Oulipo_Extended.ipynb

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        "# OuLiPo Variations\n",
        "\n",
        "*Using the spaCy NLP library for detecting parts of speech, I've created a new OuLiPo generator that replaces every verb with the 7th verb in the dictionary. Other variations tested below.*\n",
        "\n",
        "\n",
        "The [N+7 procedure](http://www.spoonbill.org/n+7/), invented in 1961 by Jean Lescure of OuLiPo, involves replacing each noun in a text with the seventh one."
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      "source": [
        "import pandas as pd\n",
        "import json\n",
        "import spacy"
      ]
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      "source": [
        "with open('dictionary.json', 'r') as f:\n",
        "    data = json.load(f)\n",
        "\n",
        "# Convert JSON data to pandas DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "types = df['pos'].unique()\n",
        "print(types)\n",
        "\n",
        "# Filter for verbs\n",
        "verbs_df = df[df['pos'] == 'v.']\n",
        "\n",
        "# Reset the index of verbs_df\n",
        "verbs_df = verbs_df.reset_index(drop=True)\n",
        "\n",
        "# # Display the DataFrame with verbs\n",
        "verbs_df.head()"
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          "text": [
            "['n.' 'prep.' 'a.' 'v.' 'adv.' 'p.' 'interj.' 'conj.' 'pron.']\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "  pos        word                                        definitions  \\\n",
              "0  v.   ABACINATE  [To blind by a red-hot metal plate held before...   \n",
              "1  v.  ABALIENATE  [To transfer the title of from one to another;...   \n",
              "2  v.       ABAND  [To abandon. [Obs.] Enforced the kingdom to ab...   \n",
              "3  v.     ABANDON  [To cast or drive out; to banish; to expel; to...   \n",
              "4  v.       ABASE  [To lower or depress; to throw or cast down; a...   \n",
              "\n",
              "                                            synonyms  \n",
              "0                                                NaN  \n",
              "1                                                NaN  \n",
              "2                                                NaN  \n",
              "3  give up; yield; forego; cede; surrender; resig...  \n",
              "4  Abase, Debase, Degrade. These words agree in t...  "
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              "      <th>0</th>\n",
              "      <td>v.</td>\n",
              "      <td>ABACINATE</td>\n",
              "      <td>[To blind by a red-hot metal plate held before...</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>1</th>\n",
              "      <td>v.</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>v.</td>\n",
              "      <td>ABAND</td>\n",
              "      <td>[To abandon. [Obs.] Enforced the kingdom to ab...</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>v.</td>\n",
              "      <td>ABANDON</td>\n",
              "      <td>[To cast or drive out; to banish; to expel; to...</td>\n",
              "      <td>give up; yield; forego; cede; surrender; resig...</td>\n",
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              "summary": "{\n  \"name\": \"verbs_df\",\n  \"rows\": 15872,\n  \"fields\": [\n    {\n      \"column\": \"pos\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"v.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"word\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12519,\n        \"samples\": [\n          \"WRIG\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"definitions\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"synonyms\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 923,\n        \"samples\": [\n          \"suggest; prescribe; enjoin; command; point out; urge; admonish.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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      "source": [
        "nlp = spacy.load(\"en_core_web_sm\")"
      ],
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        "id": "9ApsULasTA4J"
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      "cell_type": "code",
      "source": [
        "text = \"\"\"\n",
        "wet folds\n",
        "\n",
        "while marvelling at an autocorrect algorithm that replaces the word\n",
        "\"fetish\" with the phrase \"fetus god\", i find a man staring into the\n",
        "fogged window of a golden krust shaving a patch of red hair from\n",
        "his upper lip while patrons, staring back, eat yellow pockets of\n",
        "shredded meats wrapped in a beige blanket of coco bread.\n",
        "\n",
        "a sign at the butcher next door reads:\n",
        "smoked pork\n",
        "neckbones,\n",
        "tails, and\n",
        "hocks\n",
        "\n",
        "halfway home, now.\n",
        "\n",
        "i consider the last time i found you. when the cancer hollowed your\n",
        "body and made your insides fleshy tunnels for radiation.            i found you.\n",
        "\n",
        "after three chunks of congealed blood emptied from my uterus onto\n",
        "a cotton pad, eight months following when i first met a man twice\n",
