Euterpe_X_TTM_Edition.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Euterpe X TTM Edition (ver. 5.0)\n",
        "\n",
        "***\n",
        "\n",
        "Powered by tegridy-tools: https://github.com/asigalov61/tegridy-tools\n",
        "\n",
        "***\n",
        "\n",
        "WARNING: This complete implementation is a functioning model of the Artificial Intelligence. Please excercise great humility, care, and respect. https://www.nscai.gov/\n",
        "\n",
        "***\n",
        "\n",
        "#### Project Los Angeles\n",
        "\n",
        "#### Tegridy Code 2023\n",
        "\n",
        "***"
      ],
      "metadata": {
        "id": "gpy3qsulqHa5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (GPU CHECK)"
      ],
      "metadata": {
        "id": "W_So4w8fqPGL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X3rABEpKCO02",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title NVIDIA GPU check\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (SETUP ENVIRONMENT)"
      ],
      "metadata": {
        "id": "C0XxnXGFqVyh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vK40g6V_BTNj",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Install dependencies\n",
        "!git clone --depth 1 https://github.com/asigalov61/Euterpe-X\n",
        "!pip install huggingface_hub\n",
        "!pip install torch\n",
        "!pip install einops\n",
        "!pip install fuzzywuzzy[speedup]\n",
        "!pip install torch-summary\n",
        "!pip install tqdm\n",
        "!pip install matplotlib\n",
        "!apt install fluidsynth #Pip does not work for some reason. Only apt works\n",
        "!pip install midi2audio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DzCOZU_gBiQV",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Import modules\n",
        "\n",
        "print('=' * 70)\n",
        "print('Loading core Euterpe X modules...')\n",
        "\n",
        "import os\n",
        "import pickle\n",
        "\n",
        "import secrets\n",
        "import statistics\n",
        "from time import time\n",
        "import tqdm\n",
        "\n",
        "print('=' * 70)\n",
        "print('Loading main Euterpe X modules...')\n",
        "import torch\n",
        "\n",
        "%cd /content/Euterpe-X\n",
        "\n",
        "import TMIDIX\n",
        "\n",
        "from x_transformer import *\n",
        "\n",
        "%cd /content/\n",
        "\n",
        "from fuzzywuzzy import process\n",
        "\n",
        "print('=' * 70)\n",
        "print('Loading aux Euterpe X modules...')\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from torchsummary import summary\n",
        "from sklearn import metrics\n",
        "\n",
        "from midi2audio import FluidSynth\n",
        "from IPython.display import Audio, display\n",
        "\n",
        "import random\n",
        "\n",
        "from huggingface_hub import hf_hub_download\n",
        "\n",
        "print('=' * 70)\n",
        "print('Done!')\n",
        "print('Enjoy! :)')\n",
        "print('=' * 70)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eI3aQtHzqSnp"
      },
      "source": [
        "# (LOAD MODEL)\n",
        "\n",
        "## NOTE: Choose one and do not forget to restart the colab runtime if you are switching between models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rDquonbXC2je",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Load Euterpe X Small Model\n",
        "\n",
        "#@markdown Fast model, 32 layers, 141k MIDIs training corpus\n",
        "\n",
        "full_path_to_model_checkpoint = \"/content/Euterpe-X/Models/Small/Euterpe_X_Small_Trained_Model_58000_steps_0.6865_loss_0.7964_acc.pth\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown Model precision option\n",
        "\n",
        "model_precision = \"bfloat16\" # @param [\"bfloat16\", \"float16\", \"float32\"]\n",
        "\n",
        "#@markdown bfloat16 == Third precision/triple speed (if supported, otherwise the model will default to float16)\n",
        "\n",
        "#@markdown float16 == Half precision/double speed\n",
        "\n",
        "#@markdown float32 == Full precision/normal speed\n",
        "\n",
