peft_flan_t5_int8_summarization_FinancialReports-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lNGMRq7NYH3Z"
   },
   "source": [
    "# Efficiently train Large Language Models with LoRA and Hugging Face\n",
    "\n",
    "In this blog, we are going to show you how to apply [Low-Rank Adaptation of Large Language Models (LoRA)](https://arxiv.org/abs/2106.09685) to fine-tune FLAN-T5 XXL (11 billion parameters) on a single GPU. We are going to leverage Hugging Face [Transformers](https://huggingface.co/docs/transformers/index), [Accelerate](https://huggingface.co/docs/accelerate/index), and [PEFT](https://github.com/huggingface/peft).\n",
    "\n",
    "You will learn how to:\n",
    "\n",
    "1. Setup Development Environment\n",
    "2. Load and prepare the dataset\n",
    "3. Fine-Tune T5 with LoRA and bnb int-8\n",
    "4. Evaluate & run Inference with LoRA FLAN-T5\n",
    "5. Cost performance comparison\n",
    "\n",
    "### Quick intro: PEFT or Parameter Efficient Fine-tunin\n",
    "\n",
    "[PEFT](https://github.com/huggingface/peft), or Parameter Efficient Fine-tuning, is a new open-source library from Hugging Face to enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters. PEFT currently includes techniques for:\n",
    "\n",
    "- LoRA: [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685.pdf)\n",
    "- Prefix Tuning: [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://arxiv.org/pdf/2110.07602.pdf)\n",
    "- P-Tuning: [GPT Understands, Too](https://arxiv.org/pdf/2103.10385.pdf)\n",
    "- Prompt Tuning: [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/pdf/2104.08691.pdf)\n",
    "\n",
    "*Note: This tutorial was created and run on a g5.2xlarge AWS EC2 Instance, including 1 NVIDIA A10G.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pURQDvfCYH3b"
   },
   "source": [
    "## 1. Setup Development Environment\n",
    "\n",
    "In our example, we use the [PyTorch Deep Learning AMI](https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-pytorch.html) with already set up CUDA drivers and PyTorch installed. We still have to install the Hugging Face Libraries, including transformers and datasets. Running the following cell will install all the required packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 41854,
     "status": "ok",
     "timestamp": 1692076487499,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "YazVlgI6YH3c",
    "outputId": "2b3a6564-5fe0-47b2-a8f4-a1b242de3a14",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# install Hugging Face Libraries\n",
    "!pip install \"peft==0.2.0\"\n",
    "!pip install \"transformers==4.27.1\" \"datasets==2.9.0\" \"accelerate==0.17.1\" \"evaluate==0.4.0\" \"bitsandbytes==0.37.1\" loralib --upgrade --quiet\n",
    "\n",
    "# install additional dependencies needed for training\n",
    "!pip install rouge-score tensorboard py7zr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 200852,
     "status": "ok",
     "timestamp": 1692076688349,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "QkZ0k0lLMJGc",
    "outputId": "6705455c-dd05-4693-ec12-db0d27d7d1c9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')\n",
    "!cp '/content/gdrive/My Drive/Data/FINDSum/text/FINDSum-ROO/roo_input_2000/'* ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 356,
     "referenced_widgets": [
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    },
    "executionInfo": {
     "elapsed": 18327,
     "status": "ok",
     "timestamp": 1692076706658,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "nAcra2sWta_x",
    "outputId": "2eaeba4a-d5f6-4ff8-afda-bc12ad8e244c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.builder:Using custom data configuration default-e31ff96ade9443e4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-e31ff96ade9443e4/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\n"
     ]
    },
    {
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       "model_id": "b70adcc74ba1454abcf73fe5affc6f35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "716e0d134fcc45469ab503b377021ada",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e862c3a614a24e6ab288202019aeb151",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n",
      "/usr/local/lib/python3.10/dist-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "59a3839ba8c5468d905c23cec797287f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n",
      "/usr/local/lib/python3.10/dist-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-e31ff96ade9443e4/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c23efaca76b64db8b1746cc88fe6c392",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "# dataset = load_dataset(\"csv\", data_files=\"my_file.csv\")\n",
    "\n",
    "#dataset_fin = load_dataset(\"csv\", data_files=[\"val_roo_segment_0_input_2_1000.csv\", \"test_roo_segment_0_input_2_1000.csv\", \"val_roo_segment_1_input_2_1000.csv\", \"test_roo_segment_1_input_2_1000.csv\"])\n",
    "#dataset_fin = load_dataset(\"csv\", data_files={\"train\": [\"val_roo_segment_0_input_2_1000.csv\", \"test_roo_segment_0_input_2_1000.csv\", \"val_roo_segment_1_input_2_1000\"], \"test\": [\"test_roo_segment_1_input_2_1000.csv\"]})\n",
    "#dataset_fin = load_dataset(\"csv\", data_files=[\"train_roo_segment_0_input_2_1000.csv\", \"train_roo_segment_1_input_2_1000.csv\"])\n",
    "ds_fin = load_dataset(\"csv\", data_files={\"train\": [\"train_roo_segment_0_input_2_1000.csv\", \"train_roo_segment_1_input_2_1000.csv\"], \"test\": [\"test_roo_segment_0_input_2_1000.csv\",\"test_roo_segment_1_input_2_1000.csv\"]})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 38,
     "status": "ok",
     "timestamp": 1692076706658,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "g4qNZaIoufTR",
    "outputId": "eb1d9b4a-b59c-4dca-b349-ac08e07aec2b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'document': \"amounts allocated to land , buildings , equipment and fixtures are based on cost segregation studies performed by independent third parties or on the company 's analysis of story_separator_special_tag the following discussion should be read in conjunction with the consolidated financial statements of lcfh and accompanying notes included in this annual report on form 10-k. in addition to historical information , the following discussion contains forward-looking statements that reflect our plans , estimates and beliefs . our actual results could differ materially from those discussed in the forward-looking statements . factors that could cause or contribute to these differences include , but are not limited to , those discussed in our `` risk factors . '' overview we are a leading commercial real estate finance company with a proprietary loan origination platform and an established national footprint . as a non-bank operating company , we believe that we are well-positioned to benefit from the opportunities arising from the diminished supply of debt capital and the substantial demand for new financings in the commercial real estate sector . we believe our comprehensive , fully-integrated in-house infrastructure , access to a diverse array of committed financing sources and highly experienced management team of industry veterans will allow us to continue to prudently grow our business as we endeavor to capitalize on profitable opportunities in various market conditions . we conduct our business through three major business lines : commercial mortgage lending , investments in securities secured by first mortgage loans , and investments in selected net leased and other real estate assets . we apply a comprehensive best practices underwriting approach to every loan and investment that we make , rooted in management 's deep understanding of fundamental real estate values and proven expertise in these complementary business lines through multiple economic and credit cycles . our primary business strategy is originating conduit loans , which are first mortgage loans on stabilized , income-producing commercial real estate properties that are available for sale in cmbs securitizations . from our inception in october 2008 through december 30 , 2013 , we originated $ 5.9 billion of conduit loans , $ 5.2 billion of which were sold into 17 cmbs securitizations . according to market data provided by commercial mortgage alert , this makes us , by volume , the second largest non-bank contributor of loans to cmbs securitizations in the united states for in 2013. the securitization of conduit loans has been a consistently profitable business for us and enables us to reinvest our equity capital into new loan originations or allocate it to other investments . in addition to conduit loans , we originated $ 1.4 billion of balance sheet loans held for investment from inception through december 31 , 2013. during that timeframe , we also acquired $ 5.7 billion of investment grade-rated securities secured by first mortgage loans on commercial real estate and $ 785.5 million of selected net leased and other real estate assets . as of december 31 , 2013 , we had $ 3.5 billion in total assets and $ 1.2 billion in book equity capital . as of that date , our assets included $ 979.6 million of loans , $ 1.7 billion of securities , and $ 624.2 million of real estate . our primary sources of revenue include net interest income on our investments , which comprised 23.2 % and 38.6 % of our total net revenues and net income , respectively , for the year ended december 31 , 2013 , and income from sales of loans , net , which represents the income we earn from regular sales and securitizations of certain commercial mortgage loans , and which comprised 46.7 % and 77.7 % of our net revenues and net income , respectively , for the year ended december 31 , 2013. see `` \\x97non-gaap financial measures '' for a definition of net revenues and a reconciliation to total net interest income after provision for loan losses and total other income . we also generate net rental revenues from certain of our real estate and fee income from our loan originations and the management of our institutional bridge loan partnership . 66 ladder was founded in october 2008 and we are currently capitalized by our management team and a group of leading global institutional investors , including affiliates of alberta investment management corp. , gi partners , ontario municipal employees retirement system and towerbrook capital partners . we have built our operating business to include 60 full-time industry professionals by hiring experienced personnel known to us in the commercial mortgage industry . doing so has allowed us to maintain consistency in our culture and operations and to focus on strong credit practices and disciplined growth . we have a diversified and flexible financing strategy supporting our business operations , including significant committed term financing from leading financial institutions . as of december 31 , 2013 , we had $ 2.2 billion of debt financing outstanding , including $ 989.0 million of financing from the fhlb ( with an additional $ 416.0 million of committed term financing available to us ) , $ 248.2 million committed secured term repurchase agreement financing outstanding ( with an additional $ 1.7 billion of committed secured term financing available to us ) , $ 291.1 million of third-party , non-recourse mortgage debt , $ 361.6 million of other securities financing , and $ 325.0 million of notes . as of december 31 , 2013 , our debt-to-equity ratio was 1.9:1.0 , as we employ leverage prudently to maximize financial flexibility . refer to note 16 to the consolidated financial statements for disclosure regarding events subsequent to december 31 , 2013. our businesses we invest primarily in loans , securities and other interests in u.s. commercial real estate , with a focus on senior secured assets . our mix of business segments is designed to provide us with the flexibility to opportunistically allocate capital in order to generate attractive risk-adjusted returns under varying market conditions . story_separator_special_tag these properties are leased on a net basis where the tenant is generally responsible for payment of real estate taxes , property , building and general liability insurance and property and building maintenance . sixteen of our properties are leased to a national pharmacy chain , and the remaining properties are leased to a national discount retailer , a regional sporting goods store , and a regional membership warehouse club . as of december 31 , 2013 , our net leased properties comprised a total of 1.4 million square feet , had a 100 % occupancy rate , had an average age since construction of 6.6 years and a weighted average remaining lease term of 18.7 years . in addition , as of december 31 , 2013 , we owned ( i ) a 13 story office building with a book value of $ 14.8 million through a joint venture with an operating partner , ( ii ) a portfolio of 14 office buildings with a book value of $ 129.5 million through a separate joint venture with an operating partner and , ( iii ) a 26-story office building in minnesota through a consolidated , majority owned joint venture with a book value of $ 50.1 million . further details regarding our portfolio of commercial real estate properties , including state of operation , can be found in the table on page 62. residential real estate . as of december 31 , 2013 , we owned 333 residential condominium units at veer towers in las vegas , nv with a book value of $ 93.0 million through a consolidated joint venture with an operating partner . the condominium units are included in our real estate business segment . the condominium units were 53 % leased and occupied as of december 31 , 2013. depending on market conditions for new leases and renewals in this residential inventory , we may provide tenants rent concessions or abatements . we intend to sell the entire inventory of units over time . we are only leasing the units currently under short-term leases ( less than 2-year terms ) to offset operating expenses during our sales process , and therefore , any rent concessions or abatements would have no material impact on our operations . in addition , during 2013 , we acquired a multifamily apartment building in miami , fl with a book value of $ 79.8 million . the apartment units were 92 % leased and occupied as of december 31 , 2013. other investments institutional bridge loan partnership . in 2011 , we established an institutional partnership with a canadian sovereign pension fund to invest in first mortgage bridge loans that met pre-defined criteria . our partner owns 90 % of the limited partnership interests and we own the remaining 10 % on a pari passu basis and act as general partner . we retain the discretion over which loans to propose to sell to the partnership , and our partner retains the discretion to accept or reject individual loans . as the general partner , we have engaged our advisory entity to manage the assets of the partnership and earn management fees and incentive fees from the partnership . in addition , we are entitled to retain origination fees of up to 1 % on loans that we sell to the partnership and on a case-by-case basis , as approved by our partner , may retain certain exit fees . as of december 31 , 2013 , the partnership owned $ 102.6 million of first mortgage bridge loan assets that were financed by $ 34.6 million of term debt . debt of the partnership is nonrecourse to the limited and general partners , except for customary nonrecourse carve-outs for certain actions and environmental liability . as of december 31 , 2013 , the book value of our investment in the institutional partnership was $ 7.1 million . unconsolidated joint venture . in connection with the origination of a loan in april 2012 , we received a 25 % equity kicker with the right to convert upon a capital event . on march 22 , 2013 , the loan was refinanced by us and we converted our equity kicker interest into a 25 % limited liability company membership interest in grace lake jv , llc . as of december 31 , 2013 , the llc owned an office building campus with a carrying value of $ 78.5 million that is financed by $ 77.7 million of long-term debt . debt of the llc is nonrecourse to the limited liability company members , except for 70 customary nonrecourse carve-outs for certain actions and environmental liability . as of december 31 , 2013 , the book value of our investment in the llc was $ 2.1 million . other asset management activities . as of december 31 , 2013 , we also managed two separate cmbs investment accounts for private investors with combined total assets of $ 3.8 million . as of october 2012 , we are no longer purchasing any new investments for these accounts , however , we will continue to manage the existing investments until their full prepayment or other disposition . business outlook we believe the commercial real estate finance market currently presents substantial opportunities for new origination , as it is characterized by stabilizing property values , a low interest rate environment , and a supply- demand imbalance for financing . over $ 1.6 trillion of commercial real estate debt is scheduled to mature over the next five years according to trepp , while at the same time traditional real estate lenders such as banks and insurance companies face significant new capital and regulatory requirements . april 2010 marked the first new-issue , multi-borrower cmbs securitization since june 2008. for 2010 as a whole , new cmbs issuances totaled $ 11.6 billion . in 2011 , new cmbs issuances totaled $ 32.7 billion , despite a slowdown in originations of commercial real estate mortgage loans during the second half of the year because of the uncertain economic climate created by the euro-area crisis .\\n\",\n",
       " 'summary': \"factors impacting operating results there are a number of factors that influence our operating results in a meaningful way . the most significant factors include : ( 1 ) our competition ; ( 2 ) market and economic conditions ; ( 3 ) loan origination volume ; ( 4 ) profitability of securitizations ; ( 5 ) avoidance of credit losses ; ( 6 ) availability of debt and equity funding and the costs of that funding ; ( 7 ) the net interest margin on our investments ; and ( 8 ) effectiveness of our hedging and other risk management practices . jobs act on april 5 , 2012 , the jobs act was signed into law . the jobs act contains provisions that , among other things , reduce certain reporting requirements for qualifying public companies . section 107 of the jobs act also provides that an `` emerging growth company '' can take advantage of the extended transition period provided in section 7 ( a ) ( 2 ) ( b ) of the securities act for complying with new or revised 71 accounting standards . in other words , an `` emerging growth company '' can delay the adoption of certain accounting standards until those standards would otherwise apply to private companies . however , we are choosing to `` opt out '' of such extended transition period , and as a result , we will comply with new or revised accounting standards on the relevant dates on which adoption of such standards is required for non-emerging growth companies . section 107 of the jobs act provides that our decision to opt out of the extended transition period for complying with new or revised accounting standards is irrevocable . additionally , we are in the process of evaluating the benefits of relying on the other reduced reporting requirements provided by the jobs act . subject to certain conditions set forth in the jobs act , as an `` emerging growth company , '' we may choose to rely on certain exemptions .\\n\"}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ds_fin = dataset_fin[\"train\"].train_test_split(test_size=0.2, shuffle=True)\n",
