
      "Processing batch 1306/2778\n",
      "Processing batch 1307/2778\n",
      "Processing batch 1308/2778\n",
      "Processing batch 1309/2778\n",
      "Processing batch 1310/2778\n",
      "Processing batch 1311/2778\n",
      "Processing batch 1312/2778\n",
      "Processing batch 1313/2778\n",
      "Processing batch 1314/2778\n",
      "Processing batch 1315/2778\n",
      "Processing batch 1316/2778\n",
      "Processing batch 1317/2778\n",
      "Processing batch 1318/2778\n",
      "Processing batch 1319/2778\n",
      "Processing batch 1320/2778\n",
      "Processing batch 1321/2778\n",
      "Processing batch 1322/2778\n",
      "Processing batch 1323/2778\n",
      "Processing batch 1324/2778\n",
      "Processing batch 1325/2778\n",
      "Processing batch 1326/2778\n",
      "Processing batch 1327/2778\n",
      "Processing batch 1328/2778\n",
      "Processing batch 1329/2778\n",
      "Processing batch 1330/2778\n",
      "Processing batch 1331/2778\n",
      "Processing batch 1332/2778\n",
      "Processing batch 1333/2778\n",
      "Processing batch 1334/2778\n",
      "Processing batch 1335/2778\n",
      "Processing batch 1336/2778\n",
      "Processing batch 1337/2778\n",
      "Processing batch 1338/2778\n",
      "Processing batch 1339/2778\n",
      "Processing batch 1340/2778\n",
      "Processing batch 1341/2778\n",
      "Processing batch 1342/2778\n",
      "Processing batch 1343/2778\n",
      "Processing batch 1344/2778\n",
      "Processing batch 1345/2778\n",
      "Processing batch 1346/2778\n",
      "Processing batch 1347/2778\n",
      "Processing batch 1348/2778\n",
      "Processing batch 1349/2778\n",
      "Processing batch 1350/2778\n",
      "Processing batch 1351/2778\n",
      "Processing batch 1352/2778\n",
      "Processing batch 1353/2778\n",
      "Processing batch 1354/2778\n",
      "Processing batch 1355/2778\n",
      "Processing batch 1356/2778\n",
      "Processing batch 1357/2778\n",
      "Processing batch 1358/2778\n",
      "Processing batch 1359/2778\n",
      "Processing batch 1360/2778\n",
      "Processing batch 1361/2778\n",
      "Processing batch 1362/2778\n",
      "Processing batch 1363/2778\n",
      "Processing batch 1364/2778\n",
      "Processing batch 1365/2778\n",
      "Processing batch 1366/2778\n",
      "Processing batch 1367/2778\n",
      "Processing batch 1368/2778\n",
      "Processing batch 1369/2778\n",
      "Processing batch 1370/2778\n",
      "Processing batch 1371/2778\n",
      "Processing batch 1372/2778\n",
      "Processing batch 1373/2778\n",
      "Processing batch 1374/2778\n",
      "Processing batch 1375/2778\n",
      "Processing batch 1376/2778\n",
      "Processing batch 1377/2778\n",
      "Processing batch 1378/2778\n",
      "Processing batch 1379/2778\n",
      "Processing batch 1380/2778\n",
      "Processing batch 1381/2778\n",
      "Processing batch 1382/2778\n",
      "Processing batch 1383/2778\n",
      "Processing batch 1384/2778\n",
      "Processing batch 1385/2778\n",
      "Processing batch 1386/2778\n",
      "Processing batch 1387/2778\n",
      "Processing batch 1388/2778\n",
      "Processing batch 1389/2778\n",
      "Processing batch 1390/2778\n",
      "Processing batch 1391/2778\n",
      "Processing batch 1392/2778\n",
      "Processing batch 1393/2778\n",
      "Processing batch 1394/2778\n",
      "Processing batch 1395/2778\n",
      "Processing batch 1396/2778\n",
      "Processing batch 1397/2778\n",
      "Processing batch 1398/2778\n",
      "Processing batch 1399/2778\n",
      "Processing batch 1400/2778\n",
      "Processing batch 1401/2778\n",
      "Processing batch 1402/2778\n",
      "Processing batch 1403/2778\n",
      "Processing batch 1404/2778\n",
      "Processing batch 1405/2778\n",
      "Processing batch 1406/2778\n",
      "Processing batch 1407/2778\n",
      "Processing batch 1408/2778\n",
      "Processing batch 1409/2778\n",
      "Processing batch 1410/2778\n",
      "Processing batch 1411/2778\n",
      "Processing batch 1412/2778\n",
      "Processing batch 1413/2778\n",
      "Processing batch 1414/2778\n",
      "Processing batch 1415/2778\n",
      "Processing batch 1416/2778\n",
      "Processing batch 1417/2778\n",
      "Processing batch 1418/2778\n",
      "Processing batch 1419/2778\n",
      "Processing batch 1420/2778\n",
      "Processing batch 1421/2778\n",
      "Processing batch 1422/2778\n",
      "Processing batch 1423/2778\n",
      "Processing batch 1424/2778\n",
      "Processing batch 1425/2778\n",
      "Processing batch 1426/2778\n",
      "Processing batch 1427/2778\n",
      "Processing batch 1428/2778\n",
      "Processing batch 1429/2778\n",
      "Processing batch 1430/2778\n",
      "Processing batch 1431/2778\n",
      "Processing batch 1432/2778\n",
      "Processing batch 1433/2778\n",
      "Processing batch 1434/2778\n",
      "Processing batch 1435/2778\n",
      "Processing batch 1436/2778\n",
      "Processing batch 1437/2778\n",
      "Processing batch 1438/2778\n",
      "Processing batch 1439/2778\n",
      "Processing batch 1440/2778\n",
      "Processing batch 1441/2778\n",
      "Processing batch 1442/2778\n",
      "Processing batch 1443/2778\n",
      "Processing batch 1444/2778\n",
      "Processing batch 1445/2778\n",
      "Processing batch 1446/2778\n",
      "Processing batch 1447/2778\n",
      "Processing batch 1448/2778\n",
      "Processing batch 1449/2778\n",
      "Processing batch 1450/2778\n",
      "Processing batch 1451/2778\n",
      "Processing batch 1452/2778\n",
      "Processing batch 1453/2778\n",
      "Processing batch 1454/2778\n",
      "Processing batch 1455/2778\n",
      "Processing batch 1456/2778\n",
      "Processing batch 1457/2778\n",
      "Processing batch 1458/2778\n",
      "Processing batch 1459/2778\n",
      "Processing batch 1460/2778\n",
      "Processing batch 1461/2778\n",
      "Processing batch 1462/2778\n",
      "Processing batch 1463/2778\n",
      "Processing batch 1464/2778\n",
      "Processing batch 1465/2778\n",
      "Processing batch 1466/2778\n",
      "Processing batch 1467/2778\n",
      "Processing batch 1468/2778\n",
      "Processing batch 1469/2778\n",
      "Processing batch 1470/2778\n",
      "Processing batch 1471/2778\n",
      "Processing batch 1472/2778\n",
      "Processing batch 1473/2778\n",
      "Processing batch 1474/2778\n",
      "Processing batch 1475/2778\n",
      "Processing batch 1476/2778\n",
      "Processing batch 1477/2778\n",
      "Processing batch 1478/2778\n",
      "Processing batch 1479/2778\n",
      "Processing batch 1480/2778\n",
      "Processing batch 1481/2778\n",
      "Processing batch 1482/2778\n",
      "Processing batch 1483/2778\n",
      "Processing batch 1484/2778\n",
      "Processing batch 1485/2778\n",
      "Processing batch 1486/2778\n",
      "Processing batch 1487/2778\n",
      "Processing batch 1488/2778\n",
      "Processing batch 1489/2778\n",
      "Processing batch 1490/2778\n",
      "Processing batch 1491/2778\n",
      "Processing batch 1492/2778\n",
      "Processing batch 1493/2778\n",
      "Processing batch 1494/2778\n",
      "Processing batch 1495/2778\n",
      "Processing batch 1496/2778\n",
      "Processing batch 1497/2778\n",
      "Processing batch 1498/2778\n",
      "Processing batch 1499/2778\n",
      "Processing batch 1500/2778\n",
      "Processing batch 1501/2778\n",
      "Processing batch 1502/2778\n",
      "Processing batch 1503/2778\n",
      "Processing batch 1504/2778\n",
      "Processing batch 1505/2778\n",
      "Processing batch 1506/2778\n",
      "Processing batch 1507/2778\n",
      "Processing batch 1508/2778\n",
      "Processing batch 1509/2778\n",
      "Processing batch 1510/2778\n",
      "Processing batch 1511/2778\n",
      "Processing batch 1512/2778\n",
      "Processing batch 1513/2778\n",
      "Processing batch 1514/2778\n",
      "Processing batch 1515/2778\n",
      "Processing batch 1516/2778\n",
      "Processing batch 1517/2778\n",
      "Processing batch 1518/2778\n",
      "Processing batch 1519/2778\n",
      "Processing batch 1520/2778\n",
      "Processing batch 1521/2778\n",
      "Processing batch 1522/2778\n",
      "Processing batch 1523/2778\n",
      "Processing batch 1524/2778\n",
      "Processing batch 1525/2778\n",
      "Processing batch 1526/2778\n",
      "Processing batch 1527/2778\n",
      "Processing batch 1528/2778\n",
      "Processing batch 1529/2778\n",
      "Processing batch 1530/2778\n",
      "Processing batch 1531/2778\n",
      "Processing batch 1532/2778\n",
      "Processing batch 1533/2778\n",
      "Processing batch 1534/2778\n",
      "Processing batch 1535/2778\n",
      "Processing batch 1536/2778\n",
      "Processing batch 1537/2778\n",
      "Processing batch 1538/2778\n",
      "Processing batch 1539/2778\n",
      "Processing batch 1540/2778\n",
      "Processing batch 1541/2778\n",
      "Processing batch 1542/2778\n",
      "Processing batch 1543/2778\n",
      "Processing batch 1544/2778\n",
      "Processing batch 1545/2778\n",
      "Processing batch 1546/2778\n",
      "Processing batch 1547/2778\n",
      "Processing batch 1548/2778\n",
      "Processing batch 1549/2778\n",
      "Processing batch 1550/2778\n",
      "Processing batch 1551/2778\n",
      "Processing batch 1552/2778\n",
      "Processing batch 1553/2778\n",
      "Processing batch 1554/2778\n",
      "Processing batch 1555/2778\n",
      "Processing batch 1556/2778\n",
      "Processing batch 1557/2778\n",
      "Processing batch 1558/2778\n",
      "Processing batch 1559/2778\n",
      "Processing batch 1560/2778\n",
      "Processing batch 1561/2778\n",
      "Processing batch 1562/2778\n",
      "Processing batch 1563/2778\n",
      "Processing batch 1564/2778\n",
      "Processing batch 1565/2778\n",
      "Processing batch 1566/2778\n",
      "Processing batch 1567/2778\n",
      "Processing batch 1568/2778\n",
      "Processing batch 1569/2778\n",
      "Processing batch 1570/2778\n",
      "Processing batch 1571/2778\n",
      "Processing batch 1572/2778\n",
      "Processing batch 1573/2778\n",
      "Processing batch 1574/2778\n",
      "Processing batch 1575/2778\n",
      "Processing batch 1576/2778\n",
      "Processing batch 1577/2778\n",
      "Processing batch 1578/2778\n",
      "Processing batch 1579/2778\n",
      "Processing batch 1580/2778\n",
      "Processing batch 1581/2778\n",
      "Processing batch 1582/2778\n",
      "Processing batch 1583/2778\n",
      "Processing batch 1584/2778\n",
      "Processing batch 1585/2778\n",
      "Processing batch 1586/2778\n",
      "Processing batch 1587/2778\n",
      "Processing batch 1588/2778\n",
      "Processing batch 1589/2778\n",
      "Processing batch 1590/2778\n",
      "Processing batch 1591/2778\n",
      "Processing batch 1592/2778\n",
      "Processing batch 1593/2778\n",
      "Processing batch 1594/2778\n",
      "Processing batch 1595/2778\n",
      "Processing batch 1596/2778\n",
      "Processing batch 1597/2778\n",
      "Processing batch 1598/2778\n",
