multi-head_dataloader.ipynb

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 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3 (ipykernel)"
  },
  "accelerator": "GPU"
 },
 "cells": [
  {
   "metadata": {
    "id": "sqH2SEO-X9T6"
   },
   "cell_type": "markdown",
   "source": [
    "# Neural Networks on MNIST\n",
    "\n",
    "This Jupyter notebook explains various approaches for implementing neural networks that recognize digits on [MNIST](http://yann.lecun.com/exdb/mnist/) dataset."
   ]
  },
  {
   "metadata": {
    "id": "OaLeoEcDYVO_"
   },
   "cell_type": "markdown",
   "source": [
    "## Preparing the MNIST dataset\n",
    "\n",
    "Most deep learning frameworks provide APIs for loading famous datasets like MNIST (e.g., `torchvision.datasets.MNIST` in pytorch). The APIs are handy, but hide the important step for preparing a training data for a deep learning framework; when graduating from an example dataset to the real data, we must convert a training data of our interest into the data structure that is acceptable by a deep learning framework.\n",
    "\n",
    "The cell below downloads the original distribution of the MNIST dataset on the Web, converts the dataset into `numpy` arrays, and saves the arrays as the file `mnist.npz` with keyword names."
   ]
  },
  {
   "metadata": {
    "id": "OMaOcZMBPuQY",
    "ExecuteTime": {
     "end_time": "2023-10-10T15:19:19.999087597Z",
     "start_time": "2023-10-10T15:19:15.169483172Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import gzip\n",
    "import os\n",
    "import sys\n",
    "import struct\n",
    "import numpy as np\n",
    "\n",
    "def read_image(fi):\n",
    "    magic, n, rows, columns = struct.unpack(\">IIII\", fi.read(16))\n",
    "    assert magic == 0x00000803\n",
    "    assert rows == 28\n",
    "    assert columns == 28\n",
    "    rawbuffer = fi.read()\n",
    "    assert len(rawbuffer) == n * rows * columns\n",
    "    rawdata = np.frombuffer(rawbuffer, dtype='>u1', count=n*rows*columns)\n",
    "    return rawdata.reshape(n, rows, columns).astype(np.float32) / 255.0\n",
    "\n",
    "def read_label(fi):\n",
    "    magic, n = struct.unpack(\">II\", fi.read(8))\n",
    "    assert magic == 0x00000801\n",
    "    rawbuffer = fi.read()\n",
    "    assert len(rawbuffer) == n\n",
    "    return np.frombuffer(rawbuffer, dtype='>u1', count=n)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    os.system('wget -N http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz')\n",
    "    os.system('wget -N http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz')\n",
    "    os.system('wget -N http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz')\n",
    "    os.system('wget -N http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz')\n",
    "\n",
    "    np.savez_compressed(\n",
    "        'mnist',\n",
    "        train_x=read_image(gzip.open('train-images-idx3-ubyte.gz', 'rb')),\n",
    "        train_y=read_label(gzip.open('train-labels-idx1-ubyte.gz', 'rb')),\n",
    "        test_x=read_image(gzip.open('t10k-images-idx3-ubyte.gz', 'rb')),\n",
    "        test_y=read_label(gzip.open('t10k-labels-idx1-ubyte.gz', 'rb'))\n",
    "    )"
   ],
   "execution_count": 1,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2023-10-10 17:19:15--  http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Resolving yann.lecun.com (yann.lecun.com)... 2606:4700:3036::ac43:ab4c, 2606:4700:3034::6815:1d24, 172.67.171.76, ...\n",
      "Connecting to yann.lecun.com (yann.lecun.com)|2606:4700:3036::ac43:ab4c|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 9912422 (9.5M) [application/x-gzip]\n",
      "Saving to: ‘train-images-idx3-ubyte.gz’\n",
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      "\n",
      "2023-10-10 17:19:15 (104 MB/s) - ‘train-images-idx3-ubyte.gz’ saved [9912422/9912422]\n",
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      "--2023-10-10 17:19:15--  http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
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   ]
  },
  {
   "metadata": {
    "id": "TStlGwaUaZKC"
   },
   "cell_type": "markdown",
   "source": [
    "The file contains four numpy arrays (one tensor and array for each split of training and test sets) with the keywords:\n",
    "\n",
    "+ `train_x`: $60000 \\text{ (images)} \\times 28 \\text{ (y)} \\times 28 \\text{ (x)}$\n",
    "+ `train_y`: $60000 \\text{ (labels)}$\n",
    "+ `test_x`: $10000 \\text{ (images)} \\times 28 \\text{ (y)} \\times 28 \\text{ (x)}$\n",
    "+ `test_y`: $10000 \\text{ (labels)}$\n"
   ]
  },
  {
   "metadata": {
    "id": "QwF495MIe0-e"
   },
   "cell_type": "markdown",
   "source": [
    "### Install pytorch"
   ]
  },
  {
   "metadata": {
    "id": "h8U839iGXCH3",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "600dff09-c0ce-4a34-88b4-27b05fee529b",
    "ExecuteTime": {
     "end_time": "2023-10-10T15:21:44.334971602Z",
     "start_time": "2023-10-10T15:19:25.991938619Z"
    }
   },
   "cell_type": "code",
   "source": [
    "!pip install torch torchvision"
   ],
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pypi.idi.ntnu.no\r\n",
      "Requirement already satisfied: torch in ./venv/lib/python3.11/site-packages (2.0.1)\r\n",
      "Collecting torchvision\r\n",