        "my senior, a week before i started 9th grade, and three days after i\n",
        "birthed a daughter, only, on mom’s velvet couch, which at the\n",
        "time, was covered in plastic. only, born with a full bottom row of\n",
        "teeth, drew blood during each feeding.\n",
        "\n",
        "i found you shortly\n",
        "after all of that.\n",
        "that, being only.\n",
        "\n",
        "i found you on the corner of fulton and nostrand underneath a tent\n",
        "of soiled newspapers: a bag of assorted pills in your right pocket, a\n",
        "dictionary of japanese words in the left. you were wearing one red\n",
        "sock and said you just landed on earth, but the others were coming.\n",
        "coming here. you mumbled auntie martine's name as two large\n",
        "men placed your body on a tattered stretcher and shoved your\n",
        "saggy flesh into the back of an ambulance next to another black\n",
        "body lying in a pool of cloudy liquid leaking from a ruptured\n",
        "colostomy bag.\n",
        "\n",
        "i\n",
        "\n",
        "rode in the front of the truck wedged between the two large men. i\n",
        "fished a half-eaten hard boiled egg from the bottom on my\n",
        "backpack covered in crumbs and lint that clung desperately to a\n",
        "greening yolk.\n",
        "\n",
        "later, you\n",
        "disappeared\n",
        "behind hospital\n",
        "curtains for several\n",
        "hours.\n",
        "\n",
        "a strangled blue. while you were lying in the hospital bed\n",
        "\n",
        "with one lung and a slack jaw, i was eating the jars of pickled pork\n",
        "left over from the gift basket aunt martine dropped off. when the\n",
        "pork finished, i began counting the ceiling tiles (there were\n",
        "ninety-six total).\n",
        "\n",
        "your left eye was swollen and covered with a crust, like the\n",
        "cornmeal batter you used to deep fry fish on friday nights back\n",
        "when we lived in that small house in stockton that was down the\n",
        "street from the church you refused to go to. (but, you’d go there on\n",
        "saturday mornings to get free boxes of canned beans and canned\n",
        "chicken packed in water, not oil) the other eye was full and alert,\n",
        "flickering without commitment.\n",
        "\n",
        "i watched a nurse fish irregularly sized chunks of pineapple from\n",
        "your mouth with her bare fingers as you choked and i slept at the\n",
        "foot of your bed. when your shallow breaths ceased two days later,\n",
        "i fit my head into your moist armpit like a tetris block and waited\n",
        "for the nurse to collect your body.\n",
        "\n",
        "death, unlike the\n",
        "nurse, was\n",
        "punctual.\n",
        "\n",
        "you are the third person to die this year in a dank hospital with\n",
        "quiet nurses. between the wet folds of your skin, i left notes written\n",
        "on magenta construction paper. on them, i wrote questions for god.\n",
        "when i talk to god, he does not respond so i send notes with the\n",
        "dead, like you, in the hope that when you meet him, you can\n",
        "acquire some answers for me.\n",
        "\"\"\""
      ],
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      "source": [
        "# # Process the text with SpaCy\n",
        "import string\n",
        "\n",
        "# Initialize a dictionary to store verbs and their indices\n",
        "verb_indices = {}\n",
        "\n",
        "# Remove punctuation from the text\n",
        "text_without_punctuation = text.translate(str.maketrans('', '', string.punctuation))\n",
        "\n",
        "# Split the text into words\n",
        "words = text_without_punctuation.split()\n",
        "\n",
        "# Initialize a dictionary to store verbs and their indices\n",
        "verb_indices = {}\n",
        "\n",
        "# Iterate through each word in the text\n",
        "for index, word in enumerate(words):\n",
        "    # Process the word with SpaCy\n",
        "    doc = nlp(word)\n",
        "    # Check if the processed word is a verb\n",
        "    if doc[0].pos_ == 'VERB':\n",
        "        # Store the verb and its index in the dictionary\n",
        "        verb_indices[index] = word\n",
        "\n",
        "# Print the verbs and their indices\n",
        "print(\"Verbs and Indices:\", verb_indices)"
      ],
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        {
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          "name": "stdout",
          "text": [