      "Processing batch 1599/2778\n",
      "Processing batch 1600/2778\n",
      "Processing batch 1601/2778\n",
      "Processing batch 1602/2778\n",
      "Processing batch 1603/2778\n",
      "Processing batch 1604/2778\n",
      "Processing batch 1605/2778\n",
      "Processing batch 1606/2778\n",
      "Processing batch 1607/2778\n",
      "Processing batch 1608/2778\n",
      "Processing batch 1609/2778\n",
      "Processing batch 1610/2778\n",
      "Processing batch 1611/2778\n",
      "Processing batch 1612/2778\n",
      "Processing batch 1613/2778\n",
      "Processing batch 1614/2778\n",
      "Processing batch 1615/2778\n",
      "Processing batch 1616/2778\n",
      "Processing batch 1617/2778\n",
      "Processing batch 1618/2778\n",
      "Processing batch 1619/2778\n",
      "Processing batch 1620/2778\n",
      "Processing batch 1621/2778\n",
      "Processing batch 1622/2778\n",
      "Processing batch 1623/2778\n",
      "Processing batch 1624/2778\n",
      "Processing batch 1625/2778\n",
      "Processing batch 1626/2778\n",
      "Processing batch 1627/2778\n",
      "Processing batch 1628/2778\n",
      "Processing batch 1629/2778\n",
      "Processing batch 1630/2778\n",
      "Processing batch 1631/2778\n",
      "Processing batch 1632/2778\n",
      "Processing batch 1633/2778\n",
      "Processing batch 1634/2778\n",
      "Processing batch 1635/2778\n",
      "Processing batch 1636/2778\n",
      "Processing batch 1637/2778\n",
      "Processing batch 1638/2778\n",
      "Processing batch 1639/2778\n",
      "Processing batch 1640/2778\n",
      "Processing batch 1641/2778\n",
      "Processing batch 1642/2778\n",
      "Processing batch 1643/2778\n",
      "Processing batch 1644/2778\n",
      "Processing batch 1645/2778\n",
      "Processing batch 1646/2778\n",
      "Processing batch 1647/2778\n",
      "Processing batch 1648/2778\n",
      "Processing batch 1649/2778\n",
      "Processing batch 1650/2778\n",
      "Processing batch 1651/2778\n",
      "Processing batch 1652/2778\n",
      "Processing batch 1653/2778\n",
      "Processing batch 1654/2778\n",
      "Processing batch 1655/2778\n",
      "Processing batch 1656/2778\n",
      "Processing batch 1657/2778\n",
      "Processing batch 1658/2778\n",
      "Processing batch 1659/2778\n",
      "Processing batch 1660/2778\n",
      "Processing batch 1661/2778\n",
      "Processing batch 1662/2778\n",
      "Processing batch 1663/2778\n",
      "Processing batch 1664/2778\n",
      "Processing batch 1665/2778\n",
      "Processing batch 1666/2778\n",
      "Processing batch 1667/2778\n",
      "Processing batch 1668/2778\n",
      "Processing batch 1669/2778\n",
      "Processing batch 1670/2778\n",
      "Processing batch 1671/2778\n",
      "Processing batch 1672/2778\n",
      "Processing batch 1673/2778\n",
      "Processing batch 1674/2778\n",
      "Processing batch 1675/2778\n",
      "Processing batch 1676/2778\n",
      "Processing batch 1677/2778\n",
      "Processing batch 1678/2778\n",
      "Processing batch 1679/2778\n",
      "Processing batch 1680/2778\n",
      "Processing batch 1681/2778\n",
      "Processing batch 1682/2778\n",
      "Processing batch 1683/2778\n",
      "Processing batch 1684/2778\n",
      "Processing batch 1685/2778\n",
      "Processing batch 1686/2778\n",
      "Processing batch 1687/2778\n",
      "Processing batch 1688/2778\n",
      "Processing batch 1689/2778\n",
      "Processing batch 1690/2778\n",
      "Processing batch 1691/2778\n",
      "Processing batch 1692/2778\n",
      "Processing batch 1693/2778\n",
      "Processing batch 1694/2778\n",
      "Processing batch 1695/2778\n",
      "Processing batch 1696/2778\n",
      "Processing batch 1697/2778\n",
      "Processing batch 1698/2778\n",
      "Processing batch 1699/2778\n",
      "Processing batch 1700/2778\n",
      "Processing batch 1701/2778\n",
      "Processing batch 1702/2778\n",
      "Processing batch 1703/2778\n",
      "Processing batch 1704/2778\n",
      "Processing batch 1705/2778\n",
      "Processing batch 1706/2778\n",
      "Processing batch 1707/2778\n",
      "Processing batch 1708/2778\n",
      "Processing batch 1709/2778\n",
      "Processing batch 1710/2778\n",
      "Processing batch 1711/2778\n",
      "Processing batch 1712/2778\n",
      "Processing batch 1713/2778\n",
      "Processing batch 1714/2778\n",
      "Processing batch 1715/2778\n",
      "Processing batch 1716/2778\n",
      "Processing batch 1717/2778\n",
      "Processing batch 1718/2778\n",
      "Processing batch 1719/2778\n",
      "Processing batch 1720/2778\n",
      "Processing batch 1721/2778\n",
      "Processing batch 1722/2778\n",
      "Processing batch 1723/2778\n",
      "Processing batch 1724/2778\n",
      "Processing batch 1725/2778\n",
      "Processing batch 1726/2778\n",
      "Processing batch 1727/2778\n",
      "Processing batch 1728/2778\n",
      "Processing batch 1729/2778\n",
      "Processing batch 1730/2778\n",
      "Processing batch 1731/2778\n",
      "Processing batch 1732/2778\n",
      "Processing batch 1733/2778\n",
      "Processing batch 1734/2778\n",
      "Processing batch 1735/2778\n",
      "Processing batch 1736/2778\n",
      "Processing batch 1737/2778\n",
      "Processing batch 1738/2778\n",
      "Processing batch 1739/2778\n",
      "Processing batch 1740/2778\n",
      "Processing batch 1741/2778\n",
      "Processing batch 1742/2778\n",
      "Processing batch 1743/2778\n",
      "Processing batch 1744/2778\n",
      "Processing batch 1745/2778\n",
      "Processing batch 1746/2778\n",
      "Processing batch 1747/2778\n",
      "Processing batch 1748/2778\n",
      "Processing batch 1749/2778\n",
      "Processing batch 1750/2778\n",
      "Processing batch 1751/2778\n",
      "Processing batch 1752/2778\n",
      "Processing batch 1753/2778\n",
      "Processing batch 1754/2778\n",
      "Processing batch 1755/2778\n",
      "Processing batch 1756/2778\n",
      "Processing batch 1757/2778\n",
      "Processing batch 1758/2778\n",
      "Processing batch 1759/2778\n",
      "Processing batch 1760/2778\n",
      "Processing batch 1761/2778\n",
      "Processing batch 1762/2778\n",
      "Processing batch 1763/2778\n",
      "Processing batch 1764/2778\n",
      "Processing batch 1765/2778\n",
      "Processing batch 1766/2778\n",
      "Processing batch 1767/2778\n",
      "Processing batch 1768/2778\n",
      "Processing batch 1769/2778\n",
      "Processing batch 1770/2778\n",
      "Processing batch 1771/2778\n",
      "Processing batch 1772/2778\n",
      "Processing batch 1773/2778\n",
      "Processing batch 1774/2778\n",
      "Processing batch 1775/2778\n",
      "Processing batch 1776/2778\n",
      "Processing batch 1777/2778\n",
      "Processing batch 1778/2778\n",
      "Processing batch 1779/2778\n",
      "Processing batch 1780/2778\n",
      "Processing batch 1781/2778\n",
      "Processing batch 1782/2778\n",
      "Processing batch 1783/2778\n",
      "Processing batch 1784/2778\n",
      "Processing batch 1785/2778\n",
      "Processing batch 1786/2778\n",
      "Processing batch 1787/2778\n",
      "Processing batch 1788/2778\n",
      "Processing batch 1789/2778\n",
      "Processing batch 1790/2778\n",
      "Processing batch 1791/2778\n",
      "Processing batch 1792/2778\n",
      "Processing batch 1793/2778\n",
      "Processing batch 1794/2778\n",
      "Processing batch 1795/2778\n",
      "Processing batch 1796/2778\n",
      "Processing batch 1797/2778\n",
      "Processing batch 1798/2778\n",
      "Processing batch 1799/2778\n",
      "Processing batch 1800/2778\n",
      "Processing batch 1801/2778\n",
      "Processing batch 1802/2778\n",
      "Processing batch 1803/2778\n",
      "Processing batch 1804/2778\n",
      "Processing batch 1805/2778\n",
      "Processing batch 1806/2778\n",
      "Processing batch 1807/2778\n",
      "Processing batch 1808/2778\n",
      "Processing batch 1809/2778\n",
      "Processing batch 1810/2778\n",
      "Processing batch 1811/2778\n",
      "Processing batch 1812/2778\n",
      "Processing batch 1813/2778\n",
      "Processing batch 1814/2778\n",
      "Processing batch 1815/2778\n",
      "Processing batch 1816/2778\n",
      "Processing batch 1817/2778\n",
      "Processing batch 1818/2778\n",
      "Processing batch 1819/2778\n",
      "Processing batch 1820/2778\n",
      "Processing batch 1821/2778\n",
      "Processing batch 1822/2778\n",
      "Processing batch 1823/2778\n",
      "Processing batch 1824/2778\n",
      "Processing batch 1825/2778\n",
      "Processing batch 1826/2778\n",
      "Processing batch 1827/2778\n",
      "Processing batch 1828/2778\n",
      "Processing batch 1829/2778\n",
      "Processing batch 1830/2778\n",
      "Processing batch 1831/2778\n",
      "Processing batch 1832/2778\n",
      "Processing batch 1833/2778\n",
      "Processing batch 1834/2778\n",
      "Processing batch 1835/2778\n",
      "Processing batch 1836/2778\n",
      "Processing batch 1837/2778\n",
      "Processing batch 1838/2778\n",
      "Processing batch 1839/2778\n",
      "Processing batch 1840/2778\n",
      "Processing batch 1841/2778\n",
      "Processing batch 1842/2778\n",
      "Processing batch 1843/2778\n",
      "Processing batch 1844/2778\n",
      "Processing batch 1845/2778\n",
      "Processing batch 1846/2778\n",
      "Processing batch 1847/2778\n",
      "Processing batch 1848/2778\n",
      "Processing batch 1849/2778\n",
      "Processing batch 1850/2778\n",
      "Processing batch 1851/2778\n",
      "Processing batch 1852/2778\n",
      "Processing batch 1853/2778\n",
      "Processing batch 1854/2778\n",
      "Processing batch 1855/2778\n",
      "Processing batch 1856/2778\n",
      "Processing batch 1857/2778\n",
      "Processing batch 1858/2778\n",
      "Processing batch 1859/2778\n",
      "Processing batch 1860/2778\n",
      "Processing batch 1861/2778\n",
      "Processing batch 1862/2778\n",
      "Processing batch 1863/2778\n",
      "Processing batch 1864/2778\n",
      "Processing batch 1865/2778\n",
      "Processing batch 1866/2778\n",
      "Processing batch 1867/2778\n",
      "Processing batch 1868/2778\n",
      "Processing batch 1869/2778\n",
      "Processing batch 1870/2778\n",
      "Processing batch 1871/2778\n",
      "Processing batch 1872/2778\n",
      "Processing batch 1873/2778\n",
      "Processing batch 1874/2778\n",
      "Processing batch 1875/2778\n",
      "Processing batch 1876/2778\n",
      "Processing batch 1877/2778\n",
      "Processing batch 1878/2778\n",
      "Processing batch 1879/2778\n",
      "Processing batch 1880/2778\n",
      "Processing batch 1881/2778\n",
      "Processing batch 1882/2778\n",
      "Processing batch 1883/2778\n",
      "Processing batch 1884/2778\n",
      "Processing batch 1885/2778\n",
      "Processing batch 1886/2778\n",
      "Processing batch 1887/2778\n",
      "Processing batch 1888/2778\n",
      "Processing batch 1889/2778\n",
      "Processing batch 1890/2778\n",
      "Processing batch 1891/2778\n",
