code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
from flask import request, Flask
import ldap3
app = Flask(__name__)
@app.route("/normal")
def normal():
"""
A RemoteFlowSource is used directly as DN and search filter
"""
unsafe_dc = request.args['dc']
unsafe_filter = request.args['username']
dn = "dc={}".format(unsafe_dc)
search_filte... | normal | {
"blob_id": "b51591de921f6e153c1dd478cec7fad42ff4251a",
"index": 749,
"step-1": "<mask token>\n\n\n@app.route('/direct')\ndef direct():\n \"\"\"\n A RemoteFlowSource is used directly as DN and search filter using a oneline call to .search\n \"\"\"\n unsafe_dc = request.args['dc']\n unsafe_filter =... | [
1,
2,
3,
4,
5
] |
from .. import CURRENT_NAME
from ..cmd import call_cmd
from .config import Configurator
from .config import USER_INI
from icemac.install.addressbook._compat import Path
import argparse
import os
import pdb # noqa: T002
import sys
def update(stdin=None):
"""Update the current address book installation."""
cur... | normal | {
"blob_id": "f5274f5d838d484ca0c1cc5a5192a2fd698cf827",
"index": 9432,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef update(stdin=None):\n \"\"\"Update the current address book installation.\"\"\"\n curr_path = Path.cwd() / CURRENT_NAME\n if not curr_path.exists():\n print('ERROR... | [
0,
1,
2,
3,
4
] |
import types
import qt
cfg = qt.cfgman
cfg.remove_cfg('protocols')
cfg.remove_cfg('samples')
cfg.remove_cfg('setup')
cfg.add_cfg('protocols')
cfg.add_cfg('samples')
cfg.add_cfg('setup')
cfg['samples']['current'] = 'hans-sil13'
cfg['protocols']['current'] = 'hans-sil13-default'
print 'updating msmt params for {}'.for... | normal | {
"blob_id": "3f20438b0dd2ae8de470e5456dbb764eabf69645",
"index": 8092,
"step-1": "import types\nimport qt\ncfg = qt.cfgman\n\ncfg.remove_cfg('protocols')\ncfg.remove_cfg('samples')\ncfg.remove_cfg('setup')\ncfg.add_cfg('protocols')\ncfg.add_cfg('samples')\ncfg.add_cfg('setup')\n\ncfg['samples']['current'] = 'han... | [
0
] |
from project import db
from project.models import User, Recipe, Association, Ingre, Recipe_ingre
user=User.query.filter_by(username="xiaofan").first()
recipe=Recipe.query.filter_by(recipename="Jerry").first()
recipes = Recipe.query.filter(Recipe.users.any(username="xiaofan")).all()
if recipe not in recipes:
us... | normal | {
"blob_id": "07f8fd305e2311c0e37a785da0a826b8ea4e78ba",
"index": 4154,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif recipe not in recipes:\n user.add_recipes([recipe])\n db.session.commit()\n",
"step-3": "<mask token>\nuser = User.query.filter_by(username='xiaofan').first()\nrecipe = Recipe.... | [
0,
1,
2,
3,
4
] |
import weakref
from soma.controller import Controller
from soma.functiontools import SomaPartial
from traits.api import File, Undefined, Instance
class MatlabConfig(Controller):
executable = File(Undefined, output=False,
desc='Full path of the matlab executable')
def load_module(capsul_... | normal | {
"blob_id": "4a8e8994ec8734664a5965b81da9d146d8504f8d",
"index": 6096,
"step-1": "<mask token>\n\n\nclass MatlabConfig(Controller):\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass MatlabConfig(Controller):\n executable = File(Undefined, output=False, desc=\n 'Full path of t... | [
1,
4,
5,
6,
7
] |
#!/bin/python
import numpy as np
import os
from sklearn.svm.classes import SVC
import pickle
import sys
# Apply the SVM model to the testing videos; Output the score for each video
if __name__ == '__main__':
if len(sys.argv) != 5:
print("Usage: {0} model_file feat_dir feat_dim output_file".format(sys.ar... | normal | {
"blob_id": "385dccfab4d7c37d10d968658b51e231691a7b49",
"index": 1556,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n if len(sys.argv) != 5:\n print('Usage: {0} model_file feat_dir feat_dim output_file'.format(\n sys.argv[0]))\n print('model_file -... | [
0,
1,
2,
3
] |
from django.http import HttpResponseRedirect
from django.shortcuts import render
from django.views.generic import TemplateView
from pos.service.sumup import API_URL, create_checkout
from pos.models.sumup import SumUpAPIKey, SumUpOnline
from pos.forms import RemotePayForm
from pos.models.user import User
class Remote... | normal | {
"blob_id": "731d2891bbc29879fd8900a11077c93550e4e88d",
"index": 4251,
"step-1": "<mask token>\n\n\nclass RemotePayView(TemplateView):\n template_name = 'remotepay/pay.djhtml'\n\n\n<mask token>\n\n\ndef pay_callback(request, checkoutid):\n t = SumUpOnline.objects.get(transaction_id=checkoutid)\n if t.st... | [
3,
4,
7,
8,
9
] |
class Circle():
def __init__(self, radius, color="white"):
self.radius = radius
self.color = color
c1 = Circle(10, "black")
print("半径:{}, 色: {}".format(c1.radius, c1.color)) | normal | {
"blob_id": "6ce50552571594c7be77ac0bf3b5274f2f39e545",
"index": 5086,
"step-1": "class Circle:\n <mask token>\n\n\n<mask token>\n",
"step-2": "class Circle:\n\n def __init__(self, radius, color='white'):\n self.radius = radius\n self.color = color\n\n\n<mask token>\n",
"step-3": "class C... | [
1,
2,
3,
4,
5
] |
"""
Generates a temperature celsius to fahrenheit conversion table
AT
11-10-2018
"""
__author__ = "Aspen Thompson"
header = "| Celsius | Fahrenheit |"
line = "-" * len(header)
print("{0}\n{1}\n{0}".format(line, header))
for i in range(-10, 31):
print("| {:^7} | {:^10.10} |".format(i, i * 1.8 + 32))
| normal | {
"blob_id": "591d0a166af5b8d0bed851c2f56ecc3da4f3a5eb",
"index": 4367,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('{0}\\n{1}\\n{0}'.format(line, header))\nfor i in range(-10, 31):\n print('| {:^7} | {:^10.10} |'.format(i, i * 1.8 + 32))\n",
