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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TheOneTrueGuy/Janus/blob/main/Janus_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "s-CHPPvkXVw6",
        "outputId": "e68e9d54-00cf-4fa3-edca-328acdda2463"
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      "outputs": [
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          "name": "stdout",
          "text": [
            "Cloning into 'Janus'...\n",
            "remote: Enumerating objects: 35, done.\u001b[K\n",
            "remote: Counting objects: 100% (35/35), done.\u001b[K\n",
            "remote: Compressing objects: 100% (31/31), done.\u001b[K\n",
            "remote: Total 35 (delta 4), reused 35 (delta 4), pack-reused 0 (from 0)\u001b[K\n",
            "Receiving objects: 100% (35/35), 955.66 KiB | 6.73 MiB/s, done.\n",
            "Resolving deltas: 100% (4/4), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/deepseek-ai/Janus.git\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd Janus\n",
        "!pip install -e .\n",
        "!pip install flash-attn"
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      "metadata": {
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        },
        "id": "BIeDQ1ClXwL_",
        "outputId": "556e62de-2bac-4eec-9008-78f33515c790"
      },
      "execution_count": 2,
      "outputs": [
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          "text": [
            "/content/Janus\n",
            "Obtaining file:///content/Janus\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: torch>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from janus==1.0.0) (2.4.1+cu121)\n",
            "Requirement already satisfied: transformers>=4.38.2 in /usr/local/lib/python3.10/dist-packages (from janus==1.0.0) (4.44.2)\n",
            "Requirement already satisfied: timm>=0.9.16 in /usr/local/lib/python3.10/dist-packages (from janus==1.0.0) (1.0.10)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (from janus==1.0.0) (0.34.2)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from janus==1.0.0) (0.2.0)\n",
            "Collecting attrdict (from janus==1.0.0)\n",
            "  Downloading attrdict-2.0.1-py2.py3-none-any.whl.metadata (6.7 kB)\n",
            "Requirement already satisfied: einops in /usr/local/lib/python3.10/dist-packages (from janus==1.0.0) (0.8.0)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from timm>=0.9.16->janus==1.0.0) (0.19.1+cu121)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from timm>=0.9.16->janus==1.0.0) (6.0.2)\n",
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            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from timm>=0.9.16->janus==1.0.0) (0.4.5)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=2.0.1->janus==1.0.0) (3.16.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=2.0.1->janus==1.0.0) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=2.0.1->janus==1.0.0) (1.13.3)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=2.0.1->janus==1.0.0) (3.4.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=2.0.1->janus==1.0.0) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=2.0.1->janus==1.0.0) (2024.6.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.38.2->janus==1.0.0) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.38.2->janus==1.0.0) (24.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.38.2->janus==1.0.0) (2024.9.11)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers>=4.38.2->janus==1.0.0) (2.32.3)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.38.2->janus==1.0.0) (0.19.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.38.2->janus==1.0.0) (4.66.5)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate->janus==1.0.0) (5.9.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from attrdict->janus==1.0.0) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=2.0.1->janus==1.0.0) (3.0.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.38.2->janus==1.0.0) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.38.2->janus==1.0.0) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.38.2->janus==1.0.0) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.38.2->janus==1.0.0) (2024.8.30)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=2.0.1->janus==1.0.0) (1.3.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->timm>=0.9.16->janus==1.0.0) (10.4.0)\n",
            "Downloading attrdict-2.0.1-py2.py3-none-any.whl (9.9 kB)\n",
            "Building wheels for collected packages: janus\n",
            "  Building editable for janus (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for janus: filename=janus-1.0.0-0.editable-py3-none-any.whl size=13213 sha256=6549905c4ca7d80ffe93b29502638be9402c9b6e9ad02be1fe7c1de49807f601\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-3bjwhhza/wheels/db/43/fb/dfa5a5a63d3fcf28c66693ac6be0a596a4e95ee0b525246235\n",
            "Successfully built janus\n",
            "Installing collected packages: attrdict, janus\n",
            "Successfully installed attrdict-2.0.1 janus-1.0.0\n",
            "Collecting flash-attn\n",
            "  Downloading flash_attn-2.6.3.tar.gz (2.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m28.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from flash-attn) (2.4.1+cu121)\n",
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            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->flash-attn) (3.0.1)\n",
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            "Building wheels for collected packages: flash-attn\n",