        "plot_tokens_embeddings = True # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Loading Euterpe X Small Pre-Trained Model...')\n",
        "print('Please wait...')\n",
        "print('=' * 70)\n",
        "\n",
        "if os.path.isfile(full_path_to_model_checkpoint):\n",
        "  print('Model already exists...')\n",
        "\n",
        "else:\n",
        "  hf_hub_download(repo_id='asigalov61/Euterpe-X',\n",
        "                  filename='Euterpe_X_Small_Trained_Model_58000_steps_0.6865_loss_0.7964_acc.pth',\n",
        "                  local_dir='/content/Euterpe-X/Models/Small/',\n",
        "                  local_dir_use_symlinks=False)\n",
        "print('=' * 70)\n",
        "print('Instantiating model...')\n",
        "\n",
        "torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul\n",
        "torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn\n",
        "device_type = 'cuda'\n",
        "\n",
        "if model_precision == 'bfloat16' and torch.cuda.is_bf16_supported():\n",
        "  dtype = 'bfloat16'\n",
        "else:\n",
        "  dtype = 'float16'\n",
        "\n",
        "if model_precision == 'float16':\n",
        "  dtype = 'float16'\n",
        "\n",
        "if model_precision == 'float32':\n",
        "  dtype = 'float32'\n",
        "\n",
        "ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]\n",
        "ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype)\n",
        "\n",
        "SEQ_LEN = 2048\n",
        "\n",
        "# instantiate the model\n",
        "\n",
        "model = TransformerWrapper(\n",
        "    num_tokens = 3344,\n",
        "    max_seq_len = SEQ_LEN,\n",
        "    attn_layers = Decoder(dim = 1024, depth = 32, heads = 8, attn_flash=True)\n",
        ")\n",
        "\n",
        "model = AutoregressiveWrapper(model)\n",
        "\n",
        "model = torch.nn.DataParallel(model)\n",
        "\n",
        "model.cuda()\n",
        "print('=' * 70)\n",
        "\n",
        "print('Loading model checkpoint...')\n",
        "\n",
        "model.load_state_dict(torch.load(full_path_to_model_checkpoint))\n",
        "print('=' * 70)\n",
        "\n",
        "model.eval()\n",
        "\n",
        "print('Done!')\n",
        "print('=' * 70)\n",
        "\n",
        "print('Model will use', dtype, 'precision...')\n",
        "print('=' * 70)\n",
        "\n",
        "# Model stats\n",
        "print('Model summary...')\n",
        "summary(model)\n",
        "\n",
        "# Plot Token Embeddings\n",
        "if plot_tokens_embeddings:\n",
        "  tok_emb = model.module.net.token_emb.emb.weight.detach().cpu().tolist()\n",
        "\n",
        "  cos_sim = metrics.pairwise_distances(\n",
        "    tok_emb, metric='cosine'\n",
        "  )\n",
        "  plt.figure(figsize=(7, 7))\n",
        "  plt.imshow(cos_sim, cmap=\"inferno\", interpolation=\"nearest\")\n",
        "  im_ratio = cos_sim.shape[0] / cos_sim.shape[1]\n",
        "  plt.colorbar(fraction=0.046 * im_ratio, pad=0.04)\n",
        "  plt.xlabel(\"Position\")\n",
        "  plt.ylabel(\"Position\")\n",
        "  plt.tight_layout()\n",
        "  plt.plot()\n",
        "  plt.savefig(\"/content/Euterpe-X-Small-Tokens-Embeddings-Plot.png\", bbox_inches=\"tight\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load Euterpe X Large Model\n",
        "\n",
        "#@markdown Slow model, 60 layers, 314k MIDIs training corpus\n",
        "\n",
        "full_path_to_model_checkpoint = \"/content/Euterpe-X/Models/Large/Euterpe_X_Large_Trained_Model_100000_steps_0.477_loss_0.8533_acc.pth\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown Model precision option\n",
        "\n",
        "model_precision = \"bfloat16\" # @param [\"bfloat16\", \"float16\", \"float32\"]\n",
        "\n",
        "#@markdown bfloat16 == Third precision/triple speed (if supported, otherwise the model will default to float16)\n",
        "\n",
        "#@markdown float16 == Half precision/double speed\n",
        "\n",
        "#@markdown float32 == Full precision/normal speed\n",
        "\n",
        "plot_tokens_embeddings = True # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Loading Euterpe X Large Pre-Trained Model...')\n",
        "print('Please wait...')\n",
