Upload token_vectors_math.ipynb

Google Colab Notebooks/token_vectors_math.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "# NOTE : although they have 1x768 dimension , these are not text_encodings , but token vectors\n",
+        "import json\n",
+        "import pandas as pd\n",
+        "import os\n",
+        "import shelve\n",
+        "import torch\n",
+        "from safetensors.torch import save_file , load_file\n",
+        "import json\n",
+        "\n",
+        "home_directory = '/content/'\n",
+        "using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n",
+        "if using_Kaggle : home_directory = '/kaggle/working/'\n",
+        "%cd {home_directory}\n",
+        "#-------#\n",
+        "\n",
+        "# Load the data if not already loaded\n",
+        "try:\n",
+        "    loaded\n",
+        "except:\n",
+        "    %cd {home_directory}\n",
+        "    !git clone https://huggingface.co/datasets/codeShare/text-to-image-prompts\n",
+        "    loaded = True\n",
+        "#--------#\n",
+        "\n",
+        "def getPrompts(_path, separator):\n",
+        "  path = _path + '/text'\n",
+        "  path_vec = _path + '/token_vectors'\n",
+        "  _file_name = 'vocab'\n",
+        "  #-----#\n",
+        "  index = 0\n",
+        "  file_index = 0\n",
+        "  prompts = {}\n",
+        "  text_encodings = {}\n",
+        "  _text_encodings = {}\n",
+        "  #-----#\n",
+        "  for filename in os.listdir(f'{path}'):\n",
+        "    print(f'reading {filename}....')\n",
+        "    _index = 0\n",
+        "    %cd {path}\n",
+        "    with open(f'{filename}', 'r') as f:\n",
+        "      data = json.load(f)\n",
+        "    #------#\n",
+        "    _df = pd.DataFrame({'count': data})['count']\n",
+        "    _prompts = {\n",
+        "        key : value for key, value in _df.items()\n",
+        "    }\n",
+        "    #-------#\n",
+        "    %cd {path_vec}\n",
+        "    _text_encodings = load_file(f'{_file_name}.safetensors')\n",
+        "\n",
+        "    for key in _prompts:\n",
+        "      _index = int(key)\n",
+        "      value = _prompts[key]\n",
+        "      #------#\n",
+        "      #Read the text_encodings + prompts\n",
+        "      text_encodings[f'{index}'] = _text_encodings[f'{_index}']\n",
+        "      prompts[f'{index}'] = _prompts[f'{_index}'] + separator\n",
+        "      index = index + 1\n",
+        "      continue\n",
+        "      #-------#\n",
+        "    #--------#\n",
+        "    #_text_encodings.close() #close the text_encodings file\n",
+        "    file_index = file_index + 1\n",
+        "  #----------#\n",
+        "  NUM_ITEMS = index -1\n",
+        "  return prompts , text_encodings , NUM_ITEMS\n",
+        "#--------#\n",
+        "\n",
+        "def append_from_url(dictA, tensA , nA , url , separator):\n",
+        "  dictB , tensB, nB = getPrompts(url, separator)\n",
+        "  dictAB = dictA\n",
+        "  tensAB = tensA\n",
+        "  nAB = nA\n",
+        "  for key in dictB:\n",
+        "    nAB = nAB + 1\n",
+        "    dictAB[f'{nA + int(key)}'] = dictB[key]\n",
+        "    tensAB[f'{nA + int(key)}'] = tensB[key]\n",
+        "  #-----#\n",
+        "  return dictAB, tensAB , nAB-1\n",
+        "#-------#"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "V-1DrszLqEVj",
+        "outputId": "9b894182-a7e0-436e-9bf1-5a7d3d920ac7"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title Fetch the json + .safetensor pair\n",
+        "\n",
+        "#------#\n",
+        "vocab = {}\n",
+        "tokens = {}\n",
+        "nA = 0\n",
+        "#--------#\n",
+        "\n",
+        "if True:\n",
+        "  url = '/content/text-to-image-prompts/vocab'\n",
+        "  vocab , tokens, nA = append_from_url(vocab , tokens, nA , url , '')\n",
+        "#-------#\n",
+        "NUM_TOKENS = nA # NUM_TOKENS = 49407\n",
