{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-ETtu9CvVMDR"
   },
   "source": [
    "<h1>Chapter 9 - Multimodal Large Language Models</h1>\n",
    "<i>Adding vision capabilities to Large Language Models.</i>\n",
    "\n",
    "<a href=\"https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961\"><img src=\"https://img.shields.io/badge/Buy%20the%20Book!-grey?logo=amazon\"></a>\n",
    "<a href=\"https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/\"><img src=\"https://img.shields.io/badge/O'Reilly-white.svg?logo=data:image/svg%2bxml;base64,PHN2ZyB3aWR0aD0iMzQiIGhlaWdodD0iMjciIHZpZXdCb3g9IjAgMCAzNCAyNyIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPGNpcmNsZSBjeD0iMTMiIGN5PSIxNCIgcj0iMTEiIHN0cm9rZT0iI0Q0MDEwMSIgc3Ryb2tlLXdpZHRoPSI0Ii8+CjxjaXJjbGUgY3g9IjMwLjUiIGN5PSIzLjUiIHI9IjMuNSIgZmlsbD0iI0Q0MDEwMSIvPgo8L3N2Zz4K\"></a>\n",
    "<a href=\"https://github.com/HandsOnLLM/Hands-On-Large-Language-Models\"><img src=\"https://img.shields.io/badge/GitHub%20Repository-black?logo=github\"></a>\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter09/Chapter%209%20-%20Multimodal%20Large%20Language%20Models.ipynb)\n",
    "\n",
    "---\n",
    "\n",
    "This notebook is for Chapter 9 of the [Hands-On Large Language Models](https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961) book by [Jay Alammar](https://www.linkedin.com/in/jalammar) and [Maarten Grootendorst](https://www.linkedin.com/in/mgrootendorst/).\n",
    "\n",
    "---\n",
    "\n",
    "<a href=\"https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961\">\n",
    "<img src=\"https://raw.githubusercontent.com/HandsOnLLM/Hands-On-Large-Language-Models/main/images/book_cover.png\" width=\"350\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [OPTIONAL] - Installing Packages on <img src=\"https://colab.google/static/images/icons/colab.png\" width=100>\n",
    "\n",
    "If you are viewing this notebook on Google Colab (or any other cloud vendor), you need to **uncomment and run** the following codeblock to install the dependencies for this chapter:\n",
    "\n",
    "---\n",
    "\n",
    "💡 **NOTE**: We will want to use a GPU to run the examples in this notebook. In Google Colab, go to\n",
    "**Runtime > Change runtime type > Hardware accelerator > GPU > GPU type > T4**.\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%capture\n",
    "# !pip install matplotlib transformers datasets accelerate sentence-transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NMF9p8qK58Ou"
   },
   "source": [
    "## CLIP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UHUY1IBzZkgy"
   },
   "outputs": [],
   "source": [
    "from urllib.request import urlopen\n",
    "from PIL import Image\n",
    "\n",
    "# Load an AI-generated image of a puppy playing in the snow\n",
    "puppy_path = \"https://raw.githubusercontent.com/HandsOnLLM/Hands-On-Large-Language-Models/main/chapter09/images/puppy.png\"\n",
    "image = Image.open(urlopen(puppy_path)).convert(\"RGB\")\n",
    "caption = \"a puppy playing in the snow\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 529
    },
    "executionInfo": {
     "elapsed": 2092,
     "status": "ok",
     "timestamp": 1719578962939,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "kHOmvsRFZp4B",
    "outputId": "0d5bd3c8-7ea3-456e-e95f-3442bcb50c51"
   },
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 485408 characters]",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=512x512>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fpAwAij0at4o"
   },
   "source": [
    "### Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    },
    "executionInfo": {
     "elapsed": 35412,
     "status": "ok",
     "timestamp": 1719578998349,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "JLaZrGSxavbk",
    "outputId": "9709bf42-1e51-4f90-e2ee-1d634d8be22f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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"
     ]
    },
    {
     "data": {
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       "model_id": "0505dc900e8a4577abad65e740c6d17b",
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       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/592 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "c9b30582727041a49e8ea5a38a3ec45a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "vocab.json:   0%|          | 0.00/862k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "merges.txt:   0%|          | 0.00/525k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/2.22M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d9dbdbe28964632a7e0023221e5b199",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/389 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e365c9023edc43278f44a0827a2e510c",
