{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Introduction\n",
    "This notebook is used to test different ways to deploy the feature extraction model to the huggingface hub. The goal is to find the best way to deploy the model so that it can be used in the inference API and can be easy accessible for user. In the best way it would also be possible to simply use the huggingface library directly. The following methods will be tested:\n",
    "\n",
    "1. [Using ``timm`` to extract features](#Using-timm-to-extract-features) -> ❌\n",
    "2. [Using ``transformers`` to extract features](#Using-transformers-to-extract-features) --> ❌\n",
    "    1. [Feature extraction task](#Feature-extraction-task)\n",
    "    2. [``AutoModel``](#AutoModel)\n",
    "    3. [Batched feature extraction](#Batched-feature-extraction)\n",
    "3. [Using simple download](#Using-simple-download) -> ✅\n",
    "4. [Using custom model](#Using-custom-model) --> 🚧\n",
    "\n",
    "**Helpful links and resources**\n",
    "- https://huggingface.co/docs/transformers/custom_models - Alternative creating custom models\n",
    "- https://huggingface.co/templates/feature-extraction - Template for inference API\n",
    "- https://huggingface-widgets.netlify.app/ - Widgets for visualizing models in inference API\n",
    "- https://huggingface.co/docs/hub/models-widgets#how-can-i-control-my-models-widget-inference-api-parameters - Controlling inference API parameters"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Imports"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "import timm\n",
    "import torch\n",
    "from pipeline_wrapper import MyPipeline"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-15T08:30:05.075994Z",
     "start_time": "2024-02-15T08:29:59.741405700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 1. Using ``timm`` to extract features"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'torch' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[10], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m test_tensor \u001B[38;5;241m=\u001B[39m \u001B[43mtorch\u001B[49m\u001B[38;5;241m.\u001B[39mrandn(\u001B[38;5;241m2\u001B[39m, \u001B[38;5;241m3\u001B[39m, \u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m1\u001B[39m)\n",
      "\u001B[1;31mNameError\u001B[0m: name 'torch' is not defined"
     ]
    }
   ],
   "source": [
    "test_tensor = torch.randn(2, 3, 1, 1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-15T08:29:09.423837500Z",
     "start_time": "2024-02-15T08:29:09.397259300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "feature_extractor = timm.create_model('resnet18', pretrained=True, num_classes=0, global_pool='')\n",
    "features = feature_extractor.forward_features(test_tensor)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-15T07:55:57.395514300Z",
     "start_time": "2024-02-15T07:55:56.561361800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "tensor([[[[0.0000]],\n\n         [[0.6944]],\n\n         [[0.0000]],\n\n         ...,\n\n         [[0.0000]],\n\n         [[0.0000]],\n\n         [[0.0000]]],\n\n\n        [[[0.0000]],\n\n         [[0.0000]],\n\n         [[0.0143]],\n\n         ...,\n\n         [[0.0000]],\n\n         [[0.0000]],\n\n         [[0.0000]]]], grad_fn=<ReluBackward0>)"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-15T07:55:58.303122200Z",
     "start_time": "2024-02-15T07:55:58.266709100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Unknown model (ECG2HRV)",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mRuntimeError\u001B[0m                              Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[15], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m feature_extractor \u001B[38;5;241m=\u001B[39m \u001B[43mtimm\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreate_model\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mHUBII-Platform/ECG2HRV\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpretrained\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnum_classes\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mglobal_pool\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\timm\\models\\_factory.py:113\u001B[0m, in \u001B[0;36mcreate_model\u001B[1;34m(model_name, pretrained, pretrained_cfg, pretrained_cfg_overlay, checkpoint_path, scriptable, exportable, no_jit, **kwargs)\u001B[0m\n\u001B[0;32m    110\u001B[0m         pretrained_cfg \u001B[38;5;241m=\u001B[39m pretrained_tag\n\u001B[0;32m    112\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m is_model(model_name):\n\u001B[1;32m--> 113\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUnknown model (\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m)\u001B[39m\u001B[38;5;124m'\u001B[39m \u001B[38;5;241m%\u001B[39m model_name)\n\u001B[0;32m    115\u001B[0m create_fn \u001B[38;5;241m=\u001B[39m model_entrypoint(model_name)\n\u001B[0;32m    116\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m set_layer_config(scriptable\u001B[38;5;241m=\u001B[39mscriptable, exportable\u001B[38;5;241m=\u001B[39mexportable, no_jit\u001B[38;5;241m=\u001B[39mno_jit):\n",
      "\u001B[1;31mRuntimeError\u001B[0m: Unknown model (ECG2HRV)"
     ]
    }
   ],
   "source": [
    "feature_extractor = timm.create_model('HUBII-Platform/ECG2HRV', pretrained=True, num_classes=0, global_pool='')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-15T08:30:32.648960900Z",