"step-3": "<mask token>\n__author__ = 'Aspen Thom... | [
0,
1,
2,
3
] |
from django.conf import settings
from django.contrib.sites.models import RequestSite
from django.contrib.sites.models import Site
from fish.labinterface.models import *
from registration import signals
from registration.forms import RegistrationForm
from registration.models import RegistrationProfile
from labinterfac... | normal | {
"blob_id": "201279c0cba2d52b6863204bfadb6291a0065f60",
"index": 3961,
"step-1": "<mask token>\n\n\nclass CustomRegistrationBackend(object):\n <mask token>\n\n def activate(self, request, activation_key):\n activated = RegistrationProfile.objects.activate_user(activation_key)\n if activated:\... | [
5,
6,
7,
8,
9
] |
# inserting logical unit ids for splitting texts into logical chunks
import re
import os
splitter = "#META#Header#End#"
def logical_units(file):
ar_ra = re.compile("^[ذ١٢٣٤٥٦٧٨٩٠ّـضصثقفغعهخحجدًٌَُلإإشسيبلاتنمكطٍِلأأـئءؤرلاىةوزظْلآآ]+$")
with open(file, "r", encoding="utf8") as f1:
book = f1.read()
... | normal | {
"blob_id": "5c001303962315afe2512eb307376f6f7a883cf9",
"index": 6831,
"step-1": "<mask token>\n\n\ndef process_all(folder):\n exclude = ['OpenITI.github.io', 'Annotation', '_maintenance', 'i.mech']\n for root, dirs, files in os.walk(folder):\n dirs[:] = [d for d in dirs if d not in exclude]\n ... | [
1,
3,
4,
5,
6
] |
def solve(bt):
if len(bt) == n:
print(*bt, sep="")
exit()
for i in [1, 2, 3]:
if is_good(bt + [i]):
solve(bt + [i])
def is_good(arr):
for i in range(1, len(arr)//2+1):
if arr[-i:] == arr[-(i*2):-i]:
return False
return True
if __name__ == "__main__":
n = int(input())
sol... | normal | {
"blob_id": "65d5cee6899b0b75474e3898459bf2cfa8b3635b",
"index": 1042,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef is_good(arr):\n for i in range(1, len(arr) // 2 + 1):\n if arr[-i:] == arr[-(i * 2):-i]:\n return False\n return True\n\n\n<mask token>\n",
"step-3": "de... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
#############################################################################
#
# Copyright (C) 2019-Antti Kärki.
# Author: Antti Kärki.
#
# You can modify it under the terms of the GNU AFFERO
# GENERAL PUBLIC LICENSE (AGPL v3), Version 3.
#
# This program is distributed ... | normal | {
"blob_id": "96131e3d6c67c0ee4ff7f69d4ffedcbf96470f14",
"index": 7069,
"step-1": "<mask token>\n\n\nclass rocker_connection:\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass rocker_connection:\n\n @api.multi\n def create_connection(self):\n _database_record = self\n _datasource = _d... | [
1,
2,
3,
4,
5
] |
def missing_value_count_and_percent(df):
"""
Return the number and percent of missing values for each column.
Args:
df (Dataframe): A dataframe with many columns
Return:
df (Dataframe): A dataframe with one column showing number of missing values, one column showing percentage of ... | normal | {
"blob_id": "88c304f224ab60062582abbfa1146a651e1233e6",
"index": 183,
"step-1": "def missing_value_count_and_percent(df):\n \"\"\"\n Return the number and percent of missing values for each column. \n\n Args:\n df (Dataframe): A dataframe with many columns\n \n Return:\n df (Datafram... | [
0
] |
# -*- coding: utf-8 -*-
"""
@author: chris
Modified from THOMAS MCTAVISH (2010-11-04).
mpiexec -f ~/machinefile -enable-x -n 96 python Population.py --noplot
"""
from __future__ import with_statement
from __future__ import division
import sys
sys.path.append('../NET/sheff/weasel/')
sys.path.append('../NET/sheffprk/... | normal | {
"blob_id": "06ea697989f8f9ac539559690dcfd7aa73151e0f",
"index": 2700,
"step-1": "# -*- coding: utf-8 -*-\n\"\"\"\n@author: chris\n\nModified from THOMAS MCTAVISH (2010-11-04).\n\nmpiexec -f ~/machinefile -enable-x -n 96 python Population.py --noplot\n\"\"\"\n\nfrom __future__ import with_statement\nfrom __futur... | [
0
] |
import re
import random
import requests
from bs4 import BeautifulSoup
import js2py
from fake_useragent import UserAgent
def _get_request_key(session):
res = session.post("https://spys.one/en/socks-proxy-list/")
soup = BeautifulSoup(res.text, 'html.parser')
return soup.find("input", {"name": "xx0"}).get("v... | normal | {
"blob_id": "647dde6e3288ded29336062b78baacc3a92908a7",
"index": 478,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ProxyScrapper:\n\n def __init__(self):\n self._proxies = []\n\n def refresh(self):\n session = requests.Session()\n session.headers['User-Agent'] = Use... | [
0,
4,
5,
7,
8
] |
a=[1,2,3,4,5]
max=0
for i in a:
if i>=max:
max=i
elif i<=min:
min=i
print max
print min
| normal | {
"blob_id": "65da68d33aa382ed6deeff3c66a063ee299c2567",
"index": 1448,
"step-1": "a=[1,2,3,4,5]\nmax=0\nfor i in a:\n\tif i>=max:\n\t\tmax=i\n\telif i<=min:\n\t\tmin=i\nprint max\nprint min\n\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#from setup_env import *
#from mmlibrary import *
from astropy.coordinates import SkyCoord
import astropy.units as u
from mmlibrary import *
import numpy as np
import lal
from scipy.special import logsumexp
import cpnest, cpnest.model
# Oggetto per test: GW170817
#GW =... | normal | {
"blob_id": "fa5468741e9884f6c8bcacaf9d560b5c93ee781a",
"index": 8906,
"step-1": "<mask token>\n\n\nclass completeness(cpnest.model.Model):\n\n def __init__(self, catalog):\n self.names = ['z', 'h', 'om', 'ol']\n self.bounds = [[0.001, 0.012], [0.5, 1.0], [0.04, 1.0], [0.0, 1.0]]\n self.o... | [
2,
9,
13,
15,
16
] |
# -*- coding: utf-8 -*-
import json
import os
import io
import shutil
import pytest
from chi_annotator.algo_factory.common import TrainingData
from chi_annotator.task_center.config import AnnotatorConfig