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      },
      "text/plain": [
       "config.json:   0%|          | 0.00/4.19k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e7f855901e142a785345b1d69b84e9e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "preprocessor_config.json:   0%|          | 0.00/316 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6dfa69dde5a148369152c35ae08b0c76",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pytorch_model.bin:   0%|          | 0.00/605M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import CLIPTokenizerFast, CLIPProcessor, CLIPModel\n",
    "\n",
    "model_id = \"openai/clip-vit-base-patch32\"\n",
    "\n",
    "# Load a tokenizer to preprocess the text\n",
    "clip_tokenizer = CLIPTokenizerFast.from_pretrained(model_id)\n",
    "\n",
    "# Load a processor to preprocess the images\n",
    "clip_processor = CLIPProcessor.from_pretrained(model_id)\n",
    "\n",
    "# Main model for generating text and image embeddings\n",
    "model = CLIPModel.from_pretrained(model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1719578998349,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "qSx-X0-ecBv_",
    "outputId": "b4f29f5f-fca7-44ba-821b-e6f3f26209cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': tensor([[49406,   320,  6829,  1629,   530,   518,  2583, 49407]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1]])}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tokenize our input\n",
    "inputs = clip_tokenizer(caption, return_tensors=\"pt\")\n",
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1719578998349,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "ZVyHP2YwdUqB",
    "outputId": "7b3bc5e1-2943-460b-e10a-adb6561e4e73"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<|startoftext|>',\n",
       " 'a</w>',\n",
       " 'puppy</w>',\n",
       " 'playing</w>',\n",
       " 'in</w>',\n",
       " 'the</w>',\n",
       " 'snow</w>',\n",
       " '<|endoftext|>']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert our input back to tokens\n",
    "clip_tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 850,
     "status": "ok",
     "timestamp": 1719578999197,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "MEj4b22qeCm8",
    "outputId": "cb406e88-828c-41d4-b824-c7e8b4d1fc99"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 512])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a text embedding\n",
    "text_embedding = model.get_text_features(**inputs)\n",
    "text_embedding.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1719578999197,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "KwNQpueperGn",
    "outputId": "ac24e230-1f32-43e3-a3a3-5534080f165a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 3, 224, 224])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocess image\n",
    "processed_image = clip_processor(\n",
    "    text=None, images=image, return_tensors='pt'\n",
    ")['pixel_values']\n",
    "\n",
    "processed_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1719578999197,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "4P9I7JsGf7Km",
    "outputId": "0e6e29c3-5a8e-46a9-e49e-fd9d32f370d4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.5, 223.5, 223.5, -0.5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "[encoded data, 117608 characters]",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Prepare image for visualization\n",
    "img = processed_image.squeeze(0)\n",
    "img = img.permute(*torch.arange(img.ndim - 1, -1, -1))\n",
    "img = np.einsum('ijk->jik', img)\n",
    "\n",
    "# Visualize preprocessed image\n",
    "plt.imshow(img)\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1719578999197,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "gVFva16chlS3",
    "outputId": "07ded48b-35ab-423c-d59a-4423b3b46f7d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 512])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the image embedding\n",
    "image_embedding = model.get_image_features(processed_image)\n",
    "image_embedding.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1719578999197,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "6IwyPRa0R964",
    "outputId": "313ed48d-c6d6-4ed2-b883-b0174a688115"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.33149636]], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize the embeddings\n",
    "text_embedding /= text_embedding.norm(dim=-1, keepdim=True)\n",
    "image_embedding /= image_embedding.norm(dim=-1, keepdim=True)\n",
    "\n",