     "start_time": "2024-02-15T08:30:32.601345400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 2. Using ``transformers`` to extract features"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "1. Feature extraction task"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "config.json:   0%|          | 0.00/1.72k [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "b3723308dd9940aaa268f8f474eeb5df"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\merti\\anaconda3\\envs\\py310\\lib\\site-packages\\huggingface_hub\\file_download.py:149: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\merti\\.cache\\huggingface\\hub\\models--facebook--bart-base. 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": {
      "text/plain": "model.safetensors:   0%|          | 0.00/558M [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "5bffab7288404bc284624575d056c74d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "2f3359fbba014f02994e17163fcc0f1c"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "b315c7ae15b64a148df558539c3fc980"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "1b275b44c1cf4d8eaa9b57aedad04240"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example with pipeline\n",
    "from transformers import pipeline\n",
    "checkpoint = \"facebook/bart-base\"\n",
    "feature_extractor = pipeline(\"feature-extraction\", framework=\"pt\",model=checkpoint)\n",
    "text = \"Transformers is an awesome library!\""
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-15T08:02:58.086587200Z",
     "start_time": "2024-02-15T08:02:15.582501300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "It looks like the config file at 'C:\\Users\\merti\\.cache\\huggingface\\hub\\models--HUBII-Platform--ECG2HRV\\snapshots\\75f67e01de12e33cfb05cfbfed35ff621246b3f9\\config.json' is not a valid JSON file.",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mJSONDecodeError\u001B[0m                           Traceback (most recent call last)",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\configuration_utils.py:719\u001B[0m, in \u001B[0;36mPretrainedConfig._get_config_dict\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m    717\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m    718\u001B[0m     \u001B[38;5;66;03m# Load config dict\u001B[39;00m\n\u001B[1;32m--> 719\u001B[0m     config_dict \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mcls\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_dict_from_json_file\u001B[49m\u001B[43m(\u001B[49m\u001B[43mresolved_config_file\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    720\u001B[0m     config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m commit_hash\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\configuration_utils.py:818\u001B[0m, in \u001B[0;36mPretrainedConfig._dict_from_json_file\u001B[1;34m(cls, json_file)\u001B[0m\n\u001B[0;32m    817\u001B[0m     text \u001B[38;5;241m=\u001B[39m reader\u001B[38;5;241m.\u001B[39mread()\n\u001B[1;32m--> 818\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mjson\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mloads\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtext\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\json\\__init__.py:346\u001B[0m, in \u001B[0;36mloads\u001B[1;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001B[0m\n\u001B[0;32m    343\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (\u001B[38;5;28mcls\u001B[39m \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m object_hook \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m\n\u001B[0;32m    344\u001B[0m         parse_int \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m parse_float \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m\n\u001B[0;32m    345\u001B[0m         parse_constant \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m object_pairs_hook \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m kw):\n\u001B[1;32m--> 346\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_default_decoder\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdecode\u001B[49m\u001B[43m(\u001B[49m\u001B[43ms\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    347\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mcls\u001B[39m \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\json\\decoder.py:337\u001B[0m, in \u001B[0;36mJSONDecoder.decode\u001B[1;34m(self, s, _w)\u001B[0m\n\u001B[0;32m    333\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001B[39;00m\n\u001B[0;32m    334\u001B[0m \u001B[38;5;124;03mcontaining a JSON document).\u001B[39;00m\n\u001B[0;32m    335\u001B[0m \n\u001B[0;32m    336\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m--> 337\u001B[0m obj, end \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mraw_decode\u001B[49m\u001B[43m(\u001B[49m\u001B[43ms\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43midx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_w\u001B[49m\u001B[43m(\u001B[49m\u001B[43ms\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mend\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    338\u001B[0m end \u001B[38;5;241m=\u001B[39m _w(s, end)\u001B[38;5;241m.\u001B[39mend()\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\json\\decoder.py:355\u001B[0m, in \u001B[0;36mJSONDecoder.raw_decode\u001B[1;34m(self, s, idx)\u001B[0m\n\u001B[0;32m    354\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mStopIteration\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m--> 355\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m JSONDecodeError(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mExpecting value\u001B[39m\u001B[38;5;124m\"\u001B[39m, s, err\u001B[38;5;241m.\u001B[39mvalue) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m    356\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m obj, end\n",