    "# ds_fin\n",
    "\n",
    "ds_fin[\"train\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 36,
     "status": "ok",
     "timestamp": 1692076706659,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "Li45ckhz0cvI",
    "outputId": "fa565bc2-5883-4379-efe2-8991ef241dfa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset size: 33640\n",
      "Test dataset size: 4204\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(f\"Train dataset size: {len(ds_fin['train'])}\")\n",
    "print(f\"Test dataset size: {len(ds_fin['test'])}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NZ5rVvspYH3d"
   },
   "source": [
    "To train our model, we need to convert our inputs (text) to token IDs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301,
     "referenced_widgets": [
      "3d3b93d9c42c48b29e6db0b258261f64",
      "7d72f25d27404285b81c8b8316a593f0",
      "dff72c651e954ab68e45b44904fd6ee9",
      "fd97d5d6994a421abb42b61f527f46e2",
      "f802bb22de6c4510bc4d104a038b585c",
      "46f0e8130ca843149170c16030cada57",
      "9ffb014fd03c41fda9a483852de099fd",
      "47d927431d9d4e7fb88fa17a7756afc0",
      "5f6c71abfeaa44f48190b23b3b7e9731",
      "6d707397786d425d95a9101e394426fa",
      "93f80c72a6fb4825bb94bf2e7cbfa970",
      "36ae43caf46d42f78ca66df9d307a5b7",
      "a85694a8f9394464b79db9b1473990dc",
      "4debf1e433a44dfaa85ea0942be5a944",
      "7b654f3ccc8e4478b75a7b07256f272d",
      "80deb3938f6c4efd8f3818ae16ee25d0",
      "879094f81c1f4549a36f282b36d009d2",
      "44f9f6467c3547c6a4aeecb770885f88",
      "2dd59584848b473992f34356da9663a0",
      "3db3c0093f474487a8612040f0acb8da",
      "d6352e6378414ac9945e97e82b9f5100",
      "b2f041eb686047c1b949e3d029614477",
      "8b38648b53a944ee8c9e7602fe042318",
      "9e8d6ba21d19461c863b1e9d878e0a62",
      "a74e4d6054524485b6fbdae0689ae3b8",
      "70f0ad9ff06b4b9497764b020ed9f909",
      "1cb21c6df3f1496e9c81ec2a1794fcd9",
      "d21f7080864744e392b4f388ad753015",
      "2f2a890b062d4d37b8feeb2c123200a9",
      "6813f384ba8040b29e535f1930bd9d47",
      "2df059419fa44435b5c4b6d0695b1b78",
      "683c990b07f04fba8aea9a2abd8f56b9",
      "8085f2c6ed7d43499e9bc06afbf2c333",
      "9360f4e08dd64b24af985b0b933fe768",
      "b017e1e05243497e80468670a83e8f9a",
      "b906e290378c499ca13a9209f04a7682",
      "f8a4612ca3964797bed10c840474b666",
      "b29070912ae842719e04c4f274620b65",
      "c35b6c54961942428f02c54ed85142e0",
      "0e25b379b6f5465d80ee549dc0559051",
      "0d09c441995f4ab0bf570be16c06fe8c",
      "281bebbca7af45a9a7ca1572a7571687",
      "630ac86cb5ae40eda9ca3e86047f5b94",
      "e493de8583c84a5c977b631d6939aa50",
      "38a1f0a11ef34feb8a9a1378be0e52d4",
      "39cbdd70d016496294a00d6b1c38d958",
      "d4bc3cad81f84c85be7350d0c680400d",
      "fa996fd9d04946dfa88f5d8c3899ccec",
      "2c69e5b734a74a1cbc04df8c1b439eef",
      "db36ab2ec64e410e956a0b3dc02404d9",
      "37a7699af79644c0b35eff11c353e753",
      "4dd4b9102af44c7eb88d52e78436e1f1",
      "7a2ff87117544125b062003c1db0db60",
      "72216f9757114b649f6b7c82704b162d",
      "963d9aff389540c5a5ef2f6892796b32"
     ]
    },
    "executionInfo": {
     "elapsed": 48849,
     "status": "ok",
     "timestamp": 1692076755490,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "nybP0WO2YH3d",
    "outputId": "b833ca73-eb40-4f04-869d-91aedde2c2fc"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d3b93d9c42c48b29e6db0b258261f64",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.32k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36ae43caf46d42f78ca66df9d307a5b7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)ve/main/spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b38648b53a944ee8c9e7602fe042318",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9360f4e08dd64b24af985b0b933fe768",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38a1f0a11ef34feb8a9a1378be0e52d4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading model.safetensors:   0%|          | 0.00/242M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All PyTorch model weights were used when initializing TFT5ForConditionalGeneration.\n",
      "\n",
      "All the weights of TFT5ForConditionalGeneration were initialized from the PyTorch model.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5ForConditionalGeneration for predictions without further training.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pad> lcfh's financial statements reflect our plans, estimates and beliefs. the forward-looking statements could differ materially from those discussed in the forward-looking statements. we are a leading commercial real estate finance company with a proprietary loan origination platform and an established national footprint.</s>\n",
      "<pad> Wie alt sind Sie?</s>\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
    "\n",
    "model_id=\"t5-small\"\n",
    "\n",
    "sample_record = ds_fin[\"train\"][0]\n",
    "# Load tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "# to be removed\n",
    "\n",
    "from transformers import TFAutoModelForSeq2SeqLM, DataCollatorForSeq2Seq\n",
    "\n",
    "model = TFAutoModelForSeq2SeqLM.from_pretrained(model_id)\n",
    "\n",
    "# let us save the model\n",
    "model.save_pretrained('./models/base')\n",
    "#text_prompt=\"summarize: This is a very long text and sometimes this is also a long set of words. Hence such long texts are usually written with lots of words\"\n",
    "sample=\"summarize: \" + sample_record[\"document\"]\n",
    "input_tokens = tokenizer(sample, padding='max_length', max_length=512, truncation=True, return_tensors='np')\n",
    "result_sample = model.generate(**input_tokens, max_length=200, top_k=3, temperature=0.5)\n",
    "# tokenized = tokenizer([document], return_tensors='np')\n",
    "# out = model.generate(**tokenized, max_length=128\n",
    "# Greedy Search\n",
    "print(tokenizer.decode(result_sample[0], truncate_before_pattern=[r\"\\n\\n^#\", \"^'''\", \"\\n\\n\\n\"]))\n",
    "\n",
    "#summarization\n",
    "input_text = \"translate English to German: How old are you?\"\n",
    "input_ids = tokenizer(input_text,  max_length=512,truncation=True,  return_tensors=\"np\")\n",
    "\n",
    "outputs = model.generate(**input_ids, max_length=200, top_k=3, temperature=0.5)\n",
    "print(tokenizer.decode(outputs[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1692076755491,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "SJ_V_ta0jP_O",
    "outputId": "f546d873-fe5f-4df2-b2c4-2d90b31235ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['document', 'summary'],\n",
       "        num_rows: 33640\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['document', 'summary'],\n",
       "        num_rows: 4204\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_fin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UHheZ1A2YH3d"
   },
   "source": [
    "Before we can start training, we need to preprocess our data. Abstractive Summarization is a text-generation task. Our model will take a text as input and generate a summary as output. We want to understand how long our input and output will take to batch our data efficiently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 116,
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      "a1980696797e4814bb8fbfa3cd39033e"
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    },
    "executionInfo": {
     "elapsed": 347828,
     "status": "ok",
     "timestamp": 1692077103298,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "6h3GeXj5YH3d",
    "outputId": "81de8ec4-60fb-46e5-be23-8d10312fe170"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe6defe131e34c5b98ea06f8c94dc7fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/38 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max source length: 512\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f8f6a08958a41449916d85bd78f46a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/38 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max target length: 512\n"
     ]
    }
   ],
   "source": [
    "# Vish\n",
    "from datasets import concatenate_datasets\n",
    "import numpy as np\n",
    "# The maximum total input sequence length after tokenization.\n",
    "# Sequences longer than this will be truncated, sequences shorter will be padded.\n",
    "tokenized_inputs = concatenate_datasets([ds_fin[\"train\"], ds_fin[\"test\"]]).map(lambda x: tokenizer(x[\"document\"], truncation=True), batched=True, remove_columns=[\"document\", \"summary\"])\n",
    "input_lenghts = [len(x) for x in tokenized_inputs[\"input_ids\"]]\n",
    "# take 85 percentile of max length for better utilization\n",
    "max_source_length = int(np.percentile(input_lenghts, 85))\n",
    "print(f\"Max source length: {max_source_length}\")\n",
    "\n",
    "# The maximum total sequence length for target text after tokenization.\n",
    "# Sequences longer than this will be truncated, sequences shorter will be padded.\"\n",
    "tokenized_targets = concatenate_datasets([ds_fin[\"train\"], ds_fin[\"test\"]]).map(lambda x: tokenizer(x[\"summary\"], truncation=True), batched=True, remove_columns=[\"document\", \"summary\"])\n",
    "target_lenghts = [len(x) for x in tokenized_targets[\"input_ids\"]]\n",
    "# take 90 percentile of max length for better utilization\n",
    "max_target_length = int(np.percentile(target_lenghts, 90))\n",
    "print(f\"Max target length: {max_target_length}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mpzIIHGxYH3e"
   },
   "source": [
    "We preprocess our dataset before training and save it to disk. You could run this step on your local machine or a CPU and upload it to the [Hugging Face Hub](https://huggingface.co/docs/hub/datasets-overview)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 130,
     "referenced_widgets": [
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    },
    "executionInfo": {
     "elapsed": 353125,
     "status": "ok",
     "timestamp": 1692077456397,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "p0S9knqH2GZy",
    "outputId": "dbc0075c-f935-437f-bd78-643ea7f41d66"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e419cb0ea02042c48b33d81486ef7e8a",
       "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": "639f23aabcfa4217a0e0799a71fea097",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys of tokenized dataset: ['input_ids', 'attention_mask', 'labels']\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f500d22de89f4b5099f681869324d4a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/33640 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82164c1ad1f9407da841b5a9d09eb84f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/4204 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#vish\n",
    "def preprocess_function(sample,padding=\"max_length\"):\n",
    "    # add prefix to the input for t5\n",
    "    inputs = [\"summarize: \" + item for item in sample[\"document\"]]\n",
    "\n",
    "    # tokenize inputs\n",
    "    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)\n",
    "\n",
    "    # Tokenize targets with the `text_target` keyword argument\n",
    "    labels = tokenizer(text_target=sample[\"summary\"], max_length=max_target_length, padding=padding, truncation=True)\n",
    "\n",
    "    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n",
    "    # padding in the loss.\n",
    "    if padding == \"max_length\":\n",
    "        labels[\"input_ids\"] = [\n",
    "            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n",
    "        ]\n",
    "\n",
    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
    "    return model_inputs\n",
    "\n",
    "tokenized_dataset = ds_fin.map(preprocess_function, batched=True, remove_columns=[\"document\", \"summary\"])\n",
    "print(f\"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}\")\n",
    "\n",
    "# save datasets to disk for later easy loading\n",
    "tokenized_dataset[\"train\"].save_to_disk(\"data/train\")\n",
    "tokenized_dataset[\"test\"].save_to_disk(\"data/eval\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jMqkG1XiYH3e"
   },
   "source": [
    "## 3. Fine-Tune T5 with LoRA and bnb int-8\n",
    "\n",
    "In addition to the LoRA technique, we will use [bitsanbytes LLM.int8()](https://huggingface.co/blog/hf-bitsandbytes-integration) to quantize out frozen LLM to int8. This allows us to reduce the needed memory for FLAN-T5 XXL ~4x.  \n",
    "\n",
    "The first step of our training is to load the model. We are going to use [philschmid/flan-t5-xxl-sharded-fp16](https://huggingface.co/philschmid/flan-t5-xxl-sharded-fp16), which is a sharded version of [google/flan-t5-xxl](https://huggingface.co/google/flan-t5-xxl). The sharding will help us to not run off of memory when loading the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451,
     "referenced_widgets": [
      "ccf6ace51cc848ff9b12f76bc97a41c2",
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      "bc9350b7a4ea4c918155ae5d647a737f",
      "53e65ce7973c41e9a747849ce6b87efa"
     ]
    },
    "executionInfo": {
     "elapsed": 5946,
     "status": "ok",
     "timestamp": 1692077462293,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "Ye8cQWV9YH3e",
    "outputId": "598eb1f7-de5d-48dc-c796-34fe7121d2fb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /usr/lib64-nvidia did not contain libcudart.so as expected! Searching further paths...\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('http'), PosixPath('8013'), PosixPath('//172.28.0.1')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-s-1qvn9h0y8s0u8 --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//ipykernel.pylab.backend_inline'), PosixPath('module')}\n",
      "  warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================================BUG REPORT===================================\n",
      "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
      "================================================================================\n",
      "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...\n",
      "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
      "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
      "CUDA SETUP: Detected CUDA version 118\n",
      "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ccf6ace51cc848ff9b12f76bc97a41c2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import AutoModelForSeq2SeqLM\n",
    "import torch\n",
    "# huggingface hub model id\n",
    "#model_id = \"philschmid/flan-t5-xxl-sharded-fp16\"\n",
    "\n",
    "#Vish\n",
    "model_id = \"t5-small\"\n",
    "\n",
    "# model_id = \"google/flan-t5-xl\"\n",
    "\n",
    "# with torch.autocast(\"cuda\"):\n",
    "  # load model from the hub\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(model_id, load_in_8bit=True, device_map=\"auto\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Rk3Pe9wnXGY"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1692076393468,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "LF2G3gs3nLAY",
    "outputId": "aeb1fef7-746b-4397-de30-a070af636230"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t5-small\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O-GBhvmhYH3e"
   },
   "source": [
    "Now, we can prepare our model for the LoRA int-8 training using `peft`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 692,
     "status": "ok",
     "timestamp": 1692077671314,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "oQyFDz2_YH3e",
    "outputId": "bf56334e-4294-4cea-a033-8d3a6d20a38a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 589824 || all params: 61096448 || trainable%: 0.9653981848502878\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training, TaskType\n",
    "\n",
    "# Define LoRA Config\n",
    "lora_config = LoraConfig(\n",
    " r=16,\n",
    " lora_alpha=32,\n",
    " target_modules=[\"q\", \"v\"],\n",
    " lora_dropout=0.05,\n",
    " bias=\"none\",\n",
    " task_type=TaskType.SEQ_2_SEQ_LM\n",
    ")\n",
    "# prepare int-8 model for training - Commenting Vish\n",
    "model = prepare_model_for_int8_training(model)\n",
    "\n",
    "# add LoRA adaptor\n",
    "model = get_peft_model(model, lora_config)\n",
    "model.print_trainable_parameters()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f3bH3mzpYH3e"
   },
   "source": [
    "As you can see, here we are only training 0.16% of the parameters of the model! This huge memory gain will enable us to fine-tune the model without memory issues.\n",
    "\n",
    "Next is to create a `DataCollator` that will take care of padding our inputs and labels. We will use the `DataCollatorForSeq2Seq` from the 🤗 Transformers library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 540,
     "status": "ok",
     "timestamp": 1692077679559,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "6MCSkSqvYH3e"
   },
   "outputs": [],
   "source": [
    "from transformers import DataCollatorForSeq2Seq\n",
    "\n",
    "# we want to ignore tokenizer pad token in the loss\n",
    "label_pad_token_id = -100\n",
    "# Data collator\n",
    "data_collator = DataCollatorForSeq2Seq(\n",
    "    tokenizer,\n",
    "    model=model,\n",
    "    label_pad_token_id=label_pad_token_id,\n",
    "    pad_to_multiple_of=8\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a9yOoYQyYH3f"
   },
   "source": [
    "The last step is to define the hyperparameters (`TrainingArguments`) we want to use for our training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1692077683969,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "OMMpnKtQYH3f"
   },
   "outputs": [],
   "source": [
    "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
    "\n",
    "output_dir=\"lora-flan-t5-small\"\n",
    "\n",
    "# Define training args\n",
    "training_args = Seq2SeqTrainingArguments(\n",
    "    output_dir=output_dir,\n",
    "\t\tauto_find_batch_size=True,\n",
    "    learning_rate=1e-3, # higher learning rate\n",
    "    num_train_epochs=2,\n",
    "    logging_dir=f\"{output_dir}/logs\",\n",
    "    logging_strategy=\"steps\",\n",
    "    logging_steps=500,\n",
    "    save_strategy=\"no\",\n",
    "    report_to=\"tensorboard\",\n",
    ")\n",
    "\n",
    "# Create Trainer instance\n",
    "trainer = Seq2SeqTrainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    data_collator=data_collator,\n",
    "    train_dataset=tokenized_dataset[\"train\"],\n",
    ")\n",
    "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RHWIbWzIYH3f"
   },
   "source": [
    "Let's now train our model and run the cells below. Note that for T5, some layers are kept in `float32` for stability purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 719
    },
    "executionInfo": {
     "elapsed": 6640046,
     "status": "ok",
     "timestamp": 1692084328244,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "UldV0pVfYH3f",
    "outputId": "b8a9dde6-5e31-4b28-f6e6-9fa9279fbeaf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='8410' max='8410' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [8410/8410 1:50:38, Epoch 2/2]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>3.991500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>3.729100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1500</td>\n",