            "Verbs and Indices: {1: 'folds', 3: 'marvelling', 9: 'replaces', 19: 'find', 22: 'staring', 25: 'fogged', 33: 'patch', 43: 'staring', 45: 'eat', 49: 'shredded', 51: 'wrapped', 54: 'beige', 55: 'blanket', 60: 'sign', 63: 'butcher', 66: 'reads', 67: 'smoked', 77: 'consider', 82: 'found', 87: 'hollowed', 91: 'made', 93: 'insides', 99: 'found', 103: 'chunks', 105: 'congealed', 107: 'emptied', 117: 'following', 121: 'met', 131: 'started', 139: 'birthed', 152: 'covered', 156: 'born', 161: 'row', 164: 'drew', 168: 'feeding', 170: 'found', 181: 'found', 194: 'soiled', 199: 'assorted', 212: 'left', 215: 'wearing', 220: 'said', 223: 'landed', 230: 'coming', 231: 'coming', 234: 'mumbled', 242: 'placed', 247: 'tattered', 250: 'shoved', 265: 'lying', 272: 'leaking', 275: 'ruptured', 279: 'rode', 286: 'wedged', 293: 'fished', 295: 'halfeaten', 297: 'boiled', 305: 'covered', 309: 'lint', 315: 'greening', 316: 'yolk', 319: 'disappeared', 322: 'curtains', 327: 'strangled', 332: 'lying', 346: 'eating', 350: 'pickled', 352: 'left', 360: 'dropped', 365: 'finished', 367: 'began', 368: 'counting', 377: 'left', 380: 'swollen', 382: 'covered', 389: 'batter', 391: 'used', 394: 'fry', 402: 'lived', 418: 'refused', 420: 'go', 424: 'go', 430: 'get', 434: 'canned', 437: 'canned', 439: 'packed', 451: 'flickering', 455: 'watched', 460: 'sized', 461: 'chunks', 473: 'choked', 476: 'slept', 485: 'shallow', 487: 'ceased', 502: 'block', 504: 'waited', 509: 'collect', 524: 'die', 533: 'nurses', 537: 'folds', 542: 'left', 543: 'notes', 544: 'written', 552: 'wrote', 558: 'talk', 562: 'does', 564: 'respond', 567: 'send', 568: 'notes', 580: 'meet', 584: 'acquire'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lemmatized_verbs = {}\n",
        "for verb in verb_indices.values():\n",
        "    # Process the verb with SpaCy\n",
        "    doc = nlp(verb)\n",
        "    # Get the lemma of the verb\n",
        "    lemmatized_verb = doc[0].lemma_\n",
        "    # Append the lemmatized verb to the list\n",
        "    lemmatized_verbs[verb] = lemmatized_verb.upper()\n",
        "\n",
        "print(lemmatized_verbs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66v-uo4yXEXN",
        "outputId": "febd714e-0ffd-4ff6-d77c-c266190964a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'folds': 'FOLD', 'marvelling': 'MARVEL', 'replaces': 'REPLACE', 'find': 'FIND', 'staring': 'STARE', 'fogged': 'FOG', 'patch': 'PATCH', 'eat': 'EAT', 'shredded': 'SHRED', 'wrapped': 'WRAP', 'beige': 'BEIGE', 'blanket': 'BLANKET', 'sign': 'SIGN', 'butcher': 'BUTCHER', 'reads': 'READ', 'smoked': 'SMOKE', 'consider': 'CONSIDER', 'found': 'FIND', 'hollowed': 'HOLLOW', 'made': 'MAKE', 'insides': 'INSIDE', 'chunks': 'CHUNK', 'congealed': 'CONGEAL', 'emptied': 'EMPTY', 'following': 'FOLLOW', 'met': 'MEET', 'started': 'START', 'birthed': 'BIRTH', 'covered': 'COVER', 'born': 'BEAR', 'row': 'ROW', 'drew': 'DRAW', 'feeding': 'FEED', 'soiled': 'SOIL', 'assorted': 'ASSORT', 'left': 'LEAVE', 'wearing': 'WEAR', 'said': 'SAY', 'landed': 'LAND', 'coming': 'COME', 'mumbled': 'MUMBLE', 'placed': 'PLACE', 'tattered': 'TATTERE', 'shoved': 'SHOVE', 'lying': 'LIE', 'leaking': 'LEAK', 'ruptured': 'RUPTURE', 'rode': 'RIDE', 'wedged': 'WEDGE', 'fished': 'FISH', 'halfeaten': 'HALFEATEN', 'boiled': 'BOIL', 'lint': 'LINT', 'greening': 'GREEN', 'yolk': 'YOLK', 'disappeared': 'DISAPPEAR', 'curtains': 'CURTAIN', 'strangled': 'STRANGLE', 'eating': 'EAT', 'pickled': 'PICKLE', 'dropped': 'DROP', 'finished': 'FINISH', 'began': 'BEGIN', 'counting': 'COUNT', 'swollen': 'SWOLLEN', 'batter': 'BATTER', 'used': 'USE', 'fry': 'FRY', 'lived': 'LIVE', 'refused': 'REFUSE', 'go': 'GO', 'get': 'GET', 'canned': 'CAN', 'packed': 'PACK', 'flickering': 'FLICKER', 'watched': 'WATCH', 'sized': 'SIZE', 'choked': 'CHOKE', 'slept': 'SLEEP', 'shallow': 'SHALLOW', 'ceased': 'CEASE', 'block': 'BLOCK', 'waited': 'WAIT', 'collect': 'COLLECT', 'die': 'DIE', 'nurses': 'NURSE', 'notes': 'NOTE', 'written': 'WRITE', 'wrote': 'WRITE', 'talk': 'TALK', 'does': 'DO', 'respond': 'RESPOND', 'send': 'SEND', 'meet': 'MEET', 'acquire': 'ACQUIRE'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize a dictionary to store locations of lemmatized verbs\n",
        "lemmatized_verb_locations = {}\n",
        "\n",
        "# Iterate through each lemmatized word\n",
        "for verb, lemmatized_verb in lemmatized_verbs.items():\n",
        "    # Find the index of the lemmatized word in the verbs_df DataFrame\n",
        "    indices = verbs_df.index[verbs_df['word'] == lemmatized_verb].tolist()\n",
        "\n",
        "    # Store the first location of the lemmatized verb in the DataFrame\n",
        "    if indices:\n",
        "        first_index = indices[0]  # Take the first index\n",
        "        lemmatized_verb_locations[lemmatized_verb] = first_index\n",
        "\n",
        "# Print the locations of lemmatized verbs\n",
        "print(\"Lemmatized Verb Locations:\", lemmatized_verb_locations)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JiVKeL3hefft",
        "outputId": "0d3aa935-adb2-4b2b-a0b9-49220a4449ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lemmatized Verb Locations: {'FOLD': 5646, 'MARVEL': 8316, 'REPLACE': 11580, 'FIND': 5477, 'STARE': 13270, 'FOG': 5637, 'PATCH': 9851, 'EAT': 4390, 'SHRED': 12542, 'WRAP': 15775, 'BLANKET': 1333, 'SIGN': 12601, 'BUTCHER': 1703, 'READ': 11007, 'SMOKE': 12832, 'CONSIDER': 2584, 'HOLLOW': 6682, 'MAKE': 8240, 'CONGEAL': 2533, 'EMPTY': 4632, 'FOLLOW': 5653, 'MEET': 8393, 'START': 13273, 'COVER': 2820, 'BEAR': 947, 'ROW': 11993, 'DRAW': 4260, 'FEED': 5391, 'SOIL': 12936, 'ASSORT': 657, 'LEAVE': 7954, 'WEAR': 15531, 'SAY': 12138, 'LAND': 7867, 'COME': 2357, 'MUMBLE': 8902, 'PLACE': 10140, 'SHOVE': 12533, 'LIE': 8031, 'LEAK': 7941, 'RUPTURE': 12038, 'RIDE': 11851, 'WEDGE': 15547, 'FISH': 5498, 'BOIL': 1429, 'GREEN': 6303, 'DISAPPEAR': 3688, 'CURTAIN': 3007, 'STRANGLE': 13413, 'PICKLE': 10067, 'DROP': 4309, 'FINISH': 5491, 'BEGIN': 1025, 'COUNT': 2765, 'BATTER': 926, 'USE': 15221, 'FRY': 5930, 'LIVE': 8106, 'REFUSE': 11332, 'GO': 6225, 'GET': 6111, 'CAN': 1779, 'PACK': 9717, 'FLICKER': 5568, 'WATCH': 15502, 'SIZE': 12660, 'CHOKE': 2063, 'SLEEP': 12745, 'SHALLOW': 12428, 'CEASE': 1910, 'BLOCK': 1379, 'WAIT': 15434, 'COLLECT': 2331, 'DIE': 3589, 'NURSE': 9084, 'NOTE': 9056, 'WRITE': 15801, 'TALK': 13859, 'DO': 4175, 'RESPOND': 11695, 'SEND': 12359, 'ACQUIRE': 137}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_verbs_list = {}\n",
