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// Расширенная версия с отслеживанием переходов по страницам
let allOrderSums = [];
let tableOrders = [];
let isTracking = false;

function extractOrderSums() {
    const orderSumElements = document.querySelectorAll('.order-price-block.order-product__price > .order-price-block__line > .order-price-block__value.order-price-block__value_big');
    const sums = Array.from(orderSumElements).map(element => {
        const sumText = element.textContent.trim();
        const sumValue = parseInt(sumText.replace(/\s/g, '').replace('₽', ''));

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const card = "div.Panel.Focusable";
const achievement_ru = "ДОСТИЖЕНИЯ";
const perfect = '[title="Получены все достижения"]';

const cards = Array.from(document.querySelectorAll(card))
                .filter(item => !item.querySelector(perfect))
                .filter(item => Array.from(item.querySelectorAll('a'))
                .some(a => a.innerText == achievement_ru))
                .map(item => item.querySelector('a.Focusable').href)
                .map(href => href.match(/(\d+)$/)[0]);

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const FilterByModel = (size, types, names, suffix = null) => {
	return (model_name) => {
		const slices = model_name.split('-');
		
        const avaible_name = () => {
			if (names.length === 0)
				return true;
			const _name = slices[0];
			return names.find((element) => element.length > 0 && _name.includes(element));
		};

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template <class Iterator, class SignedType>
decltype(auto) nearest_binary_search(Iterator begin, Iterator end, SignedType target)
{
    auto nearest = begin;
    auto distance = std::numeric_limits<SignedType>::max();

    while (true)
    {
        const auto half = begin + (end - begin) / 2;
        const auto step = *half;

d000b

// https://stabbingwithasyringe.home.blog/

const jokes_elems = document.querySelectorAll('div.entry-content>p>span');
const jokes = Array.from(jokes_elems).map((item) => {
    return item.innerHTML;
});
console.log(jokes);

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const block_friendlists = document.querySelectorAll('.selectable');
const current_friendlist = Array.from(block_friendlists).map(item => item.getAttribute('data-steamid'));

const previous_friendlist = [
  // array of steam64's strings
];

const difference = previous_friendlist.filter(function(x) {
  return current_friendlist.indexOf(x) < 0;
});

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git fetch --prune
git branch --merged | Where-Object {$_ -notmatch "master|main|\*"} | ForEach-Object {git branch -d $_.trim()}

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const tag_containter = document.querySelector('#tag-container');
const tag_elements = Array.from(tag_containter.querySelectorAll('span.name'));
const tag_names = tag_elements.map((element) => { return element.innerText; });
console.log(tag_names);

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const example_query = 'https://nhentai.net/api/galleries/search?query=incest&page=1';
fetch(example_query)
.then((response) => {
  return response.json();
})
.then((data) => {
  const get_tag_list = (media) => {
    const get_pretty_tag = (tag) => {
      return { id: tag.id, name: tag.name, type: tag.type };
    };

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from tensorflow.keras.layers import Input, LSTM, Dense
from tensorflow.keras.models import Model

# Define the encoder
encoder_inputs = Input(shape=(None, num_encoder_tokens))
encoder = LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
encoder_states = [state_h, state_c]

# Define the decoder