{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zHkkJ5CFY2xT"
   },
   "source": [
    "# Set-up environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PuWVAAOinC8q",
    "outputId": "6b72f466-ba6d-484a-cfb7-333e46de2348",
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install --upgrade -q accelerate bitsandbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Im9YhnCi6cbZ",
    "outputId": "41a81b1a-a43d-43d0-805d-5dc8b8d723a9",
    "tags": []
   },
   "outputs": [],
   "source": [
    "!rm -r transformers\n",
    "!git clone -b llava_improvements https://github.com/NielsRogge/transformers.git\n",
    "!cd transformers\n",
    "!pip install -q ./transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Hfant_DrYrL4",
    "outputId": "18d69882-9778-4c2f-f2e1-746bdc56fd1a",
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install git+https://github.com/huggingface/transformers.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R9eOO4G5kiOA"
   },
   "source": [
    "## Load model and processor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "base_uri": "https://localhost:8080/",
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    },
    "id": "OD8C08WYY6db",
    "outputId": "5ec9a70a-cef7-4ad7-c218-436a0bfada9e",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoProcessor, LlavaForConditionalGeneration\n",
    "from transformers import BitsAndBytesConfig\n",
    "import torch\n",
    "\n",
    "quantization_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_compute_dtype=torch.float16\n",
    ")\n",
    "\n",
    "\n",
    "model_id = \"llava-hf/llava-1.5-7b-hf\"\n",
    "\n",
    "processor = AutoProcessor.from_pretrained(model_id)\n",
    "model = LlavaForConditionalGeneration.from_pretrained(model_id, quantization_config=quantization_config, device_map=\"auto\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dhkgD9rfkuFY"
   },
   "source": [
    "## Prepare image and text for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "MjlIoxq3u8ef",
    "outputId": "43365ff2-26dc-40f0-976f-e17d4ad2b5fc",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from PIL import Image\n",
    "\n",
    "image1 = Image.open('data/clock.jpeg')\n",
    "display(image1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LdzOupJqxKVZ"
   },
   "source": [
    "In the prompt, you can refer to images using the special \\<image> token. To indicate which text comes from a human vs. the model, one uses USER and ASSISTANT respectively. The format looks as follows:\n",
    "\n",
    "```bash\n",
    "USER: <image>\\n<prompt>\\nASSISTANT:\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BOnex7yYKGIE"
   },
   "source": [
    "In other words, you always need to end your prompt with `ASSISTANT:`. Here we will perform batched generation (i.e generating on several prompts)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XbTjsy-uktg5",
    "outputId": "36c66732-2c2b-4153-d108-0bf439d897aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_ids torch.Size([1, 130])\n",
      "attention_mask torch.Size([1, 130])\n",
      "pixel_values torch.Size([1, 3, 336, 336])\n"
     ]
    }
   ],
   "source": [
    "caption = 'an old fashioned clock sitting on top of a table'\n",
    "\n",
    "user_input = \"This is an intricately crafted old-fashioned clock created by a skilled Moroccan artisan back in 1988 from Chefchaoune.. it reminds me of my mother.\"\n",
    "\n",
    "prompts = [\n",
    "            f\"USER: <image>\\nBased on the caption '{caption}' and the following user input: '{user_input}', generate a detailed product name and description for this Moroccan artisanal item; the description should be minimal yet it gives the essence of the product and convinces people to buy or express their interest in it.\\nASSISTANT:\"\n",
    "          # f\"\"\"\n",
    "          # USER: <image>\\nBased on the image caption '{caption}' and the following background information: '{user_input}', generate an attention-grabbing yet concise product name and description for this authentic Moroccan artisanal item. The description should:\n",
    "          # Highlight the key features and unique selling points that make this product exceptional and desirable.\n",
    "          # Convey the cultural significance, craftsmanship, and rich heritage behind the item's creation.\n",
    "          # Use evocative language that resonates with potential buyers and piques their interest in owning this one-of-a-kind piece.\n",
