{
 "cells": [
  {
   "cell_type": "code",
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   "id": "7677cd17",
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    "execution": {
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     "status": "completed"
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    "slideshow": {
     "slide_type": ""
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hf_dataset': 'weaviate/WithoutRetrieval-SchemaSplit-Test-80',\n",
       " 'prompt_template': '## Instruction\\nYour task is to write GraphQL for the Natural Language Query provided. Use the provided Schema to generate the GraphQL. The GraphQL should be valid for Weaviate.\\n\\n## Natural Language Query\\n{nlcommand}\\n\\n## Schema\\n{schema}\\n\\n## Answer\\n```graphql\\n',\n",
       " 'push_to_hub': 'substratusai/wgql-WithoutRetrieval-SchemaSplit-Train-80'}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "from pathlib import Path\n",
    "\n",
    "params = {}\n",
    "params_path = Path(\"/content/params.json\")\n",
    "if params_path.is_file():\n",
    "    with params_path.open(\"r\", encoding=\"UTF-8\") as params_file:\n",
    "        params = json.load(params_file)\n",
    "\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bda9f3a9",
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     "exception": false,
     "start_time": "2023-10-24T03:21:54.879530",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c933c694db0f49fd8565c66bd94ef85d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "33c8774129fa404eb91ba0deaa112e41",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00028559fb3a4c74865d1667802ab9c2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/3.90M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b9d06eb8b5d8487aa2b0c758d964db8a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f868d4dcd1f7461498c5afd00aeebfe4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['input', 'output', 'nlcommand', 'apiRef', 'apiRefPath', 'schema', 'schemaPath'],\n",
       "        num_rows: 825\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "hf_dataset = params.get(\"hf_dataset\")\n",
    "if hf_dataset:\n",
    "    dataset = load_dataset(hf_dataset)\n",
    "else:\n",
    "    dataset = load_dataset(\"json\", data_files=\"/content/data/*.json*\")\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "36114732",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-24T03:22:04.622336Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Instruction\n",
      "Your task is to write GraphQL for the Natural Language Query provided. Use the provided Schema to generate the GraphQL. The GraphQL should be valid for Weaviate.\n",
      "\n",
      "## Natural Language Query\n",
      "```text\n",
      "Show me the event name, description, year, significant impact, and the countries involved with their population for the top 10 historical events.\n",
      "```\n",
      "\n",
      "## Schema\n",
      "{\n",
      "\"classes\": [\n",
      "{\n",
      "\"class\": \"HistoricalEvent\",\n",
      "\"description\": \"Information about historical events\",\n",
      "\"vectorIndexType\": \"hnsw\",\n",
      "\"vectorizer\": \"text2vec-transformers\",\n",
      "\"properties\": [\n",
      "{\n",
      "\"name\": \"eventName\",\n",
      "\"dataType\": [\"text\"],\n",
      "\"description\": \"Name of the historical event\"\n",
      "},\n",
      "{\n",
      "\"name\": \"description\",\n",
      "\"dataType\": [\"text\"],\n",
      "\"description\": \"Detailed description of the event\"\n",
      "},\n",
      "{\n",
      "\"name\": \"year\",\n",
      "\"dataType\": [\"int\"],\n",
      "\"description\": \"Year the event occurred\"\n",
      "},\n",
      "{\n",
      "\"name\": \"hadSignificantImpact\",\n",
      "\"dataType\": [\"boolean\"],\n",
      "\"description\": \"Whether the event had a significant impact\"\n",
      "},\n",
      "{\n",
      "\"name\": \"involvedCountries\",\n",
      "\"dataType\": [\"Country\"],\n",
      "\"description\": \"Countries involved in the event\"\n",
      "}{\n",
      "\"class\": \"Country\",\n",
      "\"description\": \"Information about countries\",\n",
      "\"vectorIndexType\": \"hnsw\",\n",
      "\"vectorizer\": \"text2vec-transformers\",\n",
      "\"properties\": [\n",
      "{\n",
      "\"name\": \"countryName\",\n",
