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1725
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1726
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1727
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1728
  }
1729
  },
1730
  "cells": [
@@ -1787,34 +73,16 @@
1787
  "mix_method = \"None\""
1788
  ],
1789
  "metadata": {
1790
- "id": "Ch9puvwKH1s3",
1791
- "colab": {
1792
- "base_uri": "https://localhost:8080/"
1793
- },
1794
- "outputId": "46175dd6-a875-4208-a128-fddab8752406"
1795
  },
1796
- "execution_count": 1,
1797
- "outputs": [
1798
- {
1799
- "output_type": "stream",
1800
- "name": "stdout",
1801
- "text": [
1802
- "Cloning into 'sd_tokens'...\n",
1803
- "remote: Enumerating objects: 7, done.\u001b[K\n",
1804
- "remote: Counting objects: 100% (4/4), done.\u001b[K\n",
1805
- "remote: Compressing objects: 100% (4/4), done.\u001b[K\n",
1806
- "remote: Total 7 (delta 0), reused 0 (delta 0), pack-reused 3 (from 1)\u001b[K\n",
1807
- "Unpacking objects: 100% (7/7), 305.93 KiB | 5.67 MiB/s, done.\n",
1808
- "/content/sd_tokens\n"
1809
- ]
1810
- }
1811
- ]
1812
  },
1813
  {
1814
  "cell_type": "code",
1815
  "source": [
1816
- "print(vocab[8922]) #the vocab item for ID 8922\n",
1817
- "print(token[8922].shape) #dimension of the token"
1818
  ],
1819
  "metadata": {
1820
  "id": "S_Yh9gH_OUA1"
@@ -1827,7 +95,7 @@
1827
  "source": [
1828
  "Get the IDs from a prompt text.\n",
1829
  "\n",
1830
- "The prompt will be enclosed with the <|start-of-text|> and <|end-of-text|> tokens"
1831
  ],
1832
  "metadata": {
1833
  "id": "f1-jS7YJApiO"
@@ -1838,164 +106,39 @@
1838
  "source": [
1839
  "from transformers import AutoTokenizer\n",
1840
  "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
 
1841
  "prompt= \"banana\" # @param {type:'string'}\n",
 
1842
  "tokenizer_output = tokenizer(text = prompt)\n",
1843
  "input_ids = tokenizer_output['input_ids']\n",
1844
  "print(input_ids)\n",
1845
  "id_A = input_ids[1]\n",
1846
  "A = token[id_A]\n",
1847
- "_A = LA.vector_norm(A, ord=2)"
 
 
 
 
 
 
 
 
 
 
 
 
 
1848
  ],
1849
  "metadata": {
1850
- "id": "RPdkYzT2_X85",
1851
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1852
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1853
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1854
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1910
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1911
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1913
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1914
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1915
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1916
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1917
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1918
- "data": {
1919
- "text/plain": [
1920
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1921
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1929
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1930
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1931
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1932
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1933
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1934
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1935
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1936
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1937
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1938
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1940
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1941
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1942
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1943
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1944
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1945
- "output_type": "display_data",
1946
- "data": {
1947
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1948
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1949
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1952
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1953
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1954
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1955
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1956
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1957
- },
1958
- {
1959
- "output_type": "display_data",
1960
- "data": {
1961
- "text/plain": [
1962
- "tokenizer.json: 0%| | 0.00/2.22M [00:00<?, ?B/s]"
1963
- ],
1964
- "application/vnd.jupyter.widget-view+json": {
1965
- "version_major": 2,
1966
- "version_minor": 0,
1967
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1968
- }
1969
- },
1970
- "metadata": {}
1971
- },
1972
- {
1973
- "output_type": "display_data",
1974
- "data": {
1975
- "text/plain": [
1976
- "special_tokens_map.json: 0%| | 0.00/389 [00:00<?, ?B/s]"
1977
- ],
1978
- "application/vnd.jupyter.widget-view+json": {
1979
- "version_major": 2,
1980
- "version_minor": 0,
1981
- "model_id": "f5dbb938e5f542ef8f2a4a92c98b6ed7"
1982
- }
1983
- },
1984
- "metadata": {}
1985
- },
1986
- {
1987
- "output_type": "stream",
1988
- "name": "stdout",
1989
- "text": [
1990
- "[49406, 8922, 49407]\n"
1991
- ]
1992
- }
1993
- ]
1994
  },
1995
  {
1996
  "cell_type": "markdown",
1997
  "source": [
1998
- "OPTIONAL : Add/subtract + normalize above result with another token"
1999
  ],
2000
  "metadata": {
2001
  "id": "JKnz0aLFVGXc"
@@ -2008,7 +151,11 @@
2008
  "mix_method = \"None\" # @param [\"None\" , \"Average\", \"Subtract\"] {allow-input: true}\n",
2009
  "w = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
2010
  "\n",
2011
- "\n",
 