      "Processing batch 1892/2778\n",
      "Processing batch 1893/2778\n",
      "Processing batch 1894/2778\n",
      "Processing batch 1895/2778\n",
      "Processing batch 1896/2778\n",
      "Processing batch 1897/2778\n",
      "Processing batch 1898/2778\n",
      "Processing batch 1899/2778\n",
      "Processing batch 1900/2778\n",
      "Processing batch 1901/2778\n",
      "Processing batch 1902/2778\n",
      "Processing batch 1903/2778\n",
      "Processing batch 1904/2778\n",
      "Processing batch 1905/2778\n",
      "Processing batch 1906/2778\n",
      "Processing batch 1907/2778\n",
      "Processing batch 1908/2778\n",
      "Processing batch 1909/2778\n",
      "Processing batch 1910/2778\n",
      "Processing batch 1911/2778\n",
      "Processing batch 1912/2778\n",
      "Processing batch 1913/2778\n",
      "Processing batch 1914/2778\n",
      "Processing batch 1915/2778\n",
      "Processing batch 1916/2778\n",
      "Processing batch 1917/2778\n",
      "Processing batch 1918/2778\n",
      "Processing batch 1919/2778\n",
      "Processing batch 1920/2778\n",
      "Processing batch 1921/2778\n",
      "Processing batch 1922/2778\n",
      "Processing batch 1923/2778\n",
      "Processing batch 1924/2778\n",
      "Processing batch 1925/2778\n",
      "Processing batch 1926/2778\n",
      "Processing batch 1927/2778\n",
      "Processing batch 1928/2778\n",
      "Processing batch 1929/2778\n",
      "Processing batch 1930/2778\n",
      "Processing batch 1931/2778\n",
      "Processing batch 1932/2778\n",
      "Processing batch 1933/2778\n",
      "Processing batch 1934/2778\n",
      "Processing batch 1935/2778\n",
      "Processing batch 1936/2778\n",
      "Processing batch 1937/2778\n",
      "Processing batch 1938/2778\n",
      "Processing batch 1939/2778\n",
      "Processing batch 1940/2778\n",
      "Processing batch 1941/2778\n",
      "Processing batch 1942/2778\n",
      "Processing batch 1943/2778\n",
      "Processing batch 1944/2778\n",
      "Processing batch 1945/2778\n",
      "Processing batch 1946/2778\n",
      "Processing batch 1947/2778\n",
      "Processing batch 1948/2778\n",
      "Processing batch 1949/2778\n",
      "Processing batch 1950/2778\n",
      "Processing batch 1951/2778\n",
      "Processing batch 1952/2778\n",
      "Processing batch 1953/2778\n",
      "Processing batch 1954/2778\n",
      "Processing batch 1955/2778\n",
      "Processing batch 1956/2778\n",
      "Processing batch 1957/2778\n",
      "Processing batch 1958/2778\n",
      "Processing batch 1959/2778\n",
      "Processing batch 1960/2778\n",
      "Processing batch 1961/2778\n",
      "Processing batch 1962/2778\n",
      "Processing batch 1963/2778\n",
      "Processing batch 1964/2778\n",
      "Processing batch 1965/2778\n",
      "Processing batch 1966/2778\n",
      "Processing batch 1967/2778\n",
      "Processing batch 1968/2778\n",
      "Processing batch 1969/2778\n",
      "Processing batch 1970/2778\n",
      "Processing batch 1971/2778\n",
      "Processing batch 1972/2778\n",
      "Processing batch 1973/2778\n",
      "Processing batch 1974/2778\n",
      "Processing batch 1975/2778\n",
      "Processing batch 1976/2778\n",
      "Processing batch 1977/2778\n",
      "Processing batch 1978/2778\n",
      "Processing batch 1979/2778\n",
      "Processing batch 1980/2778\n",
      "Processing batch 1981/2778\n",
      "Processing batch 1982/2778\n",
      "Processing batch 1983/2778\n",
      "Processing batch 1984/2778\n",
      "Processing batch 1985/2778\n",
      "Processing batch 1986/2778\n",
      "Processing batch 1987/2778\n",
      "Processing batch 1988/2778\n",
      "Processing batch 1989/2778\n",
      "Processing batch 1990/2778\n",
      "Processing batch 1991/2778\n",
      "Processing batch 1992/2778\n",
      "Processing batch 1993/2778\n",
      "Processing batch 1994/2778\n",
      "Processing batch 1995/2778\n",
      "Processing batch 1996/2778\n",
      "Processing batch 1997/2778\n",
      "Processing batch 1998/2778\n",
      "Processing batch 1999/2778\n",
      "Processing batch 2000/2778\n",
      "Processing batch 2001/2778\n",
      "Processing batch 2002/2778\n",
      "Processing batch 2003/2778\n",
      "Processing batch 2004/2778\n",
      "Processing batch 2005/2778\n",
      "Processing batch 2006/2778\n",
      "Processing batch 2007/2778\n",
      "Processing batch 2008/2778\n",
      "Processing batch 2009/2778\n",
      "Processing batch 2010/2778\n",
      "Processing batch 2011/2778\n",
      "Processing batch 2012/2778\n",
      "Processing batch 2013/2778\n",
      "Processing batch 2014/2778\n",
      "Processing batch 2015/2778\n",
      "Processing batch 2016/2778\n",
      "Processing batch 2017/2778\n",
      "Processing batch 2018/2778\n",
      "Processing batch 2019/2778\n",
      "Processing batch 2020/2778\n",
      "Processing batch 2021/2778\n",
      "Processing batch 2022/2778\n",
      "Processing batch 2023/2778\n",
      "Processing batch 2024/2778\n",
      "Processing batch 2025/2778\n",
      "Processing batch 2026/2778\n",
      "Processing batch 2027/2778\n",
      "Processing batch 2028/2778\n",
      "Processing batch 2029/2778\n",
      "Processing batch 2030/2778\n",
      "Processing batch 2031/2778\n",
      "Processing batch 2032/2778\n",
      "Processing batch 2033/2778\n",
      "Processing batch 2034/2778\n",
      "Processing batch 2035/2778\n",
      "Processing batch 2036/2778\n",
      "Processing batch 2037/2778\n",
      "Processing batch 2038/2778\n",
      "Processing batch 2039/2778\n",
      "Processing batch 2040/2778\n",
      "Processing batch 2041/2778\n",
      "Processing batch 2042/2778\n",
      "Processing batch 2043/2778\n",
      "Processing batch 2044/2778\n",
      "Processing batch 2045/2778\n",
      "Processing batch 2046/2778\n",
      "Processing batch 2047/2778\n",
      "Processing batch 2048/2778\n",
      "Processing batch 2049/2778\n",
      "Processing batch 2050/2778\n",
      "Processing batch 2051/2778\n",
      "Processing batch 2052/2778\n",
      "Processing batch 2053/2778\n",
      "Processing batch 2054/2778\n",
      "Processing batch 2055/2778\n",
      "Processing batch 2056/2778\n",
      "Processing batch 2057/2778\n",
      "Processing batch 2058/2778\n",
      "Processing batch 2059/2778\n",
      "Processing batch 2060/2778\n",
      "Processing batch 2061/2778\n",
      "Processing batch 2062/2778\n",
      "Processing batch 2063/2778\n",
      "Processing batch 2064/2778\n",
      "Processing batch 2065/2778\n",
      "Processing batch 2066/2778\n",
      "Processing batch 2067/2778\n",
      "Processing batch 2068/2778\n",
      "Processing batch 2069/2778\n",
      "Processing batch 2070/2778\n",
      "Processing batch 2071/2778\n",
      "Processing batch 2072/2778\n",
      "Processing batch 2073/2778\n",
      "Processing batch 2074/2778\n",
      "Processing batch 2075/2778\n",
      "Processing batch 2076/2778\n",
      "Processing batch 2077/2778\n",
      "Processing batch 2078/2778\n",
      "Processing batch 2079/2778\n",
      "Processing batch 2080/2778\n",
      "Processing batch 2081/2778\n",
      "Processing batch 2082/2778\n",
      "Processing batch 2083/2778\n",
      "Processing batch 2084/2778\n",
      "Processing batch 2085/2778\n",
      "Processing batch 2086/2778\n",
      "Processing batch 2087/2778\n",
      "Processing batch 2088/2778\n",
      "Processing batch 2089/2778\n",
      "Processing batch 2090/2778\n",
      "Processing batch 2091/2778\n",
      "Processing batch 2092/2778\n",
      "Processing batch 2093/2778\n",
      "Processing batch 2094/2778\n",
      "Processing batch 2095/2778\n",
      "Processing batch 2096/2778\n",
      "Processing batch 2097/2778\n",
      "Processing batch 2098/2778\n",
      "Processing batch 2099/2778\n",
      "Processing batch 2100/2778\n",
      "Processing batch 2101/2778\n",
      "Processing batch 2102/2778\n",
      "Processing batch 2103/2778\n",
      "Processing batch 2104/2778\n",
      "Processing batch 2105/2778\n",
      "Processing batch 2106/2778\n",
      "Processing batch 2107/2778\n",
      "Processing batch 2108/2778\n",
      "Processing batch 2109/2778\n",
      "Processing batch 2110/2778\n",
      "Processing batch 2111/2778\n",
      "Processing batch 2112/2778\n",
      "Processing batch 2113/2778\n",
      "Processing batch 2114/2778\n",
      "Processing batch 2115/2778\n",
      "Processing batch 2116/2778\n",
      "Processing batch 2117/2778\n",
      "Processing batch 2118/2778\n",
      "Processing batch 2119/2778\n",
      "Processing batch 2120/2778\n",
      "Processing batch 2121/2778\n",
      "Processing batch 2122/2778\n",
      "Processing batch 2123/2778\n",
      "Processing batch 2124/2778\n",
      "Processing batch 2125/2778\n",
      "Processing batch 2126/2778\n",
      "Processing batch 2127/2778\n",
      "Processing batch 2128/2778\n",
      "Processing batch 2129/2778\n",
      "Processing batch 2130/2778\n",
      "Processing batch 2131/2778\n",
      "Processing batch 2132/2778\n",
      "Processing batch 2133/2778\n",
      "Processing batch 2134/2778\n",
      "Processing batch 2135/2778\n",
      "Processing batch 2136/2778\n",
      "Processing batch 2137/2778\n",
      "Processing batch 2138/2778\n",
      "Processing batch 2139/2778\n",
      "Processing batch 2140/2778\n",
      "Processing batch 2141/2778\n",
      "Processing batch 2142/2778\n",
      "Processing batch 2143/2778\n",
      "Processing batch 2144/2778\n",
      "Processing batch 2145/2778\n",
      "Processing batch 2146/2778\n",
      "Processing batch 2147/2778\n",
      "Processing batch 2148/2778\n",
      "Processing batch 2149/2778\n",
      "Processing batch 2150/2778\n",
      "Processing batch 2151/2778\n",
      "Processing batch 2152/2778\n",
      "Processing batch 2153/2778\n",
      "Processing batch 2154/2778\n",
      "Processing batch 2155/2778\n",
      "Processing batch 2156/2778\n",
      "Processing batch 2157/2778\n",
      "Processing batch 2158/2778\n",
      "Processing batch 2159/2778\n",
      "Processing batch 2160/2778\n",
      "Processing batch 2161/2778\n",
      "Processing batch 2162/2778\n",
      "Processing batch 2163/2778\n",
      "Processing batch 2164/2778\n",
      "Processing batch 2165/2778\n",
      "Processing batch 2166/2778\n",
      "Processing batch 2167/2778\n",
      "Processing batch 2168/2778\n",
      "Processing batch 2169/2778\n",
      "Processing batch 2170/2778\n",
      "Processing batch 2171/2778\n",
      "Processing batch 2172/2778\n",
      "Processing batch 2173/2778\n",
      "Processing batch 2174/2778\n",
      "Processing batch 2175/2778\n",
      "Processing batch 2176/2778\n",
      "Processing batch 2177/2778\n",
      "Processing batch 2178/2778\n",
      "Processing batch 2179/2778\n",
      "Processing batch 2180/2778\n",
      "Processing batch 2181/2778\n",
      "Processing batch 2182/2778\n",
      "Processing batch 2183/2778\n",