      "\"dataType\": [\"text\"],\n",
      "\"description\": \"Name of the country\"\n",
      "},\n",
      "{\n",
      "\"name\": \"population\",\n",
      "\"dataType\": [\"int\"],\n",
      "\"description\": \"Population of the country\"\n",
      "}}}\n",
      "\n",
      "## Answer\n",
      "```graphql\n",
      "\n"
     ]
    }
   ],
   "source": [
    "default_prompt = \"\"\"\n",
    "## Instruction\n",
    "Your task is to write GraphQL for the Natural Language Query provided. Use the provided API reference and Schema to generate the GraphQL. The GraphQL should be valid for Weaviate.\n",
    "\n",
    "Only use the API reference to understand the syntax of the request.\n",
    "\n",
    "## Natural Language Query\n",
    "{nlcommand}\n",
    "\n",
    "## Schema\n",
    "{schema}\n",
    "\n",
    "## API reference\n",
    "{apiRef}\n",
    "\n",
    "## Answer\n",
    "```graphql\n",
    "\"\"\"\n",
    "\n",
    "prompt = params.get(\"prompt_template\", default_prompt)\n",
    "print(prompt.format_map(dataset[\"train\"][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "844be37e",
   "metadata": {
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    "tags": []
   },
   "outputs": [
    {
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       "version_major": 2,
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     },
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    },
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      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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    },
    {
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f1228ebf53d444208308916094b11bfd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4daa2e6354748958506738df50dc784",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import transformers\n",
    "import torch\n",
    "import sys\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "\n",
    "model_path = \"/content/model/\"\n",
    "model_id = params[\"push_to_hub\"]\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "            model_id, device_map=\"auto\", trust_remote_code=True,\n",
    "            torch_dtype=torch.bfloat16, \n",
    "            use_flash_attention_2=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af483225",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-24T03:29:55.723570Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Oct 24 03:29:55 2023       \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\r\n",
      "|-------------------------------+----------------------+----------------------+\r\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|                               |                      |               MIG M. |\r\n",
      "|===============================+======================+======================|\r\n",
      "|   0  NVIDIA L4           Off  | 00000000:00:04.0 Off |                    0 |\r\n",
      "| N/A   44C    P0    27W /  72W |  13610MiB / 23034MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "                                                                               \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| Processes:                                                                  |\r\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n",
      "|        ID   ID                                                   Usage      |\r\n",
      "|=============================================================================|\r\n",
      "+-----------------------------------------------------------------------------+\r\n"
     ]
    }
   ],
   "source": [
    "! nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5c098929",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GenerationConfig {\n",
       "  \"bos_token_id\": 1,\n",
       "  \"do_sample\": true,\n",
       "  \"eos_token_id\": 2,\n",
       "  \"max_length\": 4096,\n",
       "  \"pad_token_id\": 0,\n",
       "  \"temperature\": 0.6,\n",
       "  \"top_p\": 0.9\n",
       "}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = \"cuda\"\n",
    "model.generation_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e0308186",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-24T03:30:00.515335Z",
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    },
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([7521])\n",
      "False\n",