    "# Calculate their similarity\n",
    "text_embedding = text_embedding.detach().cpu().numpy()\n",
    "image_embedding = image_embedding.detach().cpu().numpy()\n",
    "score = text_embedding @ image_embedding.T\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-rKItoQAkQwU"
   },
   "source": [
    "### More Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kMsrjH7xkSWH"
   },
   "outputs": [],
   "source": [
    "from urllib.request import urlopen\n",
    "from PIL import Image\n",
    "\n",
    "# Load an AI-generated image of a puppy playing in the snow\n",
    "cat_path = \"https://raw.githubusercontent.com/HandsOnLLM/Hands-On-Large-Language-Models/main/chapter09/images/cat.png\"\n",
    "car_path = \"https://raw.githubusercontent.com/HandsOnLLM/Hands-On-Large-Language-Models/main/chapter09/images/car.png\"\n",
    "paths = [puppy_path, cat_path, car_path]\n",
    "images = [Image.open(urlopen(path)).convert(\"RGBA\") for path in paths]\n",
    "captions = [\n",
    "    \"a puppy playing in the snow\",\n",
    "    \"a pixelated image of a cute cat\",\n",
    "    \"A supercar on the road \\nwith the sunset in the background\"\n",
    "]\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# Embed all images\n",
    "image_embeddings = []\n",
    "for image in images:\n",
    "  image_processed = clip_processor(images=image, return_tensors='pt')['pixel_values']\n",
    "  image_embedding = model.get_image_features(image_processed).detach().cpu().numpy()[0]\n",
    "  image_embeddings.append(image_embedding)\n",
    "image_embeddings = np.array(image_embeddings)\n",
    "\n",
    "# Embed all captions\n",
    "text_embeddings = []\n",
    "for caption in captions:\n",
    "  inputs = clip_tokenizer(caption, return_tensors=\"pt\")\n",
    "  text_emb = model.get_text_features(**inputs).detach().cpu().numpy()[0]\n",
    "  text_embeddings.append(text_emb)\n",
    "text_embeddings = np.array(text_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "F5ABSnFGlGeW"
   },
   "outputs": [],
   "source": [
    "# Calculate cosine similarity between images and captions\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "sim_matrix = cosine_similarity(image_embeddings, text_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 2948,
     "status": "ok",
     "timestamp": 1719579005121,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "cmIyzyTXlIRN",
    "outputId": "c5378a78-2f0d-4a3b-b511-60afdd202caf"
   },
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 293848 characters]",
      "text/plain": [
       "<Figure size 2000x1400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create base figure\n",
    "plt.figure(figsize=(20, 14))\n",
    "plt.imshow(sim_matrix, cmap='viridis')\n",
    "\n",
    "# Adjust ticks with correct labels\n",
    "plt.yticks(range(len(captions)), captions, fontsize=18)\n",
    "plt.xticks([])\n",
    "\n",
    "# Visualize\n",
    "for i, image in enumerate(images):\n",
    "    plt.imshow(image, extent=(i - 0.5, i + 0.5, -1.6, -0.6), origin=\"lower\")\n",
    "\n",
    "# Add the captions at the correct indices\n",
    "for x in range(sim_matrix.shape[1]):\n",
    "    for y in range(sim_matrix.shape[0]):\n",
    "        plt.text(x, y, f\"{sim_matrix[y, x]:.2f}\", ha=\"center\", va=\"center\", size=30)\n",
    "\n",
    "# Remove unnecessary spines\n",
    "for side in [\"left\", \"top\", \"right\", \"bottom\"]:\n",
    "  plt.gca().spines[side].set_visible(False)\n",
    "\n",
    "# Resize blocks\n",
    "plt.xlim([-0.5, len(captions) - 0.5])\n",
    "plt.ylim([len(captions) + 0.5, -2])\n",
    "# plt.title(\"Similarity Matrix\", size=20)\n",
    "plt.savefig(\"sim_matrix.png\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E5it3Vqi0Bf5"
   },
   "source": [
    "### SBERT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "executionInfo": {
     "elapsed": 6943,
     "status": "ok",
     "timestamp": 1719577271481,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "4XFEvSss0DQf",
    "outputId": "773b7b47-8361-406a-feab-ea6579269af3"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31d685c653e44ec1b4fee196186ea494",
       "version_major": 2,
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      "text/plain": [
       "modules.json:   0%|          | 0.00/122 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "e684e1bdc96c4cbb9606a4c68f2ba2c5",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "README.md:   0%|          | 0.00/1.91k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "pytorch_model.bin:   0%|          | 0.00/605M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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      "text/plain": [