       "      <td>3.629400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>3.581000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2500</td>\n",
       "      <td>3.534500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3000</td>\n",
       "      <td>3.482800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3500</td>\n",
       "      <td>3.481500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4000</td>\n",
       "      <td>3.492600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4500</td>\n",
       "      <td>3.447000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5000</td>\n",
       "      <td>3.439600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5500</td>\n",
       "      <td>3.425600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6000</td>\n",
       "      <td>3.418700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6500</td>\n",
       "      <td>3.405400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7000</td>\n",
       "      <td>3.413300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7500</td>\n",
       "      <td>3.408500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8000</td>\n",
       "      <td>3.416200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=8410, training_loss=3.5121704682158517, metrics={'train_runtime': 6639.3284, 'train_samples_per_second': 10.134, 'train_steps_per_second': 1.267, 'total_flos': 9227703681024000.0, 'train_loss': 3.5121704682158517, 'epoch': 2.0})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  with torch.autocast(\"cuda\"):\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XM6f_lcJYH3f"
   },
   "source": [
    "The training took ~10:36:00 and cost `~13.22$` for 10h of training. For comparison a [full fine-tuning on FLAN-T5-XXL](https://www.philschmid.de/fine-tune-flan-t5-deepspeed#3-results--experiments) with the same duration (10h) requires 8x A100 40GBs and costs ~322$.\n",
    "\n",
    "We can save our model to use it for inference and evaluate it. We will save it to disk for now, but you could also upload it to the [Hugging Face Hub](https://huggingface.co/docs/hub/main) using the `model.push_to_hub` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 529,
     "status": "ok",
     "timestamp": 1692084347848,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "5FVTLWcuYH3f",
    "outputId": "b6b9ddf9-dec1-455e-b1e0-162a0b8a2828"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:1662: UserWarning: You are calling `save_pretrained` to a 8-bit converted model you may likely encounter unexepected behaviors. \n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Save our LoRA model & tokenizer results\n",
    "peft_model_id=\"results\"\n",
    "trainer.model.save_pretrained(peft_model_id)\n",
    "tokenizer.save_pretrained(peft_model_id)\n",
    "# if you want to save the base model to call\n",
    "trainer.model.base_model.save_pretrained(peft_model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "c8H9MAiIxZTP"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GaGpl6rdYH3f"
   },
   "source": [
    "Our LoRA checkpoint is only 84MB small and includes all of the learnt knowleddge for samsum.\n",
    "\n",
    "## 4. Evaluate & run Inference with LoRA FLAN-T5\n",
    "\n",
    "After the training is done we want to evaluate and test it. The most commonly used metric to evaluate summarization task is [rogue_score](https://en.wikipedia.org/wiki/ROUGE_(metric)) short for Recall-Oriented Understudy for Gisting Evaluation). This metric does not behave like the standard accuracy: it will compare a generated summary against a set of reference summaries.\n",
    "\n",
    "We are going to use `evaluate` library to evaluate the `rogue` score. We can run inference using `PEFT` and `transformers`. For our FLAN-T5 XXL model, we need at least 18GB of GPU memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3188,
     "status": "ok",
     "timestamp": 1692084634805,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "aRhYDClRYH3f",
    "outputId": "e33d80db-6052-42fe-dbfb-f1d2d62a0f16"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peft model loaded\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from peft import PeftModel, PeftConfig\n",
    "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
    "\n",
    "# Load peft config for pre-trained checkpoint etc.\n",
    "peft_model_id = \"results\"\n",
    "config = PeftConfig.from_pretrained(peft_model_id)\n",
    "\n",
    "# load base LLM model and tokenizer\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path,  load_in_8bit=True,  device_map={\"\":0})\n",
    "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
    "\n",
    "# Load the Lora model\n",
    "model = PeftModel.from_pretrained(model, peft_model_id, device_map={\"\":0})\n",
    "model.eval()\n",
    "\n",
    "print(\"Peft model loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287,
     "referenced_widgets": [
      "f920fea4cc3741159d774f4ff23d6235",
      "0c51a2b551d143c0b9d75f49ee1aad1f",
      "d1ac224efe7a4fec8a920027f9ab08ef",
      "08a6c8a9203c46ff8541455cc6b023dd",
      "84726575ab3e4c81b3e5d6c96cc9db39",
      "89299ac35b814f96b3d9ce30ba1bdefe",
      "1416d0a155ca4317b9441812d4511e78",
      "fea0a328004d49b08ceec69465d6760d",
      "959ab5d6e9a54efe818f7754cd65c2c2",
      "2f4d58b826714c058d90307e1d324599",
      "a16907d4f295428ebaa8c18c7ca0bab7",
      "a9adf1e972004e69b62cbab3a78b0a3e",
      "c3bf8e53f11444d88411e07dff0fa5f0",
      "b7bacb9ec356486a9a40cd49d3e1d13c",
      "8b61f5942b4f4609b3962eb4b33d8925",
      "485423ac1f4741a191f28566cf228df5",
      "bbc8a9c8bbc544868833ad3714665535",
      "a0692ad5c5304255b2cfb6b55649b364",
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      "14a18b21809d457b9027add0cee537d0",
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      "16304cafbc934b04b31e2cd6493fb237",
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      "c96c5ee1c2a04b58ada1dd376076e26e",
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      "7d4978641ccd4f85acf7f783dcda99a9",
      "30ab249c5962476d8db1e53c7c18b566",
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      "f809ff08d828418ab167e12d398771b1",
      "e566db9bbaee48a4a85225cb5a362501",
      "790139673ee44b059ab269f4d50b28b7"
     ]
    },
    "executionInfo": {
     "elapsed": 3028,
     "status": "ok",
     "timestamp": 1692084370332,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "OVU1NDMKUWER",
    "outputId": "c080047a-487e-462a-afec-780f65ef1279"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.builder:Using custom data configuration default-b10949de81716265\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-b10949de81716265/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f920fea4cc3741159d774f4ff23d6235",
       "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": "a9adf1e972004e69b62cbab3a78b0a3e",
       "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": "16304cafbc934b04b31e2cd6493fb237",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n",
      "/usr/local/lib/python3.10/dist-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-b10949de81716265/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec16b20981b4482e96ecca509cd5b574",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Vish : Let us look at an unseen data from Train dataset\n",
    "unseen_dataset=  load_dataset(\"csv\", data_files=[\"test_roo_segment_0_input_2_1000.csv\", \"test_roo_segment_0_input_2_1000.csv\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 649,
     "status": "ok",
     "timestamp": 1692084375669,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "JZR1ia1Zx2fE",
    "outputId": "0bea3fde-966c-4210-fcdc-4d17abf8df5c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'document': \"the one month libor rate increased from an average of ( 0.7164 % ) during december 2016 to an average of ( 1.4925 % ) during december 2017 and an average of ( 2.4582 % ) during december 2018. fixed mortgage interest rates for multi-family properties of similar class and location as omaha 's portfolio also increased during 2017 from an approximate range of 4.10 % to 4.15 % in early 2017 to 4.35 % to 4.50 % near the end of 2017and further to an approximate range of 5.05 % to 5.10 % in december 2018. mortgage interest rates may continue to increase in 2019 if the u.s. federal reserve continues a policy to increase the federal funds rate . although increases in fixed mortgage rates do not impact the operating cash flow of the omaha properties directly , increases in fixed and floating rates on commercial mortgage debt can have a negative impact on capitalization rates and the sales prices sentinel omaha may achieve in the future . since omaha has in the past two years paid off the unsecured debt along with improving performance at the properties , the debt risk to the investment is estimated to be lower . the investment in a 30 % non-controlling interest would still be valued at a discount due to the lack of control and liquidity . registrant will continue to report a reserve on the value of omaha on its books but due to omaha paying off the unsecured debt in 2018 the reserve has been reduced to 20 % as of march 31 , 2018. for additional analysis , please refer to the discussions of the individual properties below . eagle lake business center iv ( maple grove , minnesota ) total revenues increased $ 20,000 , to approximately $ 1,094,000 in 2018 compared with approximately $ 1,074,000 in 2017. net income , which includes deductions for depreciation , decreased $ 24,000 to approximately $ 632,000 in 2018 from approximately $ 656,000 in 2017. the property has been 100 % leased and occupied by the same single tenant for both years . the increase in total revenue was primarily due to higher rental income . rental income increased due to a scheduled increase in base rent for the tenant . other income decreased slightly from 2017 to 2018. the decrease in net income was due to the increase in operating expenses partially offset by an increase in revenues . the increase in operating expenses was primarily due to increases in repairs and maintenance of $ 40,000 and depreciation of $ 7,000. according to a report by cbre the minneapolis industrial market reported positive absorption and a minor increase in market rates in 2018. investments during 2007 , the registrant made a total investment in the amount of $ 37,200,000 in omaha representing a thirty percent ownership interest . the registrant 's investment in omaha is accounted for at fair value . on september 18 , 2007 , omaha acquired all the outstanding common shares of america first apartment investors , inc. , a publicly held real estate investment trust , in a transaction valued at approximately $ 532 million , including the assumption of outstanding debt and excluding transactions costs . omaha consisted of 31 wholly owned multifamily residential properties , a wholly owned commercial property and a wholly owned multifamily property that was under development . during 2018 omaha sold its garden apartment property located in asheville , north carolina , its garden apartment property located in fresno , california and its high rise apartment property located in omaha , nebraska . net sales proceeds in each transaction were used to first pay selling expenses and retire each property 's related secured mortgage loan . remaining net sales proceeds were used to retire omaha 's unsecured loan and were used to retire omaha 's secured mortgage loan encumbering its garden apartment property located in columbus , ohio to further pay down omaha 's debt . omaha reported total revenues for 2018 of approximately $ 33,786,000. net investment income before net unrealized appreciation was $ 11,892,000. major expenses included $ 5,273,000 of interest expense , $ 3,044,000 for repairs and maintenance , $ 4,319,000 for payroll and $ 3,640,000 for real estate taxes . in addition , omaha reported a net unrealized appreciation based on the valuation of the remaining real estate assets and interest rate protection agreements of $ 488,000. also omaha reported net realized gain on the sale of three real estate properties of $ 7,094,000. for the year ended december 31 , 2018 , the registrant 's equity interest in the income of omaha was approximately $ 5,842,000 . 17 management 's discussion of results of operations 2017 vs. 2016 total revenues from operations increased $ 22,000 to approximately $ 1,081,000 in 2017 from approximately $ 1,059,000 in 2016 primarily due to higher rental income . rental income increased due to a scheduled increase in base rent for the tenant at the maple grove property . other income decreased slightly while interest income increased slightly . the registrant reported a net loss from operations of approximately $ 516,000 in 2017 , an improvement of $ 16,000 as compared to a net loss from operations of approximately $ 532,000 in 2016. net loss from operations consists of net income from the maple grove property combined with partnership income and expenses . the decrease of loss from operations was due to higher total revenues partially offset by higher operating expenses . total expenses from operations for 2017 increased $ 6,000 to approximately $ 1,597,000 from approximately $ 1,591,000 in 2016 , due to increases in administration costs and investment fees partially offset by decreases in real estate taxes , professional fees and repairs and maintenance . during the twelve months ended december 31 , 2017 , omaha sold its garden apartment property in daytona , florida and sold both garden apartment properties located in fayetteville , north story_separator_special_tag the one month libor rate increased from an average of ( 0.7164 % ) during december 2016 to an average of ( 1.4925 % ) during december 2017 and an average of ( 2.4582 % ) during december 2018. fixed mortgage interest rates for multi-family properties of similar class and location as omaha 's portfolio also increased during 2017 from an approximate range of 4.10 % to 4.15 % in early 2017 to 4.35 % to 4.50 % near the end of 2017and further to an approximate range of 5.05 % to 5.10 % in december 2018. mortgage interest rates may continue to increase in 2019 if the u.s. federal reserve continues a policy to increase the federal funds rate . although increases in fixed mortgage rates do not impact the operating cash flow of the omaha properties directly , increases in fixed and floating rates on commercial mortgage debt can have a negative impact on capitalization rates and the sales prices sentinel omaha may achieve in the future . since omaha has in the past two years paid off the unsecured debt along with improving performance at the properties , the debt risk to the investment is estimated to be lower . the investment in a 30 % non-controlling interest would still be valued at a discount due to the lack of control and liquidity . registrant will continue to report a reserve on the value of omaha on its books but due to omaha paying off the unsecured debt in 2018 the reserve has been reduced to 20 % as of march 31 , 2018. for additional analysis , please refer to the discussions of the individual properties below . eagle lake business center iv ( maple grove , minnesota ) total revenues increased $ 20,000 , to approximately $ 1,094,000 in 2018 compared with approximately $ 1,074,000 in 2017. net income , which includes deductions for depreciation , decreased $ 24,000 to approximately $ 632,000 in 2018 from approximately $ 656,000 in 2017. the property has been 100 % leased and occupied by the same single tenant for both years . the increase in total revenue was primarily due to higher rental income . rental income increased due to a scheduled increase in base rent for the tenant . other income decreased slightly from 2017 to 2018. the decrease in net income was due to the increase in operating expenses partially offset by an increase in revenues . the increase in operating expenses was primarily due to increases in repairs and maintenance of $ 40,000 and depreciation of $ 7,000. according to a report by cbre the minneapolis industrial market reported positive absorption and a minor increase in market rates in 2018. investments during 2007 , the registrant made a total investment in the amount of $ 37,200,000 in omaha representing a thirty percent ownership interest . the registrant 's investment in omaha is accounted for at fair value . on september 18 , 2007 , omaha acquired all the outstanding common shares of america first apartment investors , inc. , a publicly held real estate investment trust , in a transaction valued at approximately $ 532 million , including the assumption of outstanding debt and excluding transactions costs . omaha consisted of 31 wholly owned multifamily residential properties , a wholly owned commercial property and a wholly owned multifamily property that was under development . during 2018 omaha sold its garden apartment property located in asheville , north carolina , its garden apartment property located in fresno , california and its high rise apartment property located in omaha , nebraska . net sales proceeds in each transaction were used to first pay selling expenses and retire each property 's related secured mortgage loan . remaining net sales proceeds were used to retire omaha 's unsecured loan and were used to retire omaha 's secured mortgage loan encumbering its garden apartment property located in columbus , ohio to further pay down omaha 's debt . omaha reported total revenues for 2018 of approximately $ 33,786,000. net investment income before net unrealized appreciation was $ 11,892,000. major expenses included $ 5,273,000 of interest expense , $ 3,044,000 for repairs and maintenance , $ 4,319,000 for payroll and $ 3,640,000 for real estate taxes . in addition , omaha reported a net unrealized appreciation based on the valuation of the remaining real estate assets and interest rate protection agreements of $ 488,000. also omaha reported net realized gain on the sale of three real estate properties of $ 7,094,000. for the year ended december 31 , 2018 , the registrant 's equity interest in the income of omaha was approximately $ 5,842,000 . 17 management 's discussion of results of operations 2017 vs. 2016 total revenues from operations increased $ 22,000 to approximately $ 1,081,000 in 2017 from approximately $ 1,059,000 in 2016 primarily due to higher rental income . rental income increased due to a scheduled increase in base rent for the tenant at the maple grove property . other income decreased slightly while interest income increased slightly . the registrant reported a net loss from operations of approximately $ 516,000 in 2017 , an improvement of $ 16,000 as compared to a net loss from operations of approximately $ 532,000 in 2016. net loss from operations consists of net income from the maple grove property combined with partnership income and expenses . the decrease of loss from operations was due to higher total revenues partially offset by higher operating expenses . total expenses from operations for 2017 increased $ 6,000 to approximately $ 1,597,000 from approximately $ 1,591,000 in 2016 , due to increases in administration costs and investment fees partially offset by decreases in real estate taxes , professional fees and repairs and maintenance . during the twelve months ended december 31 , 2017 , omaha sold its garden apartment property in daytona , florida and sold both garden apartment properties located in fayetteville , north\\n\",\n",
