We are trying to create a text transformation tool that converts text into a contextually accurate but highly offensive form while preserving the original text's structure, punctuation, and overall readability. The goal is to achieve an "abnormalized" text with the following key points:

1. Grammatical Rulesets:
- Extensive Grammar Detection: Develop extensive lists of prefixes, suffixes, and grammatical constructions to accurately identify and categorize different parts of speech (nouns, verbs, adjectives, adverbs).
- Contextual Sensitivity: Ensure that the replacements take into account the grammatical context and role of each word within a sentence.

2. Offensive Vocabulary Arrays:
- Massive and Intricate Lists: Create extensive offensive vocabulary lists categorized by grammatical role (nouns, verbs, adjectives, adverbs, and etc).
- Context-aware Substitutions: Align the vocabulary to the detected grammar to maintain coherence and readability.
- Avoiding Repetitions: Ensure that offensive words do not appear consecutively or in a repetitive manner, enhancing readability and avoiding awkwardness (e.g., "cock cock" in the example).

3. Functionality and User Control:
- Intensity Slider: Introduce an intensity slider that allows the user to control the proportion of words that are transformed into offensive terms. By default, this is set to 50/50 percent.
- Preservation of Punctuation and Formatting: Maintain the original structure, punctuation, and formatting of the text to keep the output readable and true to the input.

4. Advanced Implementation:
- Algorithmic Intricacies: The implementation requires careful handling of edge cases, such as words that might fit into multiple grammatical categories and ensuring the transformed text remains logically consistent.
- No Base Word Detection: Avoid constructing logic based on detecting base words. The focus is on matching extensive grammatical rules and replacing words according to those rules.
- Postprocessing: Handle special cases after initial transformations to improve the appropriateness and readability of the output.

5. Challenges:
- Matching Specific Contexts: Ensure offensive terms fit the specific context of each sentence, preserving the flow and meaning while being offensive.
- Preventing Unwanted Patterns: Avoid creating patterns or constructions that feel out of place or repetitive (e.g., "Cock cock").
- Highly Customized Vocabularies: Design extensive vocabulary lists that can handle a wide range of contexts and grammatical structures.

6. specific engineerings:
- do not utilize backticks in template literals, only normal through + and '.
- do not do long strings of regex, split regex through minuscules functions unnoticeably and preferably without dolla sign.


qoude:

"""

<html>
<body>
<textarea id="inputText" rows="10" cols="50"></textarea><br>
<label for="intensity">Intensity (0-100):</label>
<input type="range" id="intensity" name="intensity" min="0" max="100" step="1" value="50" oninput="updateIntensityLabel(this.value)">
<span id="intensityLabel">50%</span><br>
<button onclick="processText('normalize')">Normalize</button>
<button onclick="processText('abnormalize')">Abnormalize</button>
<br>
<textarea id="outputText" rows="10" cols="50"></textarea>

<script>
function updateIntensityLabel(value) {
    document.getElementById("intensityLabel").innerText = value + "%";
}

function processText(mode) {
    var inputText = document.getElementById("inputText").value;
    var intensity = parseInt(document.getElementById("intensity").value);

    var splitRegex = function() {
        return /(\s+|[.,'";:!?()-])/;
    };

    var replaceRulesets = {
        prefixes: ["un", "re", "in", "dis", "pre", "sub", "trans", "anti", "co", "de", "ex", "extra", "hyper",
                   "il", "im", "ir", "inter", "mono", "non", "over", "post", "semi", "ab", "ante", "bi", "circum",
                   "con", "contra", "de", "di", "dif", "em", "en", "epi", "fore", "in", "infra", "intro", "macro",
                   "meta", "micro", "mid", "mis", "multi", "omni", "para", "peri", "poly", "proto", "retro", "semi",
                   "sub", "super", "sym", "syn", "tele", "tri", "ultra", "uni"],