       "0_CLIPModel/config.json:   0%|          | 0.00/4.03k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5675248c79754343ac227c8cd24552bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0_CLIPModel/special_tokens_map.json:   0%|          | 0.00/389 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f7f405f1bc04255b81e43551c6d6dbf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0_CLIPModel/vocab.json:   0%|          | 0.00/961k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f52ce94c32fb4fa7a63f25882e6ae340",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0_CLIPModel/tokenizer_config.json:   0%|          | 0.00/604 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "febe44c9545a45eaa922d6b80c8a0ebd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0_CLIPModel/preprocessor_config.json:   0%|          | 0.00/316 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "012632ea2f384b08b51ec4e392343188",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0_CLIPModel/merges.txt:   0%|          | 0.00/525k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.3315, 0.1863, 0.1084],\n",
      "        [0.1488, 0.3463, 0.0947],\n",
      "        [0.0762, 0.1260, 0.3098]])\n"
     ]
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer, util\n",
    "\n",
    "# Load SBERT-compatible CLIP model\n",
    "model = SentenceTransformer('clip-ViT-B-32')\n",
    "\n",
    "# Encode the images\n",
    "image_embeddings = model.encode(images)\n",
    "\n",
    "# Encode the captions\n",
    "text_embeddings = model.encode(captions)\n",
    "\n",
    "#Compute cosine similarities\n",
    "sim_matrix = util.cos_sim(image_embeddings, text_embeddings)\n",
    "print(sim_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JsLAEG2a5-5r"
   },
   "source": [
    "## BLIP-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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    },
    "executionInfo": {
     "elapsed": 655749,
     "status": "ok",
     "timestamp": 1719579700964,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "jnNrafWu6BSJ",
    "outputId": "4e7ca3ca-2a6c-4f63-ceb1-7581cadb13d5"
   },
   "outputs": [
    {
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    {
     "data": {
      "text/plain": [
       "Blip2ForConditionalGeneration(\n",
       "  (vision_model): Blip2VisionModel(\n",
       "    (embeddings): Blip2VisionEmbeddings(\n",
       "      (patch_embedding): Conv2d(3, 1408, kernel_size=(14, 14), stride=(14, 14))\n",
       "    )\n",
       "    (encoder): Blip2Encoder(\n",
       "      (layers): ModuleList(\n",
       "        (0-38): 39 x Blip2EncoderLayer(\n",
       "          (self_attn): Blip2Attention(\n",
       "            (dropout): Dropout(p=0.0, inplace=False)\n",
       "            (qkv): Linear(in_features=1408, out_features=4224, bias=True)\n",
       "            (projection): Linear(in_features=1408, out_features=1408, bias=True)\n",
       "          )\n",
       "          (layer_norm1): LayerNorm((1408,), eps=1e-06, elementwise_affine=True)\n",
       "          (mlp): Blip2MLP(\n",
       "            (activation_fn): GELUActivation()\n",
       "            (fc1): Linear(in_features=1408, out_features=6144, bias=True)\n",
       "            (fc2): Linear(in_features=6144, out_features=1408, bias=True)\n",
       "          )\n",
       "          (layer_norm2): LayerNorm((1408,), eps=1e-06, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (post_layernorm): LayerNorm((1408,), eps=1e-06, elementwise_affine=True)\n",
       "  )\n",
       "  (qformer): Blip2QFormerModel(\n",
       "    (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "    (encoder): Blip2QFormerEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0): Blip2QFormerLayer(\n",
       "          (attention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (crossattention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=1408, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=1408, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate_query): Blip2QFormerIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output_query): Blip2QFormerOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): Blip2QFormerLayer(\n",
       "          (attention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate_query): Blip2QFormerIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output_query): Blip2QFormerOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): Blip2QFormerLayer(\n",