       " 'summary': \"condition and results of operations company overview the registrant is a new york limited partnership formed to engage in acquiring , operating and holding for investment a varying portfolio of real estate interests . the registrant 's initial public offering was in 1971 , the year it began operations . as of december 31 , 2018 , the registrant owned an industrial flex property in maple grove , minnesota and the registrant has a thirty percent interest in sentinel omaha , llc . omaha is a real estate investment company which as of december 31 , 2018 owns eight multifamily properties in five markets . omaha is an affiliate of the registrant 's general partner . the principal objectives of the registrant are , first , to obtain capital appreciation through equity investments in real estate ; second , to generate cash available for distribution , a portion of which may not be currently taxable ; and third , to the extent still permitted under the internal revenue code of 1986 , as amended , to generate tax losses which may offset the limited partners ' income from the registrant and certain other sources . the consolidated financial statements for the years ended december 31 , 2018 , 2017 and 2016 reflect the operations of one industrial flex property located in maple grove , minnesota as well as a 30 % interest in omaha . critical accounting estimates in preparing the consolidated financial statements , management has made estimates and assumptions that affect the reported amounts of assets and liabilities at the date of the financial statements and the reported amounts of revenues and expenses during the reporting periods . actual results could differ from those estimates . set forth below is a summary of the accounting policies that management believes are critical to the preparation of the consolidated financial statements .\\n\"}"
      ]
     },
     "execution_count": 19,
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    }
   ],
   "source": [
    "unseen_dataset[\"train\"][10]"
   ]
  },
  {
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   "metadata": {
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     },
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    },
    "id": "Iy8ZC47XeFBb"
   },
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   "source": [
    "import random\n",
    "sample_data= unseen_dataset[\"train\"][random.randrange(len(unseen_dataset))]"
   ]
  },
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    "id": "OJWEaVbzfo22",
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       "'the company primarily targets acquisitions of re-performing loans ( “ rpls ” ) , which are residential mortgage loans on which at least five of the seven most recent payments have been made , or the most recent payment has been made and accepted pursuant to an agreement , or the full dollar amount , to cover at least five payments has been paid in the last seven months . the company also acquires and originates small balance commercial loans ( `` sbc loans `` ) . the sbc loans that the company opportunistically targets , through acquisitions , or originations , generally have a principal balance of up to $ 5.0 million and are secured by multi-family residential and story_separator_special_tag overview great ajax corp. is a maryland corporation that is organized and operated in a manner intended to allow us to qualify as a reit . we primarily target acquisitions of rpls , which are residential mortgage loans on which at least five of the seven most recent payments have been made , or the most recent payment has been made and accepted pursuant to an agreement , or the full dollar amount , to cover at least five payments has been paid in the last seven months . we also acquire and originate sbc loans . the sbc loans that we target through acquisitions generally have a principal balance of up to $ 5.0 million and are secured by multi-family residential and commercial mixed use retail/residential properties on which at least five of the seven a most recent payments have been made , or the most recent payment has been made and accepted pursuant to an agreement , or the full dollar amount , to cover at least five payments has been paid in the last seven months . we also originate sbc loans that we believe will provide an appropriate risk-adjusted total return . additionally , we invest in single-family and smaller commercial properties directly either through a foreclosure event of a loan in its mortgage portfolio or through a direct acquisition . we may also target investments in npls either directly or with joint venture partners . npls are loans on which the most recent three payments have not been made . we may acquire npls either directly or with joint venture partners . we own a 19.8 % equity interest in our manager and an 8.0 % equity interest in the parent company of our servicer . ga-trs is a wholly owned subsidiary of the operating partnership that owns the equity interest in the manager and the servicer . we have elected to treat ga-trs as a trs under the code . our mortgage loans and real properties are serviced by the servicer , also an affiliated company . in 2014 , we formed great ajax funding llc , a wholly owned subsidiary of the operating partnership , to act as the depositor of mortgage loans into securitization trusts and to hold the subordinated securities issued by such trusts and any additional trusts we may form for additional secured borrowings . ajx mortgage trust i and ajx mortgage trust ii are wholly owned subsidiaries of the operating partnership formed to hold mortgage loans used as collateral for financings under our repurchase agreements . on february 1 , 2015 , we formed gajx real estate corp. , as a wholly owned subsidiary of the operating partnership , to own , maintain , improve and sell certain reos purchased by us . we have elected to treat gajx real estate corp. as a trs under the code . our operating partnership , through interests in certain entities as of december 31 , 2020 and december 31 , 2019 , holds 99.9 % and 99.8 % , respectively , of great ajax ii reit inc. which holds an interest in great ajax ii depositor llc which was formed to act as the depositor of mortgage loans into securitization trusts and to hold the subordinated securities issued by such trusts and any additional trusts we may form for additional secured borrowings . we have securitized mortgage loans through a securitization trust and retained subordinated securities from the secured borrowings . each such trust is considered to be a vie , and we have determined that we are the primary beneficiary of each such vie . 46 in 2018 , we formed gaea , as a wholly owned subsidiary of the operating partnership . we elected to treat gaea as a trs under the code for 2018 , and we elected to treat gaea as a reit under the code in 2019 and thereafter . also during 2018 , we formed gaea real estate operating partnership lp , a wholly owned subsidiary of gaea , to hold investments in commercial real estate assets . we also formed bfld holdings llc , gaea commercial properties llc , gaea commercial finance llc and gaea re llc as subsidiaries of gaea real estate operating partnership . in 2019 , we formed dg brooklyn holdings , llc , also a subsidiary of gaea real estate operating partnership lp , to hold investments in multi-family properties . on november 22 , 2019 gaea completed a private capital raise in which it raised $ 66.3 million from the issuance of 4,419,641 shares of its common stock to third parties to allow gaea to continue to advance its investment strategy . we retained a 23.2 % ownership interest in gaea following the transaction . at december 31 , 2020 we own approximately 23.0 % of gaea and is no longer consolidated in our financial statements . story_separator_special_tag we expect to use the net proceeds from the private placement to acquire mortgage loans and mortgage-related assets consistent with our investment strategy and that this additional capital will provide sufficient liquidity to both benefit from any investment opportunities and protect against future market disruption . notwithstanding this additional capital and liquidity , we expect continued volatility in the residential mortgage securities market in the short term and increased acquisition opportunities later in the year or early 2021. extended forbearance , foreclosure timelines and eviction timelines could result in lower yields and losses on our mortgage loan and beneficial interest portfolios and losses on our reo held-for-sale . ongoing disruption in the credit markets could result in margin calls from our financing counterparties and additional mark downs on our investments in debt securities , beneficial interests and mortgage loans . we believe that certain cyclical trends continue to drive a significant realignment within the mortgage sector notwithstanding the impact of the pandemic . through the end of the fourth quarter , the recent trends noted below have continued , including : historically low interest rates and elevated operating costs resulting from new regulatory requirements continue to drive sales of residential mortgage assets by banks and other mortgage lenders ; declining home ownership in certain areas due to rising prices , low inventory , tighter lending standards and increased down payment requirements that have increased the demand for single-family and multi-family residential rental properties ; rising home prices are increasing homeowner equity and reducing the incidence of strategic default ; rising prices have resulted in millions of homeowners being in the money to refinance ; the dodd-frank risk retention rules for asset backed securities have reduced the universe of participants in the securitization markets ; 48 the lack of a robust market for non-conforming mortgage loans will reduce the pool of buyers due to tighter credit standards as a result of the covid-19 pandemic ; and an increase in the prices of residential mortgage loans and residential real estate as a result of the covid-19 outbreak we believe will generate new opportunities in residential mortgage-related whole loan strategies . the origination of subprime and alternative residential mortgage loans remains substantially below 2008 levels and the qualified mortgage and ability-to-repay rule requirements have put pressure on new originations . additionally , many banks and other mortgage lenders have increased their credit standards and down payment requirements for originating new loans . recent market disruption from the pandemic has sharply reduced financing alternatives for borrowers not eligible for financing programs underwritten by the gses or the federal government . the combination of these factors has also resulted in a significant number of families that can not qualify to obtain new residential mortgage loans . we believe the u.s. federal regulations addressing “ qualified mortgages ” based on , among other factors such as employment status , debt-to-income level , impaired credit history or lack of savings , limit mortgage loan availability from traditional mortgage lenders . in addition , we believe that many homeowners displaced by foreclosure or who either can not afford to own or can not be approved for a mortgage will prefer to live in single-family rental properties with similar characteristics and amenities to owned homes as well as smaller multi-family residential properties . in certain demographic areas , new households are being formed at a rate that exceeds the new homes being added to the market , which we believe favors future demand for non-federally guaranteed mortgage financing for single-family and smaller multi-family rental properties . for all these reasons , we believe that demand for single-family and smaller multi-family rental properties will increase in the near term and remain at heightened levels for the foreseeable future . we believe that investments in residential rpls with positive equity provide an optimal investment value . as a result , we are currently focusing on acquiring pools of rpls , though we may acquire npls , either directly or with joint venture partners , if attractive opportunities exist . through our servicer , we work with our borrowers to improve their payment records . once there is a period of continued performance , we expect that borrowers will typically refinance these loans at or near the estimated value of the underlying property . we also believe there are significant attractive investment opportunities in the sbc loan and property markets and originate as well as purchase these loans , particularly in urban areas where there is a sustainable trend of young adults desiring to live near where they work . we focus on densely populated urban areas where we expect positive economic change based on certain demographic , economic and social statistical data . the primary lenders for smaller multi-family and mixed retail/residential properties are community banks and not regional and national banks and large institutional lenders . we believe the primary lenders and loan purchasers are less interested in these assets because they typically require significant commercial and residential mortgage credit and underwriting expertise , special servicing capability and active property management . it is also more difficult to create the large pools of these primary banks lenders and portfolio acquirers typically desire . we continually monitor opportunities to increase our holdings of these sbc loans and properties . we also believe that banks and other mortgage lenders have strengthened their capital bases and are more aggressively foreclosing on delinquent borrowers or selling these loans to dispose of their inventory . additionally , many npl buyers are now interested in reducing their investment duration and have begun selling rpls . factors that may affect our operating results acquisitions . our operating results depend heavily on sourcing residential rpls and sbc loans and , when attractive opportunities are identified , npls . we believe that there is generally a large supply of rpls available to us for acquisition and we believe the available supply provides for a steady acquisition pipeline of assets since large institutions are active sellers in the market .\\n'"
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     "text": [
      "input sentence: the company primarily targets acquisitions of re-performing loans ( “ rpls ” ) , which are residential mortgage loans on which at least five of the seven most recent payments have been made , or the most recent payment has been made and accepted pursuant to an agreement , or the full dollar amount , to cover at least five payments has been paid in the last seven months . the company also acquires and originates small balance commercial loans ( `` sbc loans `` ) . the sbc loans that the company opportunistically targets , through acquisitions , or originations , generally have a principal balance of up to $ 5.0 million and are secured by multi-family residential and story_separator_special_tag overview great ajax corp. is a maryland corporation that is organized and operated in a manner intended to allow us to qualify as a reit . we primarily target acquisitions of rpls , which are residential mortgage loans on which at least five of the seven most recent payments have been made , or the most recent payment has been made and accepted pursuant to an agreement , or the full dollar amount , to cover at least five payments has been paid in the last seven months . we also acquire and originate sbc loans . the sbc loans that we target through acquisitions generally have a principal balance of up to $ 5.0 million and are secured by multi-family residential and commercial mixed use retail/residential properties on which at least five of the seven a most recent payments have been made , or the most recent payment has been made and accepted pursuant to an agreement , or the full dollar amount , to cover at least five payments has been paid in the last seven months . we also originate sbc loans that we believe will provide an appropriate risk-adjusted total return . additionally , we invest in single-family and smaller commercial properties directly either through a foreclosure event of a loan in its mortgage portfolio or through a direct acquisition . we may also target investments in npls either directly or with joint venture partners . npls are loans on which the most recent three payments have not been made . we may acquire npls either directly or with joint venture partners . we own a 19.8 % equity interest in our manager and an 8.0 % equity interest in the parent company of our servicer . ga-trs is a wholly owned subsidiary of the operating partnership that owns the equity interest in the manager and the servicer . we have elected to treat ga-trs as a trs under the code . our mortgage loans and real properties are serviced by the servicer , also an affiliated company . in 2014 , we formed great ajax funding llc , a wholly owned subsidiary of the operating partnership , to act as the depositor of mortgage loans into securitization trusts and to hold the subordinated securities issued by such trusts and any additional trusts we may form for additional secured borrowings . ajx mortgage trust i and ajx mortgage trust ii are wholly owned subsidiaries of the operating partnership formed to hold mortgage loans used as collateral for financings under our repurchase agreements . on february 1 , 2015 , we formed gajx real estate corp. , as a wholly owned subsidiary of the operating partnership , to own , maintain , improve and sell certain reos purchased by us . we have elected to treat gajx real estate corp. as a trs under the code . our operating partnership , through interests in certain entities as of december 31 , 2020 and december 31 , 2019 , holds 99.9 % and 99.8 % , respectively , of great ajax ii reit inc. which holds an interest in great ajax ii depositor llc which was formed to act as the depositor of mortgage loans into securitization trusts and to hold the subordinated securities issued by such trusts and any additional trusts we may form for additional secured borrowings . we have securitized mortgage loans through a securitization trust and retained subordinated securities from the secured borrowings . each such trust is considered to be a vie , and we have determined that we are the primary beneficiary of each such vie . 