 
 
 
2012
  "\n",
2013
  "tokenizer_output = tokenizer(text = mix_with)\n",
2014
  "input_ids = tokenizer_output['input_ids']\n",
@@ -2016,22 +163,35 @@
2016
  "C = token[id_C]\n",
2017
  "_C = LA.vector_norm(C, ord=2)\n",
2018
  "\n",
 
 
 
 
 
 
 
 
 
 
 
2019
  "if (mix_method == \"Average\"):\n",
2020
  " A = w*A + (1-w)*C\n",
2021
  " _A = LA.vector_norm(A, ord=2)\n",
 
2022
  "\n",
2023
  "if (mix_method == \"Subtract\"):\n",
2024
  " tmp = w*A - (1-w)*C\n",
2025
  " _tmp = LA.vector_norm(tmp, ord=2)\n",
2026
  " A = tmp*((w*_A + (1-w)*_C)/_tmp)\n",
2027
  " _A = LA.vector_norm(A, ord=2)\n",
 
2028
  "\n",
2029
  "\n"
2030
  ],
2031
  "metadata": {
2032
  "id": "oXbNSRSKPgRr"
2033
  },
2034
- "execution_count": 5,
2035
  "outputs": []
2036
  },
2037
  {
@@ -2056,32 +216,29 @@
2056
  " result = absolute_value(result.item())\n",
2057
  " dots[index] = result\n",
2058
  "\n",
 
 
 
 
 
 
 
 
 