      "Processing batch 2184/2778\n",
      "Processing batch 2185/2778\n",
      "Processing batch 2186/2778\n",
      "Processing batch 2187/2778\n",
      "Processing batch 2188/2778\n",
      "Processing batch 2189/2778\n",
      "Processing batch 2190/2778\n",
      "Processing batch 2191/2778\n",
      "Processing batch 2192/2778\n",
      "Processing batch 2193/2778\n",
      "Processing batch 2194/2778\n",
      "Processing batch 2195/2778\n",
      "Processing batch 2196/2778\n",
      "Processing batch 2197/2778\n",
      "Processing batch 2198/2778\n",
      "Processing batch 2199/2778\n",
      "Processing batch 2200/2778\n",
      "Processing batch 2201/2778\n",
      "Processing batch 2202/2778\n",
      "Processing batch 2203/2778\n",
      "Processing batch 2204/2778\n",
      "Processing batch 2205/2778\n",
      "Processing batch 2206/2778\n",
      "Processing batch 2207/2778\n",
      "Processing batch 2208/2778\n",
      "Processing batch 2209/2778\n",
      "Processing batch 2210/2778\n",
      "Processing batch 2211/2778\n",
      "Processing batch 2212/2778\n",
      "Processing batch 2213/2778\n",
      "Processing batch 2214/2778\n",
      "Processing batch 2215/2778\n",
      "Processing batch 2216/2778\n",
      "Processing batch 2217/2778\n",
      "Processing batch 2218/2778\n",
      "Processing batch 2219/2778\n",
      "Processing batch 2220/2778\n",
      "Processing batch 2221/2778\n",
      "Processing batch 2222/2778\n",
      "Processing batch 2223/2778\n",
      "Processing batch 2224/2778\n",
      "Processing batch 2225/2778\n",
      "Processing batch 2226/2778\n",
      "Processing batch 2227/2778\n",
      "Processing batch 2228/2778\n",
      "Processing batch 2229/2778\n",
      "Processing batch 2230/2778\n",
      "Processing batch 2231/2778\n",
      "Processing batch 2232/2778\n",
      "Processing batch 2233/2778\n",
      "Processing batch 2234/2778\n",
      "Processing batch 2235/2778\n",
      "Processing batch 2236/2778\n",
      "Processing batch 2237/2778\n",
      "Processing batch 2238/2778\n",
      "Processing batch 2239/2778\n",
      "Processing batch 2240/2778\n",
      "Processing batch 2241/2778\n",
      "Processing batch 2242/2778\n",
      "Processing batch 2243/2778\n",
      "Processing batch 2244/2778\n",
      "Processing batch 2245/2778\n",
      "Processing batch 2246/2778\n",
      "Processing batch 2247/2778\n",
      "Processing batch 2248/2778\n",
      "Processing batch 2249/2778\n",
      "Processing batch 2250/2778\n",
      "Processing batch 2251/2778\n",
      "Processing batch 2252/2778\n",
      "Processing batch 2253/2778\n",
      "Processing batch 2254/2778\n",
      "Processing batch 2255/2778\n",
      "Processing batch 2256/2778\n",
      "Processing batch 2257/2778\n",
      "Processing batch 2258/2778\n",
      "Processing batch 2259/2778\n",
      "Processing batch 2260/2778\n",
      "Processing batch 2261/2778\n",
      "Processing batch 2262/2778\n",
      "Processing batch 2263/2778\n",
      "Processing batch 2264/2778\n",
      "Processing batch 2265/2778\n",
      "Processing batch 2266/2778\n",
      "Processing batch 2267/2778\n",
      "Processing batch 2268/2778\n",
      "Processing batch 2269/2778\n",
      "Processing batch 2270/2778\n",
      "Processing batch 2271/2778\n",
      "Processing batch 2272/2778\n",
      "Processing batch 2273/2778\n",
      "Processing batch 2274/2778\n",
      "Processing batch 2275/2778\n",
      "Processing batch 2276/2778\n",
      "Processing batch 2277/2778\n",
      "Processing batch 2278/2778\n",
      "Processing batch 2279/2778\n",
      "Processing batch 2280/2778\n",
      "Processing batch 2281/2778\n",
      "Processing batch 2282/2778\n",
      "Processing batch 2283/2778\n",
      "Processing batch 2284/2778\n",
      "Processing batch 2285/2778\n",
      "Processing batch 2286/2778\n",
      "Processing batch 2287/2778\n",
      "Processing batch 2288/2778\n",
      "Processing batch 2289/2778\n",
      "Processing batch 2290/2778\n",
      "Processing batch 2291/2778\n",
      "Processing batch 2292/2778\n",
      "Processing batch 2293/2778\n",
      "Processing batch 2294/2778\n",
      "Processing batch 2295/2778\n",
      "Processing batch 2296/2778\n",
      "Processing batch 2297/2778\n",
      "Processing batch 2298/2778\n",
      "Processing batch 2299/2778\n",
      "Processing batch 2300/2778\n",
      "Processing batch 2301/2778\n",
      "Processing batch 2302/2778\n",
      "Processing batch 2303/2778\n",
      "Processing batch 2304/2778\n",
      "Processing batch 2305/2778\n",
      "Processing batch 2306/2778\n",
      "Processing batch 2307/2778\n",
      "Processing batch 2308/2778\n",
      "Processing batch 2309/2778\n",
      "Processing batch 2310/2778\n",
      "Processing batch 2311/2778\n",
      "Processing batch 2312/2778\n",
      "Processing batch 2313/2778\n",
      "Processing batch 2314/2778\n",
      "Processing batch 2315/2778\n",
      "Processing batch 2316/2778\n",
      "Processing batch 2317/2778\n",
      "Processing batch 2318/2778\n",
      "Processing batch 2319/2778\n",
      "Processing batch 2320/2778\n",
      "Processing batch 2321/2778\n",
      "Processing batch 2322/2778\n",
      "Processing batch 2323/2778\n",
      "Processing batch 2324/2778\n",
      "Processing batch 2325/2778\n",
      "Processing batch 2326/2778\n",
      "Processing batch 2327/2778\n",
      "Processing batch 2328/2778\n",
      "Processing batch 2329/2778\n",
      "Processing batch 2330/2778\n",
      "Processing batch 2331/2778\n",
      "Processing batch 2332/2778\n",
      "Processing batch 2333/2778\n",
      "Processing batch 2334/2778\n",
      "Processing batch 2335/2778\n",
      "Processing batch 2336/2778\n",
      "Processing batch 2337/2778\n",
      "Processing batch 2338/2778\n",
      "Processing batch 2339/2778\n",
      "Processing batch 2340/2778\n",
      "Processing batch 2341/2778\n",
      "Processing batch 2342/2778\n",
      "Processing batch 2343/2778\n",
      "Processing batch 2344/2778\n",
      "Processing batch 2345/2778\n",
      "Processing batch 2346/2778\n",
      "Processing batch 2347/2778\n",
      "Processing batch 2348/2778\n",
      "Processing batch 2349/2778\n",
      "Processing batch 2350/2778\n",
      "Processing batch 2351/2778\n",
      "Processing batch 2352/2778\n",
      "Processing batch 2353/2778\n",
      "Processing batch 2354/2778\n",
      "Processing batch 2355/2778\n",
      "Processing batch 2356/2778\n",
      "Processing batch 2357/2778\n",
      "Processing batch 2358/2778\n",
      "Processing batch 2359/2778\n",
      "Processing batch 2360/2778\n",
      "Processing batch 2361/2778\n",
      "Processing batch 2362/2778\n",
      "Processing batch 2363/2778\n",
      "Processing batch 2364/2778\n",
      "Processing batch 2365/2778\n",
      "Processing batch 2366/2778\n",
      "Processing batch 2367/2778\n",
      "Processing batch 2368/2778\n",
      "Processing batch 2369/2778\n",
      "Processing batch 2370/2778\n",
      "Processing batch 2371/2778\n",
      "Processing batch 2372/2778\n",
      "Processing batch 2373/2778\n",
      "Processing batch 2374/2778\n",
      "Processing batch 2375/2778\n",
      "Processing batch 2376/2778\n",
      "Processing batch 2377/2778\n",
      "Processing batch 2378/2778\n",
      "Processing batch 2379/2778\n",
      "Processing batch 2380/2778\n",
      "Processing batch 2381/2778\n",
      "Processing batch 2382/2778\n",
      "Processing batch 2383/2778\n",
      "Processing batch 2384/2778\n",
      "Processing batch 2385/2778\n",
      "Processing batch 2386/2778\n",
      "Processing batch 2387/2778\n",
      "Processing batch 2388/2778\n",
      "Processing batch 2389/2778\n",
      "Processing batch 2390/2778\n",
      "Processing batch 2391/2778\n",
      "Processing batch 2392/2778\n",
      "Processing batch 2393/2778\n",
      "Processing batch 2394/2778\n",
      "Processing batch 2395/2778\n",
      "Processing batch 2396/2778\n",
      "Processing batch 2397/2778\n",
      "Processing batch 2398/2778\n",
      "Processing batch 2399/2778\n",
      "Processing batch 2400/2778\n",
      "Processing batch 2401/2778\n",
      "Processing batch 2402/2778\n",
      "Processing batch 2403/2778\n",
      "Processing batch 2404/2778\n",
      "Processing batch 2405/2778\n",
      "Processing batch 2406/2778\n",
      "Processing batch 2407/2778\n",
      "Processing batch 2408/2778\n",
      "Processing batch 2409/2778\n",
      "Processing batch 2410/2778\n",
      "Processing batch 2411/2778\n",
      "Processing batch 2412/2778\n",
      "Processing batch 2413/2778\n",
      "Processing batch 2414/2778\n",
      "Processing batch 2415/2778\n",
      "Processing batch 2416/2778\n",
      "Processing batch 2417/2778\n",
      "Processing batch 2418/2778\n",
      "Processing batch 2419/2778\n",
      "Processing batch 2420/2778\n",
      "Processing batch 2421/2778\n",
      "Processing batch 2422/2778\n",
      "Processing batch 2423/2778\n",
      "Processing batch 2424/2778\n",
      "Processing batch 2425/2778\n",
      "Processing batch 2426/2778\n",
      "Processing batch 2427/2778\n",
      "Processing batch 2428/2778\n",
      "Processing batch 2429/2778\n",
      "Processing batch 2430/2778\n",
      "Processing batch 2431/2778\n",
      "Processing batch 2432/2778\n",
      "Processing batch 2433/2778\n",
      "Processing batch 2434/2778\n",
      "Processing batch 2435/2778\n",
      "Processing batch 2436/2778\n",
      "Processing batch 2437/2778\n",
      "Processing batch 2438/2778\n",
      "Processing batch 2439/2778\n",
      "Processing batch 2440/2778\n",
      "Processing batch 2441/2778\n",
      "Processing batch 2442/2778\n",
      "Processing batch 2443/2778\n",
      "Processing batch 2444/2778\n",
      "Processing batch 2445/2778\n",
      "Processing batch 2446/2778\n",
      "Processing batch 2447/2778\n",
      "Processing batch 2448/2778\n",
      "Processing batch 2449/2778\n",
      "Processing batch 2450/2778\n",
      "Processing batch 2451/2778\n",
      "Processing batch 2452/2778\n",
      "Processing batch 2453/2778\n",
      "Processing batch 2454/2778\n",
      "Processing batch 2455/2778\n",
      "Processing batch 2456/2778\n",
      "Processing batch 2457/2778\n",
      "Processing batch 2458/2778\n",
      "Processing batch 2459/2778\n",
      "Processing batch 2460/2778\n",
      "Processing batch 2461/2778\n",
      "Processing batch 2462/2778\n",
      "Processing batch 2463/2778\n",
      "Processing batch 2464/2778\n",
      "Processing batch 2465/2778\n",
      "Processing batch 2466/2778\n",
      "Processing batch 2467/2778\n",
      "Processing batch 2468/2778\n",
      "Processing batch 2469/2778\n",
      "Processing batch 2470/2778\n",
      "Processing batch 2471/2778\n",
      "Processing batch 2472/2778\n",
      "Processing batch 2473/2778\n",
      "Processing batch 2474/2778\n",
      "Processing batch 2475/2778\n",
      "Processing batch 2476/2778\n",