            "  Building wheel for flash-attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for flash-attn: filename=flash_attn-2.6.3-cp310-cp310-linux_x86_64.whl size=187309225 sha256=237ef9c6157db394e1ddde4ba609a21ebb98382377a27041edc09318801a6f24\n",
            "  Stored in directory: /root/.cache/pip/wheels/7e/e3/c3/89c7a2f3c4adc07cd1c675f8bb7b9ad4d18f64a72bccdfe826\n",
            "Successfully built flash-attn\n",
            "Installing collected packages: flash-attn\n",
            "Successfully installed flash-attn-2.6.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import torch\n",
        "from transformers import AutoModelForCausalLM\n",
        "from janus.models import MultiModalityCausalLM, VLChatProcessor\n",
        "from janus.utils.io import load_pil_images\n",
        "\n",
        "# specify the path to the model\n",
        "model_path = \"deepseek-ai/Janus-1.3B\"\n",
        "vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)\n",
        "tokenizer = vl_chat_processor.tokenizer\n",
        "\n",
        "vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(\n",
        "    model_path, trust_remote_code=True\n",
        ")\n",
        "vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()\n",
        "\n",
        "conversation = [\n",
        "    {\n",
        "        \"role\": \"User\",\n",
        "        \"content\": \"<image_placeholder>\\nConvert the formula into latex code.\",\n",
        "        \"images\": [\"images/equation.png\"],\n",
        "    },\n",
        "    {\"role\": \"Assistant\", \"content\": \"\"},\n",
        "]\n",
        "\n",
        "# load images and prepare for inputs\n",
        "pil_images = load_pil_images(conversation)\n",
        "prepare_inputs = vl_chat_processor(\n",
        "    conversations=conversation, images=pil_images, force_batchify=True\n",
        ").to(vl_gpt.device)\n",
        "\n",
        "# # run image encoder to get the image embeddings\n",
        "inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)\n",
        "\n",
        "# # run the model to get the response\n",
        "outputs = vl_gpt.language_model.generate(\n",
        "    inputs_embeds=inputs_embeds,\n",
        "    attention_mask=prepare_inputs.attention_mask,\n",
        "    pad_token_id=tokenizer.eos_token_id,\n",
        "    bos_token_id=tokenizer.bos_token_id,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        "    max_new_tokens=512,\n",
        "    do_sample=False,\n",
        "    use_cache=True,\n",
        ")\n",
        "\n",
        "answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)\n",
        "print(f\"{prepare_inputs['sft_format'][0]}\", answer)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547,
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        "id": "X7B6TWTvXxRu",
        "outputId": "072387f9-6404-48f1-bcbb-f1acddf1c77f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python version is above 3.10, patching the collections module.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/image_processing_auto.py:513: FutureWarning: The image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/347 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "763db730135046ad82de98efeff17d43"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/106k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e5de18967479416bacedefaeee42e0f7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/4.72M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "957f5ed508f843909daf9a3b75470e8f"
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          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/97.0 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file you can ignore this message.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "processor_config.json:   0%|          | 0.00/210 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "5133262f73ca4e5daf973095bf4f9b4c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some kwargs in processor config are unused and will not have any effect: sft_format, ignore_id, add_special_token, mask_prompt, num_image_tokens, image_tag. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Add image tag = <image_placeholder> to the tokenizer\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.45k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f838d4eb98b54fa0a541a1cef8946969"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/4.18G [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Flash Attention 2.0 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in LlamaForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `torch_dtype` argument. Example: `model = AutoModel.from_pretrained(\"openai/whisper-tiny\", attn_implementation=\"flash_attention_2\", torch_dtype=torch.float16)`\n",
            "Flash Attention 2.0 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in LlamaModel is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `torch_dtype` argument. Example: `model = AutoModel.from_pretrained(\"openai/whisper-tiny\", attn_implementation=\"flash_attention_2\", torch_dtype=torch.float16)`\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.\n",
            "\n",
            "User: <image_placeholder>\n",
            "Convert the formula into latex code.\n",
            "\n",
            "Assistant: Sure, here is the LaTeX code for the formula:\n",
            "\n",
            "\\[ A_n = a_0 \\left[ 1 + \\frac{3}{4} \\sum_{k=1}^{n} \\left( \\frac{4}{9} \\right)^k \\right] \\]\n"
          ]
        }
      ]
    }
  ]
}