+        "#--------#\n",
+        "\n",
+        "print(NUM_TOKENS)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EDCd1IGEqj3-",
+        "outputId": "bbaab5ab-4bd3-4766-ad44-f139a0ec7a02"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "reading vocab.json....\n",
+            "/content/text-to-image-prompts/vocab/text\n",
+            "/content/text-to-image-prompts/vocab/token_vectors\n",
+            "49407\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vocab[f'{8922}']"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "o9AfUKkvwUdG",
+        "outputId": "029e1148-056b-4040-da23-7ed6caaca878"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'benedict</w>'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title Compare similiarity between tokens\n",
+        "\n",
+        "import torch\n",
+        "from transformers import AutoTokenizer\n",
+        "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
+        "\n",
+        "# @markdown Write name of token to match against\n",
+        "token_name = \"banana\" # @param {type:'string',\"placeholder\":\"leave empty for random value token\"}\n",
+        "\n",
+        "prompt = token_name\n",
+        "# @markdown (optional) Mix the token with something else\n",
+        "mix_with = \"\" # @param {\"type\":\"string\",\"placeholder\":\"leave empty for random value token\"}\n",
+        "mix_method = \"None\" # @param [\"None\" , \"Average\", \"Subtract\"] {allow-input: true}\n",
+        "w = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
+        "# @markdown Limit char size of included token\n",
+        "\n",
+        "min_char_size = 0 # param {type:\"slider\", min:0, max: 50, step:1}\n",
+        "char_range = 50 # param {type:\"slider\", min:0, max: 50, step:1}\n",
+        "\n",
+        "tokenizer_output = tokenizer(text = prompt)\n",
+        "input_ids = tokenizer_output['input_ids']\n",
+        "id_A = input_ids[1]\n",
+        "A = torch.tensor(tokens[f'{id_A}'])\n",
+        "A = A/A.norm(p=2, dim=-1, keepdim=True)\n",
+        "#-----#\n",
+        "tokenizer_output = tokenizer(text = mix_with)\n",
+        "input_ids = tokenizer_output['input_ids']\n",
+        "id_C = input_ids[1]\n",
+        "C = torch.tensor(tokens[f'{id_C}'])\n",
+        "C = C/C.norm(p=2, dim=-1, keepdim=True)\n",
+        "#-----#\n",
+        "sim_AC = torch.dot(A,C)\n",
+        "#-----#\n",
+        "print(input_ids)\n",
+        "#-----#\n",
+        "\n",
+        "#if no imput exists we just randomize the entire thing\n",
+        "if (prompt == \"\"):\n",
+        "  id_A = -1\n",
+        "  print(\"Tokenized prompt tensor A is a random valued tensor with no ID\")\n",
+        "  R = torch.rand(A.shape)\n",
+        "  R = R/R.norm(p=2, dim=-1, keepdim=True)\n",
+        "  A = R\n",
+        "  name_A = 'random_A'\n",
+        "\n",
+        "#if no imput exists we just randomize the entire thing\n",
+        "if (mix_with == \"\"):\n",
+        "  id_C = -1\n",
+        "  print(\"Tokenized prompt  'mix_with' tensor C is a random valued tensor with no ID\")\n",
+        "  R = torch.rand(A.shape)\n",
+        "  R = R/R.norm(p=2, dim=-1, keepdim=True)\n",
+        "  C = R\n",
+        "  name_C = 'random_C'\n",
+        "\n",
+        "name_A = \"A of random type\"\n",
+        "if (id_A>-1):\n",
+        "  name_A = vocab[f'{id_A}']\n",
+        "\n",
+        "name_C = \"token C of random type\"\n",
+        "if (id_C>-1):\n",
+        "  name_C = vocab[f'{id_C}']\n",
+        "\n",
+        "print(f\"The similarity between A '{name_A}' and C '{name_C}' is {round(sim_AC.item()*100,2)} %\")\n",
+        "\n",
+        "if (mix_method ==  \"None\"):\n",
+        "  print(\"No operation\")\n",
+        "\n",
+        "if (mix_method ==  \"Average\"):\n",
+        "  A = w*A + (1-w)*C\n",
+        "  _A = A.norm(p=2, dim=-1, keepdim=True)\n",