        "\n",
        "# Iterate through each verb and its locations\n",
        "for verb, location in lemmatized_verb_locations.items():\n",
        "    if location is not None:\n",
        "      new_index = location + 7\n",
        "\n",
        "      try:\n",
        "          # Try to access the verb at the calculated index\n",
        "          new_verb = verbs_df.loc[new_index, 'word']\n",
        "\n",
        "          # Store the new location and replacement in the new_verbs_list dictionary\n",
        "          if verb not in new_verbs_list:\n",
        "              new_verbs_list[verb] = new_verb\n",
        "          else:\n",
        "              new_verbs_list[verb].append(new_verb)\n",
        "\n",
        "      except KeyError:\n",
        "          # If the index is beyond the range of the DataFrame, skip this word\n",
        "          print(f\"Index {new_index} is beyond the range of the DataFrame. Skipping...\")\n",
        "          continue\n",
        "\n",
        "# Print the new_verbs_list dictionary\n",
        "print(\"New Verbs:\", new_verbs_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aDuEqG5XeFzw",
        "outputId": "8445d6c1-e0db-410e-87da-324feb91ab74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New Verbs: {'FOLD': 'FOLLOW', 'MARVEL': 'MASON', 'REPLACE': 'REPLEVIN', 'FIND': 'FINEER', 'STARE': 'STARVE', 'FOG': 'FOIN', 'PATCH': 'PATROL', 'EAT': 'ECCHYMOSE', 'SHRED': 'SHRIMP', 'WRAP': 'WREAK', 'BLANKET': 'BLAST', 'SIGN': 'SIGNIORIZE', 'BUTCHER': 'BUTTRESS', 'READ': 'READOPT', 'SMOKE': 'SMOOTH', 'CONSIDER': 'CONSOCIATE', 'HOLLOW': 'HONE', 'MAKE': 'MALIGN', 'CONGEAL': 'CONGLOBE', 'EMPTY': 'EMULE', 'FOLLOW': 'FONDLE', 'MEET': 'MELIORATE', 'START': 'STATION', 'COVER': 'COWARDIZE', 'BEAR': 'BEATIFICATE', 'ROW': 'RUB', 'DRAW': 'DREAM', 'FEED': 'FEINT', 'SOIL': 'SOLARIZE', 'ASSORT': 'ASSUME', 'LEAVE': 'LECTURE', 'WEAR': 'WEATHERBIT', 'SAY': 'SCALD', 'LAND': 'LANIATE', 'COME': 'COMMANDEER', 'MUMBLE': 'MUNCH', 'PLACE': 'PLAIN', 'SHOVE': 'SHRAG', 'LIE': 'LIGATURE', 'LEAK': 'LEARN', 'RUPTURE': 'RUSSIFY', 'RIDE': 'RIFLE', 'WEDGE': 'WEIGH', 'FISH': 'FIT', 'BOIL': 'BOLN', 'GREEN': 'GREITH', 'DISAPPEAR': 'DISARRAY', 'CURTAIN': 'CUSPIDATE', 'STRANGLE': 'STRAW', 'PICKLE': 'PIECE', 'DROP': 'DRUB', 'FINISH': 'FISH', 'BEGIN': 'BEGORE', 'COUNT': 'COUNTERBRACE', 'BATTER': 'BAWL', 'USE': 'UTTER', 'FRY': 'FUFF', 'LIVE': 'LOAM', 'REFUSE': 'REGARD', 'GO': 'GOD', 'GET': 'GIB', 'CAN': 'CANDY', 'PACK': 'PADDLE', 'FLICKER': 'FLIRT', 'WATCH': 'WATER-ROT', 'SIZE': 'SKELDER', 'CHOKE': 'CHOP', 'SLEEP': 'SLICE', 'SHALLOW': 'SHAMPOO', 'CEASE': 'CELL', 'BLOCK': 'BLOSSOM', 'WAIT': 'WAKEN', 'COLLECT': 'COLLOCATE', 'DIE': 'DIFFERENTIATE', 'NURSE': 'OAR', 'NOTE': 'NOURISH', 'WRITE': 'WROTE', 'TALK': 'TAME', 'DO': 'DOCTOR', 'RESPOND': 'RESTATE', 'SEND': 'SENSUALIZE', 'ACQUIRE': 'ACTIVATE'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_verbs = {}\n",
        "\n",
        "for verb in lemmatized_verbs:\n",
        "    try:\n",
        "        # Check if the lemmatized form of the verb exists in new_verbs_list\n",
        "        new_verb = new_verbs_list[lemmatized_verbs[verb]]\n",
        "        # If it exists, store it in new_verbs dictionary\n",
        "        new_verbs[verb] = new_verb\n",
        "    except KeyError:\n",
        "        continue\n",
        "\n",
        "print(new_verbs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PbY1ImvdpEk4",
        "outputId": "749479b2-444b-4789-f042-998812834602"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'folds': 'FOLLOW', 'marvelling': 'MASON', 'replaces': 'REPLEVIN', 'find': 'FINEER', 'staring': 'STARVE', 'fogged': 'FOIN', 'patch': 'PATROL', 'eat': 'ECCHYMOSE', 'shredded': 'SHRIMP', 'wrapped': 'WREAK', 'blanket': 'BLAST', 'sign': 'SIGNIORIZE', 'butcher': 'BUTTRESS', 'reads': 'READOPT', 'smoked': 'SMOOTH', 'consider': 'CONSOCIATE', 'found': 'FINEER', 'hollowed': 'HONE', 'made': 'MALIGN', 'congealed': 'CONGLOBE', 'emptied': 'EMULE', 'following': 'FONDLE', 'met': 'MELIORATE', 'started': 'STATION', 'covered': 'COWARDIZE', 'born': 'BEATIFICATE', 'row': 'RUB', 'drew': 'DREAM', 'feeding': 'FEINT', 'soiled': 'SOLARIZE', 'assorted': 'ASSUME', 'left': 'LECTURE', 'wearing': 'WEATHERBIT', 'said': 'SCALD', 'landed': 'LANIATE', 'coming': 'COMMANDEER', 'mumbled': 'MUNCH', 'placed': 'PLAIN', 'shoved': 'SHRAG', 'lying': 'LIGATURE', 'leaking': 'LEARN', 'ruptured': 'RUSSIFY', 'rode': 'RIFLE', 'wedged': 'WEIGH', 'fished': 'FIT', 'boiled': 'BOLN', 'greening': 'GREITH', 'disappeared': 'DISARRAY', 'curtains': 'CUSPIDATE', 'strangled': 'STRAW', 'eating': 'ECCHYMOSE', 'pickled': 'PIECE', 'dropped': 'DRUB', 'finished': 'FISH', 'began': 'BEGORE', 'counting': 'COUNTERBRACE', 'batter': 'BAWL', 'used': 'UTTER', 'fry': 'FUFF', 'lived': 'LOAM', 'refused': 'REGARD', 'go': 'GOD', 'get': 'GIB', 'canned': 'CANDY', 'packed': 'PADDLE', 'flickering': 'FLIRT', 'watched': 'WATER-ROT', 'sized': 'SKELDER', 'choked': 'CHOP', 'slept': 'SLICE', 'shallow': 'SHAMPOO', 'ceased': 'CELL', 'block': 'BLOSSOM', 'waited': 'WAKEN', 'collect': 'COLLOCATE', 'die': 'DIFFERENTIATE', 'nurses': 'OAR', 'notes': 'NOURISH', 'written': 'WROTE', 'wrote': 'WROTE', 'talk': 'TAME', 'does': 'DOCTOR', 'respond': 'RESTATE', 'send': 'SENSUALIZE', 'meet': 'MELIORATE', 'acquire': 'ACTIVATE'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "new_text = text\n",
