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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",
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"\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": []
}
]
} |