+        "  print(f\"Tokenized prompt tensor A '{name_A}' token has been recalculated as A = w*A + (1-w)*C , where C is '{name_C}' token , for w = {w}  \")\n",
+        "\n",
+        "if (mix_method ==  \"Subtract\"):\n",
+        "  tmp =  w*A - (1-w)*C\n",
+        "  tmp = tmp/tmp.norm(p=2, dim=-1, keepdim=True)\n",
+        "  A = tmp\n",
+        "  #//---//\n",
+        "  print(f\"Tokenized prompt tensor A '{name_A}' token has been recalculated as A = _A*norm(w*A  - (1-w)*C) , where C is '{name_C}' token , for w = {w} \")\n",
+        "\n",
+        "#OPTIONAL : Add/subtract + normalize above result with another token. Leave field empty to get a random value tensor\n",
+        "\n",
+        "dots = torch.zeros(NUM_TOKENS)\n",
+        "for index in range(NUM_TOKENS):\n",
+        "  id_B = index\n",
+        "  B = torch.tensor(tokens[f'{id_B}'])\n",
+        "  B = B/B.norm(p=2, dim=-1, keepdim=True)\n",
+        "  sim_AB = torch.dot(A,B)\n",
+        "  dots[index] = sim_AB\n",
+        "\n",
+        "\n",
+        "sorted, indices = torch.sort(dots,dim=0 , descending=True)\n",
+        "#----#\n",
+        "if (mix_method ==  \"Average\"):\n",
+        "  print(f'Calculated all cosine-similarities between the average of token {name_A} and {name_C} with Id_A = {id_A} and mixed Id_C = {id_C} as a 1x{sorted.shape[0]} tensor')\n",
+        "if (mix_method ==  \"Subtract\"):\n",
+        "  print(f'Calculated all cosine-similarities between the subtract of token {name_A} and {name_C} with Id_A = {id_A} and mixed Id_C = {id_C} as a 1x{sorted.shape[0]} tensor')\n",
+        "if (mix_method ==  \"None\"):\n",
+        "  print(f'Calculated all cosine-similarities between the token {name_A} with Id_A = {id_A} with the the rest of the {NUM_TOKENS} tokens as a 1x{sorted.shape[0]} tensor')\n",
+        "\n",
+        "#Produce a list id IDs that are most similiar to the prompt ID at positiion 1 based on above result\n",
+        "\n",
+        "# @markdown Set print options\n",
+        "list_size = 100 # @param {type:'number'}\n",
+        "print_ID = False # @param {type:\"boolean\"}\n",
+        "print_Similarity = True # @param {type:\"boolean\"}\n",
+        "print_Name = True # @param {type:\"boolean\"}\n",
+        "print_Divider = True # @param {type:\"boolean\"}\n",
+        "\n",
+        "\n",
+        "if (print_Divider):\n",
+        "  print('//---//')\n",
+        "\n",
+        "print('')\n",
+        "print('Here is the result : ')\n",
+        "print('')\n",
+        "\n",
+        "for index in range(list_size):\n",
+        "  id = indices[index].item()\n",
+        "  if (print_Name):\n",
+        "    print(vocab[f'{id}']) # vocab item\n",
+        "  if (print_ID):\n",
+        "    print(f'ID = {id}') # IDs\n",
+        "  if (print_Similarity):\n",
+        "    print(f'similiarity = {round(sorted[index].item()*100,2)} %')\n",
+        "  if (print_Divider):\n",
+        "    print('--------')\n",
+        "\n",
+        "#Print the sorted list from above result\n",
+        "\n",
+        "#The prompt will be enclosed with the <|start-of-text|> and <|end-of-text|> tokens, which is why output will be [49406, ... , 49407].\n",
+        "\n",
+        "#You can leave the 'prompt' field empty to get a random value tensor. Since the tensor is random value, it will not correspond to any tensor in the vocab.json list , and this it will have no ID.\n",
+        "\n",
+        "# Save results as .db file\n",
+        "import shelve\n",
+        "VOCAB_FILENAME = 'tokens_most_similiar_to_' + name_A.replace('</w>','').strip()\n",
+        "d = shelve.open(VOCAB_FILENAME)\n",
+        "#NUM TOKENS == 49407\n",
+        "for index in range(NUM_TOKENS):\n",
+        "  #print(d[f'{index}']) #<-----Use this to read values from the .db file\n",
+        "  d[f'{index}']= vocab[f'{indices[index].item()}'] #<---- write values to .db file\n",
+        "#----#\n",
+        "d.close() #close the file\n",