        "\n",
        "# Define a regular expression pattern to match whole words\n",
        "pattern = r'\\b(?:{})\\b'.format('|'.join(re.escape(word) for word in new_verbs.keys()))\n",
        "\n",
        "# Use re.sub to perform the replacements while respecting word boundaries\n",
        "new_text = re.sub(pattern, lambda match: new_verbs.get(match.group(0), match.group(0)), new_text)\n",
        "\n",
        "print(new_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TxEQsnzZsitB",
        "outputId": "cf5ce4f6-f1b8-4214-b265-eb7d820ced2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "wet FOLLOW\n",
            "\n",
            "while MASON at an autocorrect algorithm that REPLEVIN the word\n",
            "\"fetish\" with the phrase \"fetus god\", i FINEER a man STARVE into the\n",
            "FOIN window of a golden krust shaving a PATROL of red hair from\n",
            "his upper lip while patrons, STARVE back, ECCHYMOSE yellow pockets of\n",
            "SHRIMP meats WREAK in a beige BLAST of coco bread.\n",
            "\n",
            "a SIGNIORIZE at the BUTTRESS next door READOPT:\n",
            "SMOOTH pork\n",
            "neckbones,\n",
            "tails, and\n",
            "hocks\n",
            "\n",
            "halfway home, now.\n",
            "\n",
            "i CONSOCIATE the last time i FINEER you. when the cancer HONE your\n",
            "body and MALIGN your insides fleshy tunnels for radiation.            i FINEER you.\n",
            "\n",
            "after three chunks of CONGLOBE blood EMULE from my uterus onto\n",
            "a cotton pad, eight months FONDLE when i first MELIORATE a man twice\n",
            "my senior, a week before i STATION 9th grade, and three days after i\n",
            "birthed a daughter, only, on mom’s velvet couch, which at the\n",
            "time, was COWARDIZE in plastic. only, BEATIFICATE with a full bottom RUB of\n",
            "teeth, DREAM blood during each FEINT.\n",
            "\n",
            "i FINEER you shortly\n",
            "after all of that.\n",
            "that, being only.\n",
            "\n",
            "i FINEER you on the corner of fulton and nostrand underneath a tent\n",
            "of SOLARIZE newspapers: a bag of ASSUME pills in your right pocket, a\n",
            "dictionary of japanese words in the LECTURE. you were WEATHERBIT one red\n",
            "sock and SCALD you just LANIATE on earth, but the others were COMMANDEER.\n",
            "COMMANDEER here. you MUNCH auntie martine's name as two large\n",
            "men PLAIN your body on a tattered stretcher and SHRAG your\n",
            "saggy flesh into the back of an ambulance next to another black\n",
            "body LIGATURE in a pool of cloudy liquid LEARN from a RUSSIFY\n",
            "colostomy bag.\n",
            "\n",
            "i\n",
            "\n",
            "RIFLE in the front of the truck WEIGH between the two large men. i\n",
            "FIT a half-eaten hard BOLN egg from the bottom on my\n",
            "backpack COWARDIZE in crumbs and lint that clung desperately to a\n",
            "GREITH yolk.\n",
            "\n",
            "later, you\n",
            "DISARRAY\n",
            "behind hospital\n",
            "CUSPIDATE for several\n",
            "hours.\n",
            "\n",
            "a STRAW blue. while you were LIGATURE in the hospital bed\n",
            "\n",
            "with one lung and a slack jaw, i was ECCHYMOSE the jars of PIECE pork\n",
            "LECTURE over from the gift basket aunt martine DRUB off. when the\n",
            "pork FISH, i BEGORE COUNTERBRACE the ceiling tiles (there were\n",
            "ninety-six total).\n",
            "\n",
            "your LECTURE eye was swollen and COWARDIZE with a crust, like the\n",
            "cornmeal BAWL you UTTER to deep FUFF fish on friday nights back\n",
            "when we LOAM in that small house in stockton that was down the\n",
            "street from the church you REGARD to GOD to. (but, you’d GOD there on\n",
            "saturday mornings to GIB free boxes of CANDY beans and CANDY\n",
            "chicken PADDLE in water, not oil) the other eye was full and alert,\n",
            "FLIRT without commitment.\n",
            "\n",
            "i WATER-ROT a nurse fish irregularly SKELDER chunks of pineapple from\n",
            "your mouth with her bare fingers as you CHOP and i SLICE at the\n",
            "foot of your bed. when your SHAMPOO breaths CELL two days later,\n",
            "i fit my head into your moist armpit like a tetris BLOSSOM and WAKEN\n",
            "for the nurse to COLLOCATE your body.\n",
            "\n",
            "death, unlike the\n",
            "nurse, was\n",
            "punctual.\n",
            "\n",
            "you are the third person to DIFFERENTIATE this year in a dank hospital with\n",