       "          (attention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (crossattention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=1408, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=1408, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate_query): Blip2QFormerIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output_query): Blip2QFormerOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): Blip2QFormerLayer(\n",
       "          (attention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate_query): Blip2QFormerIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output_query): Blip2QFormerOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): Blip2QFormerLayer(\n",
       "          (attention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (crossattention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=1408, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=1408, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate_query): Blip2QFormerIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output_query): Blip2QFormerOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): Blip2QFormerLayer(\n",
       "          (attention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate_query): Blip2QFormerIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output_query): Blip2QFormerOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (6): Blip2QFormerLayer(\n",
       "          (attention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (crossattention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=1408, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=1408, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate_query): Blip2QFormerIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output_query): Blip2QFormerOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (7): Blip2QFormerLayer(\n",
       "          (attention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate_query): Blip2QFormerIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output_query): Blip2QFormerOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (8): Blip2QFormerLayer(\n",
       "          (attention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (crossattention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=1408, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=1408, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate_query): Blip2QFormerIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output_query): Blip2QFormerOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (9): Blip2QFormerLayer(\n",
       "          (attention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate_query): Blip2QFormerIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output_query): Blip2QFormerOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (10): Blip2QFormerLayer(\n",
       "          (attention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (crossattention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=1408, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=1408, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate_query): Blip2QFormerIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output_query): Blip2QFormerOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (11): Blip2QFormerLayer(\n",
       "          (attention): Blip2QFormerAttention(\n",
       "            (attention): Blip2QFormerMultiHeadAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): Blip2QFormerSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate_query): Blip2QFormerIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output_query): Blip2QFormerOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (language_projection): Linear(in_features=768, out_features=2560, bias=True)\n",
       "  (language_model): OPTForCausalLM(\n",
       "    (model): OPTModel(\n",
       "      (decoder): OPTDecoder(\n",
       "        (embed_tokens): Embedding(50272, 2560, padding_idx=1)\n",
       "        (embed_positions): OPTLearnedPositionalEmbedding(2050, 2560)\n",
       "        (final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
       "        (layers): ModuleList(\n",
       "          (0-31): 32 x OPTDecoderLayer(\n",
       "            (self_attn): OPTAttention(\n",
       "              (k_proj): Linear(in_features=2560, out_features=2560, bias=True)\n",
       "              (v_proj): Linear(in_features=2560, out_features=2560, bias=True)\n",
       "              (q_proj): Linear(in_features=2560, out_features=2560, bias=True)\n",
       "              (out_proj): Linear(in_features=2560, out_features=2560, bias=True)\n",
       "            )\n",
       "            (activation_fn): ReLU()\n",
       "            (self_attn_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