from chi_annotator.task_center.data_loader import load_local_data
from chi_annotator.task_center.model import Inte... | normal | {
"blob_id": "192c44540018b9e1ab857bdbfba6fdb39bb74431",
"index": 8769,
"step-1": "<mask token>\n\n\nclass TestTrainer(object):\n <mask token>\n\n @classmethod\n def teardown_class(cls):\n \"\"\" teardown any state that was previously setup with a call to\n setup_class.\n \"\"\"\n ... | [
10,
12,
13,
14,
16
] |
import json
import os
import time
import urllib.request
import pandas as pd
from lib.db.dbutils import (
check_blacklisted,
check_ticker_exists,
get_db,
update_blacklisted,
)
def get_data(url, delay=20):
while True:
df = json.loads(urllib.request.urlopen(url).read())
if df.get("N... | normal | {
"blob_id": "3c8e6a93c4d5616b9199cf473d298bfa2dc191af",
"index": 9971,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef grab_a_ticker(symbol='MSFT', apiKey=None):\n if apiKey is None:\n apiKey = os.environ.get('API_KEY')\n if not check_ticker_exists(symbol) and not check_blacklisted(sy... | [
0,
1,
2,
3,
4
] |
Ylist = ['yes', 'Yes', 'Y', 'y']
Nlist = ['no', 'No', 'N', 'n']
America = ['America', 'america', 'amer', 'rica']
TRW = ['1775', 'The Revolutionary war', 'the Revolutionary war', 'the revolutionary war', 'The Revolutionary War',
'trw', 'Trw', 'TRW']
TCW = ['1861', 'The civil war', 'The civil War', 'The Civil... | normal | {
"blob_id": "6e07dcc3f3b8c7fbf8ce8d481b9612e7496967bd",
"index": 8316,
"step-1": "<mask token>\n",
"step-2": "Ylist = ['yes', 'Yes', 'Y', 'y']\nNlist = ['no', 'No', 'N', 'n']\nAmerica = ['America', 'america', 'amer', 'rica']\nTRW = ['1775', 'The Revolutionary war', 'the Revolutionary war',\n 'the revolution... | [
0,
1,
2
] |
# coding=utf-8
from __future__ import print_function
import os
import sys
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
basedir = os.getcwd()
os.chdir(os.path.dirname(os.path.abspath(__file__)))
sys.path.append('trainer')
sys.path.append('downloader')
from gen.gen_captcha import gen_dataset, load_templates, candidates
fr... | normal | {
"blob_id": "8e34b5e15c5b6107d6841e7b567abf967c631f1b",
"index": 7440,
"step-1": "<mask token>\n\n\ndef show_im(dataset):\n data = np.uint8(dataset[0]).reshape((30, 96)) * 255\n im = Image.fromarray(data)\n im.show()\n\n\ndef test_model(captcha):\n im = Image.open(os.path.join(basedir, 'downloader', ... | [
2,
3,
4,
5,
6
] |
from app01 import models
from rest_framework.views import APIView
# from api.utils.response import BaseResponse
from rest_framework.response import Response
from rest_framework.pagination import PageNumberPagination
from api.serializers.course import DegreeCourseSerializer
# 查询所有学位课程
class DegreeCourseView(APIView):... | normal | {
"blob_id": "2b3f8b1ac4735785683c00f6e6ced85d201de53f",
"index": 8567,
"step-1": "<mask token>\n\n\nclass DegreeCourseDetailView(APIView):\n\n def get(self, request, pk, *args, **kwargs):\n response = {'code': 100, 'data': None, 'error': None}\n try:\n degree_course = models.DegreeCou... | [
2,
3,
4,
5,
6
] |
from django.contrib.auth.models import User
from rest_framework.serializers import ModelSerializer
from app_calendar.models import Holiday, Country, Event, User
class CountrySerializer(ModelSerializer):
class Meta:
model = Country
fields = '__all__'
class UserSerializer(ModelSerializer):
... | normal | {
"blob_id": "5b366b0f6813f686600df9da4a17f190f034a10c",
"index": 2046,
"step-1": "<mask token>\n\n\nclass EventSerializer(ModelSerializer):\n\n\n class Meta:\n model = Event\n fields = '__all__'\n\n\nclass HolidaySerializerRead(ModelSerializer):\n country = CountrySerializer()\n\n\n class ... | [
4,
5,
6,
7
] |
"""
时间最优
思路：
将和为目标值的那 两个 整数定义为 num1 和 num2
创建一个新字典，内容存在数组中的数字及索引
将数组nums转换为字典，
遍历字典， num1为字典中的元素（其实与数组总的元素一样），
num2 为 target减去num1， 判定num2是否在字典中，如果存在，返回字典中num2的值（也就是在数组nums中的下标）和 i（也就是num1在数组中的下标）
如果不存在，设置字典num1的值为i
"""
def two_sum(nums, target):
dct = {}
for i, num1 in enumerate(nums):
... | normal | {
"blob_id": "dac8dbb0eba78d4f8dfbe3284325735324a87dc2",
"index": 8674,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef two_sum(nums, target):\n dct = {}\n for i, num1 in enumerate(nums):\n num2 = target - num1\n if num2 in dct:\n return [dct[num2], i]\n dct[nu... | [
0,
1,
2,
3
] |
from django.urls import path
from django.conf import settings
from django.conf.urls.static import static
from . import views
urlpatterns = [path('', views.PostList.as_view(), name='blog_index'), path(
'<slug:slug>/', views.post_detail, name='post_detail'), path(
'tag/<slug:slug>/', views.TagIndexView.as_view(),... | normal | {
"blob_id": "09ea684cfb6f0a521d3bdadf977d9385636bdc83",
"index": 7150,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('', views.PostList.as_view(), name='blog_index'), path(\n '<slug:slug>/', views.post_detail, name='post_detail'), path(\n 'tag/<slug:slug>/', views.TagIndexView.... | [
0,
1,
2
] |
import socket
import time
import sys
def main():
if len(sys.argv) != 2:
print("usage : %s port")
sys.exit()
port = int(sys.argv[1])
count = 0
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
sock.setsockopt(socke... | normal | {
"blob_id": "68b9f7317f7c6dcda791338ee642dffb653ac694",
"index": 4804,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n if len(sys.argv) != 2:\n print('usage : %s port')\n sys.exit()\n port = int(sys.argv[1])\n count = 0\n sock = socket.socket(socket.AF_INET, soc... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
# encoding: utf-8
"""
plot: regularization on x axis, number of k_best features on y
Created by on 2012-01-27.
Copyright (c) 2012. All rights reserved.