46 in 2018 , we formed gaea , as a wholly owned subsidiary of the operating partnership . we elected to treat gaea as a trs under the code for 2018 , and we elected to treat gaea as a reit under the code in 2019 and thereafter . also during 2018 , we formed gaea real estate operating partnership lp , a wholly owned subsidiary of gaea , to hold investments in commercial real estate assets . we also formed bfld holdings llc , gaea commercial properties llc , gaea commercial finance llc and gaea re llc as subsidiaries of gaea real estate operating partnership . in 2019 , we formed dg brooklyn holdings , llc , also a subsidiary of gaea real estate operating partnership lp , to hold investments in multi-family properties . on november 22 , 2019 gaea completed a private capital raise in which it raised $ 66.3 million from the issuance of 4,419,641 shares of its common stock to third parties to allow gaea to continue to advance its investment strategy . we retained a 23.2 % ownership interest in gaea following the transaction . at december 31 , 2020 we own approximately 23.0 % of gaea and is no longer consolidated in our financial statements . story_separator_special_tag we expect to use the net proceeds from the private placement to acquire mortgage loans and mortgage-related assets consistent with our investment strategy and that this additional capital will provide sufficient liquidity to both benefit from any investment opportunities and protect against future market disruption . notwithstanding this additional capital and liquidity , we expect continued volatility in the residential mortgage securities market in the short term and increased acquisition opportunities later in the year or early 2021. extended forbearance , foreclosure timelines and eviction timelines could result in lower yields and losses on our mortgage loan and beneficial interest portfolios and losses on our reo held-for-sale . ongoing disruption in the credit markets could result in margin calls from our financing counterparties and additional mark downs on our investments in debt securities , beneficial interests and mortgage loans . we believe that certain cyclical trends continue to drive a significant realignment within the mortgage sector notwithstanding the impact of the pandemic . through the end of the fourth quarter , the recent trends noted below have continued , including : historically low interest rates and elevated operating costs resulting from new regulatory requirements continue to drive sales of residential mortgage assets by banks and other mortgage lenders ; declining home ownership in certain areas due to rising prices , low inventory , tighter lending standards and increased down payment requirements that have increased the demand for single-family and multi-family residential rental properties ; rising home prices are increasing homeowner equity and reducing the incidence of strategic default ; rising prices have resulted in millions of homeowners being in the money to refinance ; the dodd-frank risk retention rules for asset backed securities have reduced the universe of participants in the securitization markets ; 48 the lack of a robust market for non-conforming mortgage loans will reduce the pool of buyers due to tighter credit standards as a result of the covid-19 pandemic ; and an increase in the prices of residential mortgage loans and residential real estate as a result of the covid-19 outbreak we believe will generate new opportunities in residential mortgage-related whole loan strategies . the origination of subprime and alternative residential mortgage loans remains substantially below 2008 levels and the qualified mortgage and ability-to-repay rule requirements have put pressure on new originations . additionally , many banks and other mortgage lenders have increased their credit standards and down payment requirements for originating new loans . recent market disruption from the pandemic has sharply reduced financing alternatives for borrowers not eligible for financing programs underwritten by the gses or the federal government . the combination of these factors has also resulted in a significant number of families that can not qualify to obtain new residential mortgage loans . we believe the u.s. federal regulations addressing “ qualified mortgages ” based on , among other factors such as employment status , debt-to-income level , impaired credit history or lack of savings , limit mortgage loan availability from traditional mortgage lenders . in addition , we believe that many homeowners displaced by foreclosure or who either can not afford to own or can not be approved for a mortgage will prefer to live in single-family rental properties with similar characteristics and amenities to owned homes as well as smaller multi-family residential properties . in certain demographic areas , new households are being formed at a rate that exceeds the new homes being added to the market , which we believe favors future demand for non-federally guaranteed mortgage financing for single-family and smaller multi-family rental properties . for all these reasons , we believe that demand for single-family and smaller multi-family rental properties will increase in the near term and remain at heightened levels for the foreseeable future . we believe that investments in residential rpls with positive equity provide an optimal investment value . as a result , we are currently focusing on acquiring pools of rpls , though we may acquire npls , either directly or with joint venture partners , if attractive opportunities exist . through our servicer , we work with our borrowers to improve their payment records . once there is a period of continued performance , we expect that borrowers will typically refinance these loans at or near the estimated value of the underlying property . we also believe there are significant attractive investment opportunities in the sbc loan and property markets and originate as well as purchase these loans , particularly in urban areas where there is a sustainable trend of young adults desiring to live near where they work . we focus on densely populated urban areas where we expect positive economic change based on certain demographic , economic and social statistical data . the primary lenders for smaller multi-family and mixed retail/residential properties are community banks and not regional and national banks and large institutional lenders . we believe the primary lenders and loan purchasers are less interested in these assets because they typically require significant commercial and residential mortgage credit and underwriting expertise , special servicing capability and active property management . it is also more difficult to create the large pools of these primary banks lenders and portfolio acquirers typically desire . we continually monitor opportunities to increase our holdings of these sbc loans and properties . we also believe that banks and other mortgage lenders have strengthened their capital bases and are more aggressively foreclosing on delinquent borrowers or selling these loans to dispose of their inventory . additionally , many npl buyers are now interested in reducing their investment duration and have begun selling rpls . factors that may affect our operating results acquisitions . our operating results depend heavily on sourcing residential rpls and sbc loans and , when attractive opportunities are identified , npls . we believe that there is generally a large supply of rpls available to us for acquisition and we believe the available supply provides for a steady acquisition pipeline of assets since large institutions are active sellers in the market .\n",
      "\n",
      "------------------------------------------------------------\n",
      "summary:\n",
      "results of operations the following table sets forth the comparison of the year ended december 31, 2015, as a result of the acquisitions. the company's business results for the years ended december 31, 2015 and 2016 are subject to the company's outstanding equity of equity and equity, including the company's equity of equity, and the asset equity of equity, or a substantial percentage of equity and equity equity and net\n"
     ]
    }
   ],
   "source": [
    "input_ids = tokenizer(sample_data[\"document\"], return_tensors=\"pt\", truncation=True).input_ids.cuda()\n",
    "#with torch.inference_mode():\n",
    "outputs = model.generate(input_ids=input_ids, max_new_tokens= 100, do_sample=True, top_p=0.8)\n",
    "print(f\"input sentence: {sample_data['document']}\\n{'---'* 20}\")\n",
    "\n",
    "print(f\"summary:\\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lbgbC44UFdrA"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 29633,
     "status": "ok",
     "timestamp": 1692085155310,
     "user": {
      "displayName": "Vish Rajagopalan",
      "userId": "05526786679328024892"
     },
     "user_tz": -330
    },
    "id": "7zlSbujFFfCq",
    "outputId": "a46588de-33bb-484c-a014-245d11aacc7e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All model checkpoint layers were used when initializing TFT5ForConditionalGeneration.\n",
      "\n",
      "All the layers of TFT5ForConditionalGeneration were initialized from the model checkpoint at ./models/base.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5ForConditionalGeneration for predictions without further training.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pad> the company primarily targets acquisitions of re-performing loans ( rpls ), which are residential mortgage loans on which at least five of the seven most recent payments have been made. the company also acquires and originates small balance commercial loans ( sbc loans ). the sbc loans that the company opportunistically targets through acquisitions generally have a principal balance of up to $ 5.0 million.</s>\n",
      "<pad> Wie alt sind Sie?</s>\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
    "\n",
    "model_base=\"t5-small\"\n",
    "\n",
    "#sample_record = ds_fin[\"train\"][0]\n",
    "sample_record = sample_data # use the same record as earlier\n",
    "# Load tokenizer\n",
    "tokenizer_base = AutoTokenizer.from_pretrained(model_base)\n",
    "# to be removed\n",
    "\n",
    "from transformers import TFAutoModelForSeq2SeqLM, DataCollatorForSeq2Seq\n",
    "\n",
    "model_base = TFAutoModelForSeq2SeqLM.from_pretrained(\"./models/base\")\n",
    "\n",
    "# let us save the model\n",
    "#model.save_pretrained('/models/base')\n",
    "#text_prompt=\"summarize: This is a very long text and sometimes this is also a long set of words. Hence such long texts are usually written with lots of words\"\n",
    "sample=\"summarize: \" + sample_record[\"document\"]\n",
    "input_tokens = tokenizer_base(sample, padding='max_length', max_length=512, truncation=True, return_tensors='np')\n",
    "result_sample_base = model_base.generate(**input_tokens, max_length=200, top_k=3, temperature=0.5)\n",
    "# tokenized = tokenizer([document], return_tensors='np')\n",
    "# out = model.generate(**tokenized, max_length=128\n",
    "# Greedy Search\n",
    "print(tokenizer.decode(result_sample_base[0], truncate_before_pattern=[r\"\\n\\n^#\", \"^'''\", \"\\n\\n\\n\"]))\n",
    "\n",
    "#Translation\n",
    "input_text_trans = \"translate English to German: How old are you?\"\n",
    "input_ids_base = tokenizer_base(input_text_trans,  max_length=512,truncation=True,  return_tensors=\"np\")\n",
    "\n",
    "outputs = model_base.generate(**input_ids_base, max_length=200, top_k=3, temperature=0.5)\n",
    "print(tokenizer_base.decode(outputs[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vtLE9wp8gkzb"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6-arBDEceU9R"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pmicXVf9YH3f"
   },
   "source": [
    "Let’s load the dataset again with a random sample to try the summarization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q5yZFNfqYH3f"
   },
   "source": [
    "Nice! our model works! Now, lets take a closer look and evaluate it against the `test` set of processed dataset from `samsum`. Therefore we need to use and create some utilities to generate the summaries and group them together. The most commonly used metrics to evaluate summarization task is [rogue_score](https://en.wikipedia.org/wiki/ROUGE_(metric)) short for Recall-Oriented Understudy for Gisting Evaluation). This metric does not behave like the standard accuracy: it will compare a generated summary against a set of reference summaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66,
     "referenced_widgets": [
      "039936915e17448cbe036069d1fc10bf",
      "6fa6b1088109470d8e282b802841e683",
      "f2f8e3f076df493b83a75aa8f11cb8d5",
      "31733eddb81944659fb535bfd48fd847",
      "3a4e8e4f53284904b846091c7ecee08a",
      "3084a7b61e3348e19032a01a9bdea1e9",
      "809d1b03a8c64c0385f338bfedf2620b",
      "d84af1f8ca234eab88c7f7bb3654b706",
      "d9e052f17f7d457cafd36c80148173a6",
      "f6fa869dab854d4d88aa76b97e518ac3",
      "7c6a7325fcbc4590a5ea55b543da6ebd"
     ]
    },
    "id": "bT-m-MwsYH3f",
    "outputId": "e5f3010e-b49b-45da-aa34-6a373aea92b9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "039936915e17448cbe036069d1fc10bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 11%|█         | 464/4204 [22:45<2:55:05,  2.81s/it]"
     ]
    }
   ],
   "source": [
    "import evaluate\n",
    "import numpy as np\n",
    "from datasets import load_from_disk\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Metric\n",
    "metric = evaluate.load(\"rouge\")\n",
    "\n",
    "def evaluate_peft_model(sample,max_target_length=50):\n",
    "    # generate summary\n",
    "    outputs = model.generate(input_ids=sample[\"input_ids\"].unsqueeze(0).cuda(), do_sample=True, top_p=0.9, max_new_tokens=max_target_length)\n",
    "    prediction = tokenizer.decode(outputs[0].detach().cpu().numpy(), skip_special_tokens=True)\n",
    "    # decode eval sample\n",
    "    # Replace -100 in the labels as we can't decode them.\n",
    "    labels = np.where(sample['labels'] != -100, sample['labels'], tokenizer.pad_token_id)\n",
    "    labels = tokenizer.decode(labels, skip_special_tokens=True)\n",
    "\n",
    "    # Some simple post-processing\n",
    "    return prediction, labels\n",
    "\n",
    "# load test dataset from distk\n",
    "test_dataset = load_from_disk(\"data/eval/\").with_format(\"torch\")\n",
    "\n",
    "# run predictions\n",
    "# this can take ~45 minutes\n",
    "predictions, references = [] , []\n",
    "for sample in tqdm(test_dataset):\n",
    "    p,l = evaluate_peft_model(sample)\n",
    "    predictions.append(p)\n",
    "    references.append(l)\n",
    "\n",
    "# compute metric\n",
    "rogue = metric.compute(predictions=predictions, references=references, use_stemmer=True)\n",
    "\n",
    "# print results\n",
    "print(f\"Rogue1: {rogue['rouge1']* 100:2f}%\")\n",
    "print(f\"rouge2: {rogue['rouge2']* 100:2f}%\")\n",
    "print(f\"rougeL: {rogue['rougeL']* 100:2f}%\")\n",
    "print(f\"rougeLsum: {rogue['rougeLsum']* 100:2f}%\")\n",
    "\n",
    "# Rogue1: 50.386161%\n",
    "# rouge2: 24.842412%\n",
    "# rougeL: 41.370130%\n",
    "# rougeLsum: 41.394230%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yiPGn576YH3g"
   },
   "source": [
    "Our PEFT fine-tuned FLAN-T5-XXL achieved a rogue1 score of `50.38%` on the test dataset. For comparison a [full fine-tuning of flan-t5-base achieved a rouge1 score of 47.23](https://www.philschmid.de/fine-tune-flan-t5). That is a `3%` improvements.\n",
    "\n",
    "It is incredible to see that our LoRA checkpoint is only 84MB small and model achieves better performance than a smaller fully fine-tuned model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 657
    },
    "id": "BaVYTpea9iMC",
    "outputId": "e4509fe8-308b-4b02-a947-8330a483bf1a"
   },
   "outputs": [],
   "source": [
    "from transformers import T5Tokenizer, T5ForConditionalGeneration\n",
    "\n",
    "tokenizer = T5Tokenizer.from_pretrained(\"google/flan-t5-base\")\n",
    "model = T5ForConditionalGeneration.from_pretrained(\"google/flan-t5-base\")\n",
    "\n",
    "input_text = \"translate English to German: How old are you?\"\n",
    "input_ids = tokenizer(input_text, return_tensors=\"pt\").input_ids\n",
    "\n",
    "outputs = model.generate(input_ids)\n",
    "print(tokenizer.decode(outputs[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fl2yOrD7-XlD"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 561
    },
    "id": "P13M7nN8-YGX",
    "outputId": "08c37a6b-6db6-49bd-ee3b-7bcaff1192ee"
   },
   "outputs": [],
   "source": [
    "!pip install SentencePiece"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting s3fs\n",
      "  Obtaining dependency information for s3fs from https://files.pythonhosted.org/packages/98/19/fb5e526056deefe616530678462b02bb457089eb5bf50d47e84e105d0c6f/s3fs-2023.9.0-py3-none-any.whl.metadata\n",
      "  Downloading s3fs-2023.9.0-py3-none-any.whl.metadata (1.6 kB)\n",
      "Collecting aiobotocore~=2.5.4 (from s3fs)\n",
      "  Obtaining dependency information for aiobotocore~=2.5.4 from https://files.pythonhosted.org/packages/20/00/01780c5fa93e3feb6d776ac8c7bd05dbe9290165636c13edcbdde6853537/aiobotocore-2.5.4-py3-none-any.whl.metadata\n",
      "  Downloading aiobotocore-2.5.4-py3-none-any.whl.metadata (19 kB)\n",
      "Requirement already satisfied: fsspec==2023.9.0 in ./.local/lib/python3.9/site-packages (from s3fs) (2023.9.0)\n",
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      "Collecting botocore<1.31.18,>=1.31.17 (from aiobotocore~=2.5.4->s3fs)\n",
      "  Obtaining dependency information for botocore<1.31.18,>=1.31.17 from https://files.pythonhosted.org/packages/3d/e5/32a88f5a95e3d43c2e3ed86fc1ffdb715547a04f95a51d00e1185af63b0c/botocore-1.31.17-py3-none-any.whl.metadata\n",
      "  Downloading botocore-1.31.17-py3-none-any.whl.metadata (5.9 kB)\n",
      "Requirement already satisfied: wrapt<2.0.0,>=1.10.10 in ./.local/lib/python3.9/site-packages (from aiobotocore~=2.5.4->s3fs) (1.15.0)\n",
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      "Downloading s3fs-2023.9.0-py3-none-any.whl (28 kB)\n",
      "Downloading aiobotocore-2.5.4-py3-none-any.whl (73 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.4/73.4 kB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m0:00:01\u001b[0m\n",
      "\u001b[?25hDownloading botocore-1.31.17-py3-none-any.whl (11.1 MB)\n",
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      "\u001b[?25hInstalling collected packages: jmespath, aioitertools, botocore, aiobotocore, s3fs\n",
      "Successfully installed aiobotocore-2.5.4 aioitertools-0.11.0 botocore-1.31.17 jmespath-1.0.1 s3fs-2023.9.0\n"
     ]
    }
   ],
   "source": [
    "!pip install s3fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "K71WGAEKYH3g",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cdsw/.local/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'S3Filesystem' from 'datasets.filesystems' (/home/cdsw/.local/lib/python3.9/site-packages/datasets/filesystems/__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdatasets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_from_disk\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdatasets\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfilesystems\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m S3Filesystem\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'S3Filesystem' from 'datasets.filesystems' (/home/cdsw/.local/lib/python3.9/site-packages/datasets/filesystems/__init__.py)"
     ]
    }
   ],
   "source": [
    "from datasets import load_from_disk\n",
    "from datasets.filesystems import S3Filesystem\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting datasets==2.14.4\n",
      "  Obtaining dependency information for datasets==2.14.4 from https://files.pythonhosted.org/packages/66/f8/38298237d18d4b6a8ee5dfe390e97bed5adb8e01ec6f9680c0ddf3066728/datasets-2.14.4-py3-none-any.whl.metadata\n",
      "  Using cached datasets-2.14.4-py3-none-any.whl.metadata (19 kB)\n",
