Upload token_vectors_math.ipynb

Google Colab Notebooks/token_vectors_math.ipynb

@@ -47,10 +47,8 @@
         "  _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",
@@ -60,28 +58,19 @@
         "      data = json.load(f)\n",
         "    #------#\n",
         "    _df = pd.DataFrame({'count': data})['count']\n",
-        "    _prompts = {\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",
+        "\n",
+        "    for key in prompts:\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",
+        "    #------#\n",
+        "    %cd {path_vec}\n",
+        "    text_encodings = load_file(f'{_file_name}.safetensors')\n",
+        "    continue\n",
         "  #----------#\n",
-        "  NUM_ITEMS = index -1\n",
         "  return prompts , text_encodings , NUM_ITEMS\n",
         "#--------#\n",
         "\n",
@@ -103,7 +92,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "V-1DrszLqEVj",
-        "outputId": "9b894182-a7e0-436e-9bf1-5a7d3d920ac7"
+        "outputId": "8788d8fc-59ce-4cba-9867-4860291afcb2"
       },
       "execution_count": 5,
       "outputs": [
@@ -131,61 +120,18 @@
         "  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",
+        "NUM_TOKENS = nA\n",
         "#--------#\n",
         "\n",
-        "print(NUM_TOKENS)"
+        "if False:\n",
+        "  print(NUM_TOKENS)  # NUM_TOKENS = 49407\n",
+        "  print(vocab['8922']) #ID for banana is 8922"
       ],
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "EDCd1IGEqj3-",
-        "outputId": "bbaab5ab-4bd3-4766-ad44-f139a0ec7a02"
+        "id": "EDCd1IGEqj3-"
       },
-      "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
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -352,14 +298,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 3,
       "metadata": {
         "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 599
+          "base_uri": "https://localhost:8080/"
         },
         "id": "AyhYBlP2pYyI",
-        "outputId": "0168beb3-428c-4886-f159-adc479b9da4b"
+        "outputId": "9e2fc730-23ee-4b05-9957-6fb2db82f2cf"
       },
       "outputs": [
         {
@@ -368,33 +313,10 @@
           "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)"
+            "/content/text-to-image-prompts/vocab/raw\n",
+            "/content/output/vocab/token_vectors\n",
+            "/content/output/vocab/text\n"
           ]
         }
       ],
@@ -465,10 +387,11 @@
         "\n",
         "\n",
         "tensors = {}\n",
+        "names = {}\n",
         "for key in vocab:\n",
-        "  name = vocab[key]\n",
         "  token = tokens[int(key)]\n",
-        "  tensors[key] = token\n",
+        "  tensors[f'{key}'] = token\n",
+        "  names[f'{key}'] = vocab[key]\n",
         "#-----#\n",
         "\n",
         "%cd {output_folder_token_vectors}\n",
@@ -476,7 +399,35 @@
         "\n",
         "%cd {output_folder_text}\n",
         "with open('vocab.json', 'w') as f:\n",
-        "    json.dump(vocab, f)\n"
+        "    json.dump(names, f)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "W_Ig4ZGH18hX",
+        "outputId": "5f2c0a6e-9b6e-4135-d7de-900673f34e1c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        }
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'banana</w>'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
       ]
     },
     {
@@ -496,13 +447,34 @@
         "#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'"
+        "!zip -r {zip_dest} {root_output_folder}"
       ],
       "metadata": {
-        "id": "9uIDf9IUpzh2"
+        "id": "9uIDf9IUpzh2",
+        "outputId": "949f95c8-7657-42dd-d70a-d3cc7da2c72f",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content\n",
+            "/content\n",
+            "  adding: content/output/ (stored 0%)\n",
+            "  adding: content/output/vocab/ (stored 0%)\n",
+            "  adding: content/output/vocab/text/ (stored 0%)\n",
+            "  adding: content/output/vocab/text/vocab.json (deflated 71%)\n",
+            "  adding: content/output/vocab/text/.ipynb_checkpoints/ (stored 0%)\n",
+            "  adding: content/output/vocab/token_vectors/ (stored 0%)\n",
+            "  adding: content/output/vocab/token_vectors/vocab.safetensors (deflated 9%)\n",
+            "  adding: content/output/vocab/token_vectors/.ipynb_checkpoints/ (stored 0%)\n"
+          ]
+        }
+      ]
     }
   ]
 }