"""
import sys
import os
import json
import numpy as np
import pylab as plt
import itertools as it
from master.libs import plot_lib as plib
... | normal | {
"blob_id": "c5bbfa1a86dbbd431566205ff7d7b941bdceff58",
"index": 1233,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nreload(plib)\nreload(rdl)\n<mask token>\nplt.rcParams.update(params)\n<mask token>\nif not os.path.exists(outpath):\n os.mkdir(outpath)\nplt.close('all')\n<mask token>\nif config['plot... | [
0,
1,
2,
3,
4
] |
import os
import numpy as np
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Flatten, concatenate
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, Activation
from keras.layers.normalization import BatchNormalization
from keras.optimizers import SGD
from keras.... | normal | {
"blob_id": "ebc050544da69837cc2b8977f347380b94474bab",
"index": 576,
"step-1": "<mask token>\n\n\ndef _build(_input, *nodes):\n x = _input\n for node in nodes:\n if callable(node):\n x = node(x)\n elif isinstance(node, list):\n x = [_build(x, branch) for branch in node]... | [
1,
2,
3,
4,
5
] |
#n = int(input())
#s = input()
n, m = map(int, input().split())
#s, t = input().split()
#n, m, l = map(int, input().split())
#s, t, r = input().split()
#a = map(int, input().split())
#a = input().split()
a = [int(input()) for _ in range(n)]
#a = [input() for _ in range(n)]
#t = input()
#m = int(input())
#p, q = map(in... | normal | {
"blob_id": "a09bc84a14718422894127a519d67dc0c6b13bc9",
"index": 746,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(n - 1):\n if a[i + 1] - a[i] < m:\n ans += a[i + 1] - a[i]\n else:\n ans += m\nprint(ans)\n",
"step-3": "n, m = map(int, input().split())\na = [int(inp... | [
0,
1,
2,
3
] |
import os
from os.path import join
import json
import pandas as pd
import time
import numpy as np
import torch
def str2bool(v):
# convert string to boolean type for argparser input
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower(... | normal | {
"blob_id": "a9302dbf724f9548411fbf2959f36b4cc5742ff8",
"index": 4999,
"step-1": "<mask token>\n\n\ndef str_or_none(v):\n if v is None:\n return None\n if v.lower() == 'none':\n return None\n else:\n return v\n\n\n<mask token>\n\n\ndef name2dic(s):\n return {x.split('-')[0]: x.sp... | [
5,
8,
9,
12,
13
] |
'''
Условие
Дано два числа a и b. Выведите гипотенузу треугольника с заданными катетами.
'''
import math
a = int(input())
b = int(input())
print(math.sqrt(a * a + b * b)) | normal | {
"blob_id": "c0348fc5f51e6f7a191fea6d0e3cb84c60b03e22",
"index": 597,
"step-1": "'''\nУсловие\nДано два числа a и b. Выведите гипотенузу треугольника с заданными катетами.\n'''\nimport math\na = int(input())\nb = int(input())\nprint(math.sqrt(a * a + b * b))",
"step-2": null,
"step-3": null,
"step-4": nul... | [
0
] |
#!/usr/bin/env python3
import cgitb
import sys
from auth import is_admin
cgitb.enable()
sys.stdout.write('Content-Type: application/octet-stream\n\n')
sys.stdout.write('yes' if is_admin() else 'no')
sys.stdout.flush()
| normal | {
"blob_id": "be9972d899a167a8ca2728960e55cda538793cc5",
"index": 1576,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncgitb.enable()\nsys.stdout.write('Content-Type: application/octet-stream\\n\\n')\nsys.stdout.write('yes' if is_admin() else 'no')\nsys.stdout.flush()\n",
"step-3": "import cgitb\nimport... | [
0,
1,
2,
3
] |
# Generated by Django 3.2.7 on 2021-10-01 06:43
from django.db import migrations
import django_resized.forms
import event.models.event
import event.models.event_agenda
class Migration(migrations.Migration):
dependencies = [
('event', '0009_auto_20211001_0406'),
]
operations = [
migratio... | normal | {
"blob_id": "d0a053faccecddc84a9556aec3dff691b171df96",
"index": 9977,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('event', '00... | [
0,
1,
2,
3,
4
] |
a,b,c,d=map(int,input().split())
ans=0
if a>=0:
if c>=0:
ans=b*d
elif d>=0:
ans=b*d
else:
ans=a*d
elif b>=0:
if c>=0:
ans=b*d
elif d>=0:
ans=max(b*d,a*c)
else:
ans=a*c
else:
if c>=0:
ans=b*c
elif d>=0:
ans=a*c
else:
... | normal | {
"blob_id": "be37a7596850050af58f735e60bdf13594715caf",
"index": 4928,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif a >= 0:\n if c >= 0:\n ans = b * d\n elif d >= 0:\n ans = b * d\n else:\n ans = a * d\nelif b >= 0:\n if c >= 0:\n ans = b * d\n elif d >= 0:... | [
0,
1,
2,
3
] |
#This script reads through a Voyager import log and outputs duplicate bib IDs as well as the IDs of bibs, mfhds, and items created.
#import regular expressions and openpyxl
import re
import openpyxl
# prompt for file names
fname = input("Enter input file, including extension: ")
fout = input("Enter output file, witho... | normal | {
"blob_id": "fc06d8a26a99c16a4b38ad0b4bbb28a1dc522991",
"index": 6902,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith fh as f:\n lines = f.readlines()\n n_lines = len(lines)\n for i, line in enumerate(lines):\n line = line.rstrip()\n if line.startswith('\\tBibID & rank') and n... | [
0,
1,
2,
3,
4
] |
from .settings import *
# Heroku Configurations
# Parse database configuration from $DATABASE_URL
import dj_database_url
DATABASES = {'default': dj_database_url.config()}
# Honor the 'X-Forwarded-Proto' header for request.is_secure()
SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')
# loading local_se... | normal | {
"blob_id": "8bb86cae3387a0d4ce5987f3e3c458c8298174e0",
"index": 7342,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n from .local_settings import *\nexcept Exception as e:\n pass\n<mask token>\n",
"step-3": "<mask token>\nDATABASES = {'default': dj_database_url.config()}\nSECURE_PROXY_SSL_... | [
0,
1,
2,
3,
4
] |
K = input()
mat = "".join(raw_input() for i in xrange(4))
print ("YES", "NO")[max(mat.count(str(i)) for i in xrange(1, 10)) > K*2]
| normal | {
"blob_id": "879f7503f7f427f92109024b4646d1dc7f15d63d",
"index": 2153,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('YES', 'NO')[max(mat.count(str(i)) for i in xrange(1, 10)) > K * 2]\n",
"step-3": "K = input()\nmat = ''.join(raw_input() for i in xrange(4))\nprint('YES', 'NO')[max(mat.count(str... | [
0,
1,
2,
3
] |
from typing import Any, Optional
