tangert

//SPDX-License-Identifier: Unlicense
pragma solidity ^0.8.0;
import "./Utils.sol";

// Core SVG utilitiy library which helps us construct
// onchain SVG's with a simple, web-like API.
library svg {
    /* MAIN ELEMENTS */
    function g(string memory _props, string memory _children)
        internal

tangert

pragma solidity ^0.8.0;

import "hardhat/console.sol";
import "@openzeppelin/contracts/utils/Strings.sol";

contract Renderer {
    constructor() {
        console.log("Deploying render test");
    }

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 function getAddressChars(address _address)
        private
        pure
        returns (bytes memory)
 {
        bytes memory hexSymbols = "0123456789abcdef";
        uint256 value = uint256(uint160(_address));
        uint256 temp = value;
        uint256 length = 0;

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# USAGE:
# smolvid yourmovie.mov

# INSTALL:
# add this to your .bashrc or .zshrc or wherever:
# alias smolvid="bash path/to/smolvid.sh "$1""

# SCRIPT: 
# input file
INPUT="$1"

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syntax on
filetype plugin indent on
" show existing tab with 4 spaces width
set tabstop=4
" when indenting with '>', use 4 spaces width
set shiftwidth=4
" On pressing tab, insert 4 spaces
set expandtab

tangert

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{"format": "layers-model", "generatedBy": "keras v2.2.4", "convertedBy": "TensorFlow.js Converter v1.2.6", "modelTopology": {"keras_version": "2.2.4", "backend": "tensorflow", "model_config": {"class_name": "Sequential", "config": {"name": "sequential_18", "layers": [{"class_name": "LSTM", "config": {"name": "lstm_17", "trainable": true, "batch_input_shape": [null, 32, 1], "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "units": 128, "activation": "tanh", "recurrent_activation": "hard_sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regulari

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{"class_name": "Sequential", "config": {"name": "sequential_2", "layers": [{"class_name": "LSTM", "config": {"name": "lstm_2", "trainable": true, "batch_input_shape": [null, 32, 1], "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "units": 128, "activation": "tanh", "recurrent_activation": "hard_sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 1}}, {"class_name": "Dropout", "config":

tangert

# Code originally ported from HistWords <https://github.com/williamleif/histwords> by William Hamilton <wleif@stanford.edu>.

def align_gensim_models(models, words=None):
    """
    Returns the aligned/intersected models from a list of gensim word2vec models.
    Generalized from original two-way intersection as seen above.
    
    Also updated to work with the most recent version of gensim
    Requires reduce from functools