2059
  "sorted, indices = torch.sort(dots,dim=0 , descending=True)\n",
2060
  "#----#\n",
2061
  "if (mix_method == \"Average\"):\n",
2062
- " print(f'Calculated all cosine-similarities between the average of token {vocab[id_A]} and {vocab[id_C]} with ID = {id_A} and mixed ID = {id_C} as a 1x{sorted.shape[0]} tensor')\n",
2063
  "if (mix_method == \"Subtract\"):\n",
2064
- " print(f'Calculated all cosine-similarities between the subtract of token {vocab[id_A]} and {vocab[id_C]} with ID = {id_A} and mixed ID = {id_C} as a 1x{sorted.shape[0]} tensor')\n",
2065
  "if (mix_method == \"None\"):\n",
2066
- " print(f'Calculated all cosine-similarities between the token {vocab[id_A]} with ID = {id_A} the rest of the {NUM_TOKENS} tokens as a 1x{sorted.shape[0]} tensor')"
2067
  ],
2068
  "metadata": {
2069
- "id": "juxsvco9B0iV",
2070
- "colab": {
2071
- "base_uri": "https://localhost:8080/"
2072
- },
2073
- "outputId": "ce920e92-5ff5-412c-98d2-5f2c284ca7b4"
2074
  },
2075
- "execution_count": 6,
2076
- "outputs": [
2077
- {
2078
- "output_type": "stream",
2079
- "name": "stdout",
2080
- "text": [
2081
- "Calculated all cosine-similarities between the token banana</w> with ID = 8922 the rest of the 49407 tokens as a 1x49407 tensor\n"
2082
- ]
2083
- }
2084
- ]
2085
  },
2086
  {
2087
  "cell_type": "markdown",
@@ -2115,321 +272,10 @@
2115
  ],
2116
  "metadata": {
2117
  "id": "YIEmLAzbHeuo",
2118
- "colab": {
2119
- "base_uri": "https://localhost:8080/"
2120
- },
2121
- "collapsed": true,
2122
- "outputId": "a03b49b8-a6ec-457b-f78e-be3cb3029eae"
2123
  },
2124
- "execution_count": 7,
2125
- "outputs": [
2126
- {
2127
- "output_type": "stream",
2128
- "name": "stdout",
2129
- "text": [
2130
- "banana</w>\n",
2131
- "similiarity = 100.0 %\n",
2132
- "--------\n",
2133
- "bananas</w>\n",
2134
- "similiarity = 38.93 %\n",
2135
- "--------\n",
2136
- "banan\n",
2137
- "similiarity = 30.8 %\n",
2138
- "--------\n",
2139
- "ðŁįĮ</w>\n",
2140
- "similiarity = 27.12 %\n",
2141
- "--------\n",
2142
- "pineapple</w>\n",
2143
- "similiarity = 19.7 %\n",
2144
- "--------\n",
2145
- "chicken</w>\n",
2146
- "similiarity = 19.24 %\n",
2147
- "--------\n",
2148
- "potassium</w>\n",
2149
- "similiarity = 19.21 %\n",
2150
- "--------\n",
2151
- "sausage</w>\n",
2152
- "similiarity = 19.07 %\n",
2153
- "--------\n",
2154
- "lemon</w>\n",
2155
- "similiarity = 18.82 %\n",
2156
- "--------\n",
2157
- "orange</w>\n",
2158
- "similiarity = 18.42 %\n",
2159
- "--------\n",
2160
- "peanut</w>\n",
2161
- "similiarity = 17.84 %\n",
2162
- "--------\n",
2163
- "parachute</w>\n",
2164
- "similiarity = 17.19 %\n",
2165
- "--------\n",
2166
- "duck\n",
2167
- "similiarity = 16.8 %\n",
2168
- "--------\n",
2169
- "yellow</w>\n",
2170
- "similiarity = 16.21 %\n",
2171
- "--------\n",
2172
- "grape</w>\n",
2173
- "similiarity = 16.19 %\n",
2174
- "--------\n",
2175
- "kangaroo</w>\n",
2176
- "similiarity = 16.13 %\n",
2177
- "--------\n",
2178
- "apple</w>\n",
2179
- "similiarity = 16.13 %\n",
2180
- "--------\n",
2181
- "tangerine</w>\n",
2182
- "similiarity = 16.08 %\n",
2183
- "--------\n",
2184
- "giraffe</w>\n",
2185
- "similiarity = 16.04 %\n",
2186
- "--------\n",
2187
- "mango</w>\n",
2188
- "similiarity = 16.03 %\n",
2189
- "--------\n",
2190
- "rubber</w>\n",
2191
- "similiarity = 15.95 %\n",
2192
- "--------\n",
2193
- "bamboo</w>\n",
2194
- "similiarity = 15.88 %\n",
2195
- "--------\n",
2196
- "umbrella</w>\n",
2197
- "similiarity = 15.82 %\n",
2198
- "--------\n",
2199
- "nutella</w>\n",
2200
- "similiarity = 15.69 %\n",
2201
- "--------\n",
2202
- "ferrari</w>\n",
2203
- "similiarity = 15.69 %\n",
2204
- "--------\n",
2205
- "oranges</w>\n",
2206
- "similiarity = 15.65 %\n",
2207
- "--------\n",
2208
- "peanuts</w>\n",
2209
- "similiarity = 15.62 %\n",
2210
- "--------\n",
2211
- "ali</w>\n",
2212
- "similiarity = 15.49 %\n",
2213
- "--------\n",
2214
- "dae</w>\n",
2215
- "similiarity = 15.45 %\n",
2216
- "--------\n",
2217
- "apd</w>\n",
2218
- "similiarity = 15.43 %\n",
2219
- "--------\n",
2220
- "cucumber</w>\n",
2221
- "similiarity = 15.32 %\n",
2222
- "--------\n",
2223
- "potato</w>\n",
2224
- "similiarity = 15.22 %\n",
2225
- "--------\n",
2226
- "monkey</w>\n",
2227
- "similiarity = 15.2 %\n",
2228
- "--------\n",
2229
- "croissant</w>\n",
2230
- "similiarity = 15.18 %\n",
2231
- "--------\n",
2232
- "papaya</w>\n",
2233
- "similiarity = 15.17 %\n",
2234
- "--------\n",
2235
- "christmas</w>\n",
2236
- "similiarity = 15.12 %\n",
2237
- "--------\n",
2238
- "sandwich</w>\n",
2239
- "similiarity = 15.0 %\n",
2240
- "--------\n",
2241
- "rainbow</w>\n",
2242
- "similiarity = 14.98 %\n",
2243
- "--------\n",
2244
- "tomato</w>\n",
2245
- "similiarity = 14.96 %\n",
2246
- "--------\n",
2247
- "martini</w>\n",