            "quiet OAR. between the wet FOLLOW of your skin, i LECTURE NOURISH WROTE\n",
            "on magenta construction paper. on them, i WROTE questions for god.\n",
            "when i TAME to god, he DOCTOR not RESTATE so i SENSUALIZE NOURISH with the\n",
            "dead, like you, in the hope that when you MELIORATE him, you can\n",
            "ACTIVATE some answers for me.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "import json\n",
        "import pandas as pd\n",
        "import string\n",
        "import re\n",
        "\n",
        "# Load spaCy English model\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "def load_dictionary(json_filepath):\n",
        "    \"\"\"Loads a JSON file containing a dictionary of words and their parts of speech.\"\"\"\n",
        "    with open(json_filepath, 'r') as f:\n",
        "        data = json.load(f)\n",
        "    return data\n",
        "\n",
        "def process_a_plus_7(text, data):\n",
        "    df = pd.DataFrame(data)\n",
        "    adjectives_df = df[df['pos'] == 'a.'].reset_index(drop=True)  # Focus on adjectives\n",
        "\n",
        "    text_without_punctuation = text.translate(str.maketrans('', '', string.punctuation))\n",
        "    words = text_without_punctuation.split()\n",
        "\n",
        "    # Mapping original words to their lemmatized forms for adjectives\n",
        "    original_to_lemmatized = {\n",
        "        word: nlp(word)[0].lemma_.upper() for word in words if nlp(word)[0].pos_ == 'ADJ'\n",
        "    }\n",
        "\n",
        "    # Mapping from lemmatized adjective to its replacement (the 7th adjective following it in adjectives_df)\n",
        "    new_adjectives_list = {}\n",
        "    for original_word, lemmatized_word in original_to_lemmatized.items():\n",
        "        matching_rows = adjectives_df[adjectives_df['word'].str.upper() == lemmatized_word]\n",
        "        if not matching_rows.empty:\n",
        "            current_index = matching_rows.index[0]\n",
        "            new_index = (current_index + 7) % len(adjectives_df)\n",
        "            new_adjective = adjectives_df.iloc[new_index]['word']\n",
        "            new_adjectives_list[original_word] = new_adjective\n",
        "        else:\n",
        "            new_adjectives_list[original_word] = original_word\n",
        "\n",
        "    # Replacement function\n",
        "    def replace_func(match):\n",
        "        word = match.group(0)\n",
        "        return new_adjectives_list.get(word, word)\n",
        "\n",
        "    pattern = r'\\b(' + '|'.join(re.escape(word) for word in set(new_adjectives_list.keys())) + r')\\b'\n",
        "    new_text = re.sub(pattern, replace_func, text, flags=re.IGNORECASE)\n",
        "\n",
        "    return new_text\n",
        "\n",
        "# Example usage:\n",
        "if __name__ == \"__main__\":\n",
        "    text = \"The quick brown fox jumps over the lazy dog.\"\n",
        "    dictionary_data = load_dictionary('dictionary.json')\n",
        "    processed_text = process_a_plus_7(text, dictionary_data)\n",
        "    print(processed_text)\n"
      ],
      "metadata": {
        "id": "0bGM_mdGjZoC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f74fe3f-972c-41fe-d438-904349b57deb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The QUIDDITATIVE brown fox jumps over the LEAF-FOOTED dog.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "import random\n",
        "\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "def shuffle_every_other_word_and_rotate(text):\n",
        "    # Parse the text\n",
        "    doc = nlp(text)\n",
        "\n",
        "    # Extract words as strings, preserving whitespace\n",
        "    words = [token.text_with_ws for token in doc]\n",
        "\n",
        "    # Initialize a list to store each variation after shuffling\n",
        "    variations = []\n",
        "\n",
        "    # Set the initial starting index for shuffling to 1 (i.e., the second word)\n",
        "    start_index = 1\n",
        "\n",
        "    # Repeat the shuffling process 7 times\n",
        "    for iteration in range(7):\n",
        "        # Shuffle every other word starting from 'start_index'\n",
        "        for i in range(start_index, len(words), 2):\n",
        "            shift = random.randint(1, len(words) - 2)  # Random step within range\n",
        "            new_position = (i + shift) % len(words)  # Calculate new position with wrap-around\n",
        "\n",
        "            # Swap the current word with the word at the new position\n",
        "            words[i], words[new_position] = words[new_position], words[i]\n",
        "\n",
        "        # After each shuffle, add the current variation to the list\n",
        "        current_variation = \"\".join(words).strip()\n",
        "        variations.append(current_variation)\n",
        "\n",
        "        # Rotate the starting index for shuffling (alternate between 1 and 0)\n",
        "        start_index = (start_index + 1) % 2  # This toggles the starting index between 1 and 0\n",
        "\n",
        "    return variations\n",
        "\n",
        "# Example usage\n",
        "text = \"This is an example showing how we can shuffle every other word by a random number of positions. Each variation begins with alternating starting points.\"\n",
