diff --git "a/DONUT.ipynb" "b/DONUT.ipynb"
new file mode 100644--- /dev/null
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+ "cell_type": "markdown",
+ "source": [
+ "# Donut 🍩 : Document Understanding TransformerDonut 🍩 : Document Understanding Transformer\n",
+ "\n",
+ "Donut 🍩, Document understanding transformer, is a new method of document understanding that utilizes an OCR-free end-to-end Transformer model. Donut does not require off-the-shelf OCR engines/APIs, yet it shows state-of-the-art performances on various visual document understanding tasks, such as visual document classification or information extraction (a.k.a. document parsing). In addition, we present SynthDoG 🐶, Synthetic Document Generator, that helps the model pre-training to be flexible on various languages and domains.\n",
+ "\n",
+ ""
+ ],
+ "metadata": {
+ "id": "13QC-hhSAzDc"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "9VNVpQeDAhhl",
+ "outputId": "2ca01543-58d0-4b70-9749-dfe5bd374a9d"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m35.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m49.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.3/519.3 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "Reading package lists... Done\n",
+ "Building dependency tree... Done\n",
+ "Reading state information... Done\n",
+ "git-lfs is already the newest version (3.0.2-1ubuntu0.2).\n",
+ "0 upgraded, 0 newly installed, 0 to remove and 16 not upgraded.\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.2/251.2 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install -q git+https://github.com/huggingface/transformers.git\n",
+ "!pip install -q datasets sentencepiece tensorboard\n",
+ "!sudo apt-get install git-lfs --yes\n",
+ "!pip install -q accelerate -U"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Load SROIE dataset"
+ ],
+ "metadata": {
+ "id": "WW2iNz9jDE0q"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "%%bash\n",
+ "# clone repository\n",
+ "git clone https://github.com/zzzDavid/ICDAR-2019-SROIE.git\n",
+ "# copy data\n",
+ "cp -r ICDAR-2019-SROIE/data ./\n",
+ "# clean up\n",
+ "rm -rf ICDAR-2019-SROIE\n",
+ "rm -rf data/box"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "DfE4rPOVCySw",
+ "outputId": "a2b48151-2fa7-4e75-d5f1-64eeeed97113"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Cloning into 'ICDAR-2019-SROIE'...\n",
+ "Updating files: 47% (937/1980)\rUpdating files: 48% (951/1980)\rUpdating files: 49% (971/1980)\rUpdating files: 50% (990/1980)\rUpdating files: 51% (1010/1980)\rUpdating files: 52% (1030/1980)\rUpdating files: 53% (1050/1980)\rUpdating files: 54% (1070/1980)\rUpdating files: 55% (1089/1980)\rUpdating files: 56% (1109/1980)\rUpdating files: 57% (1129/1980)\rUpdating files: 58% (1149/1980)\rUpdating files: 58% (1163/1980)\rUpdating files: 59% (1169/1980)\rUpdating files: 60% (1188/1980)\rUpdating files: 61% (1208/1980)\rUpdating files: 62% (1228/1980)\rUpdating files: 63% (1248/1980)\rUpdating files: 64% (1268/1980)\rUpdating files: 65% (1287/1980)\rUpdating files: 66% (1307/1980)\rUpdating files: 67% (1327/1980)\rUpdating files: 68% (1347/1980)\rUpdating files: 69% (1367/1980)\rUpdating files: 70% (1386/1980)\rUpdating files: 71% (1406/1980)\rUpdating files: 72% (1426/1980)\rUpdating files: 73% (1446/1980)\rUpdating files: 74% (1466/1980)\rUpdating files: 75% (1485/1980)\rUpdating files: 76% (1505/1980)\rUpdating files: 77% (1525/1980)\rUpdating files: 78% (1545/1980)\rUpdating files: 79% (1565/1980)\rUpdating files: 80% (1584/1980)\rUpdating files: 81% (1604/1980)\rUpdating files: 82% (1624/1980)\rUpdating files: 83% (1644/1980)\rUpdating files: 84% (1664/1980)\rUpdating files: 85% (1683/1980)\rUpdating files: 86% (1703/1980)\rUpdating files: 87% (1723/1980)\rUpdating files: 88% (1743/1980)\rUpdating files: 89% (1763/1980)\rUpdating files: 90% (1782/1980)\rUpdating files: 91% (1802/1980)\rUpdating files: 92% (1822/1980)\rUpdating files: 93% (1842/1980)\rUpdating files: 94% (1862/1980)\rUpdating files: 95% (1881/1980)\rUpdating files: 96% (1901/1980)\rUpdating files: 97% (1921/1980)\rUpdating files: 98% (1941/1980)\rUpdating files: 99% (1961/1980)\rUpdating files: 100% (1980/1980)\rUpdating files: 100% (1980/1980), done.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Now we have two folders inside the data/ directory. One contains the images of the receipts and the other contains the OCR text. The nex step is to create a metadata.json file that contains the information about the images including the OCR-text. This is necessary for the imagefolder feature of datasets.\n",
+ "\n",
+ "The metadata.json should look at the end similar to the example below.\n",
+ "\n",
+ "```\n",
+ "{\"file_name\": \"0001.png\", \"text\": \"This is a golden retriever playing with a ball\"}\n",
+ "{\"file_name\": \"0002.png\", \"text\": \"A german shepherd\"}\n",
+ "```"
+ ],
+ "metadata": {
+ "id": "FGlgBypiDjwL"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import os\n",
+ "import json\n",
+ "from pathlib import Path\n",
+ "import shutil\n",
+ "\n",
+ "# define paths\n",
+ "base_path = Path(\"data\")\n",
+ "metadata_path = base_path.joinpath(\"key\")\n",
+ "image_path = base_path.joinpath(\"img\")\n",
+ "# define metadata list\n",
+ "metadata_list = []\n",
+ "\n",
+ "# parse metadata\n",
+ "for file_name in metadata_path.glob(\"*.json\"):\n",
+ " with open(file_name, \"r\") as json_file:\n",
+ " # load json file\n",
+ " data = json.load(json_file)\n",
+ " # create \"text\" column with json string\n",
+ " text = json.dumps(data)\n",
+ " # add to metadata list if image exists\n",
+ " if image_path.joinpath(f\"{file_name.stem}.jpg\").is_file():\n",
+ " metadata_list.append({\"text\":text,\"file_name\":f\"{file_name.stem}.jpg\"})\n",
+ " # delete json file\n",
+ "\n",
+ "# write jsonline file\n",