        "print('=' * 70)\n",
        "\n",
        "if os.path.isfile(full_path_to_model_checkpoint):\n",
        "  print('Model already exists...')\n",
        "\n",
        "else:\n",
        "  hf_hub_download(repo_id='asigalov61/Euterpe-X',\n",
        "                  filename='Euterpe_X_Large_Trained_Model_100000_steps_0.477_loss_0.8533_acc.pth',\n",
        "                  local_dir='/content/Euterpe-X/Models/Large',\n",
        "                  local_dir_use_symlinks=False)\n",
        "\n",
        "print('=' * 70)\n",
        "print('Instantiating model...')\n",
        "\n",
        "torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul\n",
        "torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn\n",
        "device_type = 'cuda'\n",
        "\n",
        "if model_precision == 'bfloat16' and torch.cuda.is_bf16_supported():\n",
        "  dtype = 'bfloat16'\n",
        "else:\n",
        "  dtype = 'float16'\n",
        "\n",
        "if model_precision == 'float16':\n",
        "  dtype = 'float16'\n",
        "\n",
        "if model_precision == 'float32':\n",
        "  dtype = 'float32'\n",
        "\n",
        "ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]\n",
        "ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype)\n",
        "\n",
        "SEQ_LEN = 2048\n",
        "\n",
        "# instantiate the model\n",
        "\n",
        "model = TransformerWrapper(\n",
        "    num_tokens = 3344,\n",
        "    max_seq_len = SEQ_LEN,\n",
        "    attn_layers = Decoder(dim = 1024, depth = 60, heads = 8, attn_flash=True)\n",
        ")\n",
        "\n",
        "model = AutoregressiveWrapper(model)\n",
        "\n",
        "model = torch.nn.DataParallel(model)\n",
        "\n",
        "model.cuda()\n",
        "print('=' * 70)\n",
        "\n",
        "print('Loading model checkpoint...')\n",
        "\n",
        "model.load_state_dict(torch.load(full_path_to_model_checkpoint))\n",
        "print('=' * 70)\n",
        "\n",
        "model.eval()\n",
        "\n",
        "print('Done!')\n",
        "print('=' * 70)\n",
        "\n",
        "print('Model will use', dtype, 'precision...')\n",
        "print('=' * 70)\n",
        "\n",
        "# Model stats\n",
        "print('Model summary...')\n",
        "summary(model)\n",
        "\n",
        "# Plot Token Embeddings\n",
        "if plot_tokens_embeddings:\n",
        "  tok_emb = model.module.net.token_emb.emb.weight.detach().cpu().tolist()\n",
        "\n",
        "  cos_sim = metrics.pairwise_distances(\n",
        "    tok_emb, metric='cosine'\n",
        "  )\n",
        "  plt.figure(figsize=(7, 7))\n",
        "  plt.imshow(cos_sim, cmap=\"inferno\", interpolation=\"nearest\")\n",
        "  im_ratio = cos_sim.shape[0] / cos_sim.shape[1]\n",
        "  plt.colorbar(fraction=0.046 * im_ratio, pad=0.04)\n",
        "  plt.xlabel(\"Position\")\n",
        "  plt.ylabel(\"Position\")\n",
        "  plt.tight_layout()\n",
        "  plt.plot()\n",
        "  plt.savefig(\"/content/Euterpe-X-Large-Tokens-Embeddings-Plot.png\", bbox_inches=\"tight\")"
      ],
      "metadata": {
        "cellView": "form",
        "id": "F4Yfe8-6ORr1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (LOAD AUX DATA)"
      ],
      "metadata": {
        "id": "-u40ZSHaJhZR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load Euterpe X Aux Data\n",
        "\n",
        "print('=' * 70)\n",
        "print('Loading Euterpe X Aux Data. Please wait...')\n",
        "print('=' * 70)\n",
        "\n",
        "if os.path.isfile('/content/Euterpe-X/Aux-Data/Euterpe_X_Text_To_Music_Aux_Data.pickle'):\n",
        "  print('Aux Data already exists...')\n",
        "\n",
        "else:\n",
        "  hf_hub_download(repo_id='asigalov61/Euterpe-X',\n",
        "                  filename='Euterpe_X_Text_To_Music_Aux_Data.pickle',\n",
        "                  local_dir='/content/Euterpe-X/Aux-Data/',\n",
        "                  local_dir_use_symlinks=False)\n",
        "print('=' * 70)\n",
        "AUX_DATA = TMIDIX.Tegridy_Any_Pickle_File_Reader('/content/Euterpe-X/Aux-Data/Euterpe_X_Text_To_Music_Aux_Data')\n",
        "\n",
        "print('Done!')"