        "variations = shuffle_every_other_word_and_rotate(text)\n",
        "\n",
        "# Print each variation\n",
        "for i, variation in enumerate(variations, 1):\n",
        "    print(f\"Variation {i}:\\n{variation}\\n\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUE_7bKigmEJ",
        "outputId": "d44d530e-73f5-4456-8360-4a580b0d3399"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Variation 1:\n",
            "This Each an alternating how every we a shuffle showing other positionsby pointsrandom can example begins . of variation is with number starting word .\n",
            "\n",
            "Variation 2:\n",
            "by random shuffle alternating of every pointsa positionsshowing example an word we .can variation begins starting with This is . number how other Each\n",
            "\n",
            "Variation 3:\n",
            "by This shuffle is alternating we pointscan positionsother example every with number .begins variation Each starting showing random of . a how word an\n",
            "\n",
            "Variation 4:\n",
            "number This starting how we . .can other positionspointsevery showing by random begins example Each variation with is an alternating a shuffle word of\n",
            "\n",
            "Variation 5:\n",
            "number an starting variation we positions.This other example pointsalternating begins is random word . by how can Each a every showing shuffle with of\n",
            "\n",
            "Variation 6:\n",
            "how an other variation of Each random number we example starting alternating a is This word . shuffle .can positionsbegins every showing pointswith by\n",
            "\n",
            "Variation 7:\n",
            "how Each other with of number is by we variation starting can a alternating This random . example showing begins positionsword shuffle .pointsevery an\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from music21 import stream, note\n",
        "import spacy\n",
        "\n",
        "# Load the spaCy model\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "def get_every_seventh_character(text):\n",
        "    # Select every seventh character from the text\n",
        "    return [char for i, char in enumerate(text) if (i + 1) % 7 == 0 and char.isalpha()]\n",
        "\n",
        "\n",
        "def char_to_pitch(char):\n",
        "    # Map the character's position in the alphabet to a MIDI pitch\n",
        "    base_note = 60  # Middle C\n",
        "    alphabet = 'abcdefghijklmnopqrstuvwxyz'\n",
        "    char_position = alphabet.index(char.lower()) if char.lower() in alphabet else 0\n",
        "    return base_note + (char_position % 12)  # Keep within an octave for simplicity\n",
        "\n",
        "\n",
        "def create_music_with_music21(chars):\n",
        "    # Create a music21 stream to hold the music\n",
        "    part = stream.Part()\n",
        "\n",
        "    for char in chars:\n",
        "        # Use the char_to_pitch function to determine the pitch\n",
        "        midi_pitch = char_to_pitch(char)  # Convert character position to MIDI pitch\n",
        "        new_note = note.Note(midi_pitch)\n",
        "        new_note.duration.type = 'quarter'  # Set note duration to a quarter note\n",
        "        part.append(new_note)\n",
        "\n",
        "    # Show the music as MIDI\n",
        "    part.show('midi')\n",
        "\n",
        "\n",
        "# Example usage\n",
        "text = \"\"\"late spring wind sounds an ocean\n",
        "through new leaves. later the same\n",
        "wind sounds a tide. later still the dry\n",
        "\n",
        "sound of applause: leaves chapped\n",
        "falling, an ending. this is a process.\n",
        "the ocean leaping out of ocean\n",
        "\n",
        "should be enough. the wind\n",
        "pushing the water out of itself;\n",
        "the water catching the light\n",
        "\n",
        "should be enough. I think this\n",
        "on the deck of one boat\n",
        "then another. I think this\n",
        "\n",
        "in the Salish, thought it in Stellwagen\n",
        "in the Pacific. the water leaping\n",
        "looks animal, looks open mouthed,\n",
        "\n",
        "looks toothed and rolling;\n",
        "the ocean an animal full\n",
        "of other animals.\n",
        "\n",
        "what I am looking for doesn’t matter.\n",
        "that I am looking doesn’t matter.\n",
        "I exert no meaning.\n",
        "\n",
        "a juvenile bald eagle eats\n",
        "a harbor seal’s placenta.\n",
        "its head still brown.\n",
        "\n",
        "this is a process. the land\n",
        "jutting out, seals hauled out,\n",
        "the white-headed eagles lurking\n",
        "\n",
        "ready to take their turn at what’s left.\n",
        "the lone sea otter on its back,\n",
        "toes flopped forward and curled;\n",
        "\n",
        "Friday Harbor: the phone booth\n",
        "the ghost snare of a gray whale’s call;\n",
        "an orca’s tooth in an orca’s skull\n",
        "\n",
        "mounted inside the glass box.\n",
        "remains. this is a process.\n",
        "three river otters, two adults, a pup,\n",
        "\n",
        "roll like logs parallel to the shore.\n",