      "Processing batch 2477/2778\n",
      "Processing batch 2478/2778\n",
      "Processing batch 2479/2778\n",
      "Processing batch 2480/2778\n",
      "Processing batch 2481/2778\n",
      "Processing batch 2482/2778\n",
      "Processing batch 2483/2778\n",
      "Processing batch 2484/2778\n",
      "Processing batch 2485/2778\n",
      "Processing batch 2486/2778\n",
      "Processing batch 2487/2778\n",
      "Processing batch 2488/2778\n",
      "Processing batch 2489/2778\n",
      "Processing batch 2490/2778\n",
      "Processing batch 2491/2778\n",
      "Processing batch 2492/2778\n",
      "Processing batch 2493/2778\n",
      "Processing batch 2494/2778\n",
      "Processing batch 2495/2778\n",
      "Processing batch 2496/2778\n",
      "Processing batch 2497/2778\n",
      "Processing batch 2498/2778\n",
      "Processing batch 2499/2778\n",
      "Processing batch 2500/2778\n",
      "Processing batch 2501/2778\n",
      "Processing batch 2502/2778\n",
      "Processing batch 2503/2778\n",
      "Processing batch 2504/2778\n",
      "Processing batch 2505/2778\n",
      "Processing batch 2506/2778\n",
      "Processing batch 2507/2778\n",
      "Processing batch 2508/2778\n",
      "Processing batch 2509/2778\n",
      "Processing batch 2510/2778\n",
      "Processing batch 2511/2778\n",
      "Processing batch 2512/2778\n",
      "Processing batch 2513/2778\n",
      "Processing batch 2514/2778\n",
      "Processing batch 2515/2778\n",
      "Processing batch 2516/2778\n",
      "Processing batch 2517/2778\n",
      "Processing batch 2518/2778\n",
      "Processing batch 2519/2778\n",
      "Processing batch 2520/2778\n",
      "Processing batch 2521/2778\n",
      "Processing batch 2522/2778\n",
      "Processing batch 2523/2778\n",
      "Processing batch 2524/2778\n",
      "Processing batch 2525/2778\n",
      "Processing batch 2526/2778\n",
      "Processing batch 2527/2778\n",
      "Processing batch 2528/2778\n",
      "Processing batch 2529/2778\n",
      "Processing batch 2530/2778\n",
      "Processing batch 2531/2778\n",
      "Processing batch 2532/2778\n",
      "Processing batch 2533/2778\n",
      "Processing batch 2534/2778\n",
      "Processing batch 2535/2778\n",
      "Processing batch 2536/2778\n",
      "Processing batch 2537/2778\n",
      "Processing batch 2538/2778\n",
      "Processing batch 2539/2778\n",
      "Processing batch 2540/2778\n",
      "Processing batch 2541/2778\n",
      "Processing batch 2542/2778\n",
      "Processing batch 2543/2778\n",
      "Processing batch 2544/2778\n",
      "Processing batch 2545/2778\n",
      "Processing batch 2546/2778\n",
      "Processing batch 2547/2778\n",
      "Processing batch 2548/2778\n",
      "Processing batch 2549/2778\n",
      "Processing batch 2550/2778\n",
      "Processing batch 2551/2778\n",
      "Processing batch 2552/2778\n",
      "Processing batch 2553/2778\n",
      "Processing batch 2554/2778\n",
      "Processing batch 2555/2778\n",
      "Processing batch 2556/2778\n",
      "Processing batch 2557/2778\n",
      "Processing batch 2558/2778\n",
      "Processing batch 2559/2778\n",
      "Processing batch 2560/2778\n",
      "Processing batch 2561/2778\n",
      "Processing batch 2562/2778\n",
      "Processing batch 2563/2778\n",
      "Processing batch 2564/2778\n",
      "Processing batch 2565/2778\n",
      "Processing batch 2566/2778\n",
      "Processing batch 2567/2778\n",
      "Processing batch 2568/2778\n",
      "Processing batch 2569/2778\n",
      "Processing batch 2570/2778\n",
      "Processing batch 2571/2778\n",
      "Processing batch 2572/2778\n",
      "Processing batch 2573/2778\n",
      "Processing batch 2574/2778\n",
      "Processing batch 2575/2778\n",
      "Processing batch 2576/2778\n",
      "Processing batch 2577/2778\n",
      "Processing batch 2578/2778\n",
      "Processing batch 2579/2778\n",
      "Processing batch 2580/2778\n",
      "Processing batch 2581/2778\n",
      "Processing batch 2582/2778\n",
      "Processing batch 2583/2778\n",
      "Processing batch 2584/2778\n",
      "Processing batch 2585/2778\n",
      "Processing batch 2586/2778\n",
      "Processing batch 2587/2778\n",
      "Processing batch 2588/2778\n",
      "Processing batch 2589/2778\n",
      "Processing batch 2590/2778\n",
      "Processing batch 2591/2778\n",
      "Processing batch 2592/2778\n",
      "Processing batch 2593/2778\n",
      "Processing batch 2594/2778\n",
      "Processing batch 2595/2778\n",
      "Processing batch 2596/2778\n",
      "Processing batch 2597/2778\n",
      "Processing batch 2598/2778\n",
      "Processing batch 2599/2778\n",
      "Processing batch 2600/2778\n",
      "Processing batch 2601/2778\n",
      "Processing batch 2602/2778\n",
      "Processing batch 2603/2778\n",
      "Processing batch 2604/2778\n",
      "Processing batch 2605/2778\n",
      "Processing batch 2606/2778\n",
      "Processing batch 2607/2778\n",
      "Processing batch 2608/2778\n",
      "Processing batch 2609/2778\n",
      "Processing batch 2610/2778\n",
      "Processing batch 2611/2778\n",
      "Processing batch 2612/2778\n",
      "Processing batch 2613/2778\n",
      "Processing batch 2614/2778\n",
      "Processing batch 2615/2778\n",
      "Processing batch 2616/2778\n",
      "Processing batch 2617/2778\n",
      "Processing batch 2618/2778\n",
      "Processing batch 2619/2778\n",
      "Processing batch 2620/2778\n",
      "Processing batch 2621/2778\n",
      "Processing batch 2622/2778\n",
      "Processing batch 2623/2778\n",
      "Processing batch 2624/2778\n",
      "Processing batch 2625/2778\n",
      "Processing batch 2626/2778\n",
      "Processing batch 2627/2778\n",
      "Processing batch 2628/2778\n",
      "Processing batch 2629/2778\n",
      "Processing batch 2630/2778\n",
      "Processing batch 2631/2778\n",
      "Processing batch 2632/2778\n",
      "Processing batch 2633/2778\n",
      "Processing batch 2634/2778\n",
      "Processing batch 2635/2778\n",
      "Processing batch 2636/2778\n",
      "Processing batch 2637/2778\n",
      "Processing batch 2638/2778\n",
      "Processing batch 2639/2778\n",
      "Processing batch 2640/2778\n",
      "Processing batch 2641/2778\n",
      "Processing batch 2642/2778\n",
      "Processing batch 2643/2778\n",
      "Processing batch 2644/2778\n",
      "Processing batch 2645/2778\n",
      "Processing batch 2646/2778\n",
      "Processing batch 2647/2778\n",
      "Processing batch 2648/2778\n",
      "Processing batch 2649/2778\n",
      "Processing batch 2650/2778\n",
      "Processing batch 2651/2778\n",
      "Processing batch 2652/2778\n",
      "Processing batch 2653/2778\n",
      "Processing batch 2654/2778\n",
      "Processing batch 2655/2778\n",
      "Processing batch 2656/2778\n",
      "Processing batch 2657/2778\n",
      "Processing batch 2658/2778\n",
      "Processing batch 2659/2778\n",
      "Processing batch 2660/2778\n",
      "Processing batch 2661/2778\n",
      "Processing batch 2662/2778\n",
      "Processing batch 2663/2778\n",
      "Processing batch 2664/2778\n",
      "Processing batch 2665/2778\n",
      "Processing batch 2666/2778\n",
      "Processing batch 2667/2778\n",
      "Processing batch 2668/2778\n",
      "Processing batch 2669/2778\n",
      "Processing batch 2670/2778\n",
      "Processing batch 2671/2778\n",
      "Processing batch 2672/2778\n",
      "Processing batch 2673/2778\n",
      "Processing batch 2674/2778\n",
      "Processing batch 2675/2778\n",
      "Processing batch 2676/2778\n",
      "Processing batch 2677/2778\n",
      "Processing batch 2678/2778\n",
      "Processing batch 2679/2778\n",
      "Processing batch 2680/2778\n",
      "Processing batch 2681/2778\n",
      "Processing batch 2682/2778\n",
      "Processing batch 2683/2778\n",
      "Processing batch 2684/2778\n",
      "Processing batch 2685/2778\n",
      "Processing batch 2686/2778\n",
      "Processing batch 2687/2778\n",
      "Processing batch 2688/2778\n",
      "Processing batch 2689/2778\n",
      "Processing batch 2690/2778\n",
      "Processing batch 2691/2778\n",
      "Processing batch 2692/2778\n",
      "Processing batch 2693/2778\n",
      "Processing batch 2694/2778\n",
      "Processing batch 2695/2778\n",
      "Processing batch 2696/2778\n",
      "Processing batch 2697/2778\n",
      "Processing batch 2698/2778\n",
      "Processing batch 2699/2778\n",
      "Processing batch 2700/2778\n",
      "Processing batch 2701/2778\n",
      "Processing batch 2702/2778\n",
      "Processing batch 2703/2778\n",
      "Processing batch 2704/2778\n",
      "Processing batch 2705/2778\n",
      "Processing batch 2706/2778\n",
      "Processing batch 2707/2778\n",
      "Processing batch 2708/2778\n",
      "Processing batch 2709/2778\n",
      "Processing batch 2710/2778\n",
      "Processing batch 2711/2778\n",
      "Processing batch 2712/2778\n",
      "Processing batch 2713/2778\n",
      "Processing batch 2714/2778\n",
      "Processing batch 2715/2778\n",
      "Processing batch 2716/2778\n",
      "Processing batch 2717/2778\n",
      "Processing batch 2718/2778\n",
      "Processing batch 2719/2778\n",
      "Processing batch 2720/2778\n",
      "Processing batch 2721/2778\n",
      "Processing batch 2722/2778\n",
      "Processing batch 2723/2778\n",
      "Processing batch 2724/2778\n",
      "Processing batch 2725/2778\n",
      "Processing batch 2726/2778\n",
      "Processing batch 2727/2778\n",
      "Processing batch 2728/2778\n",
      "Processing batch 2729/2778\n",
      "Processing batch 2730/2778\n",
      "Processing batch 2731/2778\n",
      "Processing batch 2732/2778\n",
      "Processing batch 2733/2778\n",
      "Processing batch 2734/2778\n",
      "Processing batch 2735/2778\n",
      "Processing batch 2736/2778\n",
      "Processing batch 2737/2778\n",
      "Processing batch 2738/2778\n",
      "Processing batch 2739/2778\n",
      "Processing batch 2740/2778\n",
      "Processing batch 2741/2778\n",
      "Processing batch 2742/2778\n",
      "Processing batch 2743/2778\n",
      "Processing batch 2744/2778\n",
      "Processing batch 2745/2778\n",
      "Processing batch 2746/2778\n",
      "Processing batch 2747/2778\n",
      "Processing batch 2748/2778\n",
      "Processing batch 2749/2778\n",
      "Processing batch 2750/2778\n",
      "Processing batch 2751/2778\n",
      "Processing batch 2752/2778\n",
      "Processing batch 2753/2778\n",
      "Processing batch 2754/2778\n",
      "Processing batch 2755/2778\n",
      "Processing batch 2756/2778\n",
      "Processing batch 2757/2778\n",
      "Processing batch 2758/2778\n",
      "Processing batch 2759/2778\n",
      "Processing batch 2760/2778\n",
      "Processing batch 2761/2778\n",
      "Processing batch 2762/2778\n",
      "Processing batch 2763/2778\n",
      "Processing batch 2764/2778\n",
      "Processing batch 2765/2778\n",
      "Processing batch 2766/2778\n",
      "Processing batch 2767/2778\n",
      "Processing batch 2768/2778\n",
      "Processing batch 2769/2778\n",