      "\u001B[1;31mJSONDecodeError\u001B[0m: Expecting value: line 1 column 1 (char 0)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001B[1;31mOSError\u001B[0m                                   Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[7], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m feature_extractor \u001B[38;5;241m=\u001B[39m \u001B[43mpipeline\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mfeature-extraction\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mHUBII-Platform/ECG2HRV\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\pipelines\\__init__.py:782\u001B[0m, in \u001B[0;36mpipeline\u001B[1;34m(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001B[0m\n\u001B[0;32m    779\u001B[0m                 adapter_config \u001B[38;5;241m=\u001B[39m json\u001B[38;5;241m.\u001B[39mload(f)\n\u001B[0;32m    780\u001B[0m                 model \u001B[38;5;241m=\u001B[39m adapter_config[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbase_model_name_or_path\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[1;32m--> 782\u001B[0m     config \u001B[38;5;241m=\u001B[39m AutoConfig\u001B[38;5;241m.\u001B[39mfrom_pretrained(\n\u001B[0;32m    783\u001B[0m         model, _from_pipeline\u001B[38;5;241m=\u001B[39mtask, code_revision\u001B[38;5;241m=\u001B[39mcode_revision, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mhub_kwargs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mmodel_kwargs\n\u001B[0;32m    784\u001B[0m     )\n\u001B[0;32m    785\u001B[0m     hub_kwargs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m config\u001B[38;5;241m.\u001B[39m_commit_hash\n\u001B[0;32m    787\u001B[0m custom_tasks \u001B[38;5;241m=\u001B[39m {}\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\models\\auto\\configuration_auto.py:1100\u001B[0m, in \u001B[0;36mAutoConfig.from_pretrained\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m   1097\u001B[0m trust_remote_code \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtrust_remote_code\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[0;32m   1098\u001B[0m code_revision \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcode_revision\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[1;32m-> 1100\u001B[0m config_dict, unused_kwargs \u001B[38;5;241m=\u001B[39m PretrainedConfig\u001B[38;5;241m.\u001B[39mget_config_dict(pretrained_model_name_or_path, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m   1101\u001B[0m has_remote_code \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mauto_map\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mAutoConfig\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mauto_map\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m   1102\u001B[0m has_local_code \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodel_type\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict \u001B[38;5;129;01mand\u001B[39;00m config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodel_type\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;129;01min\u001B[39;00m CONFIG_MAPPING\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\configuration_utils.py:634\u001B[0m, in \u001B[0;36mPretrainedConfig.get_config_dict\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m    632\u001B[0m original_kwargs \u001B[38;5;241m=\u001B[39m copy\u001B[38;5;241m.\u001B[39mdeepcopy(kwargs)\n\u001B[0;32m    633\u001B[0m \u001B[38;5;66;03m# Get config dict associated with the base config file\u001B[39;00m\n\u001B[1;32m--> 634\u001B[0m config_dict, kwargs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39m_get_config_dict(pretrained_model_name_or_path, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m    635\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict:\n\u001B[0;32m    636\u001B[0m     original_kwargs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\configuration_utils.py:722\u001B[0m, in \u001B[0;36mPretrainedConfig._get_config_dict\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m    720\u001B[0m     config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m commit_hash\n\u001B[0;32m    721\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (json\u001B[38;5;241m.\u001B[39mJSONDecodeError, \u001B[38;5;167;01mUnicodeDecodeError\u001B[39;00m):\n\u001B[1;32m--> 722\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mEnvironmentError\u001B[39;00m(\n\u001B[0;32m    723\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mIt looks like the config file at \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mresolved_config_file\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m is not a valid JSON file.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    724\u001B[0m     )\n\u001B[0;32m    726\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_local:\n\u001B[0;32m    727\u001B[0m     logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mloading configuration file \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mresolved_config_file\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
      "\u001B[1;31mOSError\u001B[0m: It looks like the config file at 'C:\\Users\\merti\\.cache\\huggingface\\hub\\models--HUBII-Platform--ECG2HRV\\snapshots\\75f67e01de12e33cfb05cfbfed35ff621246b3f9\\config.json' is not a valid JSON file."