      "False\n",
      "```\n"
     ]
    }
   ],
   "source": [
    "stop_ids = torch.LongTensor(tokenizer.encode(\"```\", add_special_tokens=False))\n",
    "## Note the stop_ids aren't correct, for some reason there are multiple possible token IDs for ```\n",
    "## so instead we're using tensor([13940, 28832], device='cuda:0') as the stop_ids, because that's\n",
    "## what the model normally generates\n",
    "print(stop_ids)\n",
    "print(tokenizer.decode([8789]) == \"```\")\n",
    "print(tokenizer.decode([13940, 28832]) == \"```\")\n",
    "print(tokenizer.decode(tokenizer.encode(\"```\", add_special_tokens=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6d55baa3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-24T03:30:02.403027Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import StoppingCriteria, StoppingCriteriaList\n",
    "\n",
    "class BacktickStoppingCriteria(StoppingCriteria):\n",
    "    def __init__(self, tokenizer):\n",
    "        self.tokenizer = tokenizer\n",
    "\n",
    "    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:\n",
    "        if self.tokenizer.decode(input_ids[0][-2:]) == \"```\" or self.tokenizer.decode(input_ids[0][-1]) == \"```\":\n",
    "            return True\n",
    "        return False\n",
    "\n",
    "\n",
    "\n",
    "stopping_criteria = StoppingCriteriaList([BacktickStoppingCriteria(tokenizer)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "598462d6",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "model.config.bos_token_id = tokenizer.bos_token_id = 1\n",
    "model.config.eos_token_id = tokenizer.eos_token_id = 2\n",
    "model.config.pad_token_id = tokenizer.pad_token_id = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b8cce97b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-24T03:30:10.372209Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Instruction\n",
      "Your task is to write GraphQL for the Natural Language Query provided. Use the provided Schema to generate the GraphQL. The GraphQL should be valid for Weaviate.\n",
      "\n",
      "## Natural Language Query\n",
      "```text\n",
      "Show me the event name, description, year, significant impact, and the countries involved with their population for the top 10 historical events.\n",
      "```\n",
      "\n",
      "## Schema\n",
      "{\n",
      "\"classes\": [\n",
      "{\n",
      "\"class\": \"HistoricalEvent\",\n",
      "\"description\": \"Information about historical events\",\n",
      "\"vectorIndexType\": \"hnsw\",\n",
      "\"vectorizer\": \"text2vec-transformers\",\n",
      "\"properties\": [\n",
      "{\n",
      "\"name\": \"eventName\",\n",
      "\"dataType\": [\"text\"],\n",
      "\"description\": \"Name of the historical event\"\n",
      "},\n",
      "{\n",
      "\"name\": \"description\",\n",
      "\"dataType\": [\"text\"],\n",
      "\"description\": \"Detailed description of the event\"\n",
      "},\n",
      "{\n",
      "\"name\": \"year\",\n",
      "\"dataType\": [\"int\"],\n",
      "\"description\": \"Year the event occurred\"\n",
      "},\n",
      "{\n",
      "\"name\": \"hadSignificantImpact\",\n",
      "\"dataType\": [\"boolean\"],\n",
      "\"description\": \"Whether the event had a significant impact\"\n",
      "},\n",
      "{\n",
      "\"name\": \"involvedCountries\",\n",
      "\"dataType\": [\"Country\"],\n",
      "\"description\": \"Countries involved in the event\"\n",
      "}{\n",
      "\"class\": \"Country\",\n",
      "\"description\": \"Information about countries\",\n",
      "\"vectorIndexType\": \"hnsw\",\n",
      "\"vectorizer\": \"text2vec-transformers\",\n",
      "\"properties\": [\n",
      "{\n",
      "\"name\": \"countryName\",\n",
      "\"dataType\": [\"text\"],\n",
      "\"description\": \"Name of the country\"\n",
      "},\n",
      "{\n",
      "\"name\": \"population\",\n",
      "\"dataType\": [\"int\"],\n",
      "\"description\": \"Population of the country\"\n",
      "}}}\n",
      "\n",
      "## Answer\n",
      "```graphql\n",
      "{\n",
      "  Get {\n",
      "    HistoricalEvent (\n",
      "      limit: 10\n",
      "    ) {\n",
      "      eventName\n",
      "      description\n",
      "      year\n",
      "      hadSignificantImpact\n",
      "      involvedCountries {\n",
      "        ... on Country {\n",
      "          countryName\n",