from aiogram import types
from aiogram.dispatcher.middlewares import BaseMiddleware
from scene_manager.loader.loader import Loader
from scene_manager.utils import content_type_checker
class ScenesMiddleware(BaseMiddleware):
def __init__(self, *, loader: Optional[Loader] = None, ... | normal | {
"blob_id": "11db76cba3dd76cad0d660a0e189d3e4c465071b",
"index": 8836,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ScenesMiddleware(BaseMiddleware):\n <mask token>\n\n async def on_post_process_message(self, message: types.Message, results:\n tuple, data: dict):\n if data... | [
0,
1,
2,
3,
4
] |
'''
Encontrar el valor mas alto el mas rapido, el mas lento
para eso son los algoritmos de optimizacion
Para eso debemos pensar en una funcion que queramos maximizar o minimizar
Se aplican mas que todo para empresas como despegar, en donde se pueden generar buenas empresas
Empresas a la optimizacion... | normal | {
"blob_id": "7163be250ae3a22931de037cb6896c2e6d5f00a8",
"index": 584,
"step-1": "<mask token>\n",
"step-2": "'''\n Encontrar el valor mas alto el mas rapido, el mas lento\n para eso son los algoritmos de optimizacion\n Para eso debemos pensar en una funcion que queramos maximizar o minimizar\n Se a... | [
0,
1
] |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
from .. import _utilities
import typing
# Export this package's modules as members:
from .authority import *
from .ca_pool import *
fro... | normal | {
"blob_id": "4ca4d4bd684802b056417be4ee3d7d10e8f5dc85",
"index": 8842,
"step-1": "<mask token>\n",
"step-2": "from .. import _utilities\nimport typing\nfrom .authority import *\nfrom .ca_pool import *\nfrom .ca_pool_iam_binding import *\nfrom .ca_pool_iam_member import *\nfrom .ca_pool_iam_policy import *\nfro... | [
0,
1,
2
] |
import flask
import flask_sqlalchemy
app = flask.Flask(__name__)
app.config.from_pyfile('settings.py')
db = flask_sqlalchemy.SQLAlchemy(app)
| normal | {
"blob_id": "2ed0ae48e8fec2c92effcbb3e495a1a9f4636c27",
"index": 6777,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp.config.from_pyfile('settings.py')\n<mask token>\n",
"step-3": "<mask token>\napp = flask.Flask(__name__)\napp.config.from_pyfile('settings.py')\ndb = flask_sqlalchemy.SQLAlchemy(app... | [
0,
1,
2,
3
] |
import re
import datetime
from django import forms
from django.utils.translation import ugettext as _
from vcg.util.forms import mobile_number_validation
from vcg.company_management.models import ConfigurationContact, ConfigurationLogo, ConfigurationHomepage, ConfigurationLocation
class ConfigurationContactForm(for... | normal | {
"blob_id": "f6f1cd95e4aaa5e434c3cf3cff0d46b45fc7b830",
"index": 6190,
"step-1": "<mask token>\n\n\nclass ConfigurationContactForm(forms.ModelForm):\n\n\n class Meta:\n model = ConfigurationContact\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def clean_phone_number_ext... | [
11,
13,
15,
16,
18
] |
# -*- coding: utf-8 -*-
from django.shortcuts import render_to_response
from django.views.generic import TemplateView
from django.core.context_processors import csrf
from django.template import RequestContext
from django.views.generic import DetailView, ListView , CreateView , UpdateView , DeleteView , FormView , View
... | normal | {
"blob_id": "8a3694f96203ae8d1e306e1c9a5a47bfe26abeb1",
"index": 5178,
"step-1": "<mask token>\n\n\nclass ListContact(ListView):\n model = Contact\n",
"step-2": "<mask token>\n\n\nclass AddContact(CreateView):\n model = Contact\n success_url = reverse_lazy('home')\n\n\nclass ListContact(ListView):\n ... | [
2,
4,
5,
6,
8
] |
from django.db import models
import eav
from django.utils import timezone
class RiskType(models.Model):
"""A model class used for storing data
about risk types
"""
name = models.CharField(max_length=255)
created = models.DateTimeField(default=timezone.now)
modified = models.DateTimeField(auto_... | normal | {
"blob_id": "635b75bc12718bccdfb9d04a54476c93fa4685ce",
"index": 4661,
"step-1": "<mask token>\n\n\nclass RiskType(models.Model):\n <mask token>\n name = models.CharField(max_length=255)\n created = models.DateTimeField(default=timezone.now)\n modified = models.DateTimeField(auto_now=True)\n\n\n c... | [
3,
4,
5,
6,
7
] |
NUM_CLASSES = 31
AUDIO_SR = 16000
AUDIO_LENGTH = 16000
LIBROSA_AUDIO_LENGTH = 22050
EPOCHS = 25
categories = {
'stop': 0,
'nine': 1,
'off': 2,
'four': 3,
'right': 4,
'eight': 5,
'one': 6,
'bird': 7,
'dog': 8,
'no': 9,
'on': 10,
'seven': 11,
'cat': 12,
'left': 1... | normal | {
"blob_id": "6a9e18cde94258b01a37f459eceaac58118b4976",
"index": 5813,
"step-1": "<mask token>\n",
"step-2": "NUM_CLASSES = 31\nAUDIO_SR = 16000\nAUDIO_LENGTH = 16000\nLIBROSA_AUDIO_LENGTH = 22050\nEPOCHS = 25\ncategories = {'stop': 0, 'nine': 1, 'off': 2, 'four': 3, 'right': 4,\n 'eight': 5, 'one': 6, 'bir... | [
0,
1,
2
] |
"""
Utilities used by other modules.
"""
import csv
import datetime
import hashlib
import json
import re
import string
import subprocess
import uuid
import xml.etree.ElementTree as ET
from alta import ConfigurationFromYamlFile
from pkg_resources import resource_filename
from ..__details__ import __appname__
from appd... | normal | {
"blob_id": "b16c847912944e0563492d35768b5b5bf3a506c7",
"index": 1569,
"step-1": "<mask token>\n\n\nclass IEMRunInfoReader:\n \"\"\"\n Illumina Experimental Manager RunInfo xml reader.\n \"\"\"\n\n def __init__(self, f):\n self.xml_file = f\n self.tree = ET.parse(self.xml_file)\n ... | [
25,
27,
28,
36,
38
] |
# -*- coding: utf-8 -*-
import pytest
from bravado.client import ResourceDecorator
from bravado.client import SwaggerClient
def test_resource_exists(petstore_client):
assert type(petstore_client.pet) == ResourceDecorator
def test_resource_not_found(petstore_client):
with pytest.raises(AttributeError) as ex... | normal | {
"blob_id": "5ee1d8ef7ec4b191e0789ceb9c6dd2d58af526a0",
"index": 7875,
"step-1": "<mask token>\n\n\ndef test_resource_not_found(petstore_client):\n with pytest.raises(AttributeError) as excinfo:\n petstore_client.foo\n assert 'foo not found' in str(excinfo.value)\n\n\n@pytest.fixture\ndef client_tag... | [
2,
3,
4,
5,
6
] |
"""
Stirng - Liste - Dosya
- Fonksiyon yazıyoruz.