+ "with open(image_path.joinpath('metadata.jsonl'), 'w') as outfile:\n",
+ " for entry in metadata_list:\n",
+ " json.dump(entry, outfile)\n",
+ " outfile.write('\\n')\n",
+ "\n",
+ "# remove old meta data\n",
+ "shutil.rmtree(metadata_path)"
+ ],
+ "metadata": {
+ "id": "nbaHuOBEDJHz"
+ },
+ "execution_count": 3,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import os\n",
+ "import json\n",
+ "from pathlib import Path\n",
+ "import shutil\n",
+ "from datasets import load_dataset\n",
+ "\n",
+ "# define paths\n",
+ "base_path = Path(\"data\")\n",
+ "metadata_path = base_path.joinpath(\"key\")\n",
+ "image_path = base_path.joinpath(\"img\")\n",
+ "\n",
+ "# Load dataset\n",
+ "dataset = load_dataset(\"imagefolder\", data_dir=image_path, split=\"train\")\n",
+ "\n",
+ "print(f\"Dataset has {len(dataset)} images\")\n",
+ "print(f\"Dataset features are: {dataset.features.keys()}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 213,
+ "referenced_widgets": [
+ "ce4184a6ae44494291b5b42ab8e72b07",
+ "6c978ad08a3c4d04950fdd0893bb2c0c",
+ "0b96971028434e1a84d6632d0bb8e07e",
+ "c4b64d5b9a664f178e3db62f88eec5d0",
+ "08910c665e084efaa7985bfccce00fbb",
+ "2eaf6089fe684888a7057075fa52e750",
+ "de0a8f24131a418b8e9e55a9c5453584",
+ "ecfd71ffbac743a19110292ea92793b9",
+ "78f0f1dd709c4ee2876da4d69d5adc39",
+ "2c13e9d8d39f4a65a7cd0ec745eb6370",
+ "580b0ef5474c462e8db83d10ec4304c0",
+ "be67dd93618f4e9f9d1b51d9a35b13ab",
+ "fb91587931434a61ab1de724cec2fc3f",
+ "dda42301cff34f78b694962a8ad0480a",
+ "53580e82e0334417bc670442f8c7e87f",
+ "b2e3eadd1f9f4a938ef915e251f49a82",
+ "92aa781ee07342a5943c470be939cb62",
+ "ab7bada26bd14dbbaaf7750330953aed",
+ "2a20d8361a244dc688351f287b25cfd7",
+ "865270470b614025be748d27c980098c",
+ "1754aa9ad47249d59b1d4ba7b0162877",
+ "c4840625cce64d58ad74ac5637ebee4f",
+ "23d3a2bc35ec430692bf003d45ba2cc2",
+ "5405bd34deb5430b983ad7dfaa7ca11b",
+ "012126ff865349709df34607e91da357",
+ "1fc967483cd140c99df02f70add905d8",
+ "c011e3b3b2bb4290a25103fb0147427e",
+ "be922bcb2e24465295afe2d46fb53899",
+ "b1e1e284229c47749f547bf6c672b354",
+ "eb3e6de11ebe46649bee15d64a46e487",
+ "9107d9c46ab04b60869eea20cd3658f6",
+ "e98e686641134d489fdd5fbac9c94e17",
+ "2f335cc60a294fc6bd5d1067a48ae71a",
+ "da71d2122b3f44e8a37c50dea1b04f98",
+ "b876eb3bff1d4a2bb08e8ea8ac064679",
+ "bb33932873314bb8b0d298ed80b11ebd",
+ "2e0c3b71d9614fb296470572512a0996",
+ "96a2d1a7bdac425ab4d51aff246dc63b",
+ "aa282e6aae0d43958669d25eb60cc089",
+ "8361210a10004b0b844407f166207e7c",
+ "471a7b40590240d9aad65fb33035bb27",
+ "aebdf70afa7a45d48807b2286ff3ea13",
+ "4767e8693596452eb1f74b3414d8d0c7",
+ "c3c1379e63f94502b7cbf86dc3a6bd95",
+ "22c35d5637ea4c7c837f7e0ab7c2a2e4",
+ "f9bc6c6de7b04924983ffd10138866ee",
+ "305d6d2a088348bd95b6ef086d5d715a",
+ "9a41689423d7412a9089cd91ac0aa546",
+ "3a10601016724248b33e1d525722e20b",
+ "45d527bdc46d49048072dd8b6737149a",
+ "d9d61b1730784bd78b15026921754c27",
+ "7b51c0b6e6414865840a0aad063c1380",
+ "b78561538711427095895741f17d1424",
+ "b7c8a2b7e4804a68b84c21d4830efc1a",
+ "10872aa32bd24a6ba1448f1c51c954df"
+ ]
+ },
+ "id": "DeUdSvLNFYFo",
+ "outputId": "4c21bb32-4e8b-4a18-8579-836fd6fe1123"
+ },
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Resolving data files: 0%| | 0/627 [00:00, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "ce4184a6ae44494291b5b42ab8e72b07"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading data files: 0%| | 0/627 [00:00, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "be67dd93618f4e9f9d1b51d9a35b13ab"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading data files: 0it [00:00, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "23d3a2bc35ec430692bf003d45ba2cc2"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Extracting data files: 0it [00:00, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "da71d2122b3f44e8a37c50dea1b04f98"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "22c35d5637ea4c7c837f7e0ab7c2a2e4"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Dataset has 626 images\n",
+ "Dataset features are: dict_keys(['image', 'text'])\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Now, lets take a closer look at our dataset"
+ ],
+ "metadata": {
+ "id": "JuUNz64CF2NN"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import random\n",
+ "\n",
+ "random_sample = random.randint(0, len(dataset))\n",
+ "\n",
+ "print(f\"Random sample is {random_sample}\")\n",
+ "print(f\"OCR text is {dataset[random_sample]['text']}\")\n",
+ "dataset[random_sample]['image'].resize((250,400))\n",
+ "# OCR text is {\"company\": \"LIM SENG THO HARDWARE TRADING\", \"date\": \"29/12/2017\", \"address\": \"NO 7, SIMPANG OFF BATU VILLAGE, JALAN IPOH BATU 5, 51200 KUALA LUMPUR MALAYSIA\", \"total\": \"6.00\"}\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 473
+ },
+ "id": "KvFetf-gF2jU",
+ "outputId": "979bd3b1-9670-4d3b-d5ab-7da6683e95c4"
+ },
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Random sample is 439\n",
+ "OCR text is {\"company\": \"UNIHAKKA INTERNATIONAL SDN BHD\", \"date\": \"27 MAY 2018\", \"address\": \"12, JALAN TAMPOI 7/4,KAWASAN PERINDUSTRIAN TAMPOI,81200 JOHOR