2248
- "similiarity = 14.93 %\n",
2249
- "--------\n",
2250
- "cabaret</w>\n",
2251
- "similiarity = 14.83 %\n",
2252
- "--------\n",
2253
- "ginger</w>\n",
2254
- "similiarity = 14.82 %\n",
2255
- "--------\n",
2256
- "µ\n",
2257
- "similiarity = 14.8 %\n",
2258
- "--------\n",
2259
- "animal</w>\n",
2260
- "similiarity = 14.76 %\n",
2261
- "--------\n",
2262
- "vanilla</w>\n",
2263
- "similiarity = 14.73 %\n",
2264
- "--------\n",
2265
- "ells</w>\n",
2266
- "similiarity = 14.66 %\n",
2267
- "--------\n",
2268
- "mustache</w>\n",
2269
- "similiarity = 14.64 %\n",
2270
- "--------\n",
2271
- "lime</w>\n",
2272
- "similiarity = 14.62 %\n",
2273
- "--------\n",
2274
- "sickle</w>\n",
2275
- "similiarity = 14.6 %\n",
2276
- "--------\n",
2277
- "vista</w>\n",
2278
- "similiarity = 14.53 %\n",
2279
- "--------\n",
2280
- "coconut</w>\n",
2281
- "similiarity = 14.52 %\n",
2282
- "--------\n",
2283
- "kara</w>\n",
2284
- "similiarity = 14.46 %\n",
2285
- "--------\n",
2286
- "alligator</w>\n",
2287
- "similiarity = 14.39 %\n",
2288
- "--------\n",
2289
- "blueberry</w>\n",
2290
- "similiarity = 14.34 %\n",
2291
- "--------\n",
2292
- "ðŁĻĤ\n",
2293
- "similiarity = 14.3 %\n",
2294
- "--------\n",
2295
- "squirrel</w>\n",
2296
- "similiarity = 14.29 %\n",
2297
- "--------\n",
2298
- "atore</w>\n",
2299
- "similiarity = 14.19 %\n",
2300
- "--------\n",
2301
- "watermelon</w>\n",
2302
- "similiarity = 14.13 %\n",
2303
- "--------\n",
2304
- "nana</w>\n",
2305
- "similiarity = 14.09 %\n",
2306
- "--------\n",
2307
- "latex</w>\n",
2308
- "similiarity = 14.08 %\n",
2309
- "--------\n",
2310
- "reas\n",
2311
- "similiarity = 14.07 %\n",
2312
- "--------\n",
2313
- "agricultural</w>\n",
2314
- "similiarity = 14.02 %\n",
2315
- "--------\n",
2316
- "davis\n",
2317
- "similiarity = 14.0 %\n",
2318
- "--------\n",
2319
- "zucchini</w>\n",
2320
- "similiarity = 14.0 %\n",
2321
- "--------\n",
2322
- "saxophone</w>\n",
2323
- "similiarity = 13.93 %\n",
2324
- "--------\n",
2325
- "rough\n",
2326
- "similiarity = 13.92 %\n",
2327
- "--------\n",
2328
- "mozzarella</w>\n",
2329
- "similiarity = 13.91 %\n",
2330
- "--------\n",
2331
- "eggplant</w>\n",
2332
- "similiarity = 13.9 %\n",
2333
- "--------\n",
2334
- "pickle</w>\n",
2335
- "similiarity = 13.89 %\n",
2336
- "--------\n",
2337
- "tortilla</w>\n",
2338
- "similiarity = 13.88 %\n",
2339
- "--------\n",
2340
- "maniac</w>\n",
2341
- "similiarity = 13.84 %\n",
2342
- "--------\n",
2343
- "milk</w>\n",
2344
- "similiarity = 13.83 %\n",
2345
- "--------\n",
2346
- "ylde</w>\n",
2347
- "similiarity = 13.82 %\n",
2348
- "--------\n",
2349
- "cellphone</w>\n",
2350
- "similiarity = 13.78 %\n",
2351
- "--------\n",
2352
- "duck</w>\n",
2353
- "similiarity = 13.73 %\n",
2354
- "--------\n",
2355
- "finely</w>\n",
2356
- "similiarity = 13.72 %\n",
2357
- "--------\n",
2358
- "umbrel\n",
2359
- "similiarity = 13.71 %\n",
2360
- "--------\n",
2361
- "fanny</w>\n",
2362
- "similiarity = 13.69 %\n",
2363
- "--------\n",
2364
- "navis</w>\n",
2365
- "similiarity = 13.68 %\n",
2366
- "--------\n",
2367
- "twister</w>\n",
2368
- "similiarity = 13.67 %\n",
2369
- "--------\n",
2370
- "moustache</w>\n",
2371
- "similiarity = 13.66 %\n",
2372
- "--------\n",
2373
- "manafort</w>\n",
2374
- "similiarity = 13.66 %\n",
2375
- "--------\n",
2376
- "nod</w>\n",
2377
- "similiarity = 13.62 %\n",
2378
- "--------\n",
2379
- "dros</w>\n",
2380
- "similiarity = 13.6 %\n",
2381
- "--------\n",
2382
- "grapefruit</w>\n",
2383
- "similiarity = 13.6 %\n",
2384
- "--------\n",
2385
- "broom</w>\n",
2386
- "similiarity = 13.59 %\n",
2387
- "--------\n",
2388
- "scorpion</w>\n",
2389
- "similiarity = 13.59 %\n",
2390
- "--------\n",
2391
- "د\n",
2392
- "similiarity = 13.58 %\n",
2393
- "--------\n",
2394
- "fruit\n",
2395
- "similiarity = 13.57 %\n",
2396
- "--------\n",
2397
- "dv\n",
2398
- "similiarity = 13.55 %\n",
2399
- "--------\n",
2400
- "agan\n",
2401
- "similiarity = 13.53 %\n",
2402
- "--------\n",
2403
- "sunflower</w>\n",
2404
- "similiarity = 13.49 %\n",
2405
- "--------\n",
2406
- "banc\n",
2407
- "similiarity = 13.46 %\n",
2408
- "--------\n",
2409
- "literature</w>\n",
2410
- "similiarity = 13.45 %\n",
2411
- "--------\n",
2412
- "pelican</w>\n",
2413
- "similiarity = 13.43 %\n",
2414
- "--------\n",
2415
- "breakfast</w>\n",
2416
- "similiarity = 13.42 %\n",
2417
- "--------\n",
2418
- "pear</w>\n",
2419
- "similiarity = 13.42 %\n",
2420
- "--------\n",
2421
- "gri\n",
2422
- "similiarity = 13.42 %\n",
2423
- "--------\n",
2424
- "orange\n",
2425
- "similiarity = 13.4 %\n",
2426
- "--------\n",
2427
- "monet</w>\n",
2428
- "similiarity = 13.4 %\n",
2429
- "--------\n"
2430
- ]
2431
- }
2432
- ]
2433
  },
2434
  {
2435
  "cell_type": "markdown",
 
