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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m4.1 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[31m6.8 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[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
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        }
      ],
      "source": [
        "# Use the Colab UI instead. Select the folder button on the left and then the drive button.\n",
        "# This way you will not be asked every execution for permissions\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "!pip install -q datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "8e6c60f7-3162-4c5f-a704-574a896b72ba"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected device: cpu\n",
            "Selected language: en\n"
          ]
        }
      ],
      "source": [
        "from transformers import GPTNeoConfig, GPTNeoForCausalLM, get_scheduler, \\\n",
        "    DataCollatorForSeq2Seq, AutoTokenizer, DataCollatorForLanguageModeling, \\\n",
        "    AutoModelForCausalLM, AutoConfig\n",
        "from datasets import load_dataset, DatasetDict, Dataset, concatenate_datasets, \\\n",
        "    Sequence, Value\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "from datetime import datetime\n",
        "from copy import deepcopy\n",
        "from tqdm.auto import tqdm\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# device\n",
        "device = \"cuda\"  if torch.cuda.is_available() else  \"cpu\"\n",
        "'''\n",
        "    if torch.cuda.is_available():\n",
        "        device = torch.device(\"cuda\")  # GPU\n",
        "    else:\n",
        "        device = torch.device(\"cpu\")  # CPU\n",
        "'''\n",
        "print(\"Selected device:\", device)\n",
        "\n",
        "# language\n",
        "lang = \"en\" # Can be \"en\" or \"de\"\n",
        "print(f\"Selected language: {lang}\")\n"
      ]
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            "9c974e7926a74a1e98ddba829f383eaf",
            "8371f579fb994dddb78711aa9e881d0d",
            "d6760bba808d40339660c10535fd072c",
            "b4717688cfa64bd6bb97150a44c575c2",
            "9c6659c169c54da0a8faa27c58bf218e",
            "6217661c9c634db3945a17c0eb450b26",
            "196bd4c4528d4d8184b4525f873c7337",
            "ac770959b9e646a5a0cd1bc079630ba5",
            "7f8d04af4a4d4b968a38beca35118e60",
            "f686e48d00514fada2e40a67c560cbd7",
            "8eb8c72234e744908f5c0bf5b39607be",
            "b9625b7b9fee498398f83d034f9b60ae",
            "f70a6d730a5046cfb639d2e52e9fbd37",
            "1777c4a496d0412cbd24c97d834dc3d4",
            "01d9ea4292a149e0a9a73c3bc91f9c8f",
            "88ba311ed1874c7ca2e368bd65885428",
            "778912ac77d64bae84a4307988672993",
            "4cf0dc812ea94ca2b13c0da7e34676b7"
          ]
        },
        "id": "pEh0auhooKBJ",
        "outputId": "477f30dc-2274-4a51-a7cc-c972561e6f91"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/727 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca8e7c14a8254f75a013c68966219088"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1e89da551aff4875a3891186d27ede6b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "51088b9e2f4b4f638796cc356a909c83"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6eb727a860184207bc4051e0c2efa4ea"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/357 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1d23e6e1fbfb438db75cca867b52695d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/865 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "20869558b9514130b4746c6ba00e907c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.43M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ac770959b9e646a5a0cd1bc079630ba5"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer_en = AutoTokenizer.from_pretrained(\"EleutherAI/gpt-neo-125M\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"dbmdz/german-gpt2\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print((len(tokenizer) - len(tokenizer_en))/len(tokenizer_en))\n",
        "print(len(tokenizer))\n",
        "print(len(tokenizer_en))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SeQ4c8n1bMBg",
        "outputId": "0f877b20-cff5-41da-a0fd-657508567628"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.00017907953120958273\n",
            "50266\n",
            "50257\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r = range(257, 270)\n",
        "print(\" & \".join([str(i) for i in r]))\n",
        "print(\" & \".join([f\"\\\"{tokenizer_en.decode([i])}\\\"\" for i in r]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DYHC4sSPT8m_",
        "outputId": "01447209-6fd4-48e9-b803-7d7e5ed1120f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "257 & 258 & 259 & 260 & 261 & 262 & 263 & 264 & 265 & 266 & 267 & 268 & 269\n",