      ],
      "metadata": {
        "cellView": "form",
        "id": "j44lPUnvKB1E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (LOAD VELOCITIES MAP)"
      ],
      "metadata": {
        "id": "zzMxnYs2Zi8r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Load velocities map\n",
        "\n",
        "#@markdown Select velocity type to apply to the generated output\n",
        "\n",
        "velocity_type_to_use = \"Velocity Map\" # @param [\"Constant Velocity\", \"Velocity Map\"]\n",
        "\n",
        "plot_velocities_map = True # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Loading Euterpe X Velocities Map...')\n",
        "print('=' * 70)\n",
        "\n",
        "instruments_patches = [0, 24, 32, 40, 42, 46, 56, 65, 73, 0, 53, 19, 0, 0, 0, 0]\n",
        "\n",
        "dynamic_velocities_map = TMIDIX.Tegridy_Any_Pickle_File_Reader('/content/Euterpe-X/Aux-Data/pitches_patches_MIDI_velocities_map')[0]\n",
        "\n",
        "print('=' * 70)\n",
        "print('Done!')\n",
        "print('=' * 70)\n",
        "\n",
        "if plot_velocities_map:\n",
        "  cos_sim = metrics.pairwise_distances(\n",
        "    dynamic_velocities_map, metric='cosine'\n",
        "  )\n",
        "  plt.figure(figsize=(7, 7))\n",
        "  plt.imshow(cos_sim, cmap=\"inferno\", interpolation=\"nearest\")\n",
        "  im_ratio = cos_sim.shape[0] / cos_sim.shape[1]\n",
        "  plt.colorbar(fraction=0.046 * im_ratio, pad=0.04)\n",
        "  plt.xlabel(\"Pitches\")\n",
        "  plt.ylabel(\"Patches\")\n",
        "  plt.tight_layout()\n",
        "  plt.plot()\n",
        "  plt.savefig(\"/content/Euterpe-X-Velocities-Map-Plot.png\", bbox_inches=\"tight\")"
      ],
      "metadata": {
        "cellView": "form",
        "id": "j91uLSIfZb2c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (GENERATE)"
      ],
      "metadata": {
        "id": "7xNyANjZsCOi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dkvXYwR_qSnx",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Text-To-Music Generation\n",
        "\n",
        "#@markdown Text-To-Music Settings\n",
        "\n",
        "#@markdown NOTE: You can enter any desired title or artist, or both\n",
        "\n",
        "enter_desired_song_title = \"Family Guy\" #@param {type:\"string\"}\n",
        "enter_desired_artist = \"TV Themes\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown Generation Settings\n",
        "\n",
        "number_of_tokens_to_generate = 300 # @param {type:\"slider\", min:30, max:2046, step:3}\n",
        "number_of_batches_to_generate = 4 #@param {type:\"slider\", min:1, max:16, step:1}\n",
        "temperature = 0.9 #@param {type:\"slider\", min:0.1, max:1, step:0.1}\n",
        "\n",
        "#@markdown Other settings\n",
        "\n",
        "allow_model_to_stop_generation_if_needed = False #@param {type:\"boolean\"}\n",
        "render_MIDI_to_audio = True # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Euterpe X TTM Model Generator')\n",
        "print('=' * 70)\n",
        "\n",
        "print('Searching titles...Please wait...')\n",
        "random.shuffle(AUX_DATA)\n",
        "\n",
        "titles_index = []\n",
        "\n",
        "for A in AUX_DATA:\n",
        "  titles_index.append(A[0])\n",
        "\n",
        "search_string = ''\n",
        "\n",
        "if enter_desired_song_title != '' and enter_desired_artist != '':\n",
        "  search_string = enter_desired_song_title + ' --- ' + enter_desired_artist\n",
        "\n",
        "else:\n",
        "  search_string = enter_desired_song_title + enter_desired_artist\n",
        "\n",
        "search_match = process.extract(query=search_string, choices=titles_index, limit=1)\n",
        "search_index = titles_index.index(search_match[0][0])\n",
        "\n",
        "print('Done!')\n",
        "print('=' * 70)\n",
        "print('Selected title:', AUX_DATA[search_index][0])\n",
        "print('=' * 70)\n",
        "\n",
        "if allow_model_to_stop_generation_if_needed:\n",
        "  min_stop_token = 3343\n",
        "else:\n",
        "  min_stop_token = None\n",
        "\n",
        "# Velocities\n",
        "velocities_map = [80, 80, 70, 100, 90, 80, 100, 100, 100, 90, 110, 100]\n",
        "vel_map = AUX_DATA[search_index][1]\n",
        "\n",
        "for i in range(12):\n",
        "  if vel_map[i] != 0:\n",
        "    velocities_map[i] = vel_map[i]\n",
        "\n",
        "# Loading data...\n",
        "outy = AUX_DATA[search_index][2][3:]\n",
        "\n",
        "block_marker = sum([(y * 8) for y in outy if y < 256]) / 1000\n",
        "\n",