      "Requirement already satisfied: numpy>=1.17 in ./.local/lib/python3.9/site-packages (from datasets==2.14.4) (1.24.3)\n",
      "Requirement already satisfied: pyarrow>=8.0.0 in ./.local/lib/python3.9/site-packages (from datasets==2.14.4) (13.0.0)\n",
      "Requirement already satisfied: dill<0.3.8,>=0.3.0 in ./.local/lib/python3.9/site-packages (from datasets==2.14.4) (0.3.6)\n",
      "Requirement already satisfied: pandas in ./.local/lib/python3.9/site-packages (from datasets==2.14.4) (2.1.0)\n",
      "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.9/site-packages (from datasets==2.14.4) (2.31.0)\n",
      "Requirement already satisfied: tqdm>=4.62.1 in ./.local/lib/python3.9/site-packages (from datasets==2.14.4) (4.66.1)\n",
      "Requirement already satisfied: xxhash in ./.local/lib/python3.9/site-packages (from datasets==2.14.4) (3.3.0)\n",
      "Requirement already satisfied: multiprocess in ./.local/lib/python3.9/site-packages (from datasets==2.14.4) (0.70.14)\n",
      "Requirement already satisfied: fsspec[http]>=2021.11.1 in ./.local/lib/python3.9/site-packages (from datasets==2.14.4) (2023.9.0)\n",
      "Requirement already satisfied: aiohttp in ./.local/lib/python3.9/site-packages (from datasets==2.14.4) (3.8.5)\n",
      "Requirement already satisfied: huggingface-hub<1.0.0,>=0.14.0 in ./.local/lib/python3.9/site-packages (from datasets==2.14.4) (0.16.4)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.9/site-packages (from datasets==2.14.4) (23.1)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/site-packages (from datasets==2.14.4) (6.0)\n",
      "Requirement already satisfied: attrs>=17.3.0 in ./.local/lib/python3.9/site-packages (from aiohttp->datasets==2.14.4) (23.1.0)\n",
      "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.9/site-packages (from aiohttp->datasets==2.14.4) (3.1.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in ./.local/lib/python3.9/site-packages (from aiohttp->datasets==2.14.4) (6.0.4)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in ./.local/lib/python3.9/site-packages (from aiohttp->datasets==2.14.4) (4.0.3)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in ./.local/lib/python3.9/site-packages (from aiohttp->datasets==2.14.4) (1.9.2)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in ./.local/lib/python3.9/site-packages (from aiohttp->datasets==2.14.4) (1.4.0)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in ./.local/lib/python3.9/site-packages (from aiohttp->datasets==2.14.4) (1.3.1)\n",
      "Requirement already satisfied: filelock in ./.local/lib/python3.9/site-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets==2.14.4) (3.12.2)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/site-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets==2.14.4) (4.3.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/site-packages (from requests>=2.19.0->datasets==2.14.4) (2.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.9/site-packages (from requests>=2.19.0->datasets==2.14.4) (1.26.6)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/site-packages (from requests>=2.19.0->datasets==2.14.4) (2023.7.22)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.9/site-packages (from pandas->datasets==2.14.4) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /runtime-addons/cmladdon-2.0.40-b157/opt/cmladdons/python/site-packages (from pandas->datasets==2.14.4) (2022.7.1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in ./.local/lib/python3.9/site-packages (from pandas->datasets==2.14.4) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas->datasets==2.14.4) (1.16.0)\n",
      "Using cached datasets-2.14.4-py3-none-any.whl (519 kB)\n",
      "Installing collected packages: datasets\n",
      "  Attempting uninstall: datasets\n",
      "    Found existing installation: datasets 2.9.0\n",
      "    Uninstalling datasets-2.9.0:\n",
      "      Successfully uninstalled datasets-2.9.0\n",
      "Successfully installed datasets-2.14.4\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets==2.14.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "s3 = S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key)\n",
    "dataset = load_from_disk('s3://vishawsbucket/findsum/test_roo_segment_0_input_2_1000.csv', fs=s3)\n",
    "print(len(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: datasets in ./.local/lib/python3.9/site-packages (2.9.0)\n",
      "Collecting boto3\n",
      "  Obtaining dependency information for boto3 from https://files.pythonhosted.org/packages/7e/3a/eb1ec341a1028ba5894924e1e3011116c7a7736060ac5f7652de1a0b187b/boto3-1.28.41-py3-none-any.whl.metadata\n",
      "  Downloading boto3-1.28.41-py3-none-any.whl.metadata (6.7 kB)\n",
      "Requirement already satisfied: numpy>=1.17 in ./.local/lib/python3.9/site-packages (from datasets) (1.24.3)\n",
      "Requirement already satisfied: pyarrow>=6.0.0 in ./.local/lib/python3.9/site-packages (from datasets) (13.0.0)\n",
      "Requirement already satisfied: dill<0.3.7 in ./.local/lib/python3.9/site-packages (from datasets) (0.3.6)\n",
      "Requirement already satisfied: pandas in ./.local/lib/python3.9/site-packages (from datasets) (2.1.0)\n",
      "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.9/site-packages (from datasets) (2.31.0)\n",
      "Requirement already satisfied: tqdm>=4.62.1 in ./.local/lib/python3.9/site-packages (from datasets) (4.66.1)\n",
      "Requirement already satisfied: xxhash in ./.local/lib/python3.9/site-packages (from datasets) (3.3.0)\n",
      "Requirement already satisfied: multiprocess in ./.local/lib/python3.9/site-packages (from datasets) (0.70.14)\n",
      "Requirement already satisfied: fsspec[http]>=2021.11.1 in ./.local/lib/python3.9/site-packages (from datasets) (2023.9.0)\n",
      "Requirement already satisfied: aiohttp in ./.local/lib/python3.9/site-packages (from datasets) (3.8.5)\n",
      "Requirement already satisfied: huggingface-hub<1.0.0,>=0.2.0 in ./.local/lib/python3.9/site-packages (from datasets) (0.16.4)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.9/site-packages (from datasets) (23.1)\n",
      "Requirement already satisfied: responses<0.19 in ./.local/lib/python3.9/site-packages (from datasets) (0.18.0)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/site-packages (from datasets) (6.0)\n",
      "Collecting botocore<1.32.0,>=1.31.41 (from boto3)\n",
      "  Obtaining dependency information for botocore<1.32.0,>=1.31.41 from https://files.pythonhosted.org/packages/1e/c3/5eeaaae524a9f867a1454cfd4f989a1864583b8b6ba45b92be800daaab9c/botocore-1.31.41-py3-none-any.whl.metadata\n",
      "  Downloading botocore-1.31.41-py3-none-any.whl.metadata (6.0 kB)\n",
      "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in ./.local/lib/python3.9/site-packages (from boto3) (1.0.1)\n",
      "Collecting s3transfer<0.7.0,>=0.6.0 (from boto3)\n",
      "  Obtaining dependency information for s3transfer<0.7.0,>=0.6.0 from https://files.pythonhosted.org/packages/d9/17/a3b666f5ef9543cfd3c661d39d1e193abb9649d0cfbbfee3cf3b51d5af02/s3transfer-0.6.2-py3-none-any.whl.metadata\n",
      "  Downloading s3transfer-0.6.2-py3-none-any.whl.metadata (1.8 kB)\n",
      "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.9/site-packages (from botocore<1.32.0,>=1.31.41->boto3) (2.8.2)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/local/lib/python3.9/site-packages (from botocore<1.32.0,>=1.31.41->boto3) (1.26.6)\n",
      "Requirement already satisfied: attrs>=17.3.0 in ./.local/lib/python3.9/site-packages (from aiohttp->datasets) (23.1.0)\n",
      "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.9/site-packages (from aiohttp->datasets) (3.1.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in ./.local/lib/python3.9/site-packages (from aiohttp->datasets) (6.0.4)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in ./.local/lib/python3.9/site-packages (from aiohttp->datasets) (4.0.3)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in ./.local/lib/python3.9/site-packages (from aiohttp->datasets) (1.9.2)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in ./.local/lib/python3.9/site-packages (from aiohttp->datasets) (1.4.0)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in ./.local/lib/python3.9/site-packages (from aiohttp->datasets) (1.3.1)\n",
      "Requirement already satisfied: filelock in ./.local/lib/python3.9/site-packages (from huggingface-hub<1.0.0,>=0.2.0->datasets) (3.12.2)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/site-packages (from huggingface-hub<1.0.0,>=0.2.0->datasets) (4.3.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/site-packages (from requests>=2.19.0->datasets) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/site-packages (from requests>=2.19.0->datasets) (2023.7.22)\n",
      "Requirement already satisfied: pytz>=2020.1 in /runtime-addons/cmladdon-2.0.40-b157/opt/cmladdons/python/site-packages (from pandas->datasets) (2022.7.1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in ./.local/lib/python3.9/site-packages (from pandas->datasets) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.32.0,>=1.31.41->boto3) (1.16.0)\n",
      "Downloading boto3-1.28.41-py3-none-any.whl (135 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m135.8/135.8 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n",
      "\u001b[?25hDownloading botocore-1.31.41-py3-none-any.whl (11.2 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.2/11.2 MB\u001b[0m \u001b[31m44.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m0:01\u001b[0m\n",
      "\u001b[?25hDownloading s3transfer-0.6.2-py3-none-any.whl (79 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.8/79.8 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0meta \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: botocore, s3transfer, boto3\n",
      "  Attempting uninstall: botocore\n",
      "    Found existing installation: botocore 1.31.17\n",
      "    Uninstalling botocore-1.31.17:\n",
      "      Successfully uninstalled botocore-1.31.17\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "aiobotocore 2.5.4 requires botocore<1.31.18,>=1.31.17, but you have botocore 1.31.41 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed boto3-1.28.41 botocore-1.31.41 s3transfer-0.6.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install datasets boto3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_from_s3(bucket_name, file_name, local_path):\n",
    "    s3 = boto3.client('s3')\n",
    "    s3.download_file(bucket_name, file_name, local_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: s3fs in ./.local/lib/python3.9/site-packages (2023.9.0)\n",
      "Requirement already satisfied: aiobotocore~=2.5.4 in ./.local/lib/python3.9/site-packages (from s3fs) (2.5.4)\n",
      "Requirement already satisfied: fsspec==2023.9.0 in ./.local/lib/python3.9/site-packages (from s3fs) (2023.9.0)\n",
      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in ./.local/lib/python3.9/site-packages (from s3fs) (3.8.5)\n",
      "Collecting botocore<1.31.18,>=1.31.17 (from aiobotocore~=2.5.4->s3fs)\n",
      "  Obtaining dependency information for botocore<1.31.18,>=1.31.17 from https://files.pythonhosted.org/packages/3d/e5/32a88f5a95e3d43c2e3ed86fc1ffdb715547a04f95a51d00e1185af63b0c/botocore-1.31.17-py3-none-any.whl.metadata\n",
      "  Using cached botocore-1.31.17-py3-none-any.whl.metadata (5.9 kB)\n",
      "Requirement already satisfied: wrapt<2.0.0,>=1.10.10 in ./.local/lib/python3.9/site-packages (from aiobotocore~=2.5.4->s3fs) (1.15.0)\n",
      "Requirement already satisfied: aioitertools<1.0.0,>=0.5.1 in ./.local/lib/python3.9/site-packages (from aiobotocore~=2.5.4->s3fs) (0.11.0)\n",
      "Requirement already satisfied: attrs>=17.3.0 in ./.local/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs) (23.1.0)\n",
      "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs) (3.1.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in ./.local/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs) (6.0.4)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in ./.local/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs) (4.0.3)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in ./.local/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs) (1.9.2)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in ./.local/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs) (1.4.0)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in ./.local/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs) (1.3.1)\n",
      "Requirement already satisfied: typing_extensions>=4.0 in /usr/local/lib/python3.9/site-packages (from aioitertools<1.0.0,>=0.5.1->aiobotocore~=2.5.4->s3fs) (4.3.0)\n",
      "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in ./.local/lib/python3.9/site-packages (from botocore<1.31.18,>=1.31.17->aiobotocore~=2.5.4->s3fs) (1.0.1)\n",
      "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.9/site-packages (from botocore<1.31.18,>=1.31.17->aiobotocore~=2.5.4->s3fs) (2.8.2)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/local/lib/python3.9/site-packages (from botocore<1.31.18,>=1.31.17->aiobotocore~=2.5.4->s3fs) (1.26.6)\n",
      "Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.9/site-packages (from yarl<2.0,>=1.0->aiohttp!=4.0.0a0,!=4.0.0a1->s3fs) (2.10)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.31.18,>=1.31.17->aiobotocore~=2.5.4->s3fs) (1.16.0)\n",
      "Using cached botocore-1.31.17-py3-none-any.whl (11.1 MB)\n",
      "Installing collected packages: botocore\n",
      "  Attempting uninstall: botocore\n",
      "    Found existing installation: botocore 1.31.41\n",
      "    Uninstalling botocore-1.31.41:\n",
      "      Successfully uninstalled botocore-1.31.41\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "boto3 1.28.41 requires botocore<1.32.0,>=1.31.41, but you have botocore 1.31.17 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed botocore-1.31.17\n"
     ]
    }
   ],
   "source": [
    "!pip install s3fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# storage_options = {\"anon\": True}  # for anonymous connection\n",
    "storage_options = {\"key\": \"ASIA6I6SNFMLCUU4QPMY\", \"secret\": \"uvj/wq2Akylgma4If4OU3K7zY94FN7Rdqt0SPSiS\"}  # for private buckets\n",
    "# import aiobotocore.session\n",
    "# s3_session = aiobotocore.session.AioSession(profile=\"my_profile_name\")\n",
    "# storage_options = {\"session\": s3_session}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import s3fs\n",
    "fs = s3fs.S3FileSystem(**storage_options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "PermissionError",
     "evalue": "Forbidden",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mClientError\u001b[0m                               Traceback (most recent call last)",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/s3fs/core.py:113\u001b[0m, in \u001b[0;36m_error_wrapper\u001b[0;34m(func, args, kwargs, retries)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 113\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    114\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m S3_RETRYABLE_ERRORS \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/aiobotocore/client.py:383\u001b[0m, in \u001b[0;36mAioBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m    382\u001b[0m     error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m--> 383\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m    384\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mClientError\u001b[0m: An error occurred (403) when calling the HeadObject operation: Forbidden",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdatasets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_dataset, load_from_disk\n\u001b[0;32m----> 2\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mload_from_disk\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43ms3://vish-cdp-demo/data/FINDSUM/FINDSUM-ROO/test_roo_segment_0_input_2_1000.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/datasets/load.py:1845\u001b[0m, in \u001b[0;36mload_from_disk\u001b[0;34m(dataset_path, fs, keep_in_memory, storage_options)\u001b[0m\n\u001b[1;32m   1842\u001b[0m     dest_dataset_path \u001b[38;5;241m=\u001b[39m dataset_path\n\u001b[1;32m   1843\u001b[0m     path_join \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin\n\u001b[0;32m-> 1845\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexists\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdest_dataset_path\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m   1846\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDirectory \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdataset_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1847\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fs\u001b[38;5;241m.\u001b[39misfile(path_join(dest_dataset_path, config\u001b[38;5;241m.\u001b[39mDATASET_INFO_FILENAME)):\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/fsspec/asyn.py:118\u001b[0m, in \u001b[0;36msync_wrapper.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    117\u001b[0m     \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m obj \u001b[38;5;129;01mor\u001b[39;00m args[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 118\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/fsspec/asyn.py:103\u001b[0m, in \u001b[0;36msync\u001b[0;34m(loop, func, timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m FSTimeoutError \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mreturn_result\u001b[39;00m\n\u001b[1;32m    102\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(return_result, \u001b[38;5;167;01mBaseException\u001b[39;00m):\n\u001b[0;32m--> 103\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m return_result\n\u001b[1;32m    104\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m return_result\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/fsspec/asyn.py:56\u001b[0m, in \u001b[0;36m_runner\u001b[0;34m(event, coro, result, timeout)\u001b[0m\n\u001b[1;32m     54\u001b[0m     coro \u001b[38;5;241m=\u001b[39m asyncio\u001b[38;5;241m.\u001b[39mwait_for(coro, timeout\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 56\u001b[0m     result[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m coro\n\u001b[1;32m     57\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m ex:\n\u001b[1;32m     58\u001b[0m     result[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m ex\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/s3fs/core.py:1012\u001b[0m, in \u001b[0;36mS3FileSystem._exists\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m   1009\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m exists_in_cache\n\u001b[1;32m   1011\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1012\u001b[0m     \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info(path, bucket, key, version_id\u001b[38;5;241m=\u001b[39mversion_id)\n\u001b[1;32m   1013\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m   1014\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m:\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/s3fs/core.py:1279\u001b[0m, in \u001b[0;36mS3FileSystem._info\u001b[0;34m(self, path, bucket, key, refresh, version_id)\u001b[0m\n\u001b[1;32m   1277\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key:\n\u001b[1;32m   1278\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1279\u001b[0m         out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_s3(\n\u001b[1;32m   1280\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhead_object\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1281\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs,\n\u001b[1;32m   1282\u001b[0m             Bucket\u001b[38;5;241m=\u001b[39mbucket,\n\u001b[1;32m   1283\u001b[0m             Key\u001b[38;5;241m=\u001b[39mkey,\n\u001b[1;32m   1284\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mversion_id_kw(version_id),\n\u001b[1;32m   1285\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreq_kw,\n\u001b[1;32m   1286\u001b[0m         )\n\u001b[1;32m   1287\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m   1288\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m\"\u001b[39m: out\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1289\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLastModified\u001b[39m\u001b[38;5;124m\"\u001b[39m: out[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLastModified\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1295\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mContentType\u001b[39m\u001b[38;5;124m\"\u001b[39m: out\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mContentType\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1296\u001b[0m         }\n\u001b[1;32m   1297\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m:\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/s3fs/core.py:348\u001b[0m, in \u001b[0;36mS3FileSystem._call_s3\u001b[0;34m(self, method, *akwarglist, **kwargs)\u001b[0m\n\u001b[1;32m    346\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCALL: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m - \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m - \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, method\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, akwarglist, kw2)\n\u001b[1;32m    347\u001b[0m additional_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_s3_method_kwargs(method, \u001b[38;5;241m*\u001b[39makwarglist, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 348\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m _error_wrapper(\n\u001b[1;32m    349\u001b[0m     method, kwargs\u001b[38;5;241m=\u001b[39madditional_kwargs, retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mretries\n\u001b[1;32m    350\u001b[0m )\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/s3fs/core.py:140\u001b[0m, in \u001b[0;36m_error_wrapper\u001b[0;34m(func, args, kwargs, retries)\u001b[0m\n\u001b[1;32m    138\u001b[0m         err \u001b[38;5;241m=\u001b[39m e\n\u001b[1;32m    139\u001b[0m err \u001b[38;5;241m=\u001b[39m translate_boto_error(err)\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m err\n",