            "\" a\" & \"he\" & \"in\" & \"re\" & \"on\" & \" the\" & \"er\" & \" s\" & \"at\" & \" w\" & \" o\" & \"en\" & \" c\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "5LJ-shR_rMWH",
        "outputId": "81e76e36-8541-4ae7-f999-84a1d24f61d7"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'GPTNeoForCausalLM' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-5b9e08506e07>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGPTNeoForCausalLM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"/content/drive/My Drive/genAI project/model-33m-de-2024-03-23T01:23:36.822703\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m#input_ids = tokenizer.encode(f\"Once upon a time Tom ate apples and\", return_tensors=\"pt\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0minput_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Eines Tages traf Lilly ein Auto und\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1024\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_beams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrepetition_penalty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meos_token_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m50256\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0moutput_text\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_special_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'GPTNeoForCausalLM' is not defined"
          ]
        }
      ],
      "source": [
        "model = GPTNeoForCausalLM.from_pretrained(f\"/content/drive/My Drive/genAI project/model-33m-de-2024-03-23T01:23:36.822703\")\n",
        "#input_ids = tokenizer.encode(f\"Once upon a time Tom ate apples and\", return_tensors=\"pt\")\n",
        "input_ids = tokenizer.encode(f\"Eines Tages traf Lilly ein Auto und\", return_tensors=\"pt\")\n",
        "output = model.generate(input_ids, max_length = 1024, num_beams=5, temperature=3, repetition_penalty=3.0, eos_token_id = 50256)\n",
        "output_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "print(output_text)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(tokenizer.decode(output[0], skip_special_tokens=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cRdMWjIC7Zut",
        "outputId": "5c3948ec-2a1f-4f26-bb4a-6ec1ba551e86"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Eines Tages traf Lilly ein Auto und fragte: „Wo gehst du hin?\" Das Auto antwortete: „Ich gehe in den Park, um mit meinen Freunden zu spielen. Willst du mit mir kommen?\" Lilly war aufgeregt und sagte: „Ja! Lass uns gehen!\" Also fuhren sie zusammen zum Park. Als sie dort ankamen, sahen sie eine große Rutsche. Lilly stieg die Leiter hinauf und setzte sich auf die Rutsche. Sie lachte und sagte: „Das ist so viel Spaß!\" Nachdem sie eine Weile gespielt hatten, gingen sie zur Rutsche. Lilly kletterte die Leiter hinauf und als sie oben ankam, schrie sie vor Freude: „Das war so viel Spaß!\"\n",
            "Es war einmal ein kleines Mädchen namens Lily. Sie liebte es, draußen im Garten mit ihren Spielzeugen zu spielen. Eines Tages bat Lilys Mutter sie, ihr Spielzeug aufzuräumen. Lily wollte nicht aufräumen, aber sie wusste, dass es wichtig war, ihrer Mutter zu helfen. Später an diesem Tag kam Lilys Vater von der Arbeit nach Hause. Er sah das Chaos und sagte: \"Lily, kannst du mir bitte helfen, dein Spielzeug aufzuräumen?\" Lily dachte einen Moment lang und sagte dann: \"Ja, ich kann dir helfen.\" Ihr Vater lächelte und sagte: \"Danke, Lily. Du bist sehr freundlich.\" Von diesem Tag an sorgte Lily dafür, dass sie immer ihre Spielzeuge weglegte, wenn sie fertig war.\n",
            "Es war einmal ein kleiner Junge namens Timmy. Timmy liebte es, mit seinen Spielzeugautos und Lastwagen zu spielen. Eines Tages spielte er mit seinem Lieblings-Spielzeugwagen, als er versehentlich sein Lieblingsspielzeugauto brach. Timmy war sehr traurig und wusste nicht, was zu tun war. Seine Mutter sah ihn weinen und fragte ihn, was falsch sei. Timmy erzählte ihr von seinem kaputten Spielzeugauto. Seine Mutter umarmte ihn und sagte: „Mach dir keine Sorgen, wir können es zusammen reparieren.\" Sie fanden etwas Kleber und reparierten das Spielzeugauto. Timmy war wieder glücklich und gab seiner Mutter eine große Umarmung. Ab diesem Tag lernte Timmy, vorsichtiger mit seinen Spielzeugen zu sein und ließ sie nie wieder brechen.\n",
            "Einmal war da ein kleines Mädchen namens Lily. Sie liebte es, mit ihrem Spielzeug zu spielen und Bücher zu lesen. Eines Tages ging sie mit ihrer Mami in den Park und sah eine große Rutsche. Sie rannte zu ihr und rutschte hinunter, kicherte vor Freude. Aber dann fiel sie herunter und verletzte ihr Knie. Es tat sehr weh und sie begann zu weinen. Ihre Mami umarmte sie und sagte: \"Es tut mir leid, Süße. Bist du okay?\" Lily nickte ihren Kopf und sagte: \"Ja, Mami. Ich bin nur ein wenig verletzt.\" Ihre Mami umarmte sie und sagte: \"Es ist okay, Unfälle passieren. Lassen Sie uns stattdessen auf den Schaukeln spielen.\" Lily fühlte sich viel besser und sie verbrachten den Rest des Tages damit, schwingen und Spaß zu haben.\n",