      "          population\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "```\n",
      "CPU times: user 6.46 s, sys: 744 ms, total: 7.21 s\n",
      "Wall time: 7.62 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import torch\n",
    "\n",
    "\n",
    "device = \"cuda\"\n",
    "model_inputs = tokenizer([prompt.format_map(dataset[\"train\"][0])],\n",
    "                         return_tensors=\"pt\").to(device)\n",
    "\n",
    "generated_ids = model.generate(**model_inputs,\n",
    "                               max_new_tokens=300,\n",
    "                               stopping_criteria=stopping_criteria)\n",
    "\n",
    "print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4d586596",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-24T03:30:19.975723Z",
     "iopub.status.busy": "2023-10-24T03:30:19.975061Z",
     "iopub.status.idle": "2023-10-24T03:30:19.980289Z",
     "shell.execute_reply": "2023-10-24T03:30:19.979604Z"
    },
    "papermill": {
     "duration": 1.01748,
     "end_time": "2023-10-24T03:30:19.982265",
     "exception": false,
     "start_time": "2023-10-24T03:30:18.964785",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 424])\n",
      "{\n",
      "  Get {\n",
      "    HistoricalEvent (\n",
      "      limit: 10\n",
      "    ) {\n",
      "      eventName\n",
      "      description\n",
      "      year\n",
      "      hadSignificantImpact\n",
      "      involvedCountries {\n",
      "        ... on Country {\n",
      "          countryName\n",
      "          population\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model_inputs[\"input_ids\"].shape)\n",
    "input_length = model_inputs[\"input_ids\"].shape[1]\n",
    "print(tokenizer.decode(generated_ids[0][input_length:], skip_special_tokens=True).strip(\"```\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f48a2dd8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-24T03:30:21.873420Z",
     "iopub.status.busy": "2023-10-24T03:30:21.872686Z",
     "iopub.status.idle": "2023-10-24T04:45:40.261618Z",
     "shell.execute_reply": "2023-10-24T04:45:40.260727Z"
    },
    "papermill": {
     "duration": 4519.365995,
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     "exception": false,
     "start_time": "2023-10-24T03:30:20.897752",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running inference for 825 entries in dataset\n",
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     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "import json\n",
    "dataset_size = len(dataset[\"train\"])\n",
    "output_path = \"/content/artifacts/test-output.json\"\n",
    "entries = []\n",
    "print(f\"Running inference for {dataset_size} entries in dataset\")\n",
    "for i in range(dataset_size):\n",
    "    print(f\"entry {i+1} of {dataset_size}\")\n",
    "    entry = dataset[\"train\"][i]\n",
    "    model_inputs = tokenizer([prompt.format_map(entry)],\n",
    "                         return_tensors=\"pt\").to(device)\n",
    "\n",
    "\n",
    "\n",
    "    generated_ids = model.generate(**model_inputs,\n",
    "                               max_new_tokens=300,\n",
    "                               stopping_criteria=stopping_criteria)\n",
    "    input_length = model_inputs[\"input_ids\"].shape[1]\n",
    "    output = tokenizer.decode(generated_ids[0][input_length:], skip_special_tokens=True)\n",
    "    entry[\"modelOutput\"] = output.strip(\"```\")\n",
    "    entries.append(entry)\n",
    "\n",
    "    with open(output_path, 'a') as file:\n",
    "        json.dump(entry, file)\n",
    "        file.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa86fcc",
   "metadata": {
    "papermill": {
     "duration": 1.059079,
     "end_time": "2023-10-24T04:45:42.478248",
     "exception": false,
     "start_time": "2023-10-24T04:45:41.419169",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Store the test dataset with model output in the original HuggingFace Model repo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e7715a2",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": false,
     "start_time": "2023-10-24T04:45:43.514575",
     "status": "running"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from huggingface_hub import HfApi\n",
    "repo_id = params.get(\"push_to_hub\")\n",
    "if repo_id:\n",
    "    hf_api = HfApi()\n",