BAHRU,JOHOR\", \"total\": \"$10.70\"}\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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2o968jz32GMuys7OzTU1NDMNYrVaKolwuVzweN5vNqNZJURRBEEwmE0EQqBj8gRdCQZ6q3CMAcv+Ctfdh7aRzYWGhu7sbPbjx/l6yLBcKBZPJJIpiIpHQarXxeHzfvn0g8VoAnJkveCAySBDEmtax3/f3CgQC77zzzoULF5aXlzs6OlKplMlkyuVyS0tLjY2NqHfAzMyMIAgOh6NYLBoMhsXFRRzHIUusRoAqhE3g9/vRSdA777xTLpcvX77scDgSicTy8vLY2Fg2m52entbpdIcPHw4EAqVSqb+/32g0+nw+iLLXCODMbI5PPvlEq9UuLCyQJOnxeLxeL8MwgiCk0+mOjo5isWi1Wm/evHn06FGCIBYWFrxebzabPXPmzJedeGD3AblvDmjSW0UeuvXfFCD3L0DpuCzLUhSFetYJgiBJkqIomUxGo9EUi0WUwl4sFo1GI8MwTU1NBEFMTEyQJIk2pidPnoxGo+l02m63nzx5str3VG8IgqAoynbOoetZ7uh08/6QSCKRcLlcyJPmef7y5cuDg4M2my2Tybz55pvt7e2zs7OoSUZTU9OVK1fsdnsmk8lms2fPnr137x7qymQymdrb29PpNJqQMTo62tPTY7VaPR7P6OhoLpfDMKy/v//b3/521e68Ttl+HUU9R2ZisdjNmzcJgujr64tEIvF4XKPRoLNMQRAGBwd/9rOfvfbaa4qizM3NPfHEE3fv3kU9HFtbWzOZDOoiZjQaLRZLuVz2+/0ul6uvry8ej5fLZZqmW1tbA4GAw+HI5/OyLC8tLfX09KyuruI4brVaq333dQhBENusGapnueM4HgqFmpubh4aGUGZiLpdrbm7OZrM6nY5l2YMHD/r9/mw229ra+s477zQ3N58/f54kSZqmOY47fPgwqkVCJ6P5fN7hcLS1tc3Pz+v1elEUOzs7u7u7jx07ZjabJUnied7j8SDHBkIxtUk9OzPFYnFpacntdvM8bzAYVldXGxsbUU8YdKSPJpWiY0447KwUtTmVCVHPcq8b4vF4LBbr7u5GKZk6nS6TyZhMptr8fJbL5ZotZ6tnZ6ZuSKVS7733HkmS8/Pz6XQaJeRcvHixNuVeyyPRa/Ebp9ZIpVLXrl3LZrPVGmbW29s7MDCAGqzmcrnPP/98dna2Nr2FGqeerXupVJqenu7p6dHr9QzDcBxnt9u3YBE/++wzm8322muv+Xy+/v7+wcHB3Z8Y09HRYbFYcBx3uVzZbLahoQHKoLZAPctdkqTPPvtsaGgon8/b7XaGYb71rW8dOHBgs9cRBGFxcdFisciyHA6HH3vssZ1Y7ToQBIGSzNaaZUPwZ2vUs9wVRXE4HKivRnd3N0VRW7OIFy5cmJqaam5uvnfv3v1pkhtZAEKSJHRAi9psSJIky7IoiqjXEs/zaKwfhmFoTJogCGu9ltBhWbFYRGXdGIZRFCUIwtmzZ2tzO1jL1HlkplQqYRhGkiRKDdjIn6C6aSRN+fcoisLzfDabJQgCCRc9gobrokeQfFFhqyAIOI7rdDpBEEiSRLkJgiCgZdy/HlS6ShCERqNBbclQ42z0T+R6oVl8iJ18t+qf+pQ7mnKKtIjEioY2kiSJxtLeL1AkSp7n0ZNRHzw0iQkF49eEiOKAqLQPFaTqdLpCoSDLMso4uHjxIprTRNP03bt3jUajy+XS6XQEQQSDwfn5+aeffhoOXKtIfcpdkqSRkRFBEFA5KfIZWltbUSIXSZKZTGbfvn2SJAWDQYvFEolEXC4XwzA8z9tstng8jppwxGKxI0eORKPRV155JZ1ODw8PI4ur0+nu3r3b0dGB4/jKysr58+enp6ctFstnn33W2tq6uLj4/e9/f2JiIplMWq3Wu3fvogPX5ubmlpaWI0eOVPvtUS/16buTJHnq1CkMw1577bWOjo5YLMbz/AsvvHD9+vXl5WU0kCwYDKKhA5OTk06nc2FhwWAw0DQ9Pz/f0NCQyWSQv4EGr3766aetra3BYJAgiOHh4e7ubpfLNTs7GwwGOY575pln0Gcpm80ajcZwOFwqlXAct9vtLMu63W7UoQkyh6tOfcodIUkSmvfLcRzyyNH5tiiKKCgpimI4HDYYDPl83mQysSzLMAwqULJYLAaDgaKofD5vNpsPHTpEEMQ3v/lNjuO6u7v9fr8kSeVy+amnntJoNG632+l04jj+N3/zN8hHt1gsxWLx4MGD9yesLi8vWyyWqr4laqc+nRmEoihXr17leX7fvn0Yhnk8nlwul0qlMAwjCMLr9WK/n4iCvG2U8k5RVE0NV5Ik6ZNPPvH5fPl8/ujRo0NDQz6fb2lp6Zvf/GbtLHKvUM/WHcfxxx9//P5HHA6Hw+F45B9uTUY79CEpFArvvPPOSy+9lEwmA4FAPp9fWFhoamoqFAqw690sdW4ekAODQjQcx4mimE6nN/uFlk6nx8fHH/k0nueRs1RZaJr2eDyhUAiF3lOpFE3Tq6urcKq6BerZmcEw7J//+Z9tNls2m3322Wdv3bpVKBTi8fjLL7/c1ta28Yugudgvv/zyji1zPRRFGR0d9Xq9LMv6/X6WZU0mE8Mwdru9KuvZ09SzM4NhmFar5Tiuv78fRdBLpZLP58tkMpuSe0NDw0ZcoB0Cx/Hjx4+v/RPN8YO+Blujzp0ZVHc3NjZWLpclSers7CQIorm5eVMX0el0p0+f3qEVArtJnTszPM/jOI4SGCGnCqhzuQPA/dS5M7MTSJKE/T518f5HEEtLS9FoNBaLFYvFcDiMHkQHW7VpWbLZLEqyUAP1b91FUUSDL1G/AJRII8syCuSxLFsoFHieN5vNsVgMJSeurKw4nc5oNKrX6y0Wy9TU1J/+6Z+SJPnJJ590dXWNj4+bTKaZmZkTJ07QNB2LxWZnZ//wD//wd7/73YkTJ2ZmZpaWlg4ePBgKhViWnZubQ/M85ubmLl68+MQTT1T7/XgQlBWnEk+vziMzH374YTwep2k6GAy++uqrHMd9+umn+XwewzCv18txHJrPYbVa0+m0zWZjGKZcLlssFnSUYzAYhoeHUcLZ/v37zWYzyj5///33XS7Xz3/+88bGxoWFhZ6eHp7nb9682dfXhy7Y2tr6wQcfnDx58uOPP0aT9LDfB1VqDVUdzda53FGXmHQ6ncvlUIYWSo9B3ajtdjtKH5BlmaZplJuu0+lQJ+tUKiUIAgpv0zSt0Wj8fr8syx0dHWiUUnd3t9PpbGlpaWpq0ul0P/3pTymKCgQC58+fNxqN3/ve91BfmrGxsXw+/7d/+7e1XLOsEurcmblx48bY2NiBAwfMZrPf7ycIAlU3GwwGp9OJpIw+CWazmWEYm82G8uANBgPP8yjZfc3z2RrIy9/O0HeWZWVZ5jgOZZjJshyPx30+n6oMc0Woc7nXB//wD//g8Xgymcy5c+cWFxf1ev21a9e+853vbOqwDMAgMrMRFEWJRCJVtAsWi0UUxZ6eHuQ75fN5p9NZKBR2eRmoZHGXX7SyVNN3Rwm3O/oSwWBwbm6OIAir1drV1ZVIJO7du9fW1rapfgSlUunNN9/88Y9/XK3wBdpduN1uSZIsFktfX58gCK2trbu/kr0ewKlzuY+Pj09NTZ07d65YLBaLxZs3b5IkGQ6HNyV3k8nU1tYmimK1khBfeumlB96ojo6O3V/GXtc6Vl1nZhd2Wj6fL5vNFgoFn8+n1+ubm5vRWODNXuf48eO730ppjTrQWY1Q51vVdDodCoW6u7sJgkAtBlBL0drsrgjsNHUudwC4H4jMVBLUjAmVgd//OMMw1VoScD91fqr6SBRF4TgOOccMw1AUVSwWFUWhaRrH8VgslsvlNBpNY2Ojy+VaWFhobGxEeTUoFUeW5cXFRZfLhcYlJJNJHMcdDkcymTQYDKjDx+rqKoZhsiwbDIZyuXzx4kU4HqoWe0nuxWJRkqRN1SOn0+mRkRGPxzM+Pv7UU081NzePjo5OTU15PB7UY8NqtV67ds1qtYqiyDAMasKhKEpHRwfHcSdPnrxy5YrNZkun06dOnZqfn//kk08YhtHr9ai1GGrdL8syOprt7+9vb2+/cuUKqrh77LHHMpmM2WxOpVJLS0tHjx6Nx+Pnz5/fzgkrsB32ktzT6bTb7d7Un4RCoYWFBYZhgsFgPB5vbm4eGxvzeDzhcBgdSabT6cbGRlSv7fF4UqmUx+PJZrM0TVssFpIkUW5jPB4PhUIkSVqt1sbGxomJCZvNhrp0dHV1LS8vt7e3oz7D7e3tsVissbGxo6NjampKUZRgMHjhwoWLFy+6XK5yuQwl1VVkL21VtxCnDwaDly5dcjqdZrO5o6Ojt7f3448/DgQCLpcLw7BDhw4xDDM5OenxeCRJYhimsbExn8/TNO12uw0Gg16v12q14XC4oaHBbDavNVgdGxvz+Xx+v19RFJIkv2pVN2/etNvthUJhYGBgmw7M/V09RFHkeR71Ut3ONVXIXpL7FpBlOZFIoN6lkiSh2HlNdU3aCENDQ8FgsKOj4/Dhw9PT02NjY7IsHzx48IEuOsAj2Uv/17cA6ham1+vXKlaxLR1vFYvFCk6qKZVKm7oajuO5XI7juGKxuLy8nEgkaJre/ZyZOmAv+e5bgOf5kZGR/v5+HMctFsvo6ChBEDzPb9Yuvv322+fOnfP7/RVZ1WZn4RqNxnw+j5LDfD4fcp8aGhoqshhVUedyX1paeuutt1AT98bGxl/96lf79u1zOp2iKG4qKaCpqQk1gK8Kg4ODAwMDKLIJCQXboc7lbrPZUOkGhmHZbNZms5XL5XU2l19Fd3c3ukhVIEkSVUKB1rdJnW9VWZa9fPnyqVOnOI6jKCqbzZrNZkEQ1mZ6Aapi9+ReKBSqaCABANsdZ4ZhmComiwPAGrth3VHX6Srmi+9pRkdHm5qa0AECRVFms/n69esWi8Vms504caIiL6GeHfBuSBAmJHIc98tf/tJms124cCEajaKcmVwu5/F4bt++PTAwkEgk7HZ7LBZDY3DGx8cTicTAwEB/f/9//ud/XrhwIZVK6fV6RVFefPFFlmVRc6hq39beAyzubkBRVEdHh8lkeuONN9BY4+Xl5ZdeemliYuLy5cvxeJzneTT8jKIoNNRydXV1//79KBmTYRi/359KpXK5XDabRYNrisVipZanEtOOgdx3BxzHXS4XSZIoES2Xy5lMJr1eLwjCCy+8UCgU3G53qVSKRqONjY3RaHRwcDCdTlMURZLk4OBgW1ubwWBA42CNRuOZM2fQPBJgs9R5ILJ2EEURlQ5qNBqUB79lB089rnbFAblXh0KhkMvl7Hb7ysqKKIrxeByN6JiYmGhubo7H42iid3t7O/LmX3vttc7OzlAo9Oqrr77xxhvNzc0cx33961+v9n3sMXbbmQHLhGGYLMuXLl2SJIkgiEwmc+LEibm5uVOnTr377rvj4+Nut3t1ddXj8aDBaSzLop9XVlY8Hs/S0lIoFHK5XBVMWVMPux0wAa1jGIa+UTOZDKptRX1s9Hr96uqqoii5XM5isTQ3N587dy4QCKD8R4IgZFkOhUKSJLW0tHi93qampmrfx94DnJnqMDExcffu3YGBgUgkcvr06Xw+39TU9MEHH0xPT586daqtrQ2F2DmOW2tKbDabUdAGTMaWAbnXFslk0mq1ojY4yPFjWZZlWZvNJopiLpdzOByzs7MWi6VUKnV1dVV7vXsMCERWh9nZWdRgXq/Xj4+PNzc3f/TRR+Vy+Vvf+lYgEOB53mazzczMnDlz5r//+79FUTSbzVardXJy8s///M9nZmYCgQBFUS6Xy2azVftW9hIg9+rw7rvvms1mgiCCwWAqlfrBD34wODiYSCR++9vfLi0tWSyWxcXFY8eOcRzn8/lmZ2fb2try+bzH4ymVSm63G8fxaDR6/3youkeWZVQZvJ2LqPpsv4r09/ezLBuPx3Ec7+npwTDMYDAYDAaKonw+H47jVqvV6XTiOG4ymSwWS1dXV29vL0VRkiT5/f7HHnuss7Nzs30Z9jSxWCyRSGzzIuC7V4d8Po/jOKqQQjNCUFdrRVHQ8RPakkLctrKA3AEVAc4MoCJA7jVNOp2Ox+Nr/0TejiRJ5XJ5amoKOq1uFojM1DQjIyP5fB7Nr/R6veVyuaurK5VKBYPBcrn84x//mKbpaq9xL1H/1r1UKq1jBWVZ5nl+ZmYmGo3OzMygRtV37tzJ5/NbGPJRWURRnJ2dDQQChUKhsbExHo8LgjA9PT03Nzc4OOh2u2HftVnqf6v6L//yL6IoPvXUU6gF5M2bN5F6EokEjuOZTKatrW12dlaj0ZRKJXSiuby8jFr1fu1rX6vu4nO5HE3TuVxOr9fLsswwjMViQYdQkiSheSTVXeHeov6dmXK57PV6P/jgg7m5ub6+Pp7nDQbD5ORkIpE4ffp0qVSiKMrv9w8PD+/bty+RSGg0GhQZ3OmZ7mhuoyzLoiiicl5JktZ+RqOD0UxjRVF4nkc/Ly4ukiSJvpT27dvn9Xp3dJF1Rv1b91u3bh09evTtt9/GMIzjuGPHjimKwrIswzCtra1oMDxN04qiGAyGbDZrt9tLpZLBYHigC7vyeyRJul+aa0LkeR7NtpZlGcdxURTL5TJ6sizLgiCgTvDIRyJJUhAE1LxSFEVkp9F/NRoNRVGiKKIevyRJiqJoMplIkiyXyyhF3mq1CoLQ19cHQ6Y2RT3LfW3sLcosz2azqK07UioSvSiKkiQhja49yHEcx3HIf0BPliQJSRDJC0kQyVEQBIPBQJKkJElarVar1VIUhb4fCIIgSVKr1ZIkqdFo0NgP9ODGy9V/+9vfrqysdHV1kSQ5PDz8ox/9aGRkZHZ2liTJV155xePx7OybWF/UszNz584dlEqFul0jU3rnzh2kM1TYj1SIHBiz2YxkimyqVqtF0lxTZ1UOOO12eyQSoWk6Ho97vd5MJtPc3MwwTDqdVlXOTEWoZ7kfPnz4yJEjsVhsZGTk3LlzU1NT7e3tQ0ND3d3dDQ0NDMN0dnZWe42PhqKofD7f2NhotVpv3LihKIpWqz19+vTw8DA0Ad4s9ezMIN5888329naCIF5//fXz589PT097PJ579+6dOXPm2WefrfbqNsSem79Qs9SzdUcYjcbJyUmSJNva2lKpFHLQv/vd7+4hT4AgiJGREZ1Ol0wmz5w5Mzw8TFHU4uLiiy++uNPho9qHZVk0KXEjT65/uZ87d25lZaWhoYGmaZ7nGYYxGo17qwROUZSPPvrIbDbjOO52u2OxWDKZNJvNkUgEJQ+rGYqiNu6h1P9XpFarbWlpQZ9+iqKsVutakKTaS9sEbW1t2WxWkqR8Pl8oFHAcD4fD0HYTwzAUTtjgk+vfd5+amhoaGurs7KRpuq2tba3BSyQSkSTJ4/EcO3as2mt8NLdv30afUo/Hw7KsXq/neb6hoWFvfWirTv3L/Z133jlw4MDU1NTS0lIymQwEAmfPno3FYmaz+cqVK2fPnv2zP/uzaq8R2CXqX+7z8/NDQ0P79++3WCzDw8MYhj355JPj4+O3b99+8sknu7u7Ozo6qr1GYJeof7k/lGg0urCwAHNJ1YZK5Q7UAjzPa7Xa3QyRwUYHqBrFYnE71lZRlM02/ga5A1XD4XBsJ7Iky3I6nd7Un4AzA1QM1EeklmfOgXXfq1S9tvCh1Lj1BOu+q0iSxDCMwWDY7Jd4Pp//6KOPDh06pNVqdTrd8PDw2NjYM888c/LkyR1aal0Cp9CVR5bl27dvNzY28jyPSqI4jksmk9PT04qizM7O/sEf/MHRo0c3dU2LxYJqDlEO8IEDB4rF4vLy8vHjx+FgdePUv9wVRYlEIjabLZFIKIpSLpcdDkc0Gj1w4AAqCdVqtaVSiabpfD6PzucdDoder9/yKwaDwbfeestkMuE4jupFBEGgaToaje7bt69QKKysrGxW7hiG9fb2chxnNpvdbncwGPzGN74hCAJofVPUvzMTCARu3LjhcDhQ5R7Lsl6vd3x83OFwRCKRbDZrNBoLhUJDQ0MsFlMURafTff/737fb7Vt+xWAwSJLk/Px8Y2NjuVw2m83ZbLa5ublYLC4sLJjNZoqijhw5ss4VUMk2Kh1cgyAIpG+WZQuFAspygyPhTVH/co9EIpcvX9br9RaLxWw20zTd0NBw584dDMNQBw5RFI1Go6IohUIBFT5vdsQXUicq0EY7SBQPFgRBURRUCMuyLIZhpVIJ1WJLkoRKuddKs3meR5Mo0YPo8bV6bVSyjb4ucBxHC7bZbC0tLZV/y+qX+pc7hmHIY0GVqainLrprZD5RcwFBEFAzunQ6bTKZUExN/D1Ihag9E6onWDPAGIZpfg/P8+iMEHUTWKuFXSuHXWsugAq31ypl76/aXr97TDwep2k6k8lgGGY2m7fzLaRC6tl3LxaLt2/fRkYXWV+kaRzHUYa0VquVZRnpEolMp9MRBIG6Zeh0OoqiaJomSZKiKCTHL9drb6Rqe3V1FXX44Hk+lUrpdLqBgYGt3dTs7KzJZAoGg263u6GhAeS+KepZ7nq9vqenBwkUeQJoBDvasNI0XSqVOI4zGAx6vT6dTlsslkKhgJyEyvZvicVi2WwWnb9EIhFRFPv6+rZWnIH03dPTo9frYVLNZql/Z4bjuKtXr5rNZpZljx49GovFaJqenJw0GAzFYlGj0XR1dd27dy8YDDqdTr1eHwqFjh07Vtmaj1gsViwWRVFE4zdQgdUeqh6sG+rZuiNQXyTkTmSz2enp6f379wuCUC6XMQzzeDwWiyWZTMqybDAYnE6n3W5HnnEF8Xq90N2uFqh/6y6K4tzcnN1uR33wdDqdoiirq6smkwlNtCNJEvVAPX78+NqsGKAuqX+5A8AaYMmqAAp9olAmVvNpVfVE/fvulQK1YdJoNPl83mAwZDIZu92eTqfdbrdGo0mlUvl8nqbplZUVu92O4jCyLCeTyYaGhnQ6jeI/09PToii6XC40DD4UCvl8PpPJdODAgWrfnyoAZ+Yr4Xn+17/+dWNjYzAYtFqtKysrTqdzYGDg0qVLWq22XC6fOnVqfHz89OnTNpvt7//+7wcGBmiaJgiCYRiapufm5vx+P7LiJpMJNcm4desWmn9dLpd1Ol0ikUin0xcuXNjUOS7qxw2BnS0A1v0riUajBEEsLi4Wi0Wbzfb4448bjUaO49LptMvlcrlcsiybzeZgMBgOhxOJhNVqRVacIAhRFJ1Op81mI0kyk8kYjca2trZcLqcoSmdnJ9K6TqfTaDTPPPPMZucr0TQNWt8aYN2/kmw2+/rrrwuC4HK5DAbDoUOHUDZvMpmMRCKRSOTxxx+/cePG/v37vV5vLBZD56ylUsnv95fLZaPRqNfrOY6z2+1oHgE630XpAxiGbXPeObAFQO7rUS6X1zJbtmxQUeAfXQT5NmgsQigUampqIkkyn8/zPE8QBEpzKBaLLpdrneyAUqmk0WgeGC4CbASQ+0NA2ZHIHq9lg3Ech6yyLMsLCws9PT3Ly8vhcLi3tzeRSORyOZ7nJUk6fvz4lStXnE7n4uIimn7j9XpDoRAaA09RlM/nC4fDXq/3xo0bP/jBDxoaGq5cubKwsNDZ2RkOhxsaGiYmJvbv3//UU0+tszwMw8Cf2QLgu39BPB7/93//91dfffXWrVvNzc0fffTRkSNHTCbTvXv3rFarx+OZnJz0eDzpdFqr1YqiyDBMOByenZ1dXFy02WyHDh369a9/vX///s8//3x6ehrNIGhubk6n0/v375+YmNBoNMlkUlGUUqk0MzPz9NNPow9DuVxGh1yiKKJMGEmSQqGQJElf5e2A0LcMyP0LGIaJxWLXrl27ceNGT09PLpcTRREFFqPRKMuy4+Pjx44dQ6P5jEbj3Nycw+Gw2WxGo5Fl2bm5OavVKssyMvMajeaxxx67cOHCz3/+82w2+8ILL6TTaZQwTFHUc889l0gkZFluaGjIZDJ9fX3PP/88hmGyLJMkefDgQZS2We23pA4BZ+YLYrHYL37xi5aWFp/Pl0qlWltbtVptY2Pj3NwcjuMmk8nlcoXDYbfbLUlSJBJxu90ul0uj0ZTLZa1Wy7KsTqdDdUa3bt3q6+s7fPgwhmEw/bSmALl/AZq5V5GZ6yh7Hv2M5vjRNK3VaiORiF6vZxhGEIRwOCxJUk9Pj8/n2/4rAhsE5P5owuHwwsLC4cOHUcY8ConwPB8KhVpaWgiCCIVCpVKpu7sbzbL0er0ajWZ1dbW3t/eNN94QBEGW5eeee+43v/kNKpBtaWlBO1e9Xn/8+HGDwbD+AkRRxDCsUsML0La7Ih/sneN+k1FBwHd/BOl0emho6N69e0tLSygoaTabr169+qMf/ejq1avvv/++0WjUaDTZbDYajQ4NDfl8PoqiMpnM3bt3/+7v/g6dp968eRNNukRZA/l8fnBwEM1aQikJ66+hslM6cByv/bEfW4g+oQL29XuY1fptV51sNmuxWHp6evr7++/evRuNRtva2pLJJLLZ/f39k5OTbrebIIjx8XGfz2c2m10uV3t7u8vlam5uXlpaSiQSTz/9tN/vb2lpaWpqQk9ua2uTZRl1BNnlO0L13bv8optlh3Y74Mw8glQqde3atfPnzxMEkUql0MZUUZSmpqZSqYT6AnAcZzQakWnZ1JAzlF2zo+sH7gfk/mhyuZzVan3k01CDDZqmA4GAwWD48vh2hmEmJycHBwdRR4NMJiMIgtFoDIVC67edASoFODPrcenSJYZhBgYGTCYTwzAEQaTT6VQq1dPTc/36dQzDnE4nRVEOh4Om6aGhIYqiTp8+/fHHH/f19Y2MjBAEEQgE7HZ7PB4/ceKEy+X6zW9+w/P8nTt3yuWyKIp6vZ4kSRSyBHYBkPt6NDU1ZTKZDz/8cGJi4u7du2azmSCIXC73l3/5lxaLJRgMrqysLC8v9/b29vT0RKPRTCZz5MgRi8VCkmQgECgWi01NTZIkTUxM9Pf353I5l8s1MzNTKpWy2axOp+vu7rZardBQYNcAua+H2+0WRfHw4cORSAQlBXR1dQUCgVwu19nZabFYZmZmLBZLNpulaXr//v2lUqmhoaGxsdFutz/xxBN6vd5ut+M4fvr0aVQFcvHixeHh4Q8++OCv//qvUTAe27Ft2Z4GpSpV/J0B3309UHc7FAMWBAG1qdkhdaJWZwzDwNx3xE6E3kHum0aSpHA4bDKZHA5HKpVCee3oV4VCIZ/PS5LU3NyMmjzKsszzvNFo/Nd//Ve3233y5Em9Xp9MJgmCWFlZYRgGVXswDBOPx71ebyaTeeaZZ3bzdnK5nE6n207H4z0EyP3/IcvyzMyM3++/c+fO4OAgy7J2uz2ZTHIcl8/n7Xb7lStXjhw58m//9m9nz54VBGF1dRV56g6HQ5Zlm82GumZPTk6+8sor169fj8fjhULhxRdffP/99wcGBq5cuSLL8tGjR9977z2Px+N0OlE+Gcp6b2ho6O3tPXPmTLXfhroFfPcHQUnnly9fTiQSH3/88V/8xV9MTExcvXr1wIED5XKZJMm+vr7W1laKou7cuVMsFimKstlsY2NjLMv+5Cc/GR0ddTqdHMdls1me50VRPHHiBMqDP3HixMjIiMPhkCTp5MmTHo8nn897PB7Uj8BmszkcDqPRWO03oJ4B6/4gr7/+OvI3mpubb9269Ud/9Efj4+OBQODpp5+ORqPpdPrs2bOyLFut1k8//dTn85EkyXFcW1ubIAg+ny8QCFgsFhzHLRYLy7IoEo86WcOWtOqA3B+kUChIkmQymbaTWIKq9dZ+FgQBx/EH/ONr167p9fquri6z2bytFQMbBuT+cGRZXlpacrvdFotl7cFkMulyudDPgiCEQqHW1lYMwxiGQdM1MpkMTdMTExMfffTRD3/4Q4ZhFEWZmZlJJpNtbW0GgwGVqPp8voWFhXA4rNfrfT4fTdPT09PHjx8/f/58de5WNYDv/nBCodB//Md/PP/887lcjqZpjuPC4XAul0NdBqxWazKZzGQyL7/8cjAYjMfj8Xic5/lCoeDz+VAJyLVr1z7//PN9+/aZTKZSqbS4uMgwzL179y5cuDA6OqooClJ/JBJBVapwtroLgNwfjiiKra2tkiS9/vrrg4ODzc3Nv/vd7/r7+5eWlnAcj8ViRqOxVCotLS1ls1kMwyRJslqtBw8eFATBbrf7fD6n09nV1XXw4EGPx4MS1tPpdE9Pz5EjR65duzYyMvLTn/4UDVriOO7EiRP3f40AD1AoFCri8oEz83BSqRTq5Hjt2rWGhgaDwYCm3mm1WkmS7HY7wzAul0sQBI1GgxppoOxI1KJDluV1NqaJRIJl2ebm5t28oz1NpY6cQO6AioDQGKAiQO6AigC