      "Processing batch 2770/2778\n",
      "Processing batch 2771/2778\n",
      "Processing batch 2772/2778\n",
      "Processing batch 2773/2778\n",
      "Processing batch 2774/2778\n",
      "Processing batch 2775/2778\n",
      "Processing batch 2776/2778\n",
      "Processing batch 2777/2778\n",
      "Processing batch 2778/2778\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Path where the feature vectors will be stored\n",
    "#\n",
    "features_path = Path(\".\") / \"features\"\n",
    "if os.path.exists(features_path):\n",
    "    shutil.rmtree(features_path)\n",
    "os.makedirs(features_path)\n",
    "\n",
    "# Function that computes the feature vectors for a batch of images\n",
    "def compute_clip_features(images_batch):\n",
    "    # Load all the images from the files\n",
    "    images = [Image.open(image_file) for image_file in images_batch]\n",
    "    #print(images)\n",
    "    # Preprocess all images\n",
    "    #images_preprocessed = torch.stack([processor(image) for image in images]).to(device)\n",
    "\n",
    "    with torch.no_grad():\n",
    "      images = processor(text=None,images=images,return_tensors='pt')['pixel_values'].to(device)\n",
    "      #print(images)\n",
    "      image_features = model.get_image_features(images)\n",
    "      image_features /= image_features.norm(dim=-1, keepdim=True)\n",
    "\n",
    "    # Transfer the feature vectors back to the CPU and convert to numpy\n",
    "    return image_features.cpu().numpy()\n",
    "\n",
    "# Define the batch size\n",
    "batch_size = 16\n",
    "\n",
    "\n",
    "# Compute how many batches are needed\n",
    "batches = math.ceil(len(images_files) / batch_size)\n",
    "\n",
    "#\n",
    "# Process each batch\n",
    "#\n",
    "for i in range(batches):\n",
    "    print(f\"Processing batch {i+1}/{batches}\")\n",
    "\n",
    "    batch_ids_path = features_path / f\"{i:010d}.csv\"\n",
    "    batch_features_path = features_path / f\"{i:010d}.npy\"\n",
    "\n",
    "    # Only do the processing if the batch wasn't processed yet\n",
    "    if not batch_features_path.exists():\n",
    "        try:\n",
    "            # Select the images for the current batch\n",
    "            batch_files = images_files[i*batch_size : (i+1)*batch_size]\n",
    "\n",
    "            # Compute the features and save to a numpy file\n",
    "            batch_features = compute_clip_features(batch_files)\n",
    "            np.save(batch_features_path, batch_features)\n",
    "\n",
    "            # Save the image IDs to a CSV file\n",
    "            image_ids = [image_file.split(\".\")[0] for image_file in batch_files]\n",
    "            image_ids_data = pd.DataFrame(image_ids, columns=['image_id'])\n",
    "            image_ids_data.to_csv(batch_ids_path, index=False)\n",
    "        except Exception as e:\n",
    "            # Catch problems with the processing to make the process more robust\n",
    "            print(f'Problem with batch {i} {e}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "-BwQ9umHc-uT"
   },
   "outputs": [],
   "source": [
    "# Load all numpy files\n",
    "features_list = [np.load(features_file) for features_file in sorted(features_path.glob(\"*.npy\"))]\n",
    "\n",
    "# Concatenate the features and store in a merged file\n",
    "features = np.concatenate(features_list)\n",
    "np.save(features_path / \"features.npy\", features)\n",
    "\n",
    "# Load all the image IDs\n",
    "image_ids = pd.concat([pd.read_csv(ids_file) for ids_file in sorted(features_path.glob(\"*.csv\"))])\n",
    "image_ids.to_csv(features_path / \"image_ids.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uP1f9WE2rAkA",
    "outputId": "2313a839-202a-4e53-fa95-f2537efcac18"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Images loaded: 44441\n"
     ]
    }
   ],
   "source": [
    "# Load the image IDs\n",
    "image_ids = pd.read_csv(features_path / 'image_ids.csv')\n",
    "image_ids = list(image_ids['image_id'])\n",
    "\n",
    "# Load the features vectors\n",
    "image_features = np.load(features_path / 'features.npy')\n",
    "\n",
    "# Convert features to Tensors: Float32 on CPU and Float16 on GPU\n",
    "if device == \"cpu\":\n",
    "    image_features = torch.from_numpy(image_features).float().to(device)\n",
    "else:\n",
    "    image_features = torch.from_numpy(image_features).to(device)\n",
    "\n",
    "# Print some statistics\n",
    "print(f\"Images loaded: {len(image_ids)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 792
    },
    "id": "3MVpeht0xk_I",
    "outputId": "c196bf17-6e5b-466b-e354-c3da1ed9e571"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [nan, nan, nan]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;module&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">49</span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">46 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>ax[i, <span style=\"color: #0000ff; text-decoration-color: #0000ff\">0</span>].text(<span style=\"color: #0000ff; text-decoration-color: #0000ff\">0.0</span>, <span style=\"color: #0000ff; text-decoration-color: #0000ff\">0.5</span>, search_query)                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">47 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>ax[i, <span style=\"color: #0000ff; text-decoration-color: #0000ff\">0</span>].axis(<span style=\"color: #808000; text-decoration-color: #808000\">'off'</span>)                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">48 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">for</span> j, image_id <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">enumerate</span>(result_image_ids):                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>49 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>image = Image.open(<span style=\"color: #808000; text-decoration-color: #808000\">f'{</span>image_id<span style=\"color: #808000; text-decoration-color: #808000\">}.jpg'</span>)                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">50 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>ax[i, j+<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1</span>].imshow(image)                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">51 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>ax[i, j+<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1</span>].axis(<span style=\"color: #808000; text-decoration-color: #808000\">'off'</span>)                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">52 </span>                                                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #808000; text-decoration-color: #808000\">c:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\PIL\\Image.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">3236</span> in     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">open</span>                                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3233 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>filename = fp                                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3234 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3235 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> filename:                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>3236 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>fp = builtins.open(filename, <span style=\"color: #808000; text-decoration-color: #808000\">\"rb\"</span>)                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3237 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>exclusive_fp = <span style=\"color: #0000ff; text-decoration-color: #0000ff\">True</span>                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3238 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3239 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">try</span>:                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
       "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">FileNotFoundError: </span><span style=\"font-weight: bold\">[</span>Errno <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">]</span> No such file or directory: <span style=\"color: #008000; text-decoration-color: #008000\">'nan.jpg'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
       "\u001b[31m│\u001b[0m in \u001b[92m<module>\u001b[0m:\u001b[94m49\u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m46 \u001b[0m\u001b[2m│   \u001b[0max[i, \u001b[94m0\u001b[0m].text(\u001b[94m0.0\u001b[0m, \u001b[94m0.5\u001b[0m, search_query)                                                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m47 \u001b[0m\u001b[2m│   \u001b[0max[i, \u001b[94m0\u001b[0m].axis(\u001b[33m'\u001b[0m\u001b[33moff\u001b[0m\u001b[33m'\u001b[0m)                                                                    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m48 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mfor\u001b[0m j, image_id \u001b[95min\u001b[0m \u001b[96menumerate\u001b[0m(result_image_ids):                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m49 \u001b[2m│   │   \u001b[0mimage = Image.open(\u001b[33mf\u001b[0m\u001b[33m'\u001b[0m\u001b[33m{\u001b[0mimage_id\u001b[33m}\u001b[0m\u001b[33m.jpg\u001b[0m\u001b[33m'\u001b[0m)                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m50 \u001b[0m\u001b[2m│   │   \u001b[0max[i, j+\u001b[94m1\u001b[0m].imshow(image)                                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m51 \u001b[0m\u001b[2m│   │   \u001b[0max[i, j+\u001b[94m1\u001b[0m].axis(\u001b[33m'\u001b[0m\u001b[33moff\u001b[0m\u001b[33m'\u001b[0m)                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m52 \u001b[0m                                                                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[33mc:\\users\\aditb\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\PIL\\Image.py\u001b[0m:\u001b[94m3236\u001b[0m in     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[92mopen\u001b[0m                                                                                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m3233 \u001b[0m\u001b[2m│   │   \u001b[0mfilename = fp                                                                     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m3234 \u001b[0m\u001b[2m│   \u001b[0m                                                                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m3235 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mif\u001b[0m filename:                                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m3236 \u001b[2m│   │   \u001b[0mfp = builtins.open(filename, \u001b[33m\"\u001b[0m\u001b[33mrb\u001b[0m\u001b[33m\"\u001b[0m)                                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m3237 \u001b[0m\u001b[2m│   │   \u001b[0mexclusive_fp = \u001b[94mTrue\u001b[0m                                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m3238 \u001b[0m\u001b[2m│   \u001b[0m                                                                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m3239 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mtry\u001b[0m:                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