11
  },
12
  "language_info": {
13
  "name": "python"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
14
  }
15
  },
16
  "cells": [
 
73
  "mix_method = \"None\""
74
  ],
75
  "metadata": {
76
+ "id": "Ch9puvwKH1s3"
 
 
 
 
77
  },
78
+ "execution_count": null,
79
+ "outputs": []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
80
  },
81
  {
82
  "cell_type": "code",
83
  "source": [
84
+ "#print(vocab[8922]) #the vocab item for ID 8922\n",
85
+ "#print(token[8922].shape) #dimension of the token"
86
  ],
87
  "metadata": {
88
  "id": "S_Yh9gH_OUA1"
 
95
  "source": [
96
  "Get the IDs from a prompt text.\n",
97
  "\n",
98
+ "The prompt will be enclosed with the <|start-of-text|> and <|end-of-text|> tokens. Leave the field empty to get a random value tensor"
99
  ],
100
  "metadata": {
101
  "id": "f1-jS7YJApiO"
 
106
  "source": [
107
  "from transformers import AutoTokenizer\n",
108
  "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
109
+ "\n",
110
  "prompt= \"banana\" # @param {type:'string'}\n",
111
+ "\n",
112
  "tokenizer_output = tokenizer(text = prompt)\n",
113
  "input_ids = tokenizer_output['input_ids']\n",
114
  "print(input_ids)\n",
115
  "id_A = input_ids[1]\n",
116
  "A = token[id_A]\n",
117
+ "_A = LA.vector_norm(A, ord=2)\n",
118
+ "\n",
119
+ "#if no imput exists we just randomize the entire thing\n",
120
+ "if (prompt == \"\"):\n",
121
+ " id_A = -1\n",
122
+ " print(\"Tokenized prompt tensor A is a random valued tensor with no ID\")\n",
123
+ " R = torch.rand(768)\n",
124
+ " _R = LA.vector_norm(R, ord=2)\n",
125
+ " A = R*(_A/_R)\n",
126
+ "\n",
127
+ "#Save a copy of the tensor A\n",
128
+ "id_P = input_ids[1]\n",
129
+ "P = token[id_A]\n",
130
+ "_P = LA.vector_norm(A, ord=2)"
131
  ],
132
  "metadata": {
133
+ "id": "RPdkYzT2_X85"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
134
  },
135
+ "execution_count": null,
136
+ "outputs": []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
137
  },
138
  {
139
  "cell_type": "markdown",
140
  "source": [
141
+ "OPTIONAL : Add/subtract + normalize above result with another token. Leave field empty to get a random value tensor"
142
  ],
143
  "metadata": {
144
  "id": "JKnz0aLFVGXc"
 