5A9tFluVsNrsnvGKQO7BdOI67efMmmkRb48BWFagA958i1zIgd0BFgDMDqAiQO6AiQO6AigC5AyoC5A6oCJA7oCJA7ltHEIRqLwHYHCD3rQOTIvcccMwEqAiw7oCKALkDKgLkDqgIkDtQAbLZbLFYrPYqHg1sVYEKIIoiKkuv9kIeAcgdUBHgzAAqAuQOqAiQO6AiQO6AigC5AyoC5A6oCJA7oCJA7oCKALkDKgLkDqgIkDugIkDugIoAuQMqAuQOqAiQO6AiQO6AigC5AyoC5A5sCFEU90Q16vqA3Pcwu6k/jUZjNBp37eV2iPqUOxrKXvfQNL2bL1eRudXVpQJyLxaLtVbfvSfGYm2f2q/8rzUqIHd403eUWjMlexpovAGoiPr03QHgoYDcARUBcgdUBMgdUBEgd+AhCIIQj8ervYrKsyG5K4rCsuxOLwWoHXieT6VS1V5F5dlQIFJRFIZhDAbDLixoyyQSCYfDAYcAwDrUT9xdUZQ6OOWuP2RZJoha8ZlrZR3bB7Reg8iyzPN8tVfxf9SPdQeAR7KD1r1cLtfUJxsA6seZAYBHsiFnRhAElmXNZvMuLAgAdo4NWff6qGTZTRRFyWazsC+qNTYkdxzHayeWtCdQFGV2dlaSpGovBPh/QGQGqEO+6hBm79lsjuMg4FN1ZFkWBKHaq/hKZFl+6ONg3YEqkEwmHQ7H7nvIe8+61wf5fD4ajVZ7FVXDbDZX5RR8KxX7kJ2yfYxGYxWz2QqFgl6v12q11VqATqeryutuxbqD/7N9SJKsbmz3q7zb+gZ8d0BFgO8OqAiQez2zsrKyvLxc2WvWeAhyfcCZqWckSVIUpbItBBVFkSRp19oSSpJUwT09WPd6hiTJCuoS7W5xHN/NFpz5fL6CuRhg3YGNksvlrFZrtVexLbYod4ZhdrnbMgBsH7DuQGUQRVEQhBo3giB3QEXAVhVQESB3oB7YoJPyCLnncjmO4yqxHgCoPo/w3UVRJAjioXnJ6A8hNRLYQzzCums0mq/KwU8mk9AnFdhbQGQGUBGwVQUqA8uy5XK52qt4BKqYPwrsAlqttvYbjYAzA6gIcGYAFQFyB7aFLMt7yEEAuQPbQhAEURSrvYqNAnIHtoVOp6tiA481FEUpFouPfBrIHagHcBynKOrRT9tDjhcAbJMate6CIKRSKXW2/gF2jhqVuyzLOp0O8s9qk1wut0ctETgzwKbhOE6r1e7FCRcgd0BF7L0PKABsGZA7oCJA7oCKALkDKgLkDqgIkDuwCTiOy+fz1V7F1gG5qxdZllmW3VQk+qvaUuwVoHhPvaDBBBRFbfz0WqvV1kL+45aBYyYAwzBMFMVtdm1fq/Oo4kTBR7KHv5iA9UGjOzbyTEVRtm/1CoVCJpPZHc++VCpt7Q/BugMqAqw7oCJA7oCKALkDKgLkDqgIkPveRpblLYcpVAhEZoAdp3ZGAWzOuld2pmuNIAhCLper9irqGUEQGIap9iowbFPWXVGUZDLpdrt3dEEAsHNs2pnhOG5TWRYAUDtsequayWTA3Qf2KLBVBf4foiiSJFkH396ZTMZisTyQrwZyr3UURdlN8a0za3FvIcvyl+9iz99V3cPz/G6apHVmLe4tHnoX27Luu2x4AGCbbP1znEwmU6lUBZcCADvN1q177RyVAcAGga0qoCLqYVMCABsE5A6oCJA7ULtUpGb8fkDuQO3C83xlp1jCVhVQEWDdgc2xp+0jyB3YHIVCYe8qvsLODJw9AbUM+O6AigBnBqhbvmzK61bu9VdCDmwKSZLK5fIDD9anM6MoCsuyNE1XeyFAbVGfcgeAh1K3zsxOI8tytZcAbBqQ+xaBYOteBOS+RUDuexGQO6AiQO6AigC5AypiN+QOsU6gRtgNuathV1csFgVBqPYqgEcAx0yVATpM7QnAd68MoPU1atmAgtyBSsKy7PT0dM0qHpwZQEWAdQdUBMh9p+A4DnLuaw2Q+w4C+9d1kCTpAXOgKMpOGwjNjl5dzeh0umovoabheR7H8fuHyeA4vtMGAraq2wUViZnN5movBHg0IPftIsuyLMsaDXxP7gFA7oCKgK0qoCJA7oCKALkDKgLkDqgIkDtQGRRFqezogZ0AIjOAigDrDqgIkDugIkDugIr4X7CfBm7YIfX/AAAAAElFTkSuQmCC\n"
+ },
+ "metadata": {},
+ "execution_count": 5
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Prepare dataset for Donut\n",
+ "As we learned in the introduction, Donut is a sequence-to-sequence model with a vision encoder and text decoder. When fine-tuning the model we want it to generate the \"text\" based on the image we pass it. Similar to NLP tasks, we have to tokenize and preprocess the text. Before we can tokenize the text, we need to transform the JSON string into a Donut compatible document.\n",
+ "\n",
+ "current JSON string\n",
+ "```\n",
+ "{\n",
+ " \"company\": \"ADVANCO COMPANY\",\n",
+ " \"date\": \"17/01/2018\",\n",
+ " \"address\": \"NO 1&3, JALAN WANGSA DELIMA 12, WANGSA LINK, WANGSA MAJU, 53300 KUALA LUMPUR\",\n",
+ " \"total\": \"7.00\"\n",
+ "}\n",
+ "\n",
+ "```\n",
+ "Donut document\n",
+ "```\n",
+ "ADVANCO COMPANY17/01/2018NO 1&3, JALAN WANGSA DELIMA 12, WANGSA LINK, WANGSA MAJU, 53300 KUALA LUMPUR7.00\n",
+ "```\n",
+ "\n",
+ "To easily create those documents the ClovaAI team has created a json2token method, which we extract and then apply.\n"
+ ],
+ "metadata": {
+ "id": "vUrJoe6eGIki"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "new_special_tokens = [] # new tokens which will be added to the tokenizer\n",
+ "task_start_token = \"\" # start of task token\n",
+ "eos_token = \"\" # eos token of tokenizer\n",
+ "\n",
+ "def json2token(obj, update_special_tokens_for_json_key: bool = True, sort_json_key: bool = True):\n",
+ " \"\"\"\n",
+ " Convert an ordered JSON object into a token sequence\n",
+ " \"\"\"\n",
+ " if type(obj) == dict:\n",
+ " if len(obj) == 1 and \"text_sequence\" in obj:\n",
+ " return obj[\"text_sequence\"]\n",
+ " else:\n",
+ " output = \"\"\n",
+ " if sort_json_key:\n",
+ " keys = sorted(obj.keys(), reverse=True)\n",
+ " else:\n",
+ " keys = obj.keys()\n",
+ " for k in keys:\n",
+ " if update_special_tokens_for_json_key:\n",
+ " new_special_tokens.append(fr\"\") if fr\"\" not in new_special_tokens else None\n",
+ " new_special_tokens.append(fr\"\") if fr\"\" not in new_special_tokens else None\n",
+ " output += (\n",
+ " fr\"\"\n",
+ " + json2token(obj[k], update_special_tokens_for_json_key, sort_json_key)\n",
+ " + fr\"\"\n",
+ " )\n",
+ " return output\n",
+ " elif type(obj) == list:\n",
+ " return r\"\".join(\n",
+ " [json2token(item, update_special_tokens_for_json_key, sort_json_key) for item in obj]\n",
+ " )\n",
+ " else:\n",
+ " # excluded special tokens for now\n",
+ " obj = str(obj)\n",
+ " if f\"<{obj}/>\" in new_special_tokens:\n",
+ " obj = f\"<{obj}/>\" # for categorical special tokens\n",
+ " return obj\n",
+ "\n",
+ "\n",
+ "def preprocess_documents_for_donut(sample):\n",
+ " # create Donut-style input\n",
+ " text = json.loads(sample[\"text\"])\n",
+ " d_doc = task_start_token + json2token(text) + eos_token\n",
+ " # convert all images to RGB\n",
+ " image = sample[\"image\"].convert('RGB')\n",
+ " return {\"image\": image, \"text\": d_doc}\n",
+ "\n",
+ "proc_dataset = dataset.map(preprocess_documents_for_donut)\n",
+ "\n",
+ "print(f\"Sample: {proc_dataset[45]['text']}\")\n",
+ "print(f\"New special tokens: {new_special_tokens + [task_start_token] + [eos_token]}\")\n",
+ "# Sample: $6.9027 MAR 2018UNIHAKKA INTERNATIONAL SDN BHD12, JALAN TAMPOI 7/4,KAWASAN PARINDUSTRIAN TAMPOI,81200 JOHOR BAHRU,JOHOR\n",
+ "# New special tokens: ['', '', '', '', '', '', '', '', '', '']\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 105,
+ "referenced_widgets": [
+ "caaf5bbead234458808d4a885c2e2955",
+ "f4181e5724dc4a1689b59e6e1518af8c",
+ "73cd944959284d12ae93747201e0f563",
+ "0b90fbf949824a00a61a18d528a57227",
+ "59beb9825c964bc88bebd30749c2d883",