- Bu fonksiyon iki parametre alacak. (dosya, string)
1. sorun : Dosyanın içinde string var ise True döndürecek yok ise False
2. sorun : Dosyanın içinde string bulunursa ilk bulunduğu konumu return edecek
3. sorun : Dosyanın içerisinde yazdığımız strinng kaç k... | normal | {
"blob_id": "0d3cc85cd18ee197b24c8b01b71afe82110bfad2",
"index": 3487,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef fonkString(text, string):\n if string in text:\n print('TRUE')\n print(text.index(string), '. sirada ilk', string, 'bulundu')\n print(text.count(string), '... | [
0,
1,
2,
3
] |
from django.shortcuts import render, get_object_or_404, redirect
from django.contrib.contenttypes.models import ContentType
from User.forms import EditProfileForm
from User import forms
from django.db.models import Q
from django.contrib import messages
from django.urls import reverse
from django.http import HttpRespons... | normal | {
"blob_id": "e9fab2bb49cfda00b8cfedafab0009f691d11ec9",
"index": 9924,
"step-1": "<mask token>\n\n\ndef post_create(request):\n form = PostForm(request.POST or None, request.FILES or None)\n if request.method == 'POST':\n user = request.POST.get('user')\n title = request.POST.get('title')\n ... | [
5,
6,
7,
8,
10
] |
#!/usr/bin/env python
# coding=utf-8
operators = ['-', '~', '++', '--', '*', '!', '/', '*', '%', '+', '-',
'>', '>=', '<', '<=', '==', '!=', '&&', '||', '=']
types = ['int ', 'double ', 'float ', 'char ']
toDelete = types + ['struct ']
toRepleace = [('printf(', 'print('), ('++', ' += 1'), ('--', ' -= 1')... | normal | {
"blob_id": "082e3350c5827ff2ca909084f2d6a206ae21a7e6",
"index": 3240,
"step-1": "<mask token>\n\n\ndef isChar(c):\n return c > 'a' and c < 'z' or c > 'A' and c < 'Z'\n\n\ndef isOperator(c):\n return c in operators\n\n\ndef isDefun(line):\n return '(' in line and ')' in line and sum([(i in line) for i i... | [
10,
12,
15,
16,
17
] |
from translit import convert_input
def openfile(name):
f = open(name, 'r', encoding = 'utf-8')
text = f.readlines()
f.close()
return text
def makedict(text):
A = []
for line in text:
if 'lex:' in line:
a = []
a.append(line[6:].replace('\n',''))
... | normal | {
"blob_id": "29e54a9ec0d65965645ac4aabf8c247a8857a25f",
"index": 3778,
"step-1": "<mask token>\n\n\ndef openfile(name):\n f = open(name, 'r', encoding='utf-8')\n text = f.readlines()\n f.close()\n return text\n\n\ndef makedict(text):\n A = []\n for line in text:\n if 'lex:' in line:\n ... | [
5,
6,
7,
8,
9
] |
import sys, os
class Extractor:
def __init__(self, prefix=''):
self.variables = {}
self.prefix = os.path.basename(prefix)
'''
Returns the variable name if a variable with
the value <value> is found.
'''
def find_variable_name(self, value):
for var, val in self.v... | normal | {
"blob_id": "dffcaf47ec8e0daa940e7047f11681ef3eabc772",
"index": 8591,
"step-1": "<mask token>\n\n\nclass Extractor:\n\n def __init__(self, prefix=''):\n self.variables = {}\n self.prefix = os.path.basename(prefix)\n <mask token>\n\n def find_variable_name(self, value):\n for var, v... | [
4,
6,
7,
8,
9
] |
import time
if __name__ == '__main__':
for i in range(10):
print('here %s' % i)
time.sleep(1)
print('TEST SUCEEDED')
| normal | {
"blob_id": "a159f9f9cc06bb9d22f84781fb2fc664ea204b64",
"index": 6856,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n for i in range(10):\n print('here %s' % i)\n time.sleep(1)\n print('TEST SUCEEDED')\n",
"step-3": "import time\nif __name__ == '__main__... | [
0,
1,
2
] |
import discord
import requests
import math
from keys import GITHUB_DISCORD_TOKEN, GITHUB_FORTNITE_API_KEY
client = discord.Client()
# Constant
DISCORD_TOKEN = GITHUB_DISCORD_TOKEN
FORTNITE_API_KEY = GITHUB_FORTNITE_API_KEY
LIST = ['Verified']
VERIFIED = 4
# Return the current season squad K/D of the fortnite player... | normal | {
"blob_id": "6c6a49dfced680fe034cbbc2fa28d57d2aa1273e",
"index": 8973,
"step-1": "<mask token>\n\n\ndef get_ratio(username):\n try:\n print(username)\n link = 'https://api.fortnitetracker.com/v1/profile/pc/' + username\n response = requests.get(link, headers={'TRN-Api-Key': FORTNITE_API_K... | [
1,
2,
3,
4,
5
] |
"""Module containing class `Station`."""
from zoneinfo import ZoneInfo
import datetime
from vesper.util.named import Named
class Station(Named):
"""Recording station."""