      "\u001b[0;31mPermissionError\u001b[0m: Forbidden"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset, load_from_disk\n",
    "ds = load_from_disk(\"s3://vish-cdp-demo/data/FINDSUM/FINDSUM-ROO/test_roo_segment_0_input_2_1000.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties\n",
      "Setting default log level to \"WARN\".\n",
      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
      "23/09/06 11:54:23 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties\n"
     ]
    }
   ],
   "source": [
    "import cml.data_v1 as cmldata\n",
    "from pyspark.sql import SparkSession\n",
    "db_name= \"norris\"\n",
    "tbl_name = []\n",
    "db_path =  's3a://goes-se-sandbox01/warehouse/tablespace/external/hive/'\n",
    "#s3://goes-se-sandbox01/warehouse/tablespace/external/hive/_SUCCESS\n",
    "spark = (SparkSession\n",
    "    .builder\n",
    "    .appName(\"demo\")\n",
    "    .config(\"spark.sql.warehouse.dir\", db_path)\n",
    "    .config(\"spark.hadoop.fs.s2a.s3guard.ddb.region\", \"us-east-1\")\n",
    "    .config(\"spark.kerberos.access.hadoopFileSystems\",\"s3a://goes-se-sandbox01/\")\n",
    "    .master(\"local[5]\") # should be possible to change this to SPARK on Yarn or SPARK on Kubernetes\n",
    "    .getOrCreate())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "df = spark.read.option('inferschema','true').csv(\n",
    "  \"s3a://goes-se-sandbox01/vishr/data/FINDSum/FINDSum-ROO/train/\",\n",
    "  header=True,\n",
    "  sep=',',\n",
    "  nullValue='NA'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6304"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(document='the company primarily targets acquisitions of re-performing loans ( “ rpls ” ) , which are residential mortgage loans on which at least five of the seven most recent payments have been made , or the most recent payment has been made and accepted pursuant to an agreement , or the full dollar amount , to cover at least five payments has been paid in the last seven months . the company also acquires and originates small balance commercial loans ( `` sbc loans `` ) . the sbc loans that the company opportunistically targets , through acquisitions , or originations , generally have a principal balance of up to $ 5.0 million and are secured by multi-family residential and story_separator_special_tag overview great ajax corp. is a maryland corporation that is organized and operated in a manner intended to allow us to qualify as a reit . we primarily target acquisitions of rpls , which are residential mortgage loans on which at least five of the seven most recent payments have been made , or the most recent payment has been made and accepted pursuant to an agreement , or the full dollar amount , to cover at least five payments has been paid in the last seven months . we also acquire and originate sbc loans . the sbc loans that we target through acquisitions generally have a principal balance of up to $ 5.0 million and are secured by multi-family residential and commercial mixed use retail/residential properties on which at least five of the seven a most recent payments have been made , or the most recent payment has been made and accepted pursuant to an agreement , or the full dollar amount , to cover at least five payments has been paid in the last seven months . we also originate sbc loans that we believe will provide an appropriate risk-adjusted total return . additionally , we invest in single-family and smaller commercial properties directly either through a foreclosure event of a loan in its mortgage portfolio or through a direct acquisition . we may also target investments in npls either directly or with joint venture partners . npls are loans on which the most recent three payments have not been made . we may acquire npls either directly or with joint venture partners . we own a 19.8 % equity interest in our manager and an 8.0 % equity interest in the parent company of our servicer . ga-trs is a wholly owned subsidiary of the operating partnership that owns the equity interest in the manager and the servicer . we have elected to treat ga-trs as a trs under the code . our mortgage loans and real properties are serviced by the servicer , also an affiliated company . in 2014 , we formed great ajax funding llc , a wholly owned subsidiary of the operating partnership , to act as the depositor of mortgage loans into securitization trusts and to hold the subordinated securities issued by such trusts and any additional trusts we may form for additional secured borrowings . ajx mortgage trust i and ajx mortgage trust ii are wholly owned subsidiaries of the operating partnership formed to hold mortgage loans used as collateral for financings under our repurchase agreements . on february 1 , 2015 , we formed gajx real estate corp. , as a wholly owned subsidiary of the operating partnership , to own , maintain , improve and sell certain reos purchased by us . we have elected to treat gajx real estate corp. as a trs under the code . our operating partnership , through interests in certain entities as of december 31 , 2020 and december 31 , 2019 , holds 99.9 % and 99.8 % , respectively , of great ajax ii reit inc. which holds an interest in great ajax ii depositor llc which was formed to act as the depositor of mortgage loans into securitization trusts and to hold the subordinated securities issued by such trusts and any additional trusts we may form for additional secured borrowings . we have securitized mortgage loans through a securitization trust and retained subordinated securities from the secured borrowings . each such trust is considered to be a vie , and we have determined that we are the primary beneficiary of each such vie . 46 in 2018 , we formed gaea , as a wholly owned subsidiary of the operating partnership . we elected to treat gaea as a trs under the code for 2018 , and we elected to treat gaea as a reit under the code in 2019 and thereafter . also during 2018 , we formed gaea real estate operating partnership lp , a wholly owned subsidiary of gaea , to hold investments in commercial real estate assets . we also formed bfld holdings llc , gaea commercial properties llc , gaea commercial finance llc and gaea re llc as subsidiaries of gaea real estate operating partnership . in 2019 , we formed dg brooklyn holdings , llc , also a subsidiary of gaea real estate operating partnership lp , to hold investments in multi-family properties . on november 22 , 2019 gaea completed a private capital raise in which it raised $ 66.3 million from the issuance of 4,419,641 shares of its common stock to third parties to allow gaea to continue to advance its investment strategy . we retained a 23.2 % ownership interest in gaea following the transaction . at december 31 , 2020 we own approximately 23.0 % of gaea and is no longer consolidated in our financial statements . story_separator_special_tag we expect to use the net proceeds from the private placement to acquire mortgage loans and mortgage-related assets consistent with our investment strategy and that this additional capital will provide sufficient liquidity to both benefit from any investment opportunities and protect against future market disruption . notwithstanding this additional capital and liquidity , we expect continued volatility in the residential mortgage securities market in the short term and increased acquisition opportunities later in the year or early 2021. extended forbearance , foreclosure timelines and eviction timelines could result in lower yields and losses on our mortgage loan and beneficial interest portfolios and losses on our reo held-for-sale . ongoing disruption in the credit markets could result in margin calls from our financing counterparties and additional mark downs on our investments in debt securities , beneficial interests and mortgage loans . we believe that certain cyclical trends continue to drive a significant realignment within the mortgage sector notwithstanding the impact of the pandemic . through the end of the fourth quarter , the recent trends noted below have continued , including : historically low interest rates and elevated operating costs resulting from new regulatory requirements continue to drive sales of residential mortgage assets by banks and other mortgage lenders ; declining home ownership in certain areas due to rising prices , low inventory , tighter lending standards and increased down payment requirements that have increased the demand for single-family and multi-family residential rental properties ; rising home prices are increasing homeowner equity and reducing the incidence of strategic default ; rising prices have resulted in millions of homeowners being in the money to refinance ; the dodd-frank risk retention rules for asset backed securities have reduced the universe of participants in the securitization markets ; 48 the lack of a robust market for non-conforming mortgage loans will reduce the pool of buyers due to tighter credit standards as a result of the covid-19 pandemic ; and an increase in the prices of residential mortgage loans and residential real estate as a result of the covid-19 outbreak we believe will generate new opportunities in residential mortgage-related whole loan strategies . the origination of subprime and alternative residential mortgage loans remains substantially below 2008 levels and the qualified mortgage and ability-to-repay rule requirements have put pressure on new originations . additionally , many banks and other mortgage lenders have increased their credit standards and down payment requirements for originating new loans . recent market disruption from the pandemic has sharply reduced financing alternatives for borrowers not eligible for financing programs underwritten by the gses or the federal government . the combination of these factors has also resulted in a significant number of families that can not qualify to obtain new residential mortgage loans . we believe the u.s. federal regulations addressing “ qualified mortgages ” based on , among other factors such as employment status , debt-to-income level , impaired credit history or lack of savings , limit mortgage loan availability from traditional mortgage lenders . in addition , we believe that many homeowners displaced by foreclosure or who either can not afford to own or can not be approved for a mortgage will prefer to live in single-family rental properties with similar characteristics and amenities to owned homes as well as smaller multi-family residential properties . in certain demographic areas , new households are being formed at a rate that exceeds the new homes being added to the market , which we believe favors future demand for non-federally guaranteed mortgage financing for single-family and smaller multi-family rental properties . for all these reasons , we believe that demand for single-family and smaller multi-family rental properties will increase in the near term and remain at heightened levels for the foreseeable future . we believe that investments in residential rpls with positive equity provide an optimal investment value . as a result , we are currently focusing on acquiring pools of rpls , though we may acquire npls , either directly or with joint venture partners , if attractive opportunities exist . through our servicer , we work with our borrowers to improve their payment records . once there is a period of continued performance , we expect that borrowers will typically refinance these loans at or near the estimated value of the underlying property . we also believe there are significant attractive investment opportunities in the sbc loan and property markets and originate as well as purchase these loans , particularly in urban areas where there is a sustainable trend of young adults desiring to live near where they work . we focus on densely populated urban areas where we expect positive economic change based on certain demographic , economic and social statistical data . the primary lenders for smaller multi-family and mixed retail/residential properties are community banks and not regional and national banks and large institutional lenders . we believe the primary lenders and loan purchasers are less interested in these assets because they typically require significant commercial and residential mortgage credit and underwriting expertise , special servicing capability and active property management . it is also more difficult to create the large pools of these primary banks lenders and portfolio acquirers typically desire . we continually monitor opportunities to increase our holdings of these sbc loans and properties . we also believe that banks and other mortgage lenders have strengthened their capital bases and are more aggressively foreclosing on delinquent borrowers or selling these loans to dispose of their inventory . additionally , many npl buyers are now interested in reducing their investment duration and have begun selling rpls . factors that may affect our operating results acquisitions . our operating results depend heavily on sourcing residential rpls and sbc loans and , when attractive opportunities are identified , npls . we believe that there is generally a large supply of rpls available to us for acquisition and we believe the available supply provides for a steady acquisition pipeline of assets since large institutions are active sellers in the market .', summary=None)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cdsw/.local/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from datasets import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'mapInArrow'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_mat\u001b[38;5;241m=\u001b[39m\u001b[43mDataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_spark\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py:1293\u001b[0m, in \u001b[0;36mDataset.from_spark\u001b[0;34m(df, split, features, keep_in_memory, cache_dir, working_dir, load_from_cache_file, **kwargs)\u001b[0m\n\u001b[1;32m   1290\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sys\u001b[38;5;241m.\u001b[39mplatform \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwin32\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m   1291\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset.from_spark is not currently supported on Windows\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSparkDatasetReader\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1294\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1295\u001b[0m \u001b[43m    \u001b[49m\u001b[43msplit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1296\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1297\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstreaming\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1298\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1299\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeep_in_memory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_in_memory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1300\u001b[0m \u001b[43m    \u001b[49m\u001b[43mworking_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mworking_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1301\u001b[0m \u001b[43m    \u001b[49m\u001b[43mload_from_cache_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mload_from_cache_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1302\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1303\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/datasets/io/spark.py:53\u001b[0m, in \u001b[0;36mSparkDatasetReader.read\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuilder\u001b[38;5;241m.\u001b[39mas_streaming_dataset(split\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msplit)\n\u001b[1;32m     52\u001b[0m download_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_load_from_cache_file \u001b[38;5;28;01melse\u001b[39;00m DownloadMode\u001b[38;5;241m.\u001b[39mFORCE_REDOWNLOAD\n\u001b[0;32m---> 53\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuilder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfile_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_file_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     56\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuilder\u001b[38;5;241m.