151
  "mix_method = \"None\" # @param [\"None\" , \"Average\", \"Subtract\"] {allow-input: true}\n",
152
  "w = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
153
  "\n",
154
+ "#prevent re-iterating A by reading from stored copy\n",
155
+ "id_A = id_P\n",
156
+ "A = P\n",
157
+ "_A = _P\n",
158
+ "#----#\n",
159
  "\n",
160
  "tokenizer_output = tokenizer(text = mix_with)\n",
161
  "input_ids = tokenizer_output['input_ids']\n",
 
163
  "C = token[id_C]\n",
164
  "_C = LA.vector_norm(C, ord=2)\n",
165
  "\n",
166
+ "#if no imput exists we just randomize the entire thing\n",
167
+ "if (mix_with == \"\"):\n",
168
+ " id_C = -1\n",
169
+ " print(\"Tokenized prompt 'mix_with' tensor C is a random valued tensor with no ID\")\n",
170
+ " R = torch.rand(768)\n",
171
+ " _R = LA.vector_norm(R, ord=2)\n",
172
+ " C = R*(_C/_R)\n",
173
+ "\n",
174
+ "if (mix_method == \"None\"):\n",
175
+ " print(\"No operation\")\n",
176
+ "\n",
177
  "if (mix_method == \"Average\"):\n",
178
  " A = w*A + (1-w)*C\n",
179
  " _A = LA.vector_norm(A, ord=2)\n",
180
+ " print(\"Tokenized prompt tensor A has been recalculated as A = w*A + (1-w)*C , where C is the tokenized prompt 'mix_with' tensor C\")\n",
181
  "\n",
182
  "if (mix_method == \"Subtract\"):\n",
183
  " tmp = w*A - (1-w)*C\n",
184
  " _tmp = LA.vector_norm(tmp, ord=2)\n",
185
  " A = tmp*((w*_A + (1-w)*_C)/_tmp)\n",
186
  " _A = LA.vector_norm(A, ord=2)\n",
187
+ " print(\"Tokenized prompt tensor A has been recalculated as A = (w*_A + (1-w)*_C) * norm(w*A - (1-w)*C) , where C is the tokenized prompt 'mix_with' tensor C\")\n",
188
  "\n",
189
  "\n"
190
  ],
191
  "metadata": {
192
  "id": "oXbNSRSKPgRr"
193
  },
194
+ "execution_count": null,
195
  "outputs": []
196
  },
197
  {
 
216
  " result = absolute_value(result.item())\n",
217
  " dots[index] = result\n",
218
  "\n",
219
+ "name_A = \"A of random type\"\n",
220
+ "if (id_A>-1):\n",
221
+ " name_A = vocab[id_A]\n",
222
+ "\n",
223
+ "name_C = \"token C of random type\"\n",
224
+ "if (id_C>-1):\n",
225
+ " name_C = vocab[id_C]\n",
226
+ "\n",
227
+ "\n",
228
  "sorted, indices = torch.sort(dots,dim=0 , descending=True)\n",
229
  "#----#\n",
230
  "if (mix_method == \"Average\"):\n",
231
+ " 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",
232
  "if (mix_method == \"Subtract\"):\n",
233
+ " 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",
234
  "if (mix_method == \"None\"):\n",
235
+ " 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')"
236
  ],
237
  "metadata": {
238
+ "id": "juxsvco9B0iV"
 
 
 
 
239
  },
240
+ "execution_count": null,
241
+ "outputs": []
 
 
 
 
 
 
 
 
242
  },
243
  {
244
  "cell_type": "markdown",
 
272
  ],
273
  "metadata": {
274
  "id": "YIEmLAzbHeuo",
275
+ "collapsed": true
 
 
 
 
276
  },
277
+ "execution_count": null,
278
+ "outputs": []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
279
  },
280
  {
281
  "cell_type": "markdown",