      "  Obtaining dependency information for torchvision from https://files.pythonhosted.org/packages/08/aa/033a0a1bca4a3b8f81f97709024e053da37cf5c970f3dc5d76233c920b99/torchvision-0.16.0-cp311-cp311-manylinux1_x86_64.whl.metadata\r\n",
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      "Collecting torch\r\n",
      "  Obtaining dependency information for torch from https://files.pythonhosted.org/packages/5b/13/fcabc86948f9e89b62a538670720f8589d63f93d3f4f3d172236a98e70f8/torch-2.1.0-cp311-cp311-manylinux1_x86_64.whl.metadata\r\n",
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      "  Obtaining dependency information for nvidia-cudnn-cu12==8.9.2.26 from https://files.pythonhosted.org/packages/ff/74/a2e2be7fb83aaedec84f391f082cf765dfb635e7caa9b49065f73e4835d8/nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata\r\n",
      "  Downloading nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\r\n",
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      "\u001B[?25hCollecting triton==2.1.0 (from torch)\r\n",
      "  Obtaining dependency information for triton==2.1.0 from https://files.pythonhosted.org/packages/5c/c1/54fffb2eb13d293d9a429fead3646752ea190de0229bcf3d591ba2481263/triton-2.1.0-0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata\r\n",
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      "  Obtaining dependency information for nvidia-nvjitlink-cu12 from https://files.pythonhosted.org/packages/0a/f8/5193b57555cbeecfdb6ade643df0d4218cc6385485492b6e2f64ceae53bb/nvidia_nvjitlink_cu12-12.2.140-py3-none-manylinux1_x86_64.whl.metadata\r\n",
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      "\u001B[?25hDownloading triton-2.1.0-0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (89.2 MB)\r\n",
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      "\u001B[?25hInstalling collected packages: triton, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, fsspec, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, torchvision\r\n",
      "  Attempting uninstall: triton\r\n",
      "    Found existing installation: triton 2.0.0\r\n",
      "    Uninstalling triton-2.0.0:\r\n",
      "      Successfully uninstalled triton-2.0.0\r\n",
      "  Attempting uninstall: torch\r\n",
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      "    Uninstalling torch-2.0.1:\r\n",
      "      Successfully uninstalled torch-2.0.1\r\n",
      "Successfully installed fsspec-2023.9.2 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.18.1 nvidia-nvjitlink-cu12-12.2.140 nvidia-nvtx-cu12-12.1.105 torch-2.1.0 torchvision-0.16.0 triton-2.1.0\r\n"
     ]
    }
   ]
  },
  {
   "metadata": {
    "id": "kjBd7K4lI3W5"
   },
   "cell_type": "markdown",
   "source": [
    "### Transformers"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "\n",
    "import re\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch.nn as nn\n",
    "from collections import Counter\n",
    "from typing import List, Tuple\n",
    "\n",
    "class BeowulfDataset(Dataset):\n",
    "    def __init__(self, file_path: str, sequence_length: int):\n",
    "        # Read the text file\n",
    "        with open(file_path, 'r') as f:\n",
    "            text = f.read().lower()\n",
    "\n",
    "        # Remove non-alphanumeric characters and tokenize\n",
    "        self.tokens = re.sub(r'[^a-zA-Z0-9\\s]', '', text).strip().split()\n",
    "\n",
    "        # Build vocabulary\n",
    "        self.vocab = {\"<PAD>\": 0, \"<UNK>\": 1}\n",
    "        self.vocab.update({word: i for i, (word, _) in enumerate(Counter(self.tokens).items(), start=2)})\n",
    "        self.token_ids = [self.vocab.get(token, self.vocab[\"<UNK>\"]) for token in self.tokens]\n",
    "\n",
    "        # Create input-output pairs using sliding window\n",
    "        self.sequence_length = sequence_length\n",
    "        self.pairs = [(self.token_ids[i:i+sequence_length-1], self.token_ids[i+sequence_length-1]) \n",
    "                      for i in range(len(self.token_ids) - sequence_length + 1)]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.pairs)\n",
    "\n",
    "    def __getitem__(self, idx) -> Tuple[torch.Tensor, torch.Tensor]:\n",
    "        x, y = self.pairs[idx]\n",
    "        return torch.tensor(x), torch.tensor(y)\n",
    "\n",
    "    def num_unique_tokens(self):\n",
    "        return len(self.vocab)\n",
    "    \n",
    "    def build_reverse_vocab(self):\n",
    "        self.reverse_vocab = {i: word for word, i in self.vocab.items()}\n",
    "\n",
    "    def tokens_to_text(self, tokens:torch.Tensor) -> str:\n",
    "        \"\"\"\n",
    "        Convert a list of token IDs back to a string of text.\n",
    "        \"\"\"\n",
    "        if not hasattr(self, \"reverse_vocab\"):\n",
    "            self.build_reverse_vocab()\n",
    "        if tokens.shape[0] == 1:\n",
    "            tokens = [tokens.item()]\n",
    "        else: \n",
    "            tokens = tokens.tolist()\n",
    "        txt = [self.reverse_vocab[token] for token in tokens if token in self.reverse_vocab.keys()]\n",
    "        return ' '.join(txt)\n",
    "    \n",
    "    def text_to_tokens(self, text: str) -> torch.Tensor:\n",
    "        \"\"\"\n",
    "        Convert a list of token IDs back to a string of text.\n",