     ]
    }
   ],
   "source": [
    "feature_extractor = pipeline(\"feature-extraction\", model = 'HUBII-Platform/ECG2HRV')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-15T08:25:26.931190300Z",
     "start_time": "2024-02-15T08:25:25.877444800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "PIPELINE_REGISTRY.register_pipeline(\n",
    "    \"ecg2hrv\",\n",
    "    pipeline_class=MyPipeline,\n",
    "    # model_class=MyModel\n",
    ")\n",
    "feature_extractor = pipeline(\"ecg2hrv\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "feature_extractor = pipeline(\"ecg2hrv\", model=\"HUBII-Platform/ECG2HRV\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "2. ``AutoModel``"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "It looks like the config file at 'C:\\Users\\merti\\.cache\\huggingface\\hub\\models--HUBII-Platform--ECG2HRV\\snapshots\\75f67e01de12e33cfb05cfbfed35ff621246b3f9\\config.json' is not a valid JSON file.",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mJSONDecodeError\u001B[0m                           Traceback (most recent call last)",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\configuration_utils.py:719\u001B[0m, in \u001B[0;36mPretrainedConfig._get_config_dict\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m    717\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m    718\u001B[0m     \u001B[38;5;66;03m# Load config dict\u001B[39;00m\n\u001B[1;32m--> 719\u001B[0m     config_dict \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mcls\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_dict_from_json_file\u001B[49m\u001B[43m(\u001B[49m\u001B[43mresolved_config_file\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    720\u001B[0m     config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m commit_hash\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\configuration_utils.py:818\u001B[0m, in \u001B[0;36mPretrainedConfig._dict_from_json_file\u001B[1;34m(cls, json_file)\u001B[0m\n\u001B[0;32m    817\u001B[0m     text \u001B[38;5;241m=\u001B[39m reader\u001B[38;5;241m.\u001B[39mread()\n\u001B[1;32m--> 818\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mjson\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mloads\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtext\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\json\\__init__.py:346\u001B[0m, in \u001B[0;36mloads\u001B[1;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001B[0m\n\u001B[0;32m    343\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (\u001B[38;5;28mcls\u001B[39m \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m object_hook \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m\n\u001B[0;32m    344\u001B[0m         parse_int \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m parse_float \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m\n\u001B[0;32m    345\u001B[0m         parse_constant \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m object_pairs_hook \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m kw):\n\u001B[1;32m--> 346\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_default_decoder\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdecode\u001B[49m\u001B[43m(\u001B[49m\u001B[43ms\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    347\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mcls\u001B[39m \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\json\\decoder.py:337\u001B[0m, in \u001B[0;36mJSONDecoder.decode\u001B[1;34m(self, s, _w)\u001B[0m\n\u001B[0;32m    333\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001B[39;00m\n\u001B[0;32m    334\u001B[0m \u001B[38;5;124;03mcontaining a JSON document).\u001B[39;00m\n\u001B[0;32m    335\u001B[0m \n\u001B[0;32m    336\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m--> 337\u001B[0m obj, end \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mraw_decode\u001B[49m\u001B[43m(\u001B[49m\u001B[43ms\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43midx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_w\u001B[49m\u001B[43m(\u001B[49m\u001B[43ms\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mend\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    338\u001B[0m end \u001B[38;5;241m=\u001B[39m _w(s, end)\u001B[38;5;241m.\u001B[39mend()\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\json\\decoder.py:355\u001B[0m, in \u001B[0;36mJSONDecoder.raw_decode\u001B[1;34m(self, s, idx)\u001B[0m\n\u001B[0;32m    354\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mStopIteration\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m--> 355\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m JSONDecodeError(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mExpecting value\u001B[39m\u001B[38;5;124m\"\u001B[39m, s, err\u001B[38;5;241m.\u001B[39mvalue) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m    356\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m obj, end\n",