            "Einmal war da ein kleines Mädchen namens Lily. Sie liebte es, Bilder mit ihren Buntstiften zu zeichnen. Eines Tages zeichnete sie ein schönes Bild einer Blume. Sie zeigte es ihrer Mutter und sagte: „Schau, Mama! Ich habe eine Blume gemacht!\" Ihre Mutter lächelte und sagte: „Das ist wunderbar, Lily. Was wirst du damit machen?\" Lily dachte für einen Moment und sagte: „Ich werde Blumen aus dem Garten pflücken und sie schön aussehen lassen.\" Ihre Mutter lächelte und sagte: „Das ist eine tolle Idee, Lily. Jetzt lass uns rausgehen und einige hübsche Blumen holen.\" Und so gingen sie hinaus und pflückten viele schöne Blumen zusammen.\n",
            "Einmal war da ein kleines Mädchen namens Lily. Sie liebte es, jeden Morgen Marmelade auf ihrem Toast zu essen. Eines Tages verschüttete sie versehentlich Marmelade auf ihren Toast und es wurde nass. Sie versuchte, es mit einem Handtuch auszutrocknen, aber es funktionierte nicht. Lily war traurig, weil sie mehr Marmelade auf ihrem Toast machen wollte. Aber dann hatte sie eine Idee! Sie nahm ein Handtuch und benutzte es, um die Marmelade vom Toaster zu trocknen. Die Marmelade war jetzt trocken und lecker. Lily war wieder glücklich und aß ihren Toast mit einem großen Lächeln auf ihrem Gesicht. Das Ende.\n",
            "Einmal war da ein kleines Mädchen namens Lily. Sie liebte es, Dress-up zu spielen und verschiedene Outfits zu tragen. Eines Tages fand sie ein glänzendes Juwel auf dem Boden liegend. Sie hob es auf und steckte es in ihre Tasche. Später an diesem Tag kam Lilys Freundin Timmy zum Spielen. Timmy sah das Juwel und sagte: \"Wow, das ist ein hübsches Juwel! Wo hast du es gefunden?\" Lily antwortete: \"Ich fand es unter meinem Bett. Es ist so glänzend und hübsch.\" Timmy sagte: \"Kann ich es sehen?\" Lily sagte: \"Sicher, aber Vorsicht, es könnte gefährlich sein.\" Timmy berührte das Juwel und legte es zurück in seine Tasche\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessed_datasets_path = \"/content/drive/My Drive/genAI project/tiny-stories-de-preprocessed\"\n",
        "ds = DatasetDict.load_from_disk(preprocessed_datasets_path)\n",
        "print(ds[\"train\"][0][\"input_ids\"])\n",
        "print(ds[\"train\"][0][\"labels\"])"
      ],
      "metadata": {
        "id": "anErKVbdEHfB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(ds[\"train\"][9][\"input_ids\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "ilco-YViFhTv",
        "outputId": "e2fb1dbb-f799-4405-b83e-1429a885e006"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'ds' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-368cbb325cb0>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"train\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"input_ids\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'ds' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "losses1 = torch.load(f\"/content/drive/My Drive/genAI project/losses_tensor-1m-en-2024-03-22T09:06:10.954130.pt\")\n",
        "losses2 = torch.load(f\"/content/drive/My Drive/genAI project/losses_tensor-1m-en-2024-03-21T12:49:59.608517.pt\")"
      ],
      "metadata": {
        "id": "H3ML7pC7Fljr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "outputId": "9c5fa240-0baa-45ae-8d98-25aacb0f09dd"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'torch' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-cdd8e75c6dde>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlosses1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"/content/drive/My Drive/genAI project/losses_tensor-1m-en-2024-03-22T09:06:10.954130.pt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mlosses2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"/content/drive/My Drive/genAI project/losses_tensor-1m-en-2024-03-21T12:49:59.608517.pt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "losses = list(losses2)\n",
        "losses.extend(list(losses1))\n",
        "print(len(losses))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "GA7L-fzOGRbO",
        "outputId": "0323063f-abc8-4f50-975e-fa18b5885021"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'losses2' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-3c6a25f1e539>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlosses2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlosses1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'losses2' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(losses)"
      ],
      "metadata": {
        "id": "WK3qC5oPGae6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "outputId": "255ea781-b10e-4fa7-8666-d3458a80da1d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'plt' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-1f71c63ba2d5>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "V0JHfBxwourr"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}