    "        \"\"\"\n",
    "        txt = text.split(\" \")\n",
    "        token_ids = [self.vocab.get(token, self.vocab[\"<UNK>\"]) for token in txt]\n",
    "        out = torch.zeros(23)\n",
    "        for i in range(min(out.shape[0], len(token_ids))):\n",
    "            out[i] = token_ids[i]\n",
    "        return out\n",
    "\n",
    "\n",
    "file_path = 'beowulf.txt'\n",
    "batch_size = 128\n",
    "shuffle = True\n",
    "sequence_length = 24  # for example, predicting 10th word based on previous 9 words\n",
    "\n",
    "dataset = BeowulfDataset(file_path, sequence_length)\n",
    "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)\n",
    "\n",
    "# Example usage:\n",
    "print(f\"Number of unique tokens in the dataset: {dataset.num_unique_tokens()}\")\n",
    "\n",
    "\n",
    "\n",
    "uniques = dataset.num_unique_tokens()\n",
    "print(uniques)\n",
    "words = list(set(dataset.vocab))\n",
    "\n"
   ],
   "metadata": {
    "id": "dSW1Tas9HKyi",
    "outputId": "99a3dfa3-a102-43e4-a15f-45479ddc4a90",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2023-10-10T15:21:51.310611953Z",
     "start_time": "2023-10-10T15:21:44.346206908Z"
    }
   },
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique tokens in the dataset: 37834\n",
      "37834\n"
     ]
    }
   ]
  },
  {
   "metadata": {
    "id": "DdSGWuLffzEg",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 436
    },
    "outputId": "64364756-6394-4c7e-87f9-9db3ba4e6bfc",
    "ExecuteTime": {
     "end_time": "2023-10-10T15:21:52.354195480Z",
     "start_time": "2023-10-10T15:21:51.293554695Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "import math\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "class self_attention(nn.Module):\n",
    "  def __init__(self, device=\"cuda:0\"):\n",
    "    super().__init__()\n",
    "    self.num_words = 12\n",
    "    self.num_dims = 12\n",
    "\n",
    "    self.pos_encoding = torch.zeros((self.num_words,self.num_dims))\n",
    "\n",
    "    for pos in range(self.num_words):\n",
    "      for i in range(self.num_dims):\n",
    "        if pos % 2 == 0:\n",
    "          self.pos_encoding[pos,i] = math.sin(pos/math.pow(10000, i/self.num_dims))\n",
    "        else:\n",
    "          self.pos_encoding[pos,i] = math.cos(pos/math.pow(10000, i-1/self.num_dims))\n",
    "    self.pos_encoding = self.pos_encoding.to(device)\n",
    "\n",
    "    self.Q = nn.Linear(self.num_dims, self.num_dims).to(device)\n",
    "    self.K = nn.Linear(self.num_dims, self.num_dims).to(device)\n",
    "    self.V = nn.Linear(self.num_dims, self.num_dims).to(device)\n",
    "    self.softmax = nn.Softmax(dim=0)\n",
    "    self.embedding = nn.Embedding(519,self.num_dims).to(device)\n",
    "    self.flatten = nn.Flatten(start_dim=0)\n",
    "    self.preoutput = nn.Linear(self.num_words*self.num_dims,512).to(device)\n",
    "    self.gelu = nn.GELU()\n",
    "    self.output = nn.Linear(512, 519).to(device)\n",
    "\n",
    "  def forward(self, x, device=\"cuda:0\"):\n",
    "    x = x.to(device)\n",
    "    x = self.embedding(x)\n",
    "    x += self.pos_encoding\n",
    "    x_q = self.Q(x)\n",
    "    x_k = self.K(x)\n",
    "    x_v = self.V(x)\n",
    "    x_qk =  x_q.matmul(x_k.transpose(0,1))/math.sqrt(self.num_dims)\n",
    "    x = self.softmax(x_qk)*x_v\n",
    "    x = self.flatten(x)\n",
    "    x=self.preoutput(x)\n",
    "    x = self.gelu(x)\n",
    "    x = self.output(x)\n",
    "\n",
    "    return x\n",
    "\n",
    "torch.manual_seed(0)\n",
    "self_atten = self_attention()\n",
    "\n",
    "#p = self_atten(numdata[:12])\n",
    "\n",
    "optimizer = torch.optim.Adam(self_atten.parameters(), lr = 0.0001)\n",
    "loss = nn.CrossEntropyLoss()\n",
    "\n",
    "for k in range(50):\n",
    "  loss_n = 0\n",
    "  for batch, i in enumerate(dataloader):\n",
    "    optimizer.zero_grad()\n",
    "    x = batch\n",
    "    y = torch.zeros(uniques)\n",
    "    y = y.to(\"cuda:0\")\n",
    "    y[numdata[i+12]] = 1\n",
    "    y_p = self_atten(x)\n",
    "    l = loss(y_p, y)\n",
    "    l.backward()\n",
    "    optimizer.step()\n",
    "    loss_n += l\n",
    "  loss_n /= len(numdata)-13\n",
    "  print(\"epoch:\", k ,\"loss:\", loss_n)\n",
    "\n",
    "\n",
    "#p = self_atten(numdata[:12])\n",
    "\n",
    "#print(p, words[p.argmax()], data[13])\n",
    "\n",
    "accuracy = 0\n",
    "for i in range(len(numdata)-13):\n",
    "  x = numdata[i:i+12]\n",
    "  y = data[i+12]\n",
    "  y_p = self_atten(x)\n",
    "  y_p = y_p.to(\"cpu\")\n",
    "  #print(y, words[y_p.argmax()])\n",
    "  if y != words[y_p.argmax()]:\n",
    "    print(\"Wrong!\")\n",
    "  else:\n",
    "    accuracy += 1\n",
    "accuracy /= len(numdata)-13\n",
    "print(\"accuracy:\", accuracy)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ],
   "execution_count": 4,
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'numdata' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[4], line 63\u001B[0m\n\u001B[1;32m     61\u001B[0m y \u001B[38;5;241m=\u001B[39m torch\u001B[38;5;241m.\u001B[39mzeros(uniques)\n\u001B[1;32m     62\u001B[0m y \u001B[38;5;241m=\u001B[39m y\u001B[38;5;241m.\u001B[39mto(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcuda:0\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m---> 63\u001B[0m y[\u001B[43mnumdata\u001B[49m[i\u001B[38;5;241m+\u001B[39m\u001B[38;5;241m12\u001B[39m]] \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[1;32m     64\u001B[0m y_p \u001B[38;5;241m=\u001B[39m self_atten(x)\n\u001B[1;32m     65\u001B[0m l \u001B[38;5;241m=\u001B[39m loss(y_p, y)\n",