       "            (fc1): Linear(in_features=2560, out_features=10240, bias=True)\n",
       "            (fc2): Linear(in_features=10240, out_features=2560, bias=True)\n",
       "            (final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (lm_head): Linear(in_features=2560, out_features=50272, bias=False)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import AutoProcessor, Blip2ForConditionalGeneration\n",
    "import torch\n",
    "\n",
    "# Load processor and main model\n",
    "blip_processor = AutoProcessor.from_pretrained(\n",
    "    \"Salesforce/blip2-opt-2.7b\",\n",
    "    revision=\"51572668da0eb669e01a189dc22abe6088589a24\"  # Choose specific model because of: https://huggingface.co/Salesforce/blip2-opt-2.7b/discussions/39\n",
    ")\n",
    "model = Blip2ForConditionalGeneration.from_pretrained(\n",
    "    \"Salesforce/blip2-opt-2.7b\",\n",
    "    revision=\"51572668da0eb669e01a189dc22abe6088589a24\",\n",
    "    torch_dtype=torch.float16\n",
    ")\n",
    "\n",
    "# Send the model to GPU to speed up inference\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xI_UWlR8c_Ey"
   },
   "source": [
    "### Preprocessing Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 537
    },
    "executionInfo": {
     "elapsed": 1726,
     "status": "ok",
     "timestamp": 1719579702683,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "W6YeV_TEQFAA",
    "outputId": "1fa221ec-a0fc-4347-9182-d5d6fc47a859"
   },
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 295140 characters]",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=492x520>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load image of a supercar\n",
    "car_path = \"https://raw.githubusercontent.com/HandsOnLLM/Hands-On-Large-Language-Models/main/chapter09/images/car.png\"\n",
    "image = Image.open(urlopen(car_path)).convert(\"RGB\")\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1719579702684,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "flZeiDFmQIIg",
    "outputId": "036a4e4f-f6a1-4e36-f355-3099df6427d6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 3, 224, 224])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocess the image\n",
    "inputs = blip_processor(image, return_tensors=\"pt\").to(device, torch.float16)\n",
    "inputs[\"pixel_values\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1719579702684,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "0lHJ7LiKUhWf",
    "outputId": "77c88807-b8a6-4ca0-bee0-3fe60d49cb0c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 67280 characters]",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Convert to numpy and go from (1, 3, 224, 224) to (224, 224, 3) in shape\n",
    "image_inputs = inputs[\"pixel_values\"][0].detach().cpu().numpy()\n",
    "image_inputs = np.einsum('ijk->kji', image_inputs)\n",
    "image_inputs = np.einsum('ijk->jik', image_inputs)\n",
    "\n",
    "# Scale image inputs to 0-255 to represent RGB values\n",
    "scaler = MinMaxScaler(feature_range=(0, 255))\n",
    "image_inputs = scaler.fit_transform(image_inputs.reshape(-1, image_inputs.shape[-1])).reshape(image_inputs.shape)\n",
    "image_inputs = np.array(image_inputs, dtype=np.uint8)\n",
    "\n",
    "# Convert numpy array to Image\n",
    "Image.fromarray(image_inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pDoBrN56dBTF"
   },
   "source": [
    "### Preprocessing Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1719579702684,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "nHJkauW9dFhB",
    "outputId": "16ca14e5-d00f-48f5-fe9f-e3d77b5e4de5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GPT2TokenizerFast(name_or_path='Salesforce/blip2-opt-2.7b', vocab_size=50265, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '</s>', 'eos_token': '</s>', 'unk_token': '</s>', 'pad_token': '<pad>'}, clean_up_tokenization_spaces=True),  added_tokens_decoder={\n",
       "\t1: AddedToken(\"<pad>\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n",
       "\t2: AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n",
       "}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blip_processor.tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1719579702684,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "21Zh5Rx8QLAy",
    "outputId": "53b71585-c169-430b-f594-28393287f6f3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['</s>', 'Her', 'Ġvocal', 'ization', 'Ġwas', 'Ġremarkably', 'Ġmel', 'odic']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocess the text\n",
    "text = \"Her vocalization was remarkably melodic\"\n",
    "token_ids = blip_processor(image, text=text, return_tensors=\"pt\")\n",