      "\u001B[1;31mJSONDecodeError\u001B[0m: Expecting value: line 1 column 1 (char 0)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001B[1;31mOSError\u001B[0m                                   Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[2], line 3\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;66;03m# Example with AutoModel\u001B[39;00m\n\u001B[0;32m      2\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mtransformers\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m AutoTokenizer, AutoModel\n\u001B[1;32m----> 3\u001B[0m model \u001B[38;5;241m=\u001B[39m \u001B[43mAutoModel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfrom_pretrained\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mHUBII-Platform/ECG2HRV\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\models\\auto\\auto_factory.py:526\u001B[0m, in \u001B[0;36m_BaseAutoModelClass.from_pretrained\u001B[1;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001B[0m\n\u001B[0;32m    523\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m kwargs\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mquantization_config\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m) \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m    524\u001B[0m     _ \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mquantization_config\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m--> 526\u001B[0m config, kwargs \u001B[38;5;241m=\u001B[39m AutoConfig\u001B[38;5;241m.\u001B[39mfrom_pretrained(\n\u001B[0;32m    527\u001B[0m     pretrained_model_name_or_path,\n\u001B[0;32m    528\u001B[0m     return_unused_kwargs\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[0;32m    529\u001B[0m     trust_remote_code\u001B[38;5;241m=\u001B[39mtrust_remote_code,\n\u001B[0;32m    530\u001B[0m     code_revision\u001B[38;5;241m=\u001B[39mcode_revision,\n\u001B[0;32m    531\u001B[0m     _commit_hash\u001B[38;5;241m=\u001B[39mcommit_hash,\n\u001B[0;32m    532\u001B[0m     \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mhub_kwargs,\n\u001B[0;32m    533\u001B[0m     \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs,\n\u001B[0;32m    534\u001B[0m )\n\u001B[0;32m    536\u001B[0m \u001B[38;5;66;03m# if torch_dtype=auto was passed here, ensure to pass it on\u001B[39;00m\n\u001B[0;32m    537\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m kwargs_orig\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtorch_dtype\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m) \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mauto\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\models\\auto\\configuration_auto.py:1100\u001B[0m, in \u001B[0;36mAutoConfig.from_pretrained\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m   1097\u001B[0m trust_remote_code \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtrust_remote_code\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[0;32m   1098\u001B[0m code_revision \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcode_revision\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[1;32m-> 1100\u001B[0m config_dict, unused_kwargs \u001B[38;5;241m=\u001B[39m PretrainedConfig\u001B[38;5;241m.\u001B[39mget_config_dict(pretrained_model_name_or_path, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m   1101\u001B[0m has_remote_code \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mauto_map\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mAutoConfig\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mauto_map\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m   1102\u001B[0m has_local_code \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodel_type\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict \u001B[38;5;129;01mand\u001B[39;00m config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodel_type\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;129;01min\u001B[39;00m CONFIG_MAPPING\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\configuration_utils.py:634\u001B[0m, in \u001B[0;36mPretrainedConfig.get_config_dict\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m    632\u001B[0m original_kwargs \u001B[38;5;241m=\u001B[39m copy\u001B[38;5;241m.\u001B[39mdeepcopy(kwargs)\n\u001B[0;32m    633\u001B[0m \u001B[38;5;66;03m# Get config dict associated with the base config file\u001B[39;00m\n\u001B[1;32m--> 634\u001B[0m config_dict, kwargs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39m_get_config_dict(pretrained_model_name_or_path, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m    635\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict:\n\u001B[0;32m    636\u001B[0m     original_kwargs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\configuration_utils.py:722\u001B[0m, in \u001B[0;36mPretrainedConfig._get_config_dict\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m    720\u001B[0m     config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m commit_hash\n\u001B[0;32m    721\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (json\u001B[38;5;241m.\u001B[39mJSONDecodeError, \u001B[38;5;167;01mUnicodeDecodeError\u001B[39;00m):\n\u001B[1;32m--> 722\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mEnvironmentError\u001B[39;00m(\n\u001B[0;32m    723\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mIt looks like the config file at \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mresolved_config_file\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m is not a valid JSON file.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    724\u001B[0m     )\n\u001B[0;32m    726\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_local:\n\u001B[0;32m    727\u001B[0m     logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mloading configuration file \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mresolved_config_file\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
      "\u001B[1;31mOSError\u001B[0m: It looks like the config file at 'C:\\Users\\merti\\.cache\\huggingface\\hub\\models--HUBII-Platform--ECG2HRV\\snapshots\\75f67e01de12e33cfb05cfbfed35ff621246b3f9\\config.json' is not a valid JSON file."