        "inp = [outy] * number_of_batches_to_generate\n",
        "\n",
        "inp = torch.LongTensor(inp).cuda()\n",
        "\n",
        "with ctx:\n",
        "  out = model.module.generate(inp,\n",
        "                        number_of_tokens_to_generate,\n",
        "                        temperature=temperature,\n",
        "                        return_prime=True,\n",
        "                        eos_token=min_stop_token,\n",
        "                        verbose=True)\n",
        "\n",
        "out0 = out.tolist()\n",
        "\n",
        "print('=' * 70)\n",
        "print('Done!')\n",
        "print('=' * 70)\n",
        "#======================================================================\n",
        "print('Rendering results...')\n",
        "\n",
        "for i in range(number_of_batches_to_generate):\n",
        "\n",
        "  print('=' * 70)\n",
        "  print('Batch #', i)\n",
        "  print('=' * 70)\n",
        "\n",
        "  out1 = out0[i]\n",
        "\n",
        "  print('Sample INTs', out1[:12])\n",
        "  print('=' * 70)\n",
        "\n",
        "  if len(out) != 0:\n",
        "\n",
        "      song = out1\n",
        "      song_f = []\n",
        "\n",
        "      time = 0\n",
        "      dur = 0\n",
        "      channel = 0\n",
        "      pitch = 0\n",
        "      vel = 90\n",
        "\n",
        "      for ss in song:\n",
        "\n",
        "        if ss > 0 and ss < 256:\n",
        "\n",
        "            time += ss * 8\n",
        "\n",
        "        if ss >= 256 and ss < 256+(12*128):\n",
        "\n",
        "            dur = ((ss-256) % 128) * 30\n",
        "\n",
        "        if ss >= 256+(12*128) and ss < 256+(12*128)+(12*128):\n",
        "            channel = (ss-(256+(12*128))) // 128\n",
        "            pitch = (ss-(256+(12*128))) % 128\n",
        "\n",
        "            if velocity_type_to_use == 'Constant Velocity':\n",
        "              vel = velocities_map[channel]\n",
        "            else:\n",
        "              vel = round(dynamic_velocities_map[pitch][instruments_patches[channel]])\n",
        "\n",
        "\n",
        "            song_f.append(['note', time, dur, channel, pitch, vel ])\n",
        "\n",
        "      detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,\n",
        "                                                          output_signature = 'Euterpe X',\n",
        "                                                          output_file_name = '/content/Euterpe-X-Music-Composition_'+str(i),\n",
        "                                                          track_name='Project Los Angeles',\n",
        "                                                          list_of_MIDI_patches=[0, 24, 32, 40, 42, 46, 56, 65, 73, 0, 53, 19, 0, 0, 0, 0]\n",
        "                                                          )\n",
        "      print('=' * 70)\n",
        "      print('Displaying resulting composition...')\n",
        "      print('=' * 70)\n",
        "\n",
        "      fname = '/content/Euterpe-X-Music-Composition_'+str(i)\n",
        "\n",
        "      x = []\n",
        "      y =[]\n",
        "      c = []\n",
        "\n",
        "      colors = ['red', 'yellow', 'green', 'cyan', 'blue', 'pink', 'orange', 'purple', 'gray', 'white', 'gold', 'silver']\n",
        "\n",
        "      for s in song_f:\n",
        "        x.append(s[1] / 1000)\n",
        "        y.append(s[4])\n",
        "        c.append(colors[s[3]])\n",
        "\n",
        "      if render_MIDI_to_audio:\n",
        "        FluidSynth(\"/usr/share/sounds/sf2/FluidR3_GM.sf2\", 16000).midi_to_audio(str(fname + '.mid'), str(fname + '.wav'))\n",
        "        display(Audio(str(fname + '.wav'), rate=16000))\n",
        "\n",
        "      plt.figure(figsize=(14,5))\n",
        "      ax=plt.axes(title=fname)\n",
        "      ax.set_facecolor('black')\n",
        "\n",
        "      plt.scatter(x,y, c=c)\n",
        "\n",
        "      ax.axvline(x=block_marker, c='w')\n",
        "\n",
        "      plt.xlabel(\"Time\")\n",
        "      plt.ylabel(\"Pitch\")\n",
        "      plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Congrats! You did it! :)"
      ],
      "metadata": {
        "id": "eoWDEy6CwDr6"
      }
    }
  ],
  "metadata": {
    "accelerator": "GPU",
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      "private_outputs": true,
      "provenance": [],
      "gpuType": "T4",
      "gpuClass": "premium"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      },
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