\u001b[39mas_dataset(split\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msplit)\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/datasets/builder.py:954\u001b[0m, in \u001b[0;36mDatasetBuilder.download_and_prepare\u001b[0;34m(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m    952\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m num_proc \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[1;32m    953\u001b[0m         prepare_split_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_proc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m num_proc\n\u001b[0;32m--> 954\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    955\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    956\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    957\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    958\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdownload_and_prepare_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    959\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    960\u001b[0m \u001b[38;5;66;03m# Sync info\u001b[39;00m\n\u001b[1;32m    961\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(split\u001b[38;5;241m.\u001b[39mnum_bytes \u001b[38;5;28;01mfor\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39msplits\u001b[38;5;241m.\u001b[39mvalues())\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/datasets/builder.py:1049\u001b[0m, in \u001b[0;36mDatasetBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verification_mode, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m   1045\u001b[0m split_dict\u001b[38;5;241m.\u001b[39madd(split_generator\u001b[38;5;241m.\u001b[39msplit_info)\n\u001b[1;32m   1047\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1048\u001b[0m     \u001b[38;5;66;03m# Prepare split will record examples associated to the split\u001b[39;00m\n\u001b[0;32m-> 1049\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prepare_split\u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_generator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1050\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m   1051\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\n\u001b[1;32m   1052\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot find data file. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1053\u001b[0m         \u001b[38;5;241m+\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmanual_download_instructions \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1054\u001b[0m         \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mOriginal error:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1055\u001b[0m         \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(e)\n\u001b[1;32m   1056\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/datasets/packaged_modules/spark/spark.py:276\u001b[0m, in \u001b[0;36mSpark._prepare_split\u001b[0;34m(self, split_generator, file_format, max_shard_size, num_proc, **kwargs)\u001b[0m\n\u001b[1;32m    273\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_cache_dir()\n\u001b[1;32m    275\u001b[0m max_shard_size \u001b[38;5;241m=\u001b[39m convert_file_size_to_int(max_shard_size \u001b[38;5;129;01mor\u001b[39;00m MAX_SHARD_SIZE)\n\u001b[0;32m--> 276\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_repartition_df_if_needed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_shard_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    277\u001b[0m is_local \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m is_remote_filesystem(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fs)\n\u001b[1;32m    278\u001b[0m path_join \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin \u001b[38;5;28;01mif\u001b[39;00m is_local \u001b[38;5;28;01melse\u001b[39;00m posixpath\u001b[38;5;241m.\u001b[39mjoin\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/datasets/packaged_modules/spark/spark.py:167\u001b[0m, in \u001b[0;36mSpark._repartition_df_if_needed\u001b[0;34m(self, max_shard_size)\u001b[0m\n\u001b[1;32m    164\u001b[0m sample_num_rows \u001b[38;5;241m=\u001b[39m df_num_rows \u001b[38;5;28;01mif\u001b[39;00m df_num_rows \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m100\u001b[39m\n\u001b[1;32m    165\u001b[0m \u001b[38;5;66;03m# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.\u001b[39;00m\n\u001b[1;32m    166\u001b[0m approx_bytes_per_row \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 167\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlimit\u001b[49m\u001b[43m(\u001b[49m\u001b[43msample_num_rows\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    168\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrepartition\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    169\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmapInArrow\u001b[49m(get_arrow_batch_size, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbatch_bytes: long\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    170\u001b[0m     \u001b[38;5;241m.\u001b[39magg(pyspark\u001b[38;5;241m.\u001b[39msql\u001b[38;5;241m.\u001b[39mfunctions\u001b[38;5;241m.\u001b[39msum(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbatch_bytes\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39malias(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_bytes\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m    171\u001b[0m     \u001b[38;5;241m.\u001b[39mcollect()[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    172\u001b[0m     \u001b[38;5;241m.\u001b[39msample_bytes\n\u001b[1;32m    173\u001b[0m     \u001b[38;5;241m/\u001b[39m sample_num_rows\n\u001b[1;32m    174\u001b[0m )\n\u001b[1;32m    175\u001b[0m approx_total_size \u001b[38;5;241m=\u001b[39m approx_bytes_per_row \u001b[38;5;241m*\u001b[39m df_num_rows\n\u001b[1;32m    176\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m approx_total_size \u001b[38;5;241m>\u001b[39m max_shard_size:\n\u001b[1;32m    177\u001b[0m     \u001b[38;5;66;03m# Make sure there is at least one row per partition.\u001b[39;00m\n",
      "File \u001b[0;32m/opt/spark/python/lib/pyspark.zip/pyspark/sql/dataframe.py:1659\u001b[0m, in \u001b[0;36mDataFrame.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1649\u001b[0m \u001b[38;5;124;03m\"\"\"Returns the :class:`Column` denoted by ``name``.\u001b[39;00m\n\u001b[1;32m   1650\u001b[0m \n\u001b[1;32m   1651\u001b[0m \u001b[38;5;124;03m.. versionadded:: 1.3.0\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1656\u001b[0m \u001b[38;5;124;03m[Row(age=2), Row(age=5)]\u001b[39;00m\n\u001b[1;32m   1657\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1658\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[0;32m-> 1659\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m   1660\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;124m'\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 (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, name))\n\u001b[1;32m   1661\u001b[0m jc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jdf\u001b[38;5;241m.\u001b[39mapply(name)\n\u001b[1;32m   1662\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Column(jc)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'mapInArrow'"
     ]
    }
   ],
   "source": [
    "df_mat=Dataset.from_spark(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.14.4\n"
     ]
    }
   ],
   "source": [
    "import datasets\n",
    "print(datasets.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from datasets import IterableDataset\n",
    "ds = IterableDataset.from_spark(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'pyspark.sql' has no attribute 'functions'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/datasets/iterable_dataset.py:1353\u001b[0m, in \u001b[0;36mIterableDataset.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1350\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m formatter\u001b[38;5;241m.\u001b[39mformat_row(pa_table)\n\u001b[1;32m   1351\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m-> 1353\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, example \u001b[38;5;129;01min\u001b[39;00m ex_iterable:\n\u001b[1;32m   1354\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeatures:\n\u001b[1;32m   1355\u001b[0m         \u001b[38;5;66;03m# `IterableDataset` automatically fills missing columns with None.\u001b[39;00m\n\u001b[1;32m   1356\u001b[0m         \u001b[38;5;66;03m# This is done with `_apply_feature_types_on_example`.\u001b[39;00m\n\u001b[1;32m   1357\u001b[0m         example \u001b[38;5;241m=\u001b[39m _apply_feature_types_on_example(\n\u001b[1;32m   1358\u001b[0m             example, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeatures, token_per_repo_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_token_per_repo_id\n\u001b[1;32m   1359\u001b[0m         )\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/datasets/packaged_modules/spark/spark.py:79\u001b[0m, in \u001b[0;36mSparkExamplesIterable.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__iter__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m---> 79\u001b[0m     \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerate_examples_fn()\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/datasets/packaged_modules/spark/spark.py:49\u001b[0m, in \u001b[0;36m_generate_iterable_examples.<locals>.generate_fn\u001b[0;34m()\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate_fn\u001b[39m():\n\u001b[0;32m---> 49\u001b[0m     df_with_partition_id \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mselect(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[43mpyspark\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msql\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunctions\u001b[49m\u001b[38;5;241m.\u001b[39mspark_partition_id()\u001b[38;5;241m.\u001b[39malias(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpart_id\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m     50\u001b[0m     partition_df \u001b[38;5;241m=\u001b[39m _reorder_dataframe_by_partition(df_with_partition_id, partition_order)\n\u001b[1;32m     51\u001b[0m     row_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'pyspark.sql' has no attribute 'functions'"
     ]
    }
   ],
   "source": [
    "print(next(iter(ds)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: boto3 in ./.local/lib/python3.9/site-packages (1.28.41)\n",
      "Requirement already satisfied: pandas in ./.local/lib/python3.9/site-packages (2.1.0)\n",
      "Requirement already satisfied: s3fs in ./.local/lib/python3.9/site-packages (2023.9.0)\n",
      "Collecting botocore<1.32.0,>=1.31.41 (from boto3)\n",
      "  Obtaining dependency information for botocore<1.32.0,>=1.31.41 from https://files.pythonhosted.org/packages/1e/c3/5eeaaae524a9f867a1454cfd4f989a1864583b8b6ba45b92be800daaab9c/botocore-1.31.41-py3-none-any.whl.metadata\n",
      "  Using cached botocore-1.31.41-py3-none-any.whl.metadata (6.0 kB)\n",
      "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in ./.local/lib/python3.9/site-packages (from boto3) (1.0.1)\n",
      "Requirement already satisfied: s3transfer<0.7.0,>=0.6.0 in ./.local/lib/python3.9/site-packages (from boto3) (0.6.2)\n",
      "Requirement already satisfied: numpy>=1.22.4 in ./.local/lib/python3.9/site-packages (from pandas) (1.24.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.9/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /runtime-addons/cmladdon-2.0.40-b157/opt/cmladdons/python/site-packages (from pandas) (2022.7.1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in ./.local/lib/python3.9/site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: aiobotocore~=2.5.4 in ./.local/lib/python3.9/site-packages (from s3fs) (2.5.4)\n",
      "Requirement already satisfied: fsspec==2023.9.0 in ./.local/lib/python3.9/site-packages (from s3fs) (2023.9.0)\n",
      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in ./.local/lib/python3.9/site-packages (from s3fs) (3.8.5)\n",
      "INFO: pip is looking at multiple versions of aiobotocore to determine which version is compatible with other requirements. This could take a while.\n",
      "Collecting s3fs\n",
      "  Obtaining dependency information for s3fs from https://files.pythonhosted.org/packages/98/19/fb5e526056deefe616530678462b02bb457089eb5bf50d47e84e105d0c6f/s3fs-2023.9.0-py3-none-any.whl.metadata\n",
      "  Using cached s3fs-2023.9.0-py3-none-any.whl.metadata (1.6 kB)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/local/lib/python3.9/site-packages (from botocore<1.32.0,>=1.31.41->boto3) (1.26.6)\n",
      "  Obtaining dependency information for s3fs from https://files.pythonhosted.org/packages/34/d0/9dbfa30e20ed5176fd4bbbd3d69b80e465025b594e63dad7402a4130e0c1/s3fs-2023.6.0-py3-none-any.whl.metadata\n",
      "  Downloading s3fs-2023.6.0-py3-none-any.whl.metadata (1.6 kB)\n",
      "Collecting fsspec==2023.6.0 (from s3fs)\n",
      "  Obtaining dependency information for fsspec==2023.6.0 from https://files.pythonhosted.org/packages/e3/bd/4c0a4619494188a9db5d77e2100ab7d544a42e76b2447869d8e124e981d8/fsspec-2023.6.0-py3-none-any.whl.metadata\n",
      "  Downloading fsspec-2023.6.0-py3-none-any.whl.metadata (6.7 kB)\n",
      "Collecting aiobotocore~=2.5.0 (from s3fs)\n",
      "  Obtaining dependency information for aiobotocore~=2.5.0 from https://files.pythonhosted.org/packages/cd/d6/b2679f6acd8705415bfd9829ecfe38dd09d6a2388cb72ba69900a44ea0f1/aiobotocore-2.5.3-py3-none-any.whl.metadata\n",
      "  Downloading aiobotocore-2.5.3-py3-none-any.whl.metadata (19 kB)\n",
      "  Obtaining dependency information for aiobotocore~=2.5.0 from https://files.pythonhosted.org/packages/73/15/49deae4077f336c543122ec17f1b4c4221b42cb5920e756397a80b130884/aiobotocore-2.5.2-py3-none-any.whl.metadata\n",
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      "  Obtaining dependency information for aiobotocore~=2.5.0 from https://files.pythonhosted.org/packages/bc/96/200526be88a7310fdd7dba18ee9854e78bbbfc99843c99a24e39b9df3fb5/aiobotocore-2.5.1-py3-none-any.whl.metadata\n",
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      "\u001b[?25hINFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.\n",
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      "\u001b[?25hCollecting s3fs\n",
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      "Collecting aiobotocore~=2.1.0 (from s3fs)\n",
      "  Obtaining dependency information for aiobotocore~=2.1.0 from https://files.pythonhosted.org/packages/f3/5f/01fd91f6bd24a14f54f9c03b7cabf51824993a6e3453731a78f26ad2df32/aiobotocore-2.1.2-py3-none-any.whl.metadata\n",
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      "\u001b[?25hCollecting s3fs\n",
      "  Downloading s3fs-2021.5.0-py3-none-any.whl (24 kB)\n",
      "Collecting fsspec==2021.05.0 (from s3fs)\n",
      "  Downloading fsspec-2021.5.0-py3-none-any.whl (111 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m111.7/111.7 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hCollecting s3fs\n",
      "  Downloading s3fs-2021.4.0-py3-none-any.whl (23 kB)\n",
      "Collecting fsspec==2021.04.0 (from s3fs)\n",
      "  Downloading fsspec-2021.4.0-py3-none-any.whl (108 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m108.3/108.3 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hCollecting s3fs\n",
      "  Downloading s3fs-0.6.0-py3-none-any.whl (23 kB)\n",
      "  Downloading s3fs-0.5.2-py3-none-any.whl (22 kB)\n",
      "  Downloading s3fs-0.5.1-py3-none-any.whl (21 kB)\n",
      "  Downloading s3fs-0.5.0-py3-none-any.whl (21 kB)\n",
      "  Downloading s3fs-0.4.2-py3-none-any.whl (19 kB)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Using cached botocore-1.31.41-py3-none-any.whl (11.2 MB)\n",
      "Installing collected packages: botocore, s3fs\n",
      "  Attempting uninstall: botocore\n",
      "    Found existing installation: botocore 1.31.17\n",
      "    Uninstalling botocore-1.31.17:\n",
      "      Successfully uninstalled botocore-1.31.17\n",
      "  Attempting uninstall: s3fs\n",
      "    Found existing installation: s3fs 2023.9.0\n",
      "    Uninstalling s3fs-2023.9.0:\n",
      "      Successfully uninstalled s3fs-2023.9.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "aiobotocore 2.5.4 requires botocore<1.31.18,>=1.31.17, but you have botocore 1.31.41 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed botocore-1.31.41 s3fs-0.4.2\n"
     ]
    }
   ],
   "source": [
    "!pip install boto3 pandas s3fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successful S3 get_object response. Status - 200\n",
      "                                               document  \\\n",
      "0     the company primarily targets acquisitions of ...   \n",
      "1     our selling , general and administrative expen...   \n",
      "2     until november 19 , 2019 , our corporate name ...   \n",
      "3     93 construction loans are underwritten utilizi...   \n",
      "4     the holders of 66 2 / 3 % of all outstanding p...   \n",
      "...                                                 ...   \n",
      "2097  commissions to agents are recognized upon the ...   \n",
      "2098  54 note 9. stock compensation 2008 omnibus inc...   \n",
      "2099  59 the children 's place , inc. and subsidiari...   \n",
      "2100  our engine management segment manufactures and...   \n",
      "2101  2016-18 , “ statement of cash flows ( topic 23...   \n",
      "\n",
      "                                                summary  \n",
      "0     results of operations for the year ended decem...  \n",
      "1     overview our principal sources of funds are ca...  \n",
      "2     consolidated summary the following table summa...  \n",
      "3     general . total assets increased $ 136.4 milli...  \n",
      "4     results of operations our historical financial...  \n",
      "...                                                 ...  \n",
      "2097  changes in financial condition and results of ...  \n",
      "2098  consolidated results of operations summary con...  \n",
      "2099  and results of operations the following discus...  \n",
      "2100  income tax provision . the income tax provisio...  \n",
      "2101  results of operations replace_table_token_7_th...  \n",
      "\n",
      "[2102 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import boto3\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# AWS_S3_BUCKET = os.getenv(\"AWS_S3_BUCKET\")\n",
    "# AWS_ACCESS_KEY_ID = os.getenv(\"AWS_ACCESS_KEY_ID\")\n",
    "# AWS_SECRET_ACCESS_KEY = os.getenv(\"AWS_SECRET_ACCESS_KEY\")\n",
    "# AWS_SESSION_TOKEN = os.getenv(\"AWS_SESSION_TOKEN\")\n",
    "\n",
    "AWS_S3_BUCKET = \"goes-se-sandbox01\" \n",
    "AWS_ACCESS_KEY_ID = \"ASIA6I6SNFMLCUU4QPMY\"\n",
    "AWS_SECRET_ACCESS_KEY = \"uvj/wq2Akylgma4If4OU3K7zY94FN7Rdqt0SPSiS\"\n",
    "AWS_SESSION_TOKEN = \"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\"\n",
    "\n",
    "s3_client = boto3.client(\n",
    "    \"s3\",\n",
    "    aws_access_key_id=AWS_ACCESS_KEY_ID,\n",
    "    aws_secret_access_key=AWS_SECRET_ACCESS_KEY,\n",
    "    aws_session_token=AWS_SESSION_TOKEN,\n",
    ")\n",
    "\n",
    "response = s3_client.get_object(Bucket=AWS_S3_BUCKET, Key=\"vishr/data/FINDSum/FINDSum-ROO/train/test_roo_segment_0_input_2_1000.csv\")\n",
    "\n",
    "status = response.get(\"ResponseMetadata\", {}).get(\"HTTPStatusCode\")\n",
    "\n",
    "if status == 200:\n",
    "    print(f\"Successful S3 get_object response. Status - {status}\")\n",
    "    df = pd.read_csv(response.get(\"Body\"))\n",
    "    print(books_df)\n",
    "else:\n",
    "    print(f\"Unsuccessful S3 get_object response. Status - {status}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#huggingface dataset : \n",
    "\n",
    "dataset = Dataset.from_pandas(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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