        "two doe, three fawns. a young buck\n",
        "stares, its antlers new, limned gold\n",
        "\n",
        "in sunset. then the wind again:\n",
        "a wave through leaves green\n",
        "with deep summer, the walnut’s\n",
        "\n",
        "green husk. we are alive in a green\n",
        "crashing world. soon winter.\n",
        "the boat forgotten. the oceans,\n",
        "\n",
        "their leaping animal light, off screen.\n",
        "past. future. this is a process. the eagles\n",
        "at the river’s edge cluster\n",
        "\n",
        "in the bare tree. they steal fish\n",
        "from ducks. they eat the hunter’s\n",
        "discards: offal and lead. the juveniles\n",
        "\n",
        "practice fighting, their feet tangle\n",
        "midair before loosing. this\n",
        "is a process. where they came from.\n",
        "\n",
        "for how long will they stay.\n",
        "that I am looking doesn’t matter.\n",
        "I will impose no meaning.\"\"\"\n",
        "chars = get_every_seventh_character(text)\n",
        "create_music_with_music21(chars)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        },
        "id": "BKEBGtrev0O9",
        "outputId": "7e5dd216-e25d-48bb-e013-616712a885fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "        <div id=\"midiPlayerDiv2354\"></div>\n",
              "        <link rel=\"stylesheet\" href=\"https://cuthbertLab.github.io/music21j/css/m21.css\">\n",
              "        \n",
              "        <script\n",
              "        src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js\"\n",
              "        ></script>\n",
              "    \n",
              "        <script>\n",
              "        function midiPlayerDiv2354_play() {\n",
              "            const rq = require.config({\n",
              "                paths: {\n",
              "                    'music21': 'https://cuthbertLab.github.io/music21j/releases/music21.debug',\n",
              "                }\n",
              "            });\n",
              "            rq(['music21'], function(music21) {\n",
              "                mp = new music21.miditools.MidiPlayer();\n",
              "                mp.addPlayer(\"#midiPlayerDiv2354\");\n",
              "                mp.base64Load(\"data:audio/midi;base64,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\");\n",
              "            });\n",
              "        }\n",
              "        if (typeof require === 'undefined') {\n",
              "            setTimeout(midiPlayerDiv2354_play, 2000);\n",
              "        } else {\n",
              "            midiPlayerDiv2354_play();\n",
              "        }\n",
              "        </script>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import spacy\n",
        "import random\n",
        "\n",
        "def fetch_random_rhyme(word):\n",
        "    \"\"\"Fetches a random rhyme for a given word using the DataMuse API.\"\"\"\n",
        "    api_url = f\"http://api.datamuse.com/words?rel_rhy={word}\"\n",
        "    response = requests.get(api_url)\n",
        "    if response.status_code == 200 and response.json():\n",
        "        rhymes = response.json()\n",
        "        random_rhyme = random.choice(rhymes)['word'].upper()  # Select a random rhyme\n",
        "        return random_rhyme + \" \"  # Return the selected rhyme in uppercase, with a space\n",
        "    else:\n",
        "        return word.upper() + \" \"  # Return the original word in uppercase, with a space, if no rhymes found\n",
        "\n",
        "def replace_every_seventh_with_random_rhyme(text):\n",
        "    \"\"\"Replaces every seventh word in the input text with a random rhyme.\"\"\"\n",
        "    nlp = spacy.load(\"en_core_web_sm\")\n",
        "    doc = nlp(text)\n",
        "    transformed_words = []\n",
        "\n",
        "    for i, token in enumerate(doc):\n",
        "        if token.is_alpha:\n",
        "            if (i + 1) % 7 == 0:\n",
        "                rhyme = fetch_random_rhyme(token.text)\n",
        "                transformed_words.append(rhyme)\n",
        "            else:\n",
        "                transformed_words.append(token.text_with_ws)\n",
        "        else:\n",
        "            transformed_words.append(token.text_with_ws)\n",
        "\n",
        "    return ''.join(transformed_words)\n",
        "\n",
        "# Example usage\n",
        "input_text = \"The quick brown fox jumps over the lazy dog and the cow jumps over the moon.\"\n",
        "transformed_text = replace_every_seventh_with_random_rhyme(input_text)\n",
        "print(\"Original Text:\")\n",
        "print(input_text)\n",
        "print(\"\\nTransformed Text:\")\n",
        "print(transformed_text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YfVHfKN_y6It",
        "outputId": "c75fc077-796b-4a5b-c03b-2d2442357f5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Text:\n",
            "The quick brown fox jumps over the lazy dog and the cow jumps over the moon.\n",
            "\n",
            "Transformed Text:\n",
            "The quick brown fox jumps over VOJTA lazy dog and the cow jumps VOICEOVER the moon.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JQNRzQOb0u1-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}