    "          # Be concise, direct, and persuasive, leaving the reader eager to learn more or acquire the product.\n",
    "\n",
    "          # Your response should follow this format:\n",
    "          # Product Name: [Compelling and relevant product name]\n",
    "          # Product Description: [Concise yet captivating description addressing the points above]\n",
    "          # ASSISTANT:\"\"\"\n",
    "\n",
    "]\n",
    "\n",
    "inputs = processor(prompts, images=[image1], padding=True, return_tensors=\"pt\").to(\"cuda\")\n",
    "for k,v in inputs.items():\n",
    "  print(k,v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x9Lviy9zk3QW"
   },
   "source": [
    "## Autoregressively generate completion\n",
    "\n",
    "Finally, we simply let the model predict the next tokens given the images + prompt. Of course one can adjust all the [generation parameters](https://huggingface.co/docs/transformers/v4.35.2/en/main_classes/text_generation#transformers.GenerationMixin.generate). By default, greedy decoding is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XN2vJCPZk1UY",
    "outputId": "09f4c392-f0ee-4a3c-851f-122b9f3e0a21"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The Moroccan artisanal clock is a beautifully crafted timepiece, reminiscent of a vintage design. Created in 1988 by a skilled Moroccan artisan in Chefchaoune, this clock is a testament to the artisan's skill and attention to detail. The clock features a combination of red and green colors, adding a touch of vibrancy to the space it occupies. Its intricate craftsmanship and unique design make it an attractive addition to any home or office, evoking a sense of nostalgia and admiration for the artisan's work.\n"
     ]
    }
   ],
   "source": [
    "output = model.generate(**inputs, max_new_tokens=200)\n",
    "generated_text = processor.batch_decode(output, skip_special_tokens=True)\n",
    "for text in generated_text:\n",
    "  print(text.split(\"ASSISTANT:\")[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eS_YdZAKztu8"
   },
   "source": [
    "## Pipeline API\n",
    "\n",
    "Alternatively, you can leverage the [pipeline](https://huggingface.co/docs/transformers/main_classes/pipelines) API which abstracts all of the logic above away for the user. We also provide the quantization config to make sure we leverage 4-bit inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67,
     "referenced_widgets": [
      "4297c20967c44f60b68d8fed9ba655db",
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      "461a053119874839890949c6a3c16931"
     ]
    },
    "id": "Xwf9cRoVqO1X",
    "outputId": "70841abf-879b-4577-a3b2-e65872e1ed6c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4297c20967c44f60b68d8fed9ba655db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "pipe = pipeline(\"image-to-text\", model=model_id, model_kwargs={\"quantization_config\": quantization_config})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "W48r3NxDRskb"
   },
   "outputs": [],
   "source": [
    "max_new_tokens = 200\n",
    "prompt = \"USER: <image>\\nWhat are the things I should be cautious about when I visit this place?\\nASSISTANT:\"\n",
    "\n",
    "outputs = pipe(image1, prompt=prompt, generate_kwargs={\"max_new_tokens\": 200})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XX80v0pgSr_L",
    "outputId": "05c2d6f8-7b3d-46bb-e044-88976de103ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USER:  \n",
      "What are the things I should be cautious about when I visit this place?\n",
      "ASSISTANT: When visiting this place, which is a pier or dock extending over a body of water, there are a few things to be cautious about. First, be aware of the weather conditions, as sudden changes in weather can make the pier unsafe to walk on. Second, be mindful of the water depth and any potential hazards, such as submerged rocks or debris, that could cause accidents or injuries. Additionally, be cautious of the presence of wildlife, such as birds or fish, and avoid disturbing their natural habitats. Lastly, be aware of any local regulations or guidelines for the use of the pier, as some areas may be restricted or prohibited for certain activities.\n"
     ]
    }
   ],
   "source": [
    "print(outputs[0][\"generated_text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pb_YcZU80YIS"
   },
   "outputs": [],
   "source": []
  }
 ],
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  "accelerator": "GPU",
  "colab": {
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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