+ "e00fec3afbd841a899227d5b343fc762",
+ "0557a08461a84c71b00f96efdeccb0f2",
+ "d019555bc44749fc87e59db73a5dc87a",
+ "238dff54c6654868b6269ae482f7251e",
+ "46d2b522800640edb18515a10ae1a9dc",
+ "839c1f51487047ada985d870589031c1"
+ ]
+ },
+ "id": "FeAdhX1hF4Lj",
+ "outputId": "1e24fa42-00a4-42ac-ad19-799e41c2e8a2"
+ },
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/626 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "caaf5bbead234458808d4a885c2e2955"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Sample: $6.9027 MAR 2018UNIHAKKA INTERNATIONAL SDN BHD12, JALAN TAMPOI 7/4,KAWASAN PARINDUSTRIAN TAMPOI,81200 JOHOR BAHRU,JOHOR\n",
+ "New special tokens: ['', '', '', '', '', '', '', '', '', '']\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The next step is to tokenize our text and encode the images into tensors. Therefore we need to load DonutProcessor, add our new special tokens and adjust the size of the images when processing from [1920, 2560] to [720, 960] to need less memory and have faster training."
+ ],
+ "metadata": {
+ "id": "3EzHlrskJ5Cl"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import DonutProcessor\n",
+ "\n",
+ "# Load processor\n",
+ "processor = DonutProcessor.from_pretrained(\"naver-clova-ix/donut-base\")\n",
+ "\n",
+ "# add new special tokens to tokenizer\n",
+ "processor.tokenizer.add_special_tokens({\"additional_special_tokens\": new_special_tokens + [task_start_token] + [eos_token]})\n",
+ "\n",
+ "# we update some settings which differ from pretraining; namely the size of the images + no rotation required\n",
+ "# resizing the image to smaller sizes from [1920, 2560] to [960,1280]\n",
+ "processor.feature_extractor.size = [720,960] # should be (width, height)\n",
+ "processor.feature_extractor.do_align_long_axis = False\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 283,
+ "referenced_widgets": [
+ "6797ab3a6f494e1997201bbf9e9c5ad6",
+ "339693f8251042aab6a49b69152c0694",
+ "262d673a09704ef58a24a5e0a8dfff7a",
+ "94aecf069a3f4df9b48243f50d6bd97e",
+ "fce4ca5477984d63a66c17946ee44cb8",
+ "bf563c14b5e349809c16004a461b9626",
+ "4a40439694974a96ade8c2d4e7577903",
+ "ed8a2555081847868ddb36d7facc2b9f",
+ "3d204583b66d4753aeae0ffedf393c7f",
+ "3b5f2086fefd4ddfa8f35214b33da671",
+ "584418c3238548e997fa48f18417c54c",
+ "90f3c8c42be84fa69e78adab91538da0",
+ "a84e63dcd8ce471dae5ab93dadca4d82",
+ "56b7985e9cb145e598d383256d99422a",
+ "3b244a5af6bb44bcb61c8fc9ab0a1581",
+ "84bd0ff908d14627be36f76b719099e0",
+ "28b4fb7667424822b251bc963c3e0cd7",
+ "ea352c2faae747b0bddf3a7aadaa92ec",
+ "77365c93c6d349ae94ce03b996a59e19",
+ "1115d978c2c945d1bd6ef20ae19cb35d",
+ "15cf9805aba74fb897fbb7572e42d9f2",
+ "5eddf8c79afd48c1bf77cc728c146982",
+ "5f6dd74137a74639aa349f4f5b47fce1",
+ "3c241a3de8a7475684ba2a7789187721",
+ "75e4681aec26471b9fdbbf6fd17324bf",
+ "5f4dcb026426401bb6b651b48c0f7215",
+ "9d24d6ad933249de90e6c76f943dceef",
+ "3b8d8ca0d8684b4fbe06d07e42f3126c",
+ "f23a246f99bf4b41988ef9ea63e472d3",
+ "bd9ee20660544dee9c75d04347900156",
+ "a61194f284f646d3a17a95f3a5bc396d",
+ "0940bc78c4b7474b8c46b98b5576b866",
+ "403c00c92fc44985a0816cd28903bcbc",
+ "d4daa24e2b7d4c48b0ed2d318d11cab6",
+ "c8933dfa355942ecac29b5368886070c",
+ "73c6009ac74c4faebf9116782bdba769",
+ "704e2515a97f4f3f8c06a47cdcb58416",
+ "88b58314f8674d6c97af8a2b7ee9be7f",
+ "bc3890312646478e8d74f28a7b46d78b",
+ "bed905dae5954275bb5012a9b5d0d56b",
+ "ba7aa7e4d0fa4efe9d260f6679e906e6",
+ "80edf26d513c407fa3154e2e95df35cc",
+ "94566d30d1c2453db71b0eccb8fa8c81",
+ "2a78f43d1ab44ef9abf4d604e4ee3ca3",
+ "b85494f5f65645bf9f722cab1640b494",
+ "79d965b2163b4d8d94b8762885c24cfd",
+ "5c0d4f550a974515afe1e00fd2b1c996",
+ "2f678a346bd449d7a0b570802f21e3ff",
+ "680b0c71943147f69c36d9ff9b308373",
+ "e3dbdeb4765d497395c11bd1f8b7893b",
+ "91604bb2b5f64ba8899873b9260ba1d6",
+ "a2d0a30fe2d9425db3055f627e4c8a3d",
+ "bdfb478412634d51bbf5d1551d905449",
+ "134569d25239463cba6f0c4f64e82952",
+ "b434f07f036546fbba15153c5f8a1c39",
+ "bf5e95d9c5e74b6da0ba720441261f36",
+ "6123c5376e0b48ada5dfca2d7ee3606b",
+ "6586e41606664d5194303ca3a782ddec",
+ "1e8add1bc7694c6eb7c34577a799a24b",
+ "07885b9878a2491297895aad7b0157f0",
+ "21fdcf64dbcb40aeaae76c3436df0395",
+ "afd6f07964674931a851537987eb1a70",
+ "05d6cf319f4e4f3b914c74238b002e25",
+ "635231066d87477fb6f1901e2da5751b",
+ "0a7a9394518f4dcabf65af9a39e8d021",
+ "982a605982064d3fb9394252738853f3"
+ ]
+ },
+ "id": "cS3gOhgPJ4Sr",
+ "outputId": "8462371d-d3cb-4054-e80b-4b4f6c68fa0c"
+ },
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)rocessor_config.json: 0%| | 0.00/362 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "6797ab3a6f494e1997201bbf9e9c5ad6"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration.\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)okenizer_config.json: 0%| | 0.00/518 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "90f3c8c42be84fa69e78adab91538da0"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)tencepiece.bpe.model: 0%| | 0.00/1.30M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "5f6dd74137a74639aa349f4f5b47fce1"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)/main/tokenizer.json: 0%| | 0.00/4.01M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d4daa24e2b7d4c48b0ed2d318d11cab6"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)in/added_tokens.json: 0%| | 0.00/71.0 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "b85494f5f65645bf9f722cab1640b494"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)cial_tokens_map.json: 0%| | 0.00/355 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "bf5e95d9c5e74b6da0ba720441261f36"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/transformers/models/donut/processing_donut.py:189: FutureWarning: `feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Now, we can prepare our dataset, which we will use for the training later."