    "token_ids = token_ids.to(device, torch.float16)[\"input_ids\"][0]\n",
    "\n",
    "# Convert input ids back to tokens\n",
    "tokens = blip_processor.tokenizer.convert_ids_to_tokens(token_ids)\n",
    "tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1719579702684,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "TFnL_Yt3elPX",
    "outputId": "4f671be1-96de-499c-af19-69606a9395c3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['</s>', 'Her', '_vocal', 'ization', '_was', '_remarkably', '_mel', 'odic']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace the space token with an underscore\n",
    "tokens = [token.replace(\"Ġ\", \"_\") for token in tokens]\n",
    "tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fDx5elnHeHnT"
   },
   "source": [
    "### Use Case 1: Image Captioning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 537
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1719579704116,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "gFJ9-l8c8u3i",
    "outputId": "a5e4b07b-c469-4991-ebf0-5c1bfe374d28"
   },
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 295140 characters]",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=492x520>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load an AI-generated image of a supercar\n",
    "image = Image.open(urlopen(car_path)).convert(\"RGB\")\n",
    "\n",
    "# Convert an image into inputs and preprocess it\n",
    "inputs = blip_processor(image, return_tensors=\"pt\").to(device, torch.float16)\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 111
    },
    "executionInfo": {
     "elapsed": 467,
     "status": "ok",
     "timestamp": 1719579704579,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "YHIoTZa6eEaR",
    "outputId": "786a25c1-c523-49e8-93e2-f1d048a8ffc5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
      "  return F.conv2d(input, weight, bias, self.stride,\n",
      "Both `max_new_tokens` (=20) and `max_length`(=51) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'an orange supercar driving on the road at sunset'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate image ids to be passed to the decoder (LLM)\n",
    "generated_ids = model.generate(**inputs, max_new_tokens=20)\n",
    "\n",
    "# Generate text from the image ids\n",
    "generated_text = blip_processor.batch_decode(generated_ids, skip_special_tokens=True)\n",
    "generated_text = generated_text[0].strip()\n",
    "generated_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 499
    },
    "executionInfo": {
     "elapsed": 2283,
     "status": "ok",
     "timestamp": 1719579706860,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "JbGnyiYhlfQi",
    "outputId": "153b7167-f70c-45b8-e7b2-fceee043a32c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 533656 characters]",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=736x482>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://upload.wikimedia.org/wikipedia/commons/7/70/Rorschach_blot_01.jpg\"\n",
    "image = Image.open(urlopen(url)).convert(\"RGB\")\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "executionInfo": {
     "elapsed": 1204,
     "status": "ok",
     "timestamp": 1719579708060,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "-bIxPEySrJW0",
    "outputId": "3671e76b-b112-4892-aade-452a81d1c9cb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Both `max_new_tokens` (=20) and `max_length`(=51) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'a black and white ink drawing of a bat'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load rorschach image\n",
    "url = \"https://upload.wikimedia.org/wikipedia/commons/7/70/Rorschach_blot_01.jpg\"\n",
    "image = Image.open(urlopen(url)).convert(\"RGB\")\n",
    "\n",
    "# Generate caption\n",
    "inputs = blip_processor(image, return_tensors=\"pt\").to(device, torch.float16)\n",
    "generated_ids = model.generate(**inputs, max_new_tokens=20)\n",
    "generated_text = blip_processor.batch_decode(generated_ids, skip_special_tokens=True)\n",
    "generated_text = generated_text[0].strip()\n",
    "generated_text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XjHfQxWGkVYF"
   },
   "source": [
    "### Use Case 2: Visual Question Answering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "269NtauNFIze"
   },
   "outputs": [],
   "source": [
    "# Load an AI-generated image of a supercar\n",
    "image = Image.open(urlopen(car_path)).convert(\"RGB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1719579708060,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "_NsMF_hMCIIj",