+        "# See this link for additional stuff to do with shelve: https://docs.python.org/3/library/shelve.html"
+      ],
+      "metadata": {
+        "id": "ZwGqg9R5s1QS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Below is code used to create the .safetensor + json files for the notebook"
+      ],
+      "metadata": {
+        "id": "dGb1KgP_p4_w"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 599
+        },
+        "id": "AyhYBlP2pYyI",
+        "outputId": "0168beb3-428c-4886-f159-adc479b9da4b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content\n",
+            "/content\n",
+            "Cloning into 'text-to-image-prompts'...\n",
+            "remote: Enumerating objects: 1552, done.\u001b[K\n",
+            "remote: Counting objects: 100% (1549/1549), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (1506/1506), done.\u001b[K\n",
+            "remote: Total 1552 (delta 190), reused 0 (delta 0), pack-reused 3 (from 1)\u001b[K\n",
+            "Receiving objects: 100% (1552/1552), 9.09 MiB | 6.30 MiB/s, done.\n",
+            "Resolving deltas: 100% (190/190), done.\n",
+            "Updating files: 100% (906/906), done.\n",
+            "Filtering content: 100% (438/438), 1.49 GiB | 56.42 MiB/s, done.\n",
+            "/content\n",
+            "/content/text-to-image-prompts/vocab/raw\n",
+            "/content/text-to-image-prompts/vocab/raw\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "JSONDecodeError",
+          "evalue": "Expecting ':' delimiter: line 28 column 7 (char 569)",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-1-542fe0f58fcc>\u001b[0m in \u001b[0;36m<cell line: 56>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'{target_raw}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{root_filename}.json'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m \u001b[0m_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;31m#reverse key and value in the dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.10/json/__init__.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    291\u001b[0m     \u001b[0mkwarg\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0motherwise\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mJSONDecoder\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mused\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m     \"\"\"\n\u001b[0;32m--> 293\u001b[0;31m     return loads(fp.read(),\n\u001b[0m\u001b[1;32m    294\u001b[0m         \u001b[0mcls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject_hook\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobject_hook\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m         \u001b[0mparse_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse_float\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparse_int\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse_int\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.10/json/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0mparse_int\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m             parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[0;32m--> 346\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    347\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.10/json/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m         \"\"\"\n\u001b[0;32m--> 337\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    338\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.10/json/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    351\u001b[0m         \"\"\"\n\u001b[1;32m    352\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m             \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    354\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expecting value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting ':' delimiter: line 28 column 7 (char 569)"
+          ]
+        }
+      ],