+ ],
+ "metadata": {
+ "id": "hdsGi4XlKc4Q"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def transform_and_tokenize(sample, processor=processor, split=\"train\", max_length=512, ignore_id=-100):\n",
+ " # create tensor from image\n",
+ " try:\n",
+ " pixel_values = processor(\n",
+ " sample[\"image\"], random_padding=split == \"train\", return_tensors=\"pt\"\n",
+ " ).pixel_values.squeeze()\n",
+ " except Exception as e:\n",
+ " print(sample)\n",
+ " print(f\"Error: {e}\")\n",
+ " return {}\n",
+ "\n",
+ " # tokenize document\n",
+ " input_ids = processor.tokenizer(\n",
+ " sample[\"text\"],\n",
+ " add_special_tokens=False,\n",
+ " max_length=max_length,\n",
+ " padding=\"max_length\",\n",
+ " truncation=True,\n",
+ " return_tensors=\"pt\",\n",
+ " )[\"input_ids\"].squeeze(0)\n",
+ "\n",
+ " labels = input_ids.clone()\n",
+ " labels[labels == processor.tokenizer.pad_token_id] = ignore_id # model doesn't need to predict pad token\n",
+ " return {\"pixel_values\": pixel_values, \"labels\": labels, \"target_sequence\": sample[\"text\"]}\n",
+ "\n",
+ "# need at least 32-64GB of RAM to run this\n",
+ "processed_dataset = proc_dataset.map(transform_and_tokenize,remove_columns=[\"image\",\"text\"])\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "e5e329024fda47f1b86ebcab25a8120c",
+ "d90c3dd6f5394aa7905409845e81f321",
+ "2989a6e115d44a36be8d07046c933ac3",
+ "c375a57298794924af70d1f8ae4301c5",
+ "cb5de398d50941e5956b82b5a3d2864a",
+ "9286c8eec00e421a8f4d0a8bc8e86723",
+ "43d82f3098aa4d3390f31f5632244f1b",
+ "ceff465e44ee442eaedcfaf01c922ab1",
+ "ac7034cd293c432ba62aeccb32ea4e8e",
+ "b71da3f83c75435385af282b2a52b389",
+ "f2d98c8bb1a14bf2bff6a4d19c0dcd41"
+ ]
+ },
+ "id": "wSmcMLQCKcF4",
+ "outputId": "87b6fcfb-6d5f-4522-caba-e2b11be63a84"
+ },
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/626 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "e5e329024fda47f1b86ebcab25a8120c"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "processed_dataset = processed_dataset.train_test_split(test_size=0.1)\n",
+ "print(processed_dataset)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "WeDKyY3IKnhK",
+ "outputId": "76e08482-25ea-4c87-82fc-3bc0736d723d"
+ },
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "DatasetDict({\n",
+ " train: Dataset({\n",
+ " features: ['pixel_values', 'labels', 'target_sequence'],\n",
+ " num_rows: 563\n",
+ " })\n",
+ " test: Dataset({\n",
+ " features: ['pixel_values', 'labels', 'target_sequence'],\n",
+ " num_rows: 63\n",
+ " })\n",
+ "})\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Before we can start our training we need to define the hyperparameters (Seq2SeqTrainingArguments) we want to use for our training. We are leveraging the Hugging Face Hub integration of the Seq2SeqTrainer to automatically push our checkpoints, logs and metrics during training into a repository."
+ ],
+ "metadata": {
+ "id": "qGMtiNCQK6bL"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from huggingface_hub import notebook_login\n",
+ "\n",
+ "notebook_login()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 145,
+ "referenced_widgets": [
+ "a57ec58d14b94c2fba4e4f1f03133e06",
+ "e1951213400c4a89b12c15140509ec63",
+ "22de3dbdf14d47099ddd3f1885bd7f5c",
+ "f1ba24931b024127bac8dbcff60f0274",
+ "a49fc9bb73d645b28e8b286ce51d9d2a",
+ "62f863a9e29c4de2a58e5ff192ec95c8",
+ "8541daa4779d423f94c22b77874e7bf4",
+ "46b35c8955f8494190e4f55e6c8bebb6",
+ "4b853daa09e54c159bf8aea64e6ebe90",
+ "03273aa97c2244e893a67857fa5fb660",
+ "7f399e62fa79436688da63d33df06ce3",
+ "f3727357b6c4475d8d6aa1071a20893a",
+ "dc2d8dc18fb749488509d534dddedf22",
+ "c7495023641d4eceb6e4a8702a56dce9",
+ "23eedd8894094d3aad7bf3c6515de791",
+ "3efc395cfc7e464282be4f38565c77d5",
+ "83e364e4022046599fb7e1fd259805ae",
+ "e8055b6511de407386915fd90fc1889b",
+ "6d57e78415974819b3370ea25d2d8a1e",
+ "61f776905fc34f93b5ecb802c8bfc7f2",
+ "6ed262034bf849adaa62b2d1d946c218",
+ "9018e04e18db47d1aa61c0077b5194e1",
+ "23acee24c3c641d7b7ada2e3ff0d7014",
+ "4046a1dc9a374c34b9867b0e4b7ab19c",
+ "fbf2c9f2e21b4400b3936e06a93cbfb7",
+ "4bd896eff13b42b48336801ff01b9159",
+ "d304742889ad4f629bf710954aab457c",
+ "c2429dd6546741d2a8d1e19e9ae21611",
+ "b80281f6936c41e1bbb90211c154e2fe",
+ "f81c03c7b82e410d98c0cd88f1bceeaa",
+ "2e8ac550b27e4c799864020d0be83b8e",
+ "f64566c9f7654902bd5ef43bbf9e00bc"
+ ]
+ },
+ "id": "TctHX06WLgIP",
+ "outputId": "ce710198-119d-487e-db91-1b914946d867"
+ },
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "VBox(children=(HTML(value='
'])[0]\n",
+ "\n",
+ "# is done by Trainer\n",
+ "# device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+ "# model.to(device)\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 172,
+ "referenced_widgets": [
+ "318f32e91dff43eb81885922ca70b2ee",
+ "efc4f3cfb55f487fbb08f07b9f33ec25",
+ "5405159dad13480089f79920cb0a6d7a",
+ "846a5bd47cc74a52a5f7f6580d5209b9",
+ "b3aebd3ccc2f496f89b36bf2e2505742",
+ "97061e456f414c1cb9261446f766982a",
+ "713e698e11bd4094b95f94ae9657e2ab",
+ "33bf2ddb148940b1933811b56ae67f61",
+ "f27e41ce9eae4752bbc0c6a408297dce",
+ "5215b0e2d4cf4212b8cd18514a647d78",
+ "60ae55d3b94a40e1833d4ae1dbcaca08",
+ "ba5e8739e5bc439a8ae85fdd0b6f4851",
+ "2eac6b728a65430cb02bf84a69bc2399",
+ "d86bfc76698d4740a702e346f741558b",
+ "b1ebab7fb6844d5fa3d3c589a51deedf",
+ "701baa3438f348ce93222fe32deebd3f",
+ "ca826084ae674380be4436edb630ef12",
+ "b42e453ab07a40388ebbe0f3579baec3",
+ "324c154ae45a48ddb14281ca4501fd09",
+ "b9720aec012747bea33718783867c0dc",
+ "29ff9bddb9ba4f30938201dde04dd71c",
+ "bc827f9bf43242838035ced7f9bbb51c"
+ ]
+ },
+ "id": "hZGRAVAQNsne",
+ "outputId": "b31bf7e5-72e3-434f-d8d9-41314e3c8207"
+ },
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)lve/main/config.json: 0%| | 0.00/4.74k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "318f32e91dff43eb81885922ca70b2ee"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading pytorch_model.bin: 0%| | 0.00/809M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "ba5e8739e5bc439a8ae85fdd0b6f4851"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "You are resizing the embedding layer without providing a `pad_to_multiple_of` parameter. This means that the new embedding dimension will be 57533. This might induce some performance reduction as *Tensor Cores* will not be available. For more details about this, or help on choosing the correct value for resizing, refer to this guide: https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "New embedding size: Embedding(57533, 1024)\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/transformers/models/donut/processing_donut.py:189: FutureWarning: `feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from huggingface_hub import HfFolder\n",