       "\u001b[1;91mFileNotFoundError: \u001b[0m\u001b[1m[\u001b[0mErrno \u001b[1;36m2\u001b[0m\u001b[1m]\u001b[0m No such file or directory: \u001b[32m'nan.jpg'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def encode_search_query(search_query):\n",
    "    with torch.no_grad():\n",
    "        # Encode and normalize the search query using CLIP\n",
    "        tokens = processor(\n",
    "                           text = search_query,\n",
    "                           padding=True,\n",
    "                          images=None,\n",
    "                           return_tensors='pt').to(device)\n",
    "\n",
    "        text_encoded =  model.get_text_features(**tokens).to(device)\n",
    "        text_encoded /= text_encoded.norm(dim=-1, keepdim=True)\n",
    "\n",
    "    # Retrieve the feature vector\n",
    "    return text_encoded\n",
    "\n",
    "def find_best_matches(text_features, image_features, image_ids, results_count=3):\n",
    "  # Compute the similarity between the search query and each image using the Cosine similarity\n",
    "  similarities = (image_features @ text_features.T).squeeze(1)\n",
    "\n",
    "  # Sort the images by their similarity score\n",
    "  best_image_idx = (-similarities).argsort()\n",
    "\n",
    "  # Return the image IDs of the best matches\n",
    "  return [image_ids[i] for i in best_image_idx[:results_count]]\n",
    "\n",
    "def search(search_query, image_features, image_ids, results_count=1):\n",
    "  # Encode the search query\n",
    "  text_features = encode_search_query(search_query)\n",
    "\n",
    "  # Find the best matches\n",
    "  return find_best_matches(text_features, image_features, image_ids, results_count)\n",
    "\n",
    "#\n",
    "# Search for images and visualize the results\n",
    "#\n",
    "search_queries = ['a black pant',\n",
    "                  'short sleeved shirt',\n",
    "                  'skirt',\n",
    "                  'blue jeans']\n",
    "n_results_per_query = 3\n",
    "\n",
    "fig, ax = plt.subplots(len(search_queries), n_results_per_query + 1, figsize=(15, 10))\n",
    "for i, search_query in enumerate(search_queries):\n",
    "    result_image_ids = search(search_query, image_features, image_ids, n_results_per_query)\n",
    "    print(i, result_image_ids)\n",
    "    ax[i, 0].text(0.0, 0.5, search_query)\n",
    "    ax[i, 0].axis('off')\n",
    "    for j, image_id in enumerate(result_image_ids):\n",
    "        image = Image.open(f'{image_id}.jpg')\n",
    "        ax[i, j+1].imshow(image)\n",
    "        ax[i, j+1].axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "OmyUuhVXxqp3"
   },
   "outputs": [],
   "source": [
    "import pinecone\n",
    "\n",
    "# Load CSV file with IDs\n",
    "#df_ids = pd.read_csv('../features/image_ids.csv')\n",
    "#df_ids['image_id'] = df_ids['image_id'].apply(lambda x: x.replace('../Data/images/fashion-dataset/images/', ''))\n",
    "# Load Numpy file with vectors\n",
    "features = np.load('./features/features.npy')\n",
    "\n",
    "# pinecone.create_index(\"vectors\", dimension=512, metric=\"cosine\")\n",
    "# pinecone.describe_index(\"vectors\")\n",
    "\n",
    "# api_key = 'bb5c549b-3015-44a6-9e7b-f7939252ffbc'\n",
    "#pinecone.init(api_key = 'bb5c549b-3015-44a6-9e7b-f7939252ffbc', environment=\"gcp-starter\")\n",
    "pinecone.init(api_key = \"751e7796-d00e-4382-98aa-557196118a24\", environment=\"gcp-starter\")\n",
    "\n",
    "index = pinecone.Index(\"vector\")\n",
    "\n",
    "\n",
    "for i in range(features.shape[0]):\n",
    "                index.upsert(\n",
    "                    vectors = [\n",
    "                        {\n",
    "                            'id': l[i],\n",
    "                            'values': features[i].tolist()\n",
    "                        }\n",
    "                    ]\n",
    "                )\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;module&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1</span>                                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1 index_info = index.get_index_info()                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2 </span>num_rows = index_info[<span style=\"color: #808000; text-decoration-color: #808000\">'stats'</span>][<span style=\"color: #808000; text-decoration-color: #808000\">'totalDocuments'</span>]                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3 </span>                                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
       "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">AttributeError: </span><span style=\"color: #008000; text-decoration-color: #008000\">'Index'</span> object has no attribute <span style=\"color: #008000; text-decoration-color: #008000\">'get_index_info'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
       "\u001b[31m│\u001b[0m in \u001b[92m<module>\u001b[0m:\u001b[94m1\u001b[0m                                                                                    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1 index_info = index.get_index_info()                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m2 \u001b[0mnum_rows = index_info[\u001b[33m'\u001b[0m\u001b[33mstats\u001b[0m\u001b[33m'\u001b[0m][\u001b[33m'\u001b[0m\u001b[33mtotalDocuments\u001b[0m\u001b[33m'\u001b[0m]                                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m3 \u001b[0m                                                                                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
       "\u001b[1;91mAttributeError: \u001b[0m\u001b[32m'Index'\u001b[0m object has no attribute \u001b[32m'get_index_info'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index_info = index.get_index_info()\n",
    "num_rows = index_info['stats']['totalDocuments']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(batches):\n",
    "    print(f\"Processing batch {i+1}/{batches}\")\n",
    "\n",
    "    #batch_ids_path = features_path / f\"{i:010d}.csv\"\n",
    "    #batch_features_path = features_path / f\"{i:010d}.npy\"\n",
    "\n",
    "    # Only do the processing if the batch wasn't processed yet\n",
    "    if not batch_features_path.exists():\n",
    "        try:\n",
    "            # Select the images for the current batch\n",
    "            batch_files = images_files[i*batch_size : (i+1)*batch_size]\n",
    "\n",
    "            # Compute the features and save to a numpy file\n",
    "            #batch_features = compute_clip_features(batch_files)\n",
    "            #np.save(batch_features_path, batch_features)\n",
    "\n",
    "            # Save the image IDs to a CSV file\n",
    "            image_ids = [image_file.split(\".\")[0] for image_file in batch_files]\n",
    "            image_ids_data = pd.DataFrame(image_ids, columns=['image_id'])\n",
    "            image_ids_data.to_csv(batch_ids_path, index=False)\n",
    "        except Exception as e:\n",
    "            # Catch problems with the processing to make the process more robust\n",
    "            print(f'Problem with batch {i} {e}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'10000'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_files[0].replace('../Data/images/fashion-dataset/images\\\\','')[:-4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "l  =[]\n",
    "for i in images_files:\n",
    "    l.append(i.replace('../Data/images/fashion-dataset/images\\\\','')[:-4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../Data/images/fashion-dataset/images\\\\15874.jpg'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_files]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.5"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "0104e07a3b2d4e1698443ff15746ebef": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "0a9b07ae0494464fbdd64604f9285bba": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "0c6416dd3aa247689d3be44a7ddcb0bf": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "0c8132f9c02a4a38bb4968185d4b4823": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "0cdafbda19384d5095a66f9bcc689224": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "0f407b11fe7c4088bc4b74f5b1c3cf98": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_909d1de5cb0d4c9290a4f07b133602ab",
      "max": 316,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_20e5fb6c1d304d3784ca9ce3029d6475",
      "value": 316
     }
    },
    "1375ead1b20642dab07347917fc243f3": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "173a8ad645fa473992d9fe34f0240dde": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_7e47a501ab324eef879a5b170c140180",
      "placeholder": "​",
      "style": "IPY_MODEL_0c8132f9c02a4a38bb4968185d4b4823",
      "value": "tokenizer.json: 100%"
     }
    },
    "18369f4b9def452094a0301cd440c6e6": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "185b7cddcce048f8816cab4d7802927a": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "1d4d9939f5364d1ba478e703fa058d49": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "20e5fb6c1d304d3784ca9ce3029d6475": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "2b27967a8e1b4bb5a890c62d5fa56e9c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "2cddf874788141cba26c1dee82703442": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "2d491944d6d842c49642bda3b71f41c1": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_c197bca1cf494167ae2811f9d27b83a0",
      "placeholder": "​",
      "style": "IPY_MODEL_a97841dcef32419eacded75f1c29fb3a",
      "value": "tokenizer_config.json: 100%"
     }
    },
    "310a5b7f438d49a385e527651817dbe1": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "31800f95310b4154aef30bfba659cd58": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "31e50a9e9b0b41038aaf61b6b2545eae": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "35816dbcf41e4ae8874f20bc0d9a53a9": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "37f0add343f14fc19a0bb017ba5d5085": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_53deda350140485481d9499c85cf663d",
      "max": 4186,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_0104e07a3b2d4e1698443ff15746ebef",
      "value": 4186
     }
    },
    "3c031da1ec3a43e8ab992ce931b4f873": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "423c5757d8504cf7bf012602a5402a65": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_79d4a6fff8dc481d9ad5fcd7a40d4401",
       "IPY_MODEL_4432bf5237264a5f9a86addc732757c4",
       "IPY_MODEL_562b81b168874220a3e3df28baefaf1f"
      ],
      "layout": "IPY_MODEL_1375ead1b20642dab07347917fc243f3"
     }
    },