      "\u001B[0;31mNameError\u001B[0m: name 'numdata' is not defined"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import math\n",
    "from matplotlib import pyplot as plt\n",
    "#torch.manual_seed(0)\n",
    "class self_attention_pure(nn.Module):\n",
    "  def __init__(self, device=\"cuda:0\", p_in=12, mid = 512, dims=0, num_heads=4):\n",
    "    super().__init__()\n",
    "    self.num_words = p_in\n",
    "    self.num_dims = p_in\n",
    "    self.num_heads = num_heads\n",
    "    if dims != 0:\n",
    "      self.num_dims = dims\n",
    "    if self.num_dims % num_heads  != 0:\n",
    "      raise \"num heads must divide num dims\"\n",
    "\n",
    "    self.Q = nn.Linear(self.num_dims, self.num_dims).to(device)\n",
    "    self.K = nn.Linear(self.num_dims, self.num_dims).to(device)\n",
    "    self.V = nn.Linear(self.num_dims, self.num_dims).to(device)\n",
    "    self.softmax = nn.Softmax(dim=3)\n",
    "    self.preoutput_0 = nn.Linear(self.num_dims,mid).to(device)\n",
    "    self.preoutput_1 = nn.Linear(mid, self.num_dims).to(device)\n",
    "    self.gelu = nn.GELU()\n",
    "    self.device=device\n",
    "\n",
    "\n",
    "  def forward(self, x, device=\"cuda:0\", visualization=False):\n",
    "    x = x.to(self.device)\n",
    "    id = x\n",
    "    x_q = self.Q(x).view(x.shape[0], self.num_words, self.num_heads, self.num_dims//self.num_heads)\n",
    "    x_k = self.K(x).view(x.shape[0], self.num_words, self.num_heads, self.num_dims//self.num_heads)\n",
    "    x_v = self.V(x).view(x.shape[0], self.num_words, self.num_heads, self.num_dims//self.num_heads)\n",
    "    x_q = x_q.permute(0, 2,1,3)\n",
    "    x_k = x_k.permute(0, 2,1,3)\n",
    "    x_v = x_v.permute(0, 2,1,3)\n",
    "\n",
    "    x_qk = x_q.matmul(x_k.transpose(2,3))/math.sqrt(self.num_dims)\n",
    "    x = self.softmax(x_qk).matmul(x_v)\n",
    "    if visualization:\n",
    "      vis_softmax = nn.Softmax(dim=1)\n",
    "      for i in range(x_qk.shape[1]):\n",
    "        print(x_qk.shape)\n",
    "        plt.imshow(vis_softmax(x_qk[0][i]).clone().detach().cpu(), vmin=0, vmax=1)\n",
    "        plt.show()\n",
    "      print(self.softmax(x_qk).shape)\n",
    "    x = x.permute(0, 2, 1, 3)\n",
    "    x = x.reshape(x.shape[0], self.num_words, self.num_dims)\n",
    "    x = self.preoutput_0(x)\n",
    "    x = self.preoutput_1(x)\n",
    "    x += id\n",
    "    x = self.gelu(x)\n",
    "\n",
    "    return x\n",
    "\n",
    "\n",
    "class self_attention_w_pos_enc(nn.Module):\n",
    "  def __init__(self, device=\"cuda:0\", words = 12, dims = 12):\n",
    "    super().__init__()\n",
    "    self.num_words = words\n",
    "    self.num_dims = dims\n",
    "    self.embedding = nn.Embedding(uniques,self.num_dims).to(device)\n",
    "    self.pos_encoding = torch.zeros((self.num_words,self.num_dims))\n",
    "\n",
    "    for i in range(self.num_words):\n",
    "      for j in range(self.num_dims):\n",
    "        if j % 2 == 0:\n",
    "          self.pos_encoding[i,j] = math.sin(i/math.pow(10000, j/self.num_dims))\n",
    "        else:\n",
    "          self.pos_encoding[i,j] = math.cos(i/math.pow(10000, j-1/self.num_dims))\n",
    "    self.pos_encoding = self.pos_encoding.to(device)\n",
    "\n",
    "    self.attention = self_attention_pure(device=device, p_in = self.num_dims, dims = self.num_dims, mid=512)\n",
    "    self.output = nn.Linear(512, 519).to(device)\n",
    "\n",
    "  def forward(self, x, device=\"cuda:0\", visualization=False):\n",
    "    x = x.to(device)\n",
    "    x = self.embedding(x)\n",
    "    x += self.pos_encoding\n",
    "    x = self.attention(x, visualization=visualization)\n",
    "    x = self.output(x)\n",
    "\n",
    "    return x\n",
    "\n",
    "class self_attention_w_pos_enc_pure(nn.Module):\n",
    "  def __init__(self, device=\"cuda:0\", num_words = 12, num_dims=12, out=512, num_heads=4):\n",
    "    super().__init__()\n",
    "    self.num_words = num_words\n",
    "    self.num_dims = num_dims\n",
    "    self.embedding = nn.Embedding(uniques,self.num_dims).to(device)\n",
    "    self.pos_encoding = torch.zeros((self.num_words,self.num_dims))\n",
    "    self.device=device\n",
    "\n",
    "    for i in range(self.num_words):\n",
    "      for j in range(self.num_dims):\n",
    "        if j % 2 == 0:\n",
    "          self.pos_encoding[i,j] = math.sin(i/math.pow(10000, j/self.num_dims))\n",
    "        else:\n",
    "          self.pos_encoding[i,j] = math.cos(i/math.pow(10000, (j-1)/self.num_dims))\n",
    "    self.pos_encoding = self.pos_encoding.to(device)\n",
    "\n",
    "    self.attention = self_attention_pure(device=device, p_in = self.num_words, dims = self.num_dims, mid=out, num_heads=num_heads)\n",
    "\n",
    "  def forward(self, x, device=\"cuda:0\", visualization=False):\n",
    "    x = x.to(self.device)\n",
    "    x = self.embedding(x)\n",