+ "from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
+ "\n",
+ "# hyperparameters used for multiple args\n",
+ "hf_repository_id = \"donut-base-sroie\"\n",
+ "\n",
+ "# Arguments for training\n",
+ "training_args = Seq2SeqTrainingArguments(\n",
+ " output_dir=hf_repository_id,\n",
+ " num_train_epochs=3,\n",
+ " learning_rate=2e-5,\n",
+ " per_device_train_batch_size=2,\n",
+ " weight_decay=0.01,\n",
+ " fp16=True,\n",
+ " logging_steps=100,\n",
+ " save_total_limit=2,\n",
+ " evaluation_strategy=\"no\",\n",
+ " save_strategy=\"epoch\",\n",
+ " predict_with_generate=True,\n",
+ " # push to hub parameters\n",
+ " report_to=\"tensorboard\",\n",
+ " push_to_hub=True,\n",
+ " hub_strategy=\"every_save\",\n",
+ " hub_model_id=hf_repository_id,\n",
+ " hub_token=HfFolder.get_token(),\n",
+ ")\n",
+ "\n",
+ "# Create Trainer\n",
+ "trainer = Seq2SeqTrainer(\n",
+ " model=model,\n",
+ " args=training_args,\n",
+ " train_dataset=processed_dataset[\"train\"],\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "-B4-OUWdK7Xj"
+ },
+ "execution_count": 13,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "We can start our training by using the train method of the Seq2SeqTrainer."
+ ],
+ "metadata": {
+ "id": "gjjpYugkN6ib"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Start training\n",
+ "trainer.train()\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 362
+ },
+ "id": "e-DEpItcN7Fd",
+ "outputId": "81d99ed7-4680-4a22-8e65-12003571409a"
+ },
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [846/846 40:11, Epoch 3/3]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Step | \n",
+ " Training Loss | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 100 | \n",
+ " 3.406900 | \n",
+ "
\n",
+ " \n",
+ " | 200 | \n",
+ " 0.936700 | \n",
+ "
\n",
+ " \n",
+ " | 300 | \n",
+ " 0.712400 | \n",
+ "
\n",
+ " \n",
+ " | 400 | \n",
+ " 0.561900 | \n",
+ "
\n",
+ " \n",
+ " | 500 | \n",
+ " 0.435400 | \n",
+ "
\n",
+ " \n",
+ " | 600 | \n",
+ " 0.474700 | \n",
+ "
\n",
+ " \n",
+ " | 700 | \n",
+ " 0.299700 | \n",
+ "
\n",
+ " \n",
+ " | 800 | \n",
+ " 0.339100 | \n",
+ "
\n",
+ " \n",
+ "
"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=846, training_loss=0.8639062685323945, metrics={'train_runtime': 2423.0321, 'train_samples_per_second': 0.697, 'train_steps_per_second': 0.349, 'total_flos': 2.970761500447949e+18, 'train_loss': 0.8639062685323945, 'epoch': 3.0})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 14
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "After our training is done we also want to save our processor to the Hugging Face Hub and create a model card."
+ ],
+ "metadata": {
+ "id": "Gv0rSORaN9RC"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Save processor and create model card\n",
+ "processor.save_pretrained(hf_repository_id)\n",
+ "trainer.create_model_card()\n",
+ "trainer.push_to_hub()\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 67,
+ "referenced_widgets": [
+ "b8211547dd4144aba540bd724f182727",
+ "f07a515374474d57840b511290ed6cd8",
+ "dd6bd55ba00d41e498d47351a63e4f78",
+ "19c40eb37e4a48919a50b338a2fc121e",
+ "df105857dcbd4c30b997e696eeb56748",
+ "83f85692c54a4546ae5574b160e27bba",
+ "a6f752a4007740599651240988808831",
+ "41337203335c4fab9ef3a84acd80923d",
+ "eb40117d811a49ad92b16e5bd756acb8",
+ "f84eaa861e2e47bea8212bfe7c15e3d0",
+ "5b74996dad984dd787729ee66649e168"
+ ]
+ },
+ "id": "sKhOv7BLOC0e",
+ "outputId": "4b1563fe-062c-4f01-8022-38c9cb28592e"
+ },
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "sentencepiece.bpe.model: 0%| | 0.00/1.30M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "b8211547dd4144aba540bd724f182727"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'https://huggingface.co/Andyrasika/donut-base-sroie/tree/main/'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 15
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import re\n",
+ "import transformers\n",
+ "from PIL import Image\n",
+ "from transformers import DonutProcessor, VisionEncoderDecoderModel\n",
+ "import torch\n",
+ "import random\n",
+ "import numpy as np\n",
+ "\n",
+ "# hidde logs\n",
+ "transformers.logging.disable_default_handler()\n",
+ "\n",
+ "\n",
+ "# Load our model from Hugging Face\n",
+ "processor = DonutProcessor.from_pretrained(\"Andyrasika/donut-base-sroie\")\n",
+ "model = VisionEncoderDecoderModel.from_pretrained(\"Andyrasika/donut-base-sroie\")\n",
+ "\n",
+ "# Move model to GPU\n",
+ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+ "model.to(device)\n",
+ "\n",
+ "# Load random document image from the test set\n",
+ "test_sample = processed_dataset[\"test\"][random.randint(1, 50)]\n",
+ "\n",
+ "def run_prediction(sample, model=model, processor=processor):\n",
+ " # prepare inputs\n",
+ " pixel_values = torch.tensor(test_sample[\"pixel_values\"]).unsqueeze(0)\n",
+ " task_prompt = \"\"\n",
+ " decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors=\"pt\").input_ids\n",
+ "\n",
+ " # run inference\n",
+ " outputs = model.generate(\n",
+ " pixel_values.to(device),\n",
+ " decoder_input_ids=decoder_input_ids.to(device),\n",
+ " max_length=model.decoder.config.max_position_embeddings,\n",
+ " early_stopping=True,\n",
+ " pad_token_id=processor.tokenizer.pad_token_id,\n",
+ " eos_token_id=processor.tokenizer.eos_token_id,\n",
+ " use_cache=True,\n",
+ " num_beams=1,\n",
+ " bad_words_ids=[[processor.tokenizer.unk_token_id]],\n",
+ " return_dict_in_generate=True,\n",
+ " )\n",
+ "\n",
+ " # process output\n",
+ " prediction = processor.batch_decode(outputs.sequences)[0]\n",
+ " prediction = processor.token2json(prediction)\n",
+ "\n",
+ " # load reference target\n",
+ " target = processor.token2json(test_sample[\"target_sequence\"])\n",
+ " return prediction, target\n",
+ "\n",
+ "prediction, target = run_prediction(test_sample)\n",
+ "print(f\"Reference:\\n {target}\")\n",
+ "print(f\"Prediction:\\n {prediction}\")\n",
+ "# processor.feature_extractor.to_pil_image(np.array(test_sample[\"pixel_values\"])).resize((350,600))\n"
+ ],
+ "metadata": {
+ "id": "Hp9g9jbzaFSO",
+ "outputId": "2f342500-701e-40bb-c4e3-bcc13b72d227",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": 22,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Reference:\n",
+ " {'total': '2.10', 'date': '04-06-16', 'company': '99 SPEED MART S/B', 'address': 'LOT P.T. 33198, BATU 4 JALAN KAPAR, MUKIM KAPAR 42100 KLANG, SELANGOR 1174-PERMATA MAGNA'}\n",
+ "Prediction:\n",
+ " {'total': '2.10', 'date': '04-06-16', 'company': '99 SPEED MART S/B', 'address': 'LOT P.T. 33198, BATU 4 JALAN KAPAR, MUKIM KAPAR 42100 KLANG, SELANGOR 1174-PERMATA MAGNA'}\n"
+ ]
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file