     ]
    }
   ],
   "source": [
    "# Example with AutoModel\n",
    "from transformers import AutoTokenizer, AutoModel\n",
    "model = AutoModel.from_pretrained('HUBII-Platform/ECG2HRV')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-15T08:19:30.471734700Z",
     "start_time": "2024-02-15T08:19:29.806536100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "3. Batched feature extraction - not supported (see https://huggingface.co/docs/transformers/main_classes/feature_extractor#transformers.BatchFeature)\n",
    "Not possible since it is not a model itself but a component used in the pipeline"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 3. Using simple download\n",
    "(See https://huggingface.co/julien-c/wine-quality?structured_data=%7B%7D)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "from huggingface_hub import hf_hub_download\n",
    "import joblib\n",
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "from src.model import ECG2HRV"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Instantiate model and save the model as a joblib file in the huggingface repository**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# Instantiate model\n",
    "model = ECG2HRV()\n",
    "# Save\n",
    "joblib.dump(model, \"..\\ECG2HRV.joblib\")\n",
    "# Load in notebook\n",
    "model = joblib.load(\"..\\ECG2HRV.joblib\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-21T16:08:51.659030Z",
     "start_time": "2024-02-21T16:08:51.605730100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Test the model locally with random ecg**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "duration_seconds = 10 # Time duration for ECG signal (in seconds)\n",
    "sample_rate = 100 # Sample rate (samples per second)\n",
    "num_samples = duration_seconds * sample_rate # Number of samples\n",
    "\n",
    "t = np.linspace(0, duration_seconds, num_samples) # Time array\n",
    "\n",
    "# Generate ECG signal (example synthetic data)\n",
    "ecg_signal = (\n",
    "    0.2 * np.sin(2 * np.pi * 1 * t) +\n",
    "    0.5 * np.sin(2 * np.pi * 0.5 * t) -\n",
    "    0.1 * np.sin(2 * np.pi * 2.5 * t)\n",
    ")\n",
    "\n",
    "# Add some random noise\n",
    "ecg_signal += np.random.normal(scale=0.1, size=num_samples)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-21T16:08:51.669938Z",
     "start_time": "2024-02-21T16:08:51.635032600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "[{'HRV_MeanNN': 413.4782608695652,\n  'HRV_SDNN': 100.97743652790477,\n  'HRV_SDANN1': nan,\n  'HRV_SDNNI1': nan,\n  'HRV_SDANN2': nan,\n  'HRV_SDNNI2': nan,\n  'HRV_SDANN5': nan,\n  'HRV_SDNNI5': nan,\n  'HRV_RMSSD': 92.78518690551262,\n  'HRV_SDSD': 94.96410805236795,\n  'HRV_CVNN': 0.24421462041449105,\n  'HRV_CVSD': 0.22440160870944167,\n  'HRV_MedianNN': 400.0,\n  'HRV_MadNN': 118.60799999999999,\n  'HRV_MCVNN': 0.29651999999999995,\n  'HRV_IQRNN': 150.0,\n  'HRV_SDRMSSD': 1.0882926455785953,\n  'HRV_Prc20NN': 320.0,\n  'HRV_Prc80NN': 490.0,\n  'HRV_pNN50': 52.17391304347826,\n  'HRV_pNN20': 69.56521739130434,\n  'HRV_MinNN': 310.0,\n  'HRV_MaxNN': 640.0,\n  'HRV_HTI': 5.75,\n  'HRV_TINN': 0.0}]"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model(input_data=ecg_signal, frequency=100.0)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-21T16:08:51.755181400Z",
     "start_time": "2024-02-21T16:08:51.671014900Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Test if the model can be loaded from the hub and used**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "# Load from hub\n",
    "REPO_ID = \"HUBII-Platform/ECG2HRV\"\n",
    "FILENAME = \"feature-extractor.joblib\"\n",
    "\n",
    "model = joblib.load(\n",
    "    hf_hub_download(repo_id=REPO_ID, filename=FILENAME)\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-21T11:36:52.302912800Z",
     "start_time": "2024-02-21T11:36:52.145834500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'rmssd': tensor(1.), 'mean_rr': tensor(3.), 'sdnn': tensor(1.), 'mean_hr': tensor(20000.)}\n"
     ]
    }
   ],
   "source": [
    "# Run model\n",
    "model(input_data=ecg_signal, frequency=100.0)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-21T11:37:12.239246200Z",
     "start_time": "2024-02-21T11:37:12.219556200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 4. Using custom model (not tested yet)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  }
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