    "    x += self.pos_encoding\n",
    "    x = self.attention(x, visualization=visualization)\n",
    "    return x\n",
    "\n",
    "\n",
    "class multi_layer_self_attention(nn.Module):\n",
    "  def __init__(self, device=\"cuda:0\", outs=[144,256,512,519], seq_length=12, dims=12, num_heads=4):\n",
    "    super().__init__()\n",
    "    self.attention_w_enc = self_attention_w_pos_enc_pure(device=device, num_words=seq_length, num_dims=dims, out=outs[0], num_heads=num_heads)\n",
    "    self.attention_1 = self_attention_pure(device=device, p_in=seq_length, dims=dims, mid=outs[0], num_heads=num_heads)\n",
    "    self.attention_2 = self_attention_pure(device=device, p_in=seq_length, dims=dims, mid=outs[1], num_heads=num_heads)\n",
    "    self.attention_3 = self_attention_pure(device=device, p_in=seq_length, dims=dims, mid=outs[2], num_heads=num_heads)\n",
    "    self.linear_1 = nn.Linear(seq_length*dims, outs[0]).to(device)\n",
    "    self.linear_2 = nn.Linear(outs[0], outs[1]).to(device)\n",
    "    self.flatten = nn.Flatten(start_dim=1)\n",
    "    self.output = nn.Linear(outs[1], outs[3]).to(device)\n",
    "    self.device=device\n",
    "\n",
    "  def forward(self, x, visualization=False):\n",
    "    x = x.to(self.device)\n",
    "    x = self.attention_w_enc(x, visualization=visualization)\n",
    "    x = self.attention_1(x, visualization=visualization)\n",
    "    x = self.attention_2(x, visualization=False)\n",
    "    x = self.attention_3(x, visualization=False)\n",
    "\n",
    "    x = self.flatten(x)\n",
    "    x = self.linear_1(x)\n",
    "    x = self.linear_2(x)\n",
    "    x = self.output(x)\n",
    "    return x\n",
    "\n",
    "seq_length = sequence_length - 1\n",
    "self_atten = multi_layer_self_attention(seq_length=seq_length, dims=256, num_heads=8, outs=[768,1024,512,uniques])\n",
    "#p = self_atten(numdata[:12])\n",
    "\n",
    "optimizer = torch.optim.Adam(self_atten.parameters(), lr = 0.00001, weight_decay=0.000005)\n",
    "loss = nn.CrossEntropyLoss()\n",
    "schedule = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=2, cooldown=3, factor=0.333)\n",
    "\n",
    "\n",
    "\n",
    "for k in range(25):\n",
    "  loss_n = 0\n",
    "  acc = 0\n",
    "  for i, batch in enumerate(dataloader):\n",
    "    optimizer.zero_grad()\n",
    "    x = batch[0]\n",
    "    y = torch.zeros((batch[1].shape[0], uniques))\n",
    "    y = y.to(\"cuda:0\")\n",
    "    y[:,batch[1]] = 1\n",
    "    y_p = self_atten(x, visualization=False)\n",
    "    l = loss(y_p, y)\n",
    "    l.backward()\n",
    "    optimizer.step()\n",
    "    loss_n += l/batch[0].shape[0]\n",
    "    acc += torch.eq(y.argmax(dim=1), y_p.argmax(dim=1)).sum().item()/batch[0].shape[0]\n",
    "    print(f\"\\rit: {i+1}/{len(dataloader)}, loss: {loss_n/(i+1)}, acc: {acc/(i+1)}\", end=\"\")\n",
    "  loss_n /= len(dataloader)\n",
    "  acc /= len(dataloader)\n",
    "  schedule.step(loss_n)\n",
    "  print(\"epoch:\", k ,\"loss:\", loss_n, \"acc:\",acc)\n",
    "\n",
    "\n",
    "#p = self_atten(numdata[:12])\n",
    "\n",
    "#print(p, words[p.argmax()], data[13])\n",
    "\n",
    "accuracy = 0\n",
    "for i, batch in enumerate(dataloader):\n",
    "  x = batch[0]\n",
    "  y = torch.zeros((batch[1].shape[0], uniques))\n",
    "  y[:,batch[1]] = 1\n",
    "  y_p = self_atten(x, visualization=False)\n",
    "  y_p = y_p.to(\"cpu\")\n",
    "  #print(y, words[y_p.argmax()])\n",
    "  accuracy += torch.eq(y.argmax(dim=1), y_p.argmax(dim=1)).sum().item()/batch[0].shape[0]\n",
    "\n",
    "accuracy /= len(dataloader)-seq_length-1\n",
    "print(\"accuracy:\", accuracy)\n",
    "\n",
    "print(dataset.tokens_to_text(dataset[128][0]), dataset.tokens_to_text(torch.Tensor([dataset[128][1]])))\n",
    "print(dataset[128][0].shape)\n",
    "x = torch.Tensor(dataset[128][0]).unsqueeze(0)\n",
    "self_atten(x, visualization=True)\n",
    "x = torch.Tensor(dataset[129][0]).unsqueeze(0)\n",
    "self_atten(x, visualization=True)\n"
   ],
   "metadata": {
    "id": "X8H7JxBoLXeh",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "80e2fc86-f9a0-407d-bd70-2dd1357bedf3",
    "ExecuteTime": {
     "end_time": "2023-10-10T17:34:58.815392814Z",
     "start_time": "2023-10-10T15:22:29.581406266Z"
    }
   },
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it: 8554/8554, loss: 6.186310768127441, acc: 0.2624795417348609epoch: 0 loss: tensor(6.1863, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.2624795417348609\n",
      "it: 8554/8554, loss: 6.084527969360352, acc: 0.33421217954130183epoch: 1 loss: tensor(6.0845, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.33421217954130183\n",
      "it: 8554/8554, loss: 6.0768961906433105, acc: 0.3826532110275461epoch: 2 loss: tensor(6.0769, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.3826532110275461\n",
      "it: 8554/8554, loss: 6.076301574707031, acc: 0.47145412396409736epoch: 3 loss: tensor(6.0763, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.47145412396409736\n",
      "it: 8554/8554, loss: 6.0742340087890625, acc: 0.4930210525290312epoch: 4 loss: tensor(6.0742, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.4930210525290312\n",
      "it: 8554/8554, loss: 6.0730061531066895, acc: 0.5999751578209025epoch: 5 loss: tensor(6.0730, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.5999751578209025\n",