+      "source": [
+        "# @title Process the raw vocab into json + .safetensor pair\n",
+        "\n",
+        "# NOTE : although they have 1x768 dimension , these are not text_encodings , but token vectors\n",
+        "import json\n",
+        "import pandas as pd\n",
+        "import os\n",
+        "import shelve\n",
+        "import torch\n",
+        "from safetensors.torch import save_file , load_file\n",
+        "import json\n",
+        "\n",
+        "home_directory = '/content/'\n",
+        "using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n",
+        "if using_Kaggle : home_directory = '/kaggle/working/'\n",
+        "%cd {home_directory}\n",
+        "#-------#\n",
+        "\n",
+        "# Load the data if not already loaded\n",
+        "try:\n",
+        "    loaded\n",
+        "except:\n",
+        "    %cd {home_directory}\n",
+        "    !git clone https://huggingface.co/datasets/codeShare/text-to-image-prompts\n",
+        "    loaded = True\n",
+        "#--------#\n",
+        "\n",
+        "# User input\n",
+        "target = home_directory + 'text-to-image-prompts/vocab/'\n",
+        "root_output_folder = home_directory + 'output/'\n",
+        "output_folder = root_output_folder  + 'vocab/'\n",
+        "root_filename = 'vocab'\n",
+        "NUM_FILES = 1\n",
+        "#--------#\n",
+        "\n",
+        "# Setup environment\n",
+        "def my_mkdirs(folder):\n",
+        "    if os.path.exists(folder)==False:\n",
+        "        os.makedirs(folder)\n",
+        "#--------#\n",
+        "output_folder_text = output_folder + 'text/'\n",
+        "output_folder_text = output_folder + 'text/'\n",
+        "output_folder_token_vectors = output_folder + 'token_vectors/'\n",
+        "target_raw = target + 'raw/'\n",
+        "%cd {home_directory}\n",
+        "my_mkdirs(output_folder)\n",
+        "my_mkdirs(output_folder_text)\n",
+        "my_mkdirs(output_folder_token_vectors)\n",
+        "#-------#\n",
+        "\n",
+        "%cd {target_raw}\n",
+        "model = torch.load(f'{root_filename}.pt' , weights_only=True)\n",
+        "tokens = model.clone().detach()\n",
+        "\n",
+        "\n",
+        "%cd {target_raw}\n",
+        "with open(f'{root_filename}.json', 'r') as f:\n",
+        "    data = json.load(f)\n",
+        "_df = pd.DataFrame({'count': data})['count']\n",
+        "#reverse key and value in the dict\n",
+        "vocab = {\n",
+        "    value : key for key, value in _df.items()\n",
+        "}\n",
+        "#------#\n",
+        "\n",
+        "\n",
+        "tensors = {}\n",
+        "for key in vocab:\n",
+        "  name = vocab[key]\n",
+        "  token = tokens[int(key)]\n",
+        "  tensors[key] = token\n",
+        "#-----#\n",
+        "\n",
+        "%cd {output_folder_token_vectors}\n",
+        "save_file(tensors, \"vocab.safetensors\")\n",
+        "\n",
+        "%cd {output_folder_text}\n",
+        "with open('vocab.json', 'w') as f:\n",
+        "    json.dump(vocab, f)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Determine if this notebook is running on Colab or Kaggle\n",
+        "#Use https://www.kaggle.com/ if Google Colab GPU is busy\n",
+        "home_directory = '/content/'\n",
+        "using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n",
+        "if using_Kaggle : home_directory = '/kaggle/working/'\n",
+        "%cd {home_directory}\n",
+        "#-------#\n",
+        "\n",
+        "# @title Download the vocab as .zip\n",
+        "import os\n",
+        "%cd {home_directory}\n",
+        "#os.remove(f'{home_directory}results.zip')\n",
+        "root_output_folder = home_directory + 'output/'\n",
+        "zip_dest = f'{home_directory}results.zip'\n",
+        "!zip -r {zip_dest} '/content/text-to-image-prompts/tokens'"
+      ],
+      "metadata": {
+        "id": "9uIDf9IUpzh2"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}