    "    hf_api.upload_file(\n",
    "            path_or_fileobj=Path(output_path),\n",
    "            path_in_repo=Path(output_path).name,\n",
    "            repo_id=repo_id,\n",
    "    )\n",
    "    logs_path = Path(\"/content/artifacts/eval.ipynb\")\n",
    "    if logs_path.exists():\n",
    "        hf_api.upload_file(\n",
    "            path_or_fileobj=logs_path,\n",
    "            path_in_repo=logs_path.name,\n",
    "            repo_id=repo_id,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "931e3b5a",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "## Execute the model output on a live Weaviate cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1809d7c2",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import weaviate\n",
    "from weaviate.embedded import EmbeddedOptions\n",
    "\n",
    "client = weaviate.Client(\n",
    "    embedded_options=EmbeddedOptions()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9de4c1fd",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "! cat ToySchemas/{dataset[\"train\"][0][\"schemaPath\"]}\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5124f713",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from typing import Dict\n",
    "import json\n",
    "def json_reader(file_path):\n",
    "    with open(file_path, 'r', encoding='utf-8', errors='replace') as api_ref_fh:\n",
    "        data = json.load(api_ref_fh)\n",
    "    return data\n",
    "\n",
    "def remove_vectorizer(classes: list[Dict]) -> list[Dict]:\n",
    "    new_list = []\n",
    "    for c in classes:\n",
    "        if \"vectorizer\" in c:\n",
    "            del c[\"vectorizer\"]\n",
    "        new_list.append(c)\n",
    "    return new_list\n",
    "\n",
    "def didItExecute(schemaPath, modelOutput):\n",
    "    client.schema.delete_all()\n",
    "    schema = json_reader(f'ToySchemas/{schemaPath}')\n",
    "    schema[\"classes\"] = remove_vectorizer(schema[\"classes\"])\n",
    "    client.schema.create(schema)\n",
    "    WeaviateResponse = client.query.raw(modelOutput)\n",
    "    return WeaviateResponse\n",
    "\n",
    "sample = entries[0]\n",
    "didItExecute(sample[\"schemaPath\"], sample[\"modelOutput\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7f8a824",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "counter = 1\n",
    "successfulQueries = []\n",
    "failedQueries = []\n",
    "failedAPIsCount = {}\n",
    "failedSchemasCount = {}\n",
    "for idx, example in enumerate(entries):\n",
    "    failed = False\n",
    "    modelQuery = example[\"modelOutput\"]\n",
    "\n",
    "    weaviateResponse = didItExecute(example[\"schemaPath\"], modelQuery)\n",
    "\n",
    "    if \"errors\" in weaviateResponse.keys():\n",
    "        failed = True\n",
    "\n",
    "    if failed:\n",
    "        print(\"FAILED! FAILED! FAILED! \\n\")\n",
    "        print(idx)\n",
    "        failedQueries.append(example)\n",
    "        # Update failed Schema tracker\n",
    "        if example[\"schemaPath\"] in failedSchemasCount.keys():\n",
    "            failedSchemasCount[example[\"schemaPath\"]] += 1\n",
    "        else:\n",
    "            failedSchemasCount[example[\"schemaPath\"]] = 1\n",
    "        # Update API tracker\n",
    "        if example[\"apiRefPath\"] in failedAPIsCount.keys():\n",
    "            failedAPIsCount[example[\"apiRefPath\"]] += 1\n",
    "        else:\n",
    "            failedAPIsCount[example[\"apiRefPath\"]] = 1\n",
    "    else:\n",
    "        successfulQueries.append(example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94e02a6b",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(f\"{len(successfulQueries)} Queries successfully executed!\")\n",
    "print(f\"{len(failedQueries)} Queries failed to execute!\")\n",
    "print(\"FAILED API Count \\n\")\n",
    "print(failedAPIsCount)\n",
    "print(\"FAILE SCHEMA COUNT \\n\")\n",
    "print(failedSchemasCount)"
   ]
  }
 ],
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  "papermill": {
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   "duration": null,
   "end_time": null,
   "environment_variables": {},
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   "output_path": "/content/artifacts/eval.ipynb",
   "parameters": {},
   "start_time": "2023-10-24T03:21:48.360391",
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