      "it: 8554/8554, loss: 6.069760799407959, acc: 0.6879192455987669epoch: 6 loss: tensor(6.0698, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.6879192455987669\n",
      "it: 8554/8554, loss: 6.0705413818359375, acc: 0.7143824242394721epoch: 7 loss: tensor(6.0705, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.7143824242394721\n",
      "it: 8554/8554, loss: 6.070863246917725, acc: 0.7702177249218473epoch: 8 loss: tensor(6.0709, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.7702177249218473\n",
      "it: 8554/8554, loss: 6.067889213562012, acc: 0.7420598388018601epoch: 9 loss: tensor(6.0679, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.7420598388018601\n",
      "it: 8554/8554, loss: 6.067895889282227, acc: 0.8471310936404022epoch: 10 loss: tensor(6.0679, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.8471310936404022\n",
      "it: 8554/8554, loss: 6.067321300506592, acc: 0.7963628668415902epoch: 11 loss: tensor(6.0673, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.7963628668415902\n",
      "it: 8554/8554, loss: 6.066120147705078, acc: 0.7974366889759177epoch: 12 loss: tensor(6.0661, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.7974366889759177\n",
      "it: 8554/8554, loss: 6.065666675567627, acc: 0.8249008951302834epoch: 13 loss: tensor(6.0657, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.8249008951302834\n",
      "it: 8554/8554, loss: 6.065090656280518, acc: 0.8645690247252747epoch: 14 loss: tensor(6.0651, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.8645690247252747\n",
      "it: 8554/8554, loss: 6.0654096603393555, acc: 0.8265559241290624epoch: 15 loss: tensor(6.0654, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.8265559241290624\n",
      "it: 8554/8554, loss: 6.0646891593933105, acc: 0.8784906914893617epoch: 16 loss: tensor(6.0647, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.8784906914893617\n",
      "it: 8554/8554, loss: 6.063124179840088, acc: 0.9071879749240122epoch: 17 loss: tensor(6.0631, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.9071879749240122\n",
      "it: 8554/8554, loss: 6.06221866607666, acc: 0.8842911576455459epoch: 18 loss: tensor(6.0622, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.8842911576455459\n",
      "it: 8554/8554, loss: 6.063520431518555, acc: 0.9363343051009274epoch: 19 loss: tensor(6.0635, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.9363343051009274\n",
      "it: 8554/8554, loss: 6.0624799728393555, acc: 0.9206694236614449epoch: 20 loss: tensor(6.0625, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.9206694236614449\n",
      "it: 8554/8554, loss: 6.062249660491943, acc: 0.9293532265606733epoch: 21 loss: tensor(6.0622, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.9293532265606733\n",
      "it: 8554/8554, loss: 6.058876037597656, acc: 0.9944561754734627epoch: 22 loss: tensor(6.0589, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.9944561754734627\n",
      "it: 8554/8554, loss: 6.060120582580566, acc: 0.9942296732522796epoch: 23 loss: tensor(6.0601, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.9942296732522796\n",
      "it: 8554/8554, loss: 6.058049201965332, acc: 0.9997826309328969epoch: 24 loss: tensor(6.0580, device='cuda:0', grad_fn=<DivBackward0>) acc: 0.9997826309328969\n",
      "accuracy: 1.0028135990621336\n",
      "make a donation to project gutenberg and how to get involved welcome to the world of free plain vanilla electronic texts ebooks readable by\n",
      "torch.Size([23])\n",
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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md0IIqir8NsCdEODhihtTV+KPGhoaUllZ2Q3Xm/Rp2IsWLdLcuXN16tSp6z4fjUZVVlaWtQAApr5JD6Bz587p4sWLqqysnOxdAQBCZMLXuSMjI1lXM729vTp+/LjKy8tVXl6uV199VatWrVI8Htfp06e1ceNG3X333Wpqaspr4QCAcJtwAH322Wd68sknM193dHRIklpbW7Vjxw797W9/0x/+8AcNDg6qqqpKy5cv169+9StFo9H8VQ0ACL0JB9ATTzzxrW+w/vnPf/YqCAAwPRTuVJOiIikoym2s5wys4AZTxqeLYPZs7224Ec+eQoHn25MR235C1jMR01+e8RofzPL7i4XvLDbfWYT5OP6FUIMX35mot2AmKTcjBQCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACAicLtBwQ707wf0pTgPPshwb+fTwj68VjjCggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACAicJtSOfSknJtqlWUz0qmnwi/l4RewPfQ3DRoKOeLsxQAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYKtx8Q7KRz7cOUR64AakDuIoHf+BS9dKYDroAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCfkDAVOTbj8dXmn4+uDmugAAAJgggAIAJAggAYGJCAdTZ2amHH35YpaWlmj9/vlauXKkTJ05krXP58mW1tbXpjjvu0OzZs7Vq1SoNDAzktWgAQPhNKIC6u7vV1tamI0eO6MMPP9T4+LiWL1+u0dHRzDovvfSS3n//fe3bt0/d3d3q6+vTM888k/fCAQDhFjjncp6u8tVXX2n+/Pnq7u7WT37yEw0NDWnevHnas2ePfvrTn0qS/vGPf+gHP/iBenp69Mgjj9x0m4lEQrFYTMvm/EwzguLcCisqym3cVamU3/iQC0pKvLfhLl3KQyWGQj6LK51Meo0PinN87V0dH/jNwvP4sZSX/U8JvjMhPV4DV9yYuhJ/1NDQkMrKym64ntd7QENDQ5Kk8vJySdKxY8c0Pj6uxsbGzDqLFy9WTU2Nenp6rruNZDKpRCKRtQAApr6cAyidTmvDhg169NFH9cADD0iS+vv7VVxcrDlz5mStW1FRof7+/utup7OzU7FYLLNUV1fnWhIAIERyDqC2tjZ98cUXeuedd7wK2LRpk4aGhjLL2bNnvbYHAAiHnO6E0N7erg8++ECHDx/WggULMo/H43GNjY1pcHAw6ypoYGBA8Xj8utuKRqOKRqO5lAEACLEJXQE559Te3q79+/fr448/Vm1tbdbzS5Ys0cyZM9XV1ZV57MSJEzpz5owaGhryUzEAYEqY0BVQW1ub9uzZo/fee0+lpaWZ93VisZhKSkoUi8W0fv16dXR0qLy8XGVlZXrxxRfV0NDwnWbAAQCmjwkF0I4dOyRJTzzxRNbjb7/9ttauXStJ+s1vfqNIJKJVq1YpmUyqqalJv/vd7/JSLABg6vD6HNBk4HNA9vgckPgcEJ8DCr+p/jkgAAByRT8gXKuwLoqRg8DzrwBcQeBW4AoIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmpmY/IN9uliHvhundCRHh59sV2FOBNVpGgeIKCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgouHYMV2/jfsWNeWzFM1ddym+8NefXjiFI+/9e4ry+fwUg5O0EfNshBPI7h6zbMfjWPyV4/hzweQ1c/fl9s/Og4AJoeHhYktQ99K5xJQAAH8PDw4rFYjd8PnDWv6p8QzqdVl9fn0pLSxUE1yZ4IpFQdXW1zp49q7KyMoMKw49j6Ifj54fj56/Qj6FzTsPDw6qqqlIkcuO/qBTcFVAkEtGCBQtuul5ZWVlBHvgw4Rj64fj54fj5K+Rj+G1XPlcxCQEAYIIAAgCYCF0ARaNRbd26VdFo1LqU0OIY+uH4+eH4+Zsqx7DgJiEAAKaH0F0BAQCmBgIIAGCCAAIAmCCAAAAmQhdA27dv15133qlZs2apvr5en376qXVJofDKK68oCIKsZfHixdZlFbTDhw9rxYoVqqqqUhAEOnDgQNbzzjlt2bJFlZWVKikpUWNjo06ePGlTbAG62fFbu3btNedkc3OzTbEFqLOzUw8//LBKS0s1f/58rVy5UidOnMha5/Lly2pra9Mdd9yh2bNna9WqVRoYGDCqeOJCFUDvvvuuOjo6tHXrVn3++eeqq6tTU1OTLly4YF1aKNx///06f/58ZvnLX/5iXVJBGx0dVV1dnbZv337d59944w299dZb2rlzp44eParbb79dTU1Nunz58i2utDDd7PhJUnNzc9Y5uXfv3ltYYWHr7u5WW1ubjhw5og8//FDj4+Navny5RkdHM+u89NJLev/997Vv3z51d3err69PzzzzjGHVE+RCZOnSpa6trS3zdSqVclVVVa6zs9OwqnDYunWrq6ursy4jtCS5/fv3Z75Op9MuHo+7X//615nHBgcHXTQadXv37jWosLB98/g551xra6t76qmnTOoJowsXLjhJrru72zn37/Nt5syZbt++fZl1/v73vztJrqenx6rMCQnNFdDY2JiOHTumxsbGzGORSESNjY3q6ekxrCw8Tp48qaqqKi1atEjPPfeczpw5Y11SaPX29qq/vz/rfIzFYqqvr+d8nIBDhw5p/vz5uu+++/TCCy/o4sWL1iUVrKGhIUlSeXm5JOnYsWMaHx/POgcXL16smpqa0JyDoQmgr7/+WqlUShUVFVmPV1RUqL+/36iq8Kivr9euXbt08OBB7dixQ729vXr88ccz7S8wMVfPOc7H3DU3N2v37t3q6urS66+/ru7ubrW0tCiVCnk/rkmQTqe1YcMGPfroo3rggQck/fscLC4u1pw5c7LWDdM5WHB3w8bkaGlpyfz7wQcfVH19vRYuXKg//elPWr9+vWFlmK6effbZzL9/9KMf6cEHH9Rdd92lQ4cOadmyZYaVFZ62tjZ98cUXU+5929BcAc2dO1dFRUXXzPAYGBhQPB43qiq85syZo3vvvVenTp2yLiWUrp5znI/5s2jRIs2dO5dz8hva29v1wQcf6JNPPslqVROPxzU2NqbBwcGs9cN0DoYmgIqLi7VkyRJ1dXVlHkun0+rq6lJDQ4NhZeE0MjKi06dPq7Ky0rqUUKqtrVU8Hs86HxOJhI4ePcr5mKNz587p4sWLnJP/n3NO7e3t2r9/vz7++GPV1tZmPb9kyRLNnDkz6xw8ceKEzpw5E5pzMFR/guvo6FBra6seeughLV26VNu2bdPo6KjWrVtnXVrBe/nll7VixQotXLhQfX192rp1q4qKirRmzRrr0grWyMhI1m/jvb29On78uMrLy1VTU6MNGzbotdde0z333KPa2lpt3rxZVVVVWrlypV3RBeTbjl95ebleffVVrVq1SvF4XKdPn9bGjRt19913q6mpybDqwtHW1qY9e/bovffeU2lpaeZ9nVgsppKSEsViMa1fv14dHR0qLy9XWVmZXnzxRTU0NOiRRx4xrv47sp6GN1G//e1vXU1NjSsuLnZLly51R44csS4pFFavXu0qKytdcXGx+/73v+9Wr17tTp06ZV1WQfvkk0+cpGuW1tZW59y/p2Jv3rzZVVRUuGg06pYtW+ZOnDhhW3QB+bbj969//cstX77czZs3z82cOdMtXLjQPf/8866/v9+67IJxvWMnyb399tuZdS5duuR+8YtfuO9973vutttuc08//bQ7f/68XdETRDsGAICJ0LwHBACYWgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJj4f/1nsAhqRm/5AAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n",
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n",
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n",
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAVRElEQVR4nO3db2xVhd3A8V/50ytqe1kFWjoLgv/YdLIEBYm6R0ND4QURZYkaY9AQl7higo0xMZmimUmjS5xxYfhqOpf4Z74Aoy9YtGrJMsCIMYvJRoCxgMFWJWkL3SgI53mxxz5PFfQptP568fNJTsI959x7fjk56Zfbe9pWFUVRBAB8y8ZlDwDAd5MAAZBCgABIIUAApBAgAFIIEAApBAiAFAIEQIoJ2QN82fHjx2P//v1RU1MTVVVV2eMAMExFUcTBgwejsbExxo07+fucMReg/fv3R1NTU/YYAJymffv2xfnnn3/S7WMuQDU1NRER8V/lW2JCVXXyNAAM1+fFkejsfXnw6/nJjLkAffFttwlV1QIEUMG+6WMUNyEAkGLUArRu3bq44IIL4qyzzooFCxbEu+++O1qHAqACjUqAXn755Whra4u1a9fG+++/H3Pnzo2Wlpb45JNPRuNwAFSgUQnQk08+GXfffXfcdddd8cMf/jCeeeaZOPvss+N3v/vdaBwOgAo04gE6cuRIbN++PZqbm//3IOPGRXNzc2zZsuUr+w8MDERfX9+QBYAz34gH6LPPPotjx45FfX39kPX19fXR1dX1lf3b29ujXC4PLn4GCOC7If0uuAcffDB6e3sHl3379mWPBMC3YMR/DmjKlCkxfvz46O7uHrK+u7s7GhoavrJ/qVSKUqk00mMAMMaN+Dug6urqmDdvXnR0dAyuO378eHR0dMTChQtH+nAAVKhR+U0IbW1tsXLlyrjyyitj/vz58dRTT0V/f3/cddddo3E4ACrQqATolltuiU8//TQefvjh6Orqih//+MexadOmr9yYAMB3V1VRFEX2EP9XX19flMvlWDT5Dr8LDqACfV4ciY6eP0Rvb2/U1taedL/0u+AA+G4SIABSCBAAKQQIgBQCBEAKAQIghQABkEKAAEghQACkECAAUggQACkECIAUAgRACgECIIUAAZBCgABIIUAApBAgAFIIEAApBAiAFAIEQAoBAiCFAAGQQoAASCFAAKQQIABSCBAAKQQIgBQCBEAKAQIghQABkEKAAEghQACkECAAUggQACkECIAUAgRACgECIIUAAZBCgABIIUAApBAgAFIIEAApBAiAFAIEQAoBAiCFAAGQQoAASCFAAKQQIABSCBAAKQQIgBQCBEAKAQIghQABkEKAAEghQACkECAAUggQACkECIAUAgRACgECIIUAAZBCgABIIUAApBAgAFIIEAApBAiAFAIEQIoRD9AjjzwSVVVVQ5Y5c+aM9GEAqHATRuNFL7vssnjzzTf/9yATRuUwAFSwUSnDhAkToqGhYTReGoAzxKh8BrRz585obGyM2bNnx+233x579+496b4DAwPR19c3ZAHgzDfiAVqwYEE899xzsWnTpli/fn3s2bMnrrvuujh48OAJ929vb49yuTy4NDU1jfRIAIxBVUVRFKN5gJ6enpg5c2Y8+eSTsWrVqq9sHxgYiIGBgcHHfX190dTUFIsm3xETqqpHczQARsHnxZHo6PlD9Pb2Rm1t7Un3G/W7AyZPnhyXXHJJ7Nq164TbS6VSlEql0R4DgDFm1H8O6NChQ7F79+6YPn36aB8KgAoy4gG6//77o7OzM/75z3/GX/7yl7jpppti/Pjxcdttt430oQCoYCP+LbiPPvoobrvttjhw4EBMnTo1rr322ti6dWtMnTp1pA8FQAUb8QC99NJLI/2SAJyB/C44AFIIEAApBAiAFAIEQAoBAiCFAAGQQoAASCFAAKQQIABSCBAAKQQIgBQCBEAKAQIghQABkEKAAEghQACkECAAUggQACkECIAUAgRACgECIIUAAZBCgABIIUAApBAgAFIIEAApBAiAFAIEQAoBAiCFAAGQQoAASCFAAKQQIABSCBAAKQQIgBQCBEAKAQIghQABkEKAAEghQACkECAAUggQACkECIAUAgRACgECIIUAAZBCgABIIUAApBAgAFIIEAApBAiAFAIEQAoBAiCFAAGQQoAASCFAAKQQIABSCBAAKQQIgBQCBEAKAQIghQABkEKAAEghQACkECAAUggQACkECIAUAgRACgECIMWwA7R58+ZYtmxZNDY2RlVVVWzcuHHI9qIo4uGHH47p06fHpEmTorm5OXbu3DlS8wJwhhh2gPr7+2Pu3Lmxbt26E25/4okn4umnn45nnnkmtm3bFuecc060tLTE4cOHT3tYAM4cE4b7hKVLl8bSpUtPuK0oinjqqafiF7/4Rdx4440REfH8889HfX19bNy4MW699dbTmxaAM8aIfga0Z8+e6Orqiubm5sF15XI5FixYEFu2bDnhcwYGBqKvr2/IAsCZb0QD1NXVFRER9fX1Q9bX19cPbvuy9vb2KJfLg0tTU9NIjgTAGJV+F9yDDz4Yvb29g8u+ffuyRwLgWzCiAWpoaIiIiO7u7iHru7u7B7d9WalUitra2iELAGe+EQ3QrFmzoqGhITo6OgbX9fX1xbZt22LhwoUjeSgAKtyw74I7dOhQ7Nq1a/Dxnj174oMPPoi6urqYMWNGrFmzJh577LG4+OKLY9asWfHQQw9FY2NjLF++fCTnBqDCDTtA7733Xtxwww2Dj9va2iIiYuXKlfHcc8/FAw88EP39/fGzn/0senp64tprr41NmzbFWWedNXJTA1DxqoqiKLKH+L/6+vqiXC7Hosl3xISq6uxxABimz4sj0dHzh+jt7f3az/XT74ID4LtJgABIIUAApBAgAFIIEAApBAiAFAIEQAoBAiCFAAGQQoAASCFAAKQQIABSCBAAKQQIgBQCBEAKAQIghQABkEKAAEghQACkECAAUggQACkECIAUAgRACgECIIUAAZBCgABIIUAApBAgAFIIEAApBAiAFAIEQAoBAiCFAAGQQoAASCFAAKQQIABSCBAAKQQIgBQCBEAKAQIghQABkEKAAEghQACkECAAUggQACkECIAUAgRACgECIIUAAZBCgABIIUAApBAgAFIIEAApBAiAFAIEQAoBAiCFAAGQQoAASCFAAKQQIABSCBAAKQQIgBQCBEAKAQIghQABkEKAAEghQACkECAAUggQACkECIAUww7Q5s2bY9myZdHY2BhVVVWxcePGIdvvvPPOqKqqGrIsWbJkpOYF4Awx7AD19/fH3LlzY926dSfdZ8mSJfHxxx8PLi+++OJpDQnAmWfCcJ+wdOnSWLp06dfuUyqVoqGh4ZSHAuDMNyqfAb3zzjsxbdq0uPTSS+Oee+6JAwcOnHTfgYGB6OvrG7IAcOYb8QAtWbIknn/++ejo6IjHH388Ojs7Y+nSpXHs2LET7t/e3h7lcnlwaWpqGumRABiDqoqiKE75yVVVsWHDhli+fPlJ9/nHP/4RF154Ybz55puxaNGir2wfGBiIgYGBwcd9fX3R1NQUiybfEROqqk91NACSfF4ciY6eP0Rvb2/U1taedL9Rvw179uzZMWXKlNi1a9cJt5dKpaitrR2yAHDmG/UAffTRR3HgwIGYPn36aB8KgAoy7LvgDh06NOTdzJ49e+KDDz6Iurq6qKuri0cffTRWrFgRDQ0NsXv37njggQfioosuipaWlhEdHIDKNuwAvffee3HDDTcMPm5ra4uIiJUrV8b69evjr3/9a/z+97+Pnp6eaGxsjMWLF8cvf/nLKJVKIzc1ABVv2AG6/vrr4+vuW/jTn/50WgMB8N3gd8EBkEKAAEghQACkECAAUggQACkECIAUAgRACgECIIUAAZBCgABIIUAApBAgAFIIEAApBAiAFAIEQAoBAiCFAAGQQoAASCFAAKQQIABSCBAAKQQIgBQCBEAKAQIghQABkEKAAEghQACkECAAUggQACkECIAUAgRACgECIIUAAZBCgABIIUAApBAgAFIIEAApBAiAFAIEQAoBAiCFAAGQQoAASCFAAKQQIABSCBAAKQQIgBQCBEAKAQIghQABkEKAAEghQACkECAAUggQACkECIAUAgRACgECIIUAAZBCgABIIUAApBAgAFIIEAApBAiAFAIEQAoBAiCFAAGQQoAASCFAAKQQIABSCBAAKYYVoPb29rjqqquipqYmpk2bFsuXL48dO3YM2efw4cPR2toa5513Xpx77rmxYsWK6O7uHtGhAah8wwpQZ2dntLa2xtatW+ONN96Io0ePxuLFi6O/v39wn/vuuy9ee+21eOWVV6KzszP2798fN99884gPDkBlqyqKojjVJ3/66acxbdq06OzsjJ/85CfR29sbU6dOjRdeeCF++tOfRkTE3//+9/jBD34QW7ZsiauvvvobX7Ovry/K5XIsmnxHTKiqPtXRAEjyeXEkOnr+EL29vVFbW3vS/U7rM6De3t6IiKirq4uIiO3bt8fRo0ejubl5cJ85c+bEjBkzYsuWLSd8jYGBgejr6xuyAHDmO+UAHT9+PNasWRPXXHNNXH755RER0dXVFdXV1TF58uQh+9bX10dXV9cJX6e9vT3K5fLg0tTUdKojAVBBTjlAra2t8eGHH8ZLL710WgM8+OCD0dvbO7js27fvtF4PgMow4VSetHr16nj99ddj8+bNcf755w+ub2hoiCNHjkRPT8+Qd0Hd3d3R0NBwwtcqlUpRKpVOZQwAKtiw3gEVRRGrV6+ODRs2xFtvvRWzZs0asn3evHkxceLE6OjoGFy3Y8eO2Lt3byxcuHBkJgbgjDCsd0Ctra3xwgsvxKuvvho1NTWDn+uUy+WYNGlSlMvlWLVqVbS1tUVdXV3U1tbGvffeGwsXLvx/3QEHwHfHsAK0fv36iIi4/vrrh6x/9tln484774yIiF//+tcxbty4WLFiRQwMDERLS0v89re/HZFhAThznNbPAY0GPwcEUNm+lZ8DAoBTJUAApBAgAFIIEAApBAiAFAIEQAoBAiCFAAGQQoAASCFAAKQQIABSCBAAKQQIgBQCBEAKAQIghQABkEKAAEghQACkECAAUggQACkECIAUAgRACgECIIUAAZBCgABIIUAApBAgAFIIEAApBAiAFAIEQAoBAiCFAAGQYkL2AF9WFEVERHxeHEmeBIBT8cXX7y++np/MmAvQwYMHIyKis/fl5EkAOB0HDx6Mcrl80u1VxTcl6lt2/Pjx2L9/f9TU1ERVVdVXtvf19UVTU1Ps27cvamtrEyasfM7h6XH+To/zd/rG+jksiiIOHjwYjY2NMW7cyT/pGXPvgMaNGxfnn3/+N+5XW1s7Jk98JXEOT4/zd3qcv9M3ls/h173z+YKbEABIIUAApKi4AJVKpVi7dm2USqXsUSqWc3h6nL/T4/ydvjPlHI65mxAA+G6ouHdAAJwZBAiAFAIEQAoBAiBFxQVo3bp1ccEFF8RZZ50VCxYsiHfffTd7pIrwyCOPRFVV1ZBlzpw52WONaZs3b45ly5ZFY2NjVFVVxcaNG4dsL4oiHn744Zg+fXpMmjQpmpubY+fOnTnDjkHfdP7uvPPOr1yTS5YsyRl2DGpvb4+rrroqampqYtq0abF8+fLYsWPHkH0OHz4cra2tcd5558W5554bK1asiO7u7qSJh6+iAvTyyy9HW1tbrF27Nt5///2YO3dutLS0xCeffJI9WkW47LLL4uOPPx5c/vznP2ePNKb19/fH3LlzY926dSfc/sQTT8TTTz8dzzzzTGzbti3OOeecaGlpicOHD3/Lk45N33T+IiKWLFky5Jp88cUXv8UJx7bOzs5obW2NrVu3xhtvvBFHjx6NxYsXR39//+A+9913X7z22mvxyiuvRGdnZ+zfvz9uvvnmxKmHqagg8+fPL1pbWwcfHzt2rGhsbCza29sTp6oMa9euLebOnZs9RsWKiGLDhg2Dj48fP140NDQUv/rVrwbX9fT0FKVSqXjxxRcTJhzbvnz+iqIoVq5cWdx4440p81SiTz75pIiIorOzsyiK/1xvEydOLF555ZXBff72t78VEVFs2bIla8xhqZh3QEeOHInt27dHc3Pz4Lpx48ZFc3NzbNmyJXGyyrFz585obGyM2bNnx+233x579+7NHqli7dmzJ7q6uoZcj+VyORYsWOB6HIZ33nknpk2bFpdeemncc889ceDAgeyRxqze3t6IiKirq4uIiO3bt8fRo0eHXINz5syJGTNmVMw1WDEB+uyzz+LYsWNRX18/ZH19fX10dXUlTVU5FixYEM8991xs2rQp1q9fH3v27Inrrrtu8M9fMDxfXHOux1O3ZMmSeP7556OjoyMef/zx6OzsjKVLl8axY8eyRxtzjh8/HmvWrIlrrrkmLr/88oj4zzVYXV0dkydPHrJvJV2DY+63YTM6li5dOvjvK664IhYsWBAzZ86MP/7xj7Fq1arEyfiuuvXWWwf//aMf/SiuuOKKuPDCC+Odd96JRYsWJU429rS2tsaHH354xn1uWzHvgKZMmRLjx4//yh0e3d3d0dDQkDRV5Zo8eXJccsklsWvXruxRKtIX15zrceTMnj07pkyZ4pr8ktWrV8frr78eb7/99pA/VdPQ0BBHjhyJnp6eIftX0jVYMQGqrq6OefPmRUdHx+C648ePR0dHRyxcuDBxssp06NCh2L17d0yfPj17lIo0a9asaGhoGHI99vX1xbZt21yPp+ijjz6KAwcOuCb/R1EUsXr16tiwYUO89dZbMWvWrCHb582bFxMnThxyDe7YsSP27t1bMddgRX0Lrq2tLVauXBlXXnllzJ8/P5566qno7++Pu+66K3u0Me/++++PZcuWxcyZM2P//v2xdu3aGD9+fNx2223Zo41Zhw4dGvK/8T179sQHH3wQdXV1MWPGjFizZk089thjcfHFF8esWbPioYceisbGxli+fHne0GPI152/urq6ePTRR2PFihXR0NAQu3fvjgceeCAuuuiiaGlpSZx67GhtbY0XXnghXn311aipqRn8XKdcLsekSZOiXC7HqlWroq2tLerq6qK2tjbuvffeWLhwYVx99dXJ0/8/Zd+GN1y/+c1vihkzZhTV1dXF/Pnzi61bt2aPVBFuueWWYvr06UV1dXXx/e9/v7jllluKXbt2ZY81pr399ttFRHxlWblyZVEU/7kV+6GHHirq6+uLUqlULFq0qNixY0fu0GPI152/f/3rX8XixYuLqVOnFhMnTixmzpxZ3H333UVXV1f22GPGic5dRBTPPvvs4D7//ve/i5///OfF9773veLss88ubrrppuLjjz/OG3qY/DkGAFJUzGdAAJxZBAiAFAIEQAoBAiCFAAGQQoAASCFAAKQQIABSCBAAKQQIgBQCBEAKAQIgxX8Db88wHMsg280AAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 8, 23, 23])\n"
     ]
    },
    {
     "data": {
      "text/plain": "tensor([[-7.9647, -7.9647,  3.8007,  ..., -6.2867, -6.0678, -5.7902]],\n       device='cuda:0', grad_fn=<AddmmBackward0>)"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the\n"
     ]
    }
   ],
   "source": [
    "print(dataset.tokens_to_text(self_atten(dataset.text_to_tokens(\"i am a super person who went to school with bond and i ate two ice creams with potato sticks and sugar cane\").to(torch.long).unsqueeze(0)).argmax(dim=1)))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-05T07:33:01.621152839Z",
     "start_time": "2023-10-05T07:33:01.600087159Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ]
}