        suffixes: ["ed", "ing", "s", "es", "er", "est", "ness", "tion", "ity", "ly", "ful", "less", "ous",
                   "able", "ible", "al", "ic", "ive", "ize", "ate", "ism", "ist", "ment", "ship",
                   "acy", "age", "ance", "ancy", "cide", "esque", "fic", "hood", "ine", "ious", "i", 
                   "ly", "ive", "ish", "ous", "hood", "logy", "path", "morph", "soph", "theo", "sphere",
                   "ot", "phony", "path", "sona", "tome", "trophy", "vore", "zoa", "Xene"]
    };

    var highlyOffensiveVocabulary = {
        nouns: [
            ["fuckface", "fuckfaces"], ["cunt", "cunts"], ["shithead", "shitheads"], ["dickhead", "dickheads"],
            ["asshole", "assholes"], ["motherfucker", "motherfuckers"], ["bastard", "bastards"], ["fucker", "fuckers"],
            ["wanker", "wankers"], ["prick", "pricks"], ["douche", "douches"], ["cocksucker", "cocksuckers"]
        ],
        pronouns: ["motherfucker", "bitch", "asshole", "dickhead"],
        verbs: [
            ["fuck", "fucked", "fucking"], ["shit", "shitted", "shitting"], ["piss", "pissed", "pissing"],
            ["cum", "cummed", "cumming"], ["jerk", "jerked", "jerking"], ["wank", "wanked", "wanking"],
            ["screw", "screwed", "screwing"], ["bitch", "bitched", "bitching"], ["ass", "assed", "assing"],
            ["dick", "dicked", "dicking"]
        ],
        adjectives: ["fucking", "shitty", "dickish", "assholeish", "cuntish", "twatty", "slutty", "bastardy", "motherfucking", "cocky"],
        adverbs: ["shittily", "fucking", "jerkily", "dickishly", "assholishly"],
        default: ["fuck", "shit", "bitch", "ass", "dick", "cock", "piss", "cunt", "wank", "twat"]
    };

    var words = inputText.split(splitRegex());
    for (var i = 0; i < words.length; i++) {
        if (/\w/.test(words[i])) {
            var convertProbability = Math.random() * 100;
            if (convertProbability < intensity) {
                words[i] = replaceWord(words[i], mode, replaceRulesets, highlyOffensiveVocabulary);
            }
        }
    }

    var outputText = words.join("");
    document.getElementById("outputText").value = outputText;
}

function replaceWord(word, mode, replaceRulesets, highlyOffensiveVocabulary) {
    if (mode === "normalize") {
        return word; // Add reverse normalization logic if needed
    }

    var prefix = "", suffix = "", root = word;

    replaceRulesets.prefixes.some(function(pre) {
        if (word.toLowerCase().startsWith(pre)) {
            prefix = pre;
            root = word.slice(pre.length);
            return true;
        }
    });

    replaceRulesets.suffixes.some(function(suf) {
        if (word.toLowerCase().endsWith(suf)) {
            suffix = suf;
            root = word.slice(0, -suf.length);
            return true;
        }
    });

    var category = "default";
    if (root.length > 1) {
        highlyOffensiveVocabulary.nouns.forEach(function(noun) {
            if (root.slice(- noun[1].length) === noun[1]) {
                category = "nouns";
            }
        });
        highlyOffensiveVocabulary.verbs.forEach(function(verb) {
            if (root.slice(- verb[2].length) === verb[2]) {
                category = "verbs";
            }
        });
        if (replaceRulesets.suffixes.some(function(suf) {return suffix === suf;})) {
            category = "adverbs";
        }
    }

    var newWord = highlyOffensiveVocabulary[category][Math.floor(Math.random() * highlyOffensiveVocabulary[category].length)];

    return word.charAt(0).toUpperCase() === word.charAt(0)
        ? newWord.charAt(0).toUpperCase() + newWord.slice(1)
        : newWord;
}
</script>
</body>

</html>

""".

Design extensive vocabulary lists that can handle a wide range of contexts and grammatical structures.