def __init__(
self, name, long_name, time_zone_name,
latitude=None, longitude=None, elevation=None... | normal | {
"blob_id": "ad09880b9e06a129b9623be2a086ebcc8dc55c2c",
"index": 9079,
"step-1": "<mask token>\n\n\nclass Station(Named):\n <mask token>\n\n def __init__(self, name, long_name, time_zone_name, latitude=None,\n longitude=None, elevation=None):\n super().__init__(name)\n self._long_name ... | [
6,
7,
9,
10,
11
] |
import csv
import glob
import random
import sys
from math import ceil, floor
from os.path import basename, exists, dirname, isfile
import numpy as np
import keras
from keras import Model, Input, regularizers
from keras.layers import TimeDistributed, LSTMCell, Reshape, Dense, Lambda, Dropout, Concatenate
from keras.cal... | normal | {
"blob_id": "c925bed2f4d8120e156caebbe8e6bf9d6a51ee37",
"index": 3330,
"step-1": "<mask token>\n\n\nclass VideoClassifier:\n\n def __init__(self, train_mode='late_fusion', video_model_path=None,\n time_step=16, base_path='/user/vlongobardi/AFEW/aligned/',\n feature_name='emobase2010_100', stride... | [
7,
11,
13,
14,
18
] |
#!/usr/bin/env python
from distutils.core import setup
setup(
name='RBM',
version='0.0.1',
description='Restricted Boltzmann Machines',
long_description='README',
install_requires=['numpy','pandas'],
)
| normal | {
"blob_id": "fab7ee8a7336ba2c044adce4cc8483af78b775ba",
"index": 1827,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='RBM', version='0.0.1', description=\n 'Restricted Boltzmann Machines', long_description='README',\n install_requires=['numpy', 'pandas'])\n",
"step-3": "from distutils... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
import psycopg2
DBNAME = "news"
query1 = """
select title, count(*) as numOfViews from articles,log
where concat('/article/', articles.slug) = log.path
group by title order by numOfViews desc limit 3;
"""
query2 = """
select authors.name, count(*) as numOfViews
from articles, authors, log
where... | normal | {
"blob_id": "612a3d168a09fc26530b95d258cbb4de6728419d",
"index": 3721,
"step-1": "<mask token>\n\n\ndef fileWrite(content):\n \"\"\" write result to result.txt \"\"\"\n file = open('./result.txt', 'w')\n file.write(content)\n file.close()\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_d... | [
1,
2,
5,
6,
7
] |
def getGC(st):
n = 0
for char in st:
if char == 'C' or char == 'G':
n += 1
return n
while True:
try:
DNA = input()
ln = int(input())
maxLen = 0
subDNA = ''
for i in range(len(DNA) - ln + 1):
sub = DNA[i:i + ln]
if getG... | normal | {
"blob_id": "afe63f94c7107cf79e57f695df8543e0786a155f",
"index": 6556,
"step-1": "<mask token>\n",
"step-2": "def getGC(st):\n n = 0\n for char in st:\n if char == 'C' or char == 'G':\n n += 1\n return n\n\n\n<mask token>\n",
"step-3": "def getGC(st):\n n = 0\n for char in st... | [
0,
1,
2
] |
from django.contrib import admin
from .models import Sport
from .models import Action
admin.site.register(Sport)
admin.site.register(Action)
| normal | {
"blob_id": "ab38371ee3941e214344497b7e56786908a9b3d1",
"index": 2236,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nadmin.site.register(Sport)\nadmin.site.register(Action)\n",
"step-3": "from django.contrib import admin\nfrom .models import Sport\nfrom .models import Action\nadmin.site.register(Sport... | [
0,
1,
2
] |
import logging
from pathlib import Path
import numpy as np
import torch
import re
import json
from helpers import init_helper, data_helper, vsumm_helper, bbox_helper
from modules.model_zoo import get_model
logger = logging.getLogger()
def evaluate(model, val_loader, nms_thresh, device):
model.eval()
stats ... | normal | {
"blob_id": "dd3419f42a3b1aafd1d4f5d88189fb3c6bd0c67e",
"index": 4233,
"step-1": "<mask token>\n\n\ndef evaluate(model, val_loader, nms_thresh, device):\n model.eval()\n stats = data_helper.AverageMeter('fscore', 'diversity')\n json_file = []\n with torch.no_grad():\n for test_key, seq, gt, cp... | [
4,
5,
7,
8,
10
] |
# Author: Sam Erickson
# Date: 2/23/2016
#
# Program Description: This program gives the integer coefficients x,y to the
# equation ax+by=gcd(a,b) given by the extended Euclidean Algorithm.
def extendedEuclid(a,b):
"""
Preconditions - a and b are both positive integers.
Posconditions - The equation for ax... | normal | {
"blob_id": "36e5b0f40b8016f39120f839766db0ac518c9bed",
"index": 4712,
"step-1": "<mask token>\n",
"step-2": "def extendedEuclid(a, b):\n \"\"\"\n Preconditions - a and b are both positive integers.\n Posconditions - The equation for ax+by=gcd(a,b) has been returned where\n x and y ... | [
0,
1,
2
] |
# -*- coding: utf-8 -*-
"""Labeled entry widget.
The goal of these widgets is twofold: to make it easier for developers
to implement dialogs with compound widgets, and to naturally
standardize the user interface presented to the user.
"""
import logging
import seamm_widgets as sw
import tkinter as tk
import tkinter.... | normal | {
"blob_id": "111186f1d45b9cf3bf9065c7fa83a8f3f796bbe1",
"index": 5841,
"step-1": "<mask token>\n\n\nclass LabeledEntry(sw.LabeledWidget):\n <mask token>\n\n @property\n def value(self):\n return self.get()\n <mask token>\n\n def show(self, *args):\n \"\"\"Show only the specified subw... | [
5,
6,
8,
9,
11
] |
from __future__ import print_function
import os
import shutil
import pymake
import flopy
# set up paths
dstpth = os.path.join('temp')
if not os.path.exists(dstpth):
os.makedirs(dstpth)
mp6pth = os.path.join(dstpth, 'Modpath_7_1_000')
expth = os.path.join(mp6pth, 'examples')
exe_name = 'mp7'
srcpth = os.path.join(... | normal | {
"blob_id": "ddaba7a8b53072da36224dd4618696ebf0e9a4e4",
"index": 1015,
"step-1": "<mask token>\n\n\ndef compile_code():\n if os.path.isdir(mp6pth):\n shutil.rmtree(mp6pth)\n url = 'https://water.usgs.gov/ogw/modpath/Modpath_7_1_000.zip'\n pymake.download_and_unzip(url, pth=dstpth)\n pth = os.p... | [
7,
8,
9,
11,
12
] |
# Generated by Django 3.0.10 on 2020-12-19 15:07
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
("wagtailadmin", "0001_create_admin_access_permissions"),
]
operations = [
migrations.CreateModel(
name="Admi... | normal | {
"blob_id": "52a4213a1729e25f96faebc5fd4f299017446c5a",
"index": 6370,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n initial = T... | [
0,
1,
2,
3,
4
] |
from django.urls import reverse
from django.utils.translation import get_language
from drf_dynamic_fields import DynamicFieldsMixin
from geotrek.api.v2.serializers import AttachmentSerializer
from mapentity.serializers import MapentityGeojsonModelSerializer
from rest_framework import serializers as rest_serializers
fro... | normal | {
"blob_id": "dfd5915428dc8f15fb61c5d81f22dfecfe29af15",
"index": 6409,
"step-1": "<mask token>\n\n\nclass SpeciesSerializer(TranslatedModelSerializer, PictogramSerializerMixin):\n <mask token>\n <mask token>\n <mask token>\n\n\n class Meta:\n model = sensitivity_models.Species\n fields ... | [
10,
11,
12,
13,
16
] |
import os , sys , time
print("""
███████████████████████████████
█ █
█═╬═════════════════════════╬═█
█ ║░░░░░░░░░░░░░░░░░░░░░░░░░║ █
█ ║░░░░Wi-fi Fucker Tool░░░░║ █
█ ║░░░░░░░░░░░░░░░░░░░░░░░░░║ █
█ ║░░░░░coded by arda6░░░░░░║ █
█ ║░░░░░░░░░░░░░░░░░... | normal | {
"blob_id": "15eb205e6bd36844fdfc8c05efbc3a3d584c122d",
"index": 7238,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(\n \"\"\"\n\n ███████████████████████████████\n █ █\n █═╬═════════════════════════╬═█\n █ ║░░░░░░░░░░░░░░░░░░░░░░░░░║ █\n █ ║░░░░Wi-fi ... | [
0,
1,
2,
3,
4
] |
"""
Data pre-processing
"""
import os
import corenlp
import numpy as np
import ujson as json
from tqdm import tqdm
from collections import Counter
from bilm import dump_token_embeddings
import sys
sys.path.append('../..')