    "outputId": "c38e3114-b1ba-40ed-9e76-2bf13d970d4f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Both `max_new_tokens` (=30) and `max_length`(=51) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'A sports car driving on the road at sunset'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual Question Answering\n",
    "prompt = \"Question: Write down what you see in this picture. Answer:\"\n",
    "\n",
    "# Process both the image and the prompt\n",
    "inputs = blip_processor(image, text=prompt, return_tensors=\"pt\").to(device, torch.float16)\n",
    "\n",
    "# Generate text\n",
    "generated_ids = model.generate(**inputs, max_new_tokens=30)\n",
    "generated_text = blip_processor.batch_decode(generated_ids, skip_special_tokens=True)\n",
    "generated_text = generated_text[0].strip()\n",
    "generated_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1719579708060,
     "user": {
      "displayName": "Maarten Grootendorst",
      "userId": "11015108362723620659"
     },
     "user_tz": -120
    },
    "id": "NtpMIgUaScBD",
    "outputId": "c1fb7526-7557-4304-d0c1-8b7c0725027f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Both `max_new_tokens` (=30) and `max_length`(=51) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'$1,000,000'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chat-like prompting\n",
    "prompt = \"Question: Write down what you see in this picture. Answer: A sports car driving on the road at sunset. Question: What would it cost me to drive that car? Answer:\"\n",
    "\n",
    "# Generate output\n",
    "inputs = blip_processor(image, text=prompt, return_tensors=\"pt\").to(device, torch.float16)\n",
    "generated_ids = model.generate(**inputs, max_new_tokens=30)\n",
    "generated_text = blip_processor.batch_decode(generated_ids, skip_special_tokens=True)\n",
    "generated_text = generated_text[0].strip()\n",
    "generated_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
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      "displayName": "Maarten Grootendorst",
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    },
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    "outputId": "bb13c5e4-2aad-46d1-ce6c-29b11491f6fa"
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e6e7ef762134ab4bd886ae583104c6e",
       "version_major": 2,
       "version_minor": 0
      },
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       "VBox(children=(Output(), Text(value='', continuous_update=False)), layout=Layout(display='inline-flex', flex_f…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import HTML, display\n",
    "import ipywidgets as widgets\n",
    "\n",
    "def text_eventhandler(*args):\n",
    "  question = args[0][\"new\"]\n",
    "  if question:\n",
    "    args[0][\"owner\"].value = \"\"\n",
    "\n",
    "    # Create prompt\n",
    "    if not memory:\n",
    "      prompt = \" Question: \" + question + \" Answer:\"\n",
    "    else:\n",
    "      template = \"Question: {} Answer: {}.\"\n",
    "      prompt = \" \".join(\n",
    "          [\n",
    "              template.format(memory[i][0], memory[i][1])\n",
    "              for i in range(len(memory))\n",
    "          ]\n",
    "      ) + \" Question: \" + question + \" Answer:\"\n",
    "\n",
    "    # Generate text\n",
    "    inputs = blip_processor(image, text=prompt, return_tensors=\"pt\")\n",
    "    inputs = inputs.to(device, torch.float16)\n",
    "    generated_ids = model.generate(**inputs, max_new_tokens=100)\n",
    "    generated_text = blip_processor.batch_decode(\n",
    "        generated_ids,\n",
    "        skip_special_tokens=True\n",
    "    )\n",
    "    generated_text = generated_text[0].strip().split(\"Question\")[0]\n",
    "\n",
    "    # Update memory\n",
    "    memory.append((question, generated_text))\n",
    "\n",
    "    # Assign to output\n",
    "    output.append_display_data(HTML(\"<b>USER:</b> \" + question))\n",
    "    output.append_display_data(HTML(\"<b>BLIP-2:</b> \" + generated_text))\n",
    "    output.append_display_data(HTML(\"<br>\"))\n",
    "\n",
    "# Prepare widgets\n",
    "in_text = widgets.Text()\n",
    "in_text.continuous_update = False\n",
    "in_text.observe(text_eventhandler, \"value\")\n",
    "output = widgets.Output()\n",
    "memory = []\n",
    "\n",
    "# Display chat box\n",
    "display(\n",
    "    widgets.VBox(\n",
    "        children=[output, in_text],\n",
    "        layout=widgets.Layout(display=\"inline-flex\", flex_flow=\"column-reverse\"),\n",
    "    )\n",
    ")"
   ]
  },
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   "execution_count": null,
   "metadata": {
    "id": "fOHMAa6_MEgX"
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