Mizzlr

# from selenium import webdriver
from seleniumwire import webdriver
from selenium.webdriver.common.action_chains import ActionChains

from selenium.webdriver.chrome.options import Options
import time
import os
import calendar
import traceback
import json

Mizzlr

from github import Github

g = Github('your-github-login', 'your-github-token-XXXX')


if __name__ == '__main__':
    for repo in g.get_user().get_repos():
        print(repo.name)

Mizzlr

import tabulate


def calc_indian_tds(salary):
    if salary < 2.5e5:
        tds = 0
    elif salary < 5e5:
        tds = salary * 0.05
    elif salary < 1e6:
        tds = (salary - 5e5) * 0.20 + 5e5 * 0.05

Mizzlr

"""This module provides tools to clone github repos. The GithubOrgCloner
can clone an entire org worth of codebases and create a local corpus of projects.
"""

import git
import shutil
import traceback
import urllib.parse
from argparse import ArgumentParser
from functools import partial

Mizzlr

#!/bin/bash

# Dependencies:
#   ripgrep https://github.com/BurntSushi/ripgrep#installation
#   realpath from GNU coreutils
#   pip3 install pylint isort dewildcard
# Usage: ./fix-python-wild-card-imports.sh "path/to/python/source/to/fix/"

# set -x # uncomment this line to see all commands

Mizzlr

import tokenize, collections, pathlib, pprint

counter = collections.Counter()
source_path = pathlib.Path('.')

for filename in source_path.glob('**/*.py'):
    print(f'Scanning tokens {filename} ...')
    tokens = tokenize.tokenize(open(filename, 'rb').__next__)
    for token in tokens:
        if token.type == 1 or token.type == '3' and ' ' not in token.string:

Mizzlr

import stringcase, tokenize, pathlib, tqdm, os

os.system("mkdir -p converted/src/")
os.system("rm -rf converted/src/*")
os.system("cp -r src/* converted/src/")

IGNORE = {'dictConfig', 'Box'}

keywords = set()
source_path = pathlib.Path('converted/src/')

Mizzlr

#pragma once

// @generated from tools/autograd/templates/VariableType.h

#include <ATen/ATen.h>

#include <cstdint> // for size_t
#include <functional> // for function
#include <memory> // for unique_ptr
#include <string>

Mizzlr

clear ; close all; clc
% Specify the architecture
input_layer_size  = 900;  % 30x30 Input Images of Digits
hidden_layer_size = 300;   % 300 hidden units
num_labels = 92;    
% set maximum number of training iteration as you please
% larger MaxIter results in better accuracy
options = optimset('MaxIter', 10);
lambda = 1; % learning rate for regularization

Mizzlr

function [J ,grad] = nnCostFunction(nn_params, ...
                                   input_layer_size, ...
                                   hidden_layer_size, ...
                                   num_labels, ...
                                   X, y, lambda)

Theta1 = reshape(nn_params(1:hidden_layer_size * ...
				(input_layer_size + 1)), ...
                hidden_layer_size, (input_layer_size + 1));