    "426d55dc50854faaa55b47df79e04758": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "42d1c341c71948928681151575c63c70": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_85c471edbf68497faf73bb6d2d99b5d3",
      "placeholder": "​",
      "style": "IPY_MODEL_430dc21cfb624a2581678fbbc2c8f54e",
      "value": "special_tokens_map.json: 100%"
     }
    },
    "430dc21cfb624a2581678fbbc2c8f54e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "4432bf5237264a5f9a86addc732757c4": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_1d4d9939f5364d1ba478e703fa058d49",
      "max": 862328,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_0cdafbda19384d5095a66f9bcc689224",
      "value": 862328
     }
    },
    "44b5e736d7b2490da03a3d59928b0f67": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "481badd6e8814883a72e13576c96d360": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_66432a1ec191454ea590fc845820ebfc",
       "IPY_MODEL_88cd7d33a37a4b0caf8cd731bf0e0ea0",
       "IPY_MODEL_6fd2c302d2d041578a8e89dc41344d45"
      ],
      "layout": "IPY_MODEL_bb06c8556b9840fab73cfca87ed30293"
     }
    },
    "4f5025d3eb804e8486d38539b4b6fa84": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_9a2b34e4b21947cd8a6ebd101952343f",
       "IPY_MODEL_e046b0189d6349a2827ea994231150be",
       "IPY_MODEL_f8c22041d23d4b43b7a53ae8acc19ed5"
      ],
      "layout": "IPY_MODEL_6a3278a2520948438f7356259da1327d"
     }
    },
    "53deda350140485481d9499c85cf663d": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "549534e4209747f8a46c3d74c13c4fd0": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_e2de6858f8ff4185b6c3e2341fdb5342",
      "placeholder": "​",
      "style": "IPY_MODEL_31e50a9e9b0b41038aaf61b6b2545eae",
      "value": " 316/316 [00:00&lt;00:00, 11.9kB/s]"
     }
    },
    "562b81b168874220a3e3df28baefaf1f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_185b7cddcce048f8816cab4d7802927a",
      "placeholder": "​",
      "style": "IPY_MODEL_0c6416dd3aa247689d3be44a7ddcb0bf",
      "value": " 862k/862k [00:00&lt;00:00, 16.8MB/s]"
     }
    },
    "5a780a1e435e49c6b683defdf332e1bd": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "5dff26e881ab4a238571f53ce552457c": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "628665a2ee534b10a97edca1e422222a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_173a8ad645fa473992d9fe34f0240dde",
       "IPY_MODEL_c2f5c46882ae44b29e0eef57200c309e",
       "IPY_MODEL_6b26c07e4e5249a2a95eb2ce097c689f"
      ],
      "layout": "IPY_MODEL_d0855d3098ee4b34a3343cbb8d620caa"
     }
    },
    "66432a1ec191454ea590fc845820ebfc": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_35816dbcf41e4ae8874f20bc0d9a53a9",
      "placeholder": "​",
      "style": "IPY_MODEL_5a780a1e435e49c6b683defdf332e1bd",
      "value": "merges.txt: 100%"
     }
    },
    "68241006363a4dc8b7e8daca3dc88193": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "6a3278a2520948438f7356259da1327d": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "6a459f78c07241ee91168210dac47159": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_426d55dc50854faaa55b47df79e04758",
      "max": 389,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_86a6ee1b58b54d179099c690e9e09348",
      "value": 389
     }
    },
    "6b26c07e4e5249a2a95eb2ce097c689f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_a833ed19b40f4f8bbdc4c81bddc35d14",
      "placeholder": "​",
      "style": "IPY_MODEL_2b27967a8e1b4bb5a890c62d5fa56e9c",
      "value": " 2.22M/2.22M [00:00&lt;00:00, 44.1MB/s]"
     }
    },
    "6c8633070d3946e0a474ae5923494bbb": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "6e369f81de2344b397c2e12407fd9c80": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "6fd2c302d2d041578a8e89dc41344d45": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_a2bb3c3cd786492bbe67466d5ed2037b",
      "placeholder": "​",
      "style": "IPY_MODEL_6e369f81de2344b397c2e12407fd9c80",
      "value": " 525k/525k [00:00&lt;00:00, 21.9MB/s]"
     }
    },
    "76e1654f77354b70bb5e7999733244c1": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "7755479a4e63438e93c5bd8212bf4fcd": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_83536728637644208dd5351c34a69016",
      "placeholder": "​",
      "style": "IPY_MODEL_821cf3c8abff4e2294ac6b6ea21a1e58",
      "value": "config.json: 100%"
     }
    },
    "79d4a6fff8dc481d9ad5fcd7a40d4401": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_a79d6ea298464884987d46e728f83547",
      "placeholder": "​",
      "style": "IPY_MODEL_f9c3e9753edc4761926aa05d146c23c2",
      "value": "vocab.json: 100%"
     }
    },
    "7d618bcf3cd246149bcd6ade71fb2134": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "7e47a501ab324eef879a5b170c140180": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "821cf3c8abff4e2294ac6b6ea21a1e58": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "83536728637644208dd5351c34a69016": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "85c471edbf68497faf73bb6d2d99b5d3": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "86a6ee1b58b54d179099c690e9e09348": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "874db23882294d94aab8e5c7f7b81b53": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "88cd7d33a37a4b0caf8cd731bf0e0ea0": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_310a5b7f438d49a385e527651817dbe1",
      "max": 524657,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_b1ba2edd7a0b49c2884efbb064e8e0db",
      "value": 524657
     }
    },
    "8c3936cd3c2b4b51879f197e2c3a4a23": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "8f0024b9a26c4d6d8d8437ae17369f10": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "8f3dbb4682be4c6695bcaa31f75a3e6d": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_44b5e736d7b2490da03a3d59928b0f67",
      "placeholder": "​",
      "style": "IPY_MODEL_874db23882294d94aab8e5c7f7b81b53",
      "value": " 4.19k/4.19k [00:00&lt;00:00, 202kB/s]"
     }
    },
    "909d1de5cb0d4c9290a4f07b133602ab": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "910d142a4051466ebbc7489f0bc70b18": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_2d491944d6d842c49642bda3b71f41c1",
       "IPY_MODEL_fc342a521465480781fde984ac5addac",
       "IPY_MODEL_b77e6c39b94247a1b6b8f4977969249c"
      ],
      "layout": "IPY_MODEL_7d618bcf3cd246149bcd6ade71fb2134"
     }
    },
    "9626d5793c4945d490bb8d404cf9135b": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_b116aabfa0434162b88ca76fd130143c",
       "IPY_MODEL_0f407b11fe7c4088bc4b74f5b1c3cf98",
       "IPY_MODEL_549534e4209747f8a46c3d74c13c4fd0"
      ],
      "layout": "IPY_MODEL_a4b79101a6264969ae76aaf3a5c601c3"
     }
    },
    "9a2b34e4b21947cd8a6ebd101952343f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_2cddf874788141cba26c1dee82703442",
      "placeholder": "​",
      "style": "IPY_MODEL_a32357d77c4e4591b4661a67d87720d2",
      "value": "pytorch_model.bin: 100%"
     }
    },
    "9b4207ce4268455eb6d9e6903e62d508": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "9b46bda89b8640c286a98d8ab36dd834": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "a2bb3c3cd786492bbe67466d5ed2037b": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "a32357d77c4e4591b4661a67d87720d2": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "a4b79101a6264969ae76aaf3a5c601c3": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "a79d6ea298464884987d46e728f83547": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "a7fa2f3e15d143728b756f5138f783e7": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_7755479a4e63438e93c5bd8212bf4fcd",
       "IPY_MODEL_37f0add343f14fc19a0bb017ba5d5085",
       "IPY_MODEL_8f3dbb4682be4c6695bcaa31f75a3e6d"
      ],
      "layout": "IPY_MODEL_9b46bda89b8640c286a98d8ab36dd834"
     }
    },
    "a833ed19b40f4f8bbdc4c81bddc35d14": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "a97841dcef32419eacded75f1c29fb3a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "afc004423fb743739cd0955b30d06193": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "b02b1a2c2511453a8bd747110a3e8751": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "b116aabfa0434162b88ca76fd130143c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_5dff26e881ab4a238571f53ce552457c",
      "placeholder": "​",
      "style": "IPY_MODEL_b02b1a2c2511453a8bd747110a3e8751",
      "value": "preprocessor_config.json: 100%"
     }
    },
    "b1ba2edd7a0b49c2884efbb064e8e0db": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "b77e6c39b94247a1b6b8f4977969249c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_8c3936cd3c2b4b51879f197e2c3a4a23",
      "placeholder": "​",
      "style": "IPY_MODEL_6c8633070d3946e0a474ae5923494bbb",
      "value": " 568/568 [00:00&lt;00:00, 28.2kB/s]"
     }
    },
    "bb06c8556b9840fab73cfca87ed30293": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "c197bca1cf494167ae2811f9d27b83a0": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "c2f5c46882ae44b29e0eef57200c309e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_76e1654f77354b70bb5e7999733244c1",
      "max": 2224041,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_e7e43dbfd90947178665741e09087df8",
      "value": 2224041
     }
    },
    "d0855d3098ee4b34a3343cbb8d620caa": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "d0993928ea0b43e2b8bab4f43965ac83": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "d281157460a14a35980444d1bddefd9c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_68241006363a4dc8b7e8daca3dc88193",
      "placeholder": "​",
      "style": "IPY_MODEL_3c031da1ec3a43e8ab992ce931b4f873",
      "value": " 389/389 [00:00&lt;00:00, 19.1kB/s]"
     }
    },
    "e046b0189d6349a2827ea994231150be": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_31800f95310b4154aef30bfba659cd58",
      "max": 605247071,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_8f0024b9a26c4d6d8d8437ae17369f10",
      "value": 605247071
     }
    },
    "e2de6858f8ff4185b6c3e2341fdb5342": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "e7e43dbfd90947178665741e09087df8": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "f4e61479e89b49b599c04f53a7ddcd99": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_42d1c341c71948928681151575c63c70",
       "IPY_MODEL_6a459f78c07241ee91168210dac47159",
       "IPY_MODEL_d281157460a14a35980444d1bddefd9c"
      ],
      "layout": "IPY_MODEL_18369f4b9def452094a0301cd440c6e6"
     }
    },
    "f8c22041d23d4b43b7a53ae8acc19ed5": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_d0993928ea0b43e2b8bab4f43965ac83",
      "placeholder": "​",
      "style": "IPY_MODEL_afc004423fb743739cd0955b30d06193",
      "value": " 605M/605M [00:04&lt;00:00, 169MB/s]"
     }
    },
    "f9c3e9753edc4761926aa05d146c23c2": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "fc342a521465480781fde984ac5addac": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_0a9b07ae0494464fbdd64604f9285bba",
      "max": 568,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_9b4207ce4268455eb6d9e6903e62d508",
      "value": 568
     }
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}