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import time
import logging

from airflow.utils.db import create_session
from airflow.utils import timezone
from airflow.models import TaskInstance, DagRun
from airflow.models.serialized_dag import SerializedDagModel
from airflow.utils.types import DagRunType

logger = logging.getLogger(__name__)

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from sqlalchemy import create_engine, Column, String
from sqlalchemy.ext.declarative import declarative_base, declared_attr
from sqlalchemy.orm import sessionmaker, scoped_session

engine = create_engine(
    'mysql://root@mysql/airflow',
    echo=True
)

db_session = scoped_session(

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from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'start_date': datetime(2018, 1, 1),
    'email_on_failure': False,
    'email_on_retry': False,

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from flask import Blueprint

import os

"""
Create this file as ./plugins/staticfiles.py in your ARIFLOW_HOME directory

It will serve all files under ./static/ available under /a/static/ (The "a" comes from the ``url_prefix`` argument.)
"""

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# Unofficial brew formula for proxychains 4
# Instruction:
#   $ git clone https://gist.github.com/628c1d98fab731d809d883c24eedd9d4.git gist-3792521
#   $ brew install --HEAD gist-3792521/proxychains4.rb
#
# The default config file will be located in /usr/local/etc/proxychains.conf
#
require 'formula'

class Proxychains4 < Formula

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#!/bin/bash -e

set -o pipefail


case "$1" in
  --list|-l)
    list_only=1
    ;;
  --dry-run|-n)

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#!/bin/bash
#
# shellcheck disable=SC2001

set -e -o pipefail


input="$1"

[[ -n "$input" ]] || {

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{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "AllowListingOfOwnState",
            "Effect": "Allow",
            "Principal": "*",
            "Action": "s3:ListBucket",
            "Resource": "arn:aws:s3:::my-terraform-state",
            "Condition": {

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ensure_kube_apiserver_config() {
  local dry_run="$2"
  # If we want to make changes to the apiserver manifest we do it via an
  # Azure CustomLinuxCommand extension
  # https://github.com/Azure/custom-script-extension-linux which will run the
  # command for us without having to log in. The down side to this is that it
  # takes a while to "deploy" the extension, which is espeically a pain if
  # the config is already correct.
  #
  # To speed it up and only deploy the extension if something is changed we

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#!/usr/bin/env python
from __future__ import print_function
import argparse
import boto
import boto.provider
import boto.sdb
import getpass
import os
import signal
import subprocess