from LIB.utils import save
def process(json_file, outpur_dir, exclude_titles=None, include_titl... | normal | {
"blob_id": "0c37806f0a7c0976711edd685fd64d2616147cb6",
"index": 4623,
"step-1": "<mask token>\n\n\ndef process(json_file, outpur_dir, exclude_titles=None, include_titles=None):\n \"\"\"\n :param json_file: original data in json format\n :param outpur_dir: the output directory of pre-processed data\n ... | [
5,
6,
7,
8,
9
] |
import seaborn as sns
tips = sns.load_dataset('iris')
sns.violinplot(x='species', y='sepal_length', data=tips, palette='rainbow')
| normal | {
"blob_id": "274af2a0b758472ca4116f1dfa47069647babf57",
"index": 8543,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsns.violinplot(x='species', y='sepal_length', data=tips, palette='rainbow')\n",
"step-3": "<mask token>\ntips = sns.load_dataset('iris')\nsns.violinplot(x='species', y='sepal_length', d... | [
0,
1,
2,
3
] |
import torch
import torch.nn.functional as F
import csv
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x... | normal | {
"blob_id": "e221553f866de8b3e175197a40982506bf8c1ef9",
"index": 205,
"step-1": "<mask token>\n\n\nclass Net(torch.nn.Module):\n\n def __init__(self, n_feature, n_hidden, n_output):\n super(Net, self).__init__()\n self.hidden = torch.nn.Linear(n_feature, n_hidden)\n self.predict = torch.n... | [
3,
4,
5,
6,
7
] |
#!/usr/bin/env python
from setuptools import setup, find_packages
#if sys.argv[-1] == 'publish':
# os.system('python setup.py sdist upload')
# sys.exit()
with open('bace/__init__.py') as fid:
for line in fid:
if line.startswith('__version__'):
VERSION = line.strip().split()[-1][1:-1]
... | normal | {
"blob_id": "d28571214805df766c2cc2f45a6b5bea88d7ac18",
"index": 9371,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('bace/__init__.py') as fid:\n for line in fid:\n if line.startswith('__version__'):\n VERSION = line.strip().split()[-1][1:-1]\n break\nwith open... | [
0,
1,
2,
3,
4
] |
from multiprocessing import Process, Queue
def f(q):
for i in range(0,100):
print("come on baby")
q.put([42, None, 'hello'])
if __name__ == '__main__':
q = Queue()
p = Process(target=f, args=(q,))
p.start()
for j in range(0, 2000):
if j == 1800:
print(q.get())
... | normal | {
"blob_id": "c7258d77db2fe6e1470c972ddd94b2ed02f48003",
"index": 3390,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef f(q):\n for i in range(0, 100):\n print('come on baby')\n q.put([42, None, 'hello'])\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef f(q):\n for i in rang... | [
0,
1,
2,
3,
4
] |
import os
import pprint
import math
import sys
import datetime as dt
from pathlib import Path
import RotateCipher
import ShiftCipher
import TranspositionCipher
def process_textfile(
string_path: str,
encryption_algorithm: str,
algorithm_key: float,
output_folderpath: str = str(
... | normal | {
"blob_id": "5dccd015a90927e8d2a9c0ea4b11b24bfd4bb65e",
"index": 5690,
"step-1": "<mask token>\n\n\ndef manual_test():\n dict_processedtext = process_textfile(string_path=\n 'C:\\\\Users\\\\Rives\\\\Downloads\\\\Quizzes\\\\Quiz 0 Overwrite Number 1.txt',\n encryption_algorithm='rotate', algorith... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/python
# ~~~~~============== HOW TO RUN ==============~~~~~
# 1) Configure things in CONFIGURATION section
# 2) Change permissions: chmod +x bot.py
# 3) Run in loop: while true; do ./bot.py; sleep 1; done
from __future__ import print_function
import sys
import socket
import json
import time
# ~~~~~==... | normal | {
"blob_id": "56c5c515de8490f2e3516563e037c375aba03667",
"index": 3221,
"step-1": "<mask token>\n\n\ndef connect():\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((exchange_hostname, port))\n return s.makefile('rw', 1)\n\n\ndef write_to_exchange(exchange, obj):\n json.dump(obj, ex... | [
5,
8,
9,
10,
16
] |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans
from kneed import KneeLocator
#Create a panda data frame from the csv file
df = pd.read_csv('ClusterPlot.csv', usecols=['V1','V2'])
#Convert the panda data frame to a NumPy array
arr = df.to_numpy()
#Code used t... | normal | {
"blob_id": "09417014963172fc71b4268aafdec1405c04f34d",
"index": 3472,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(1, 11):\n km = KMeans(n_clusters=i, init='random', n_init=10, max_iter=300, tol=\n 0.0001, random_state=0)\n km.fit(arr)\n distortions.append(km.inertia_)\n... | [
0,
1,
2,
3,
4
] |
a, b = map(int, input().split())
def mult(a, b):
if a > 9 or b > 9 or a < 1 or b < 1:
print(-1)
else:
print(a * b)
mult(a, b)
| normal | {
"blob_id": "991fa5f9c83a1821e62f7baacbc56a4d31982312",
"index": 3681,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef mult(a, b):\n if a > 9 or b > 9 or a < 1 or b < 1:\n print(-1)\n else:\n print(a * b)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef mult(a, b):\n ... | [
0,
1,
2,
3
] |
import os, datetime
import urllib
from flask import (Flask, flash, json, jsonify, redirect, render_template,
request, session, url_for)
import util.database as db
template_path=os.path.dirname(__file__)+"/templates"
file=""
if template_path!="/templates":
app = Flask("__main__",tem... | normal | {
"blob_id": "5c20eefe8111d44a36e69b873a71377ee7bfa23d",
"index": 6768,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef home():\n if 'username' in session:\n id_num = db.search_user_list(session['username'], is_usrname=True)[0][2\n ]\n finavail = db.search_finance_list(id_num)\n ... | [
14,
15,
16,
18,
19
] |
from connect_to_elasticsearch import *
# returns the name of all indices in the elasticsearch server
def getAllIndiciesNames():
indicies = set()
for index in connect_to_elasticsearch().indices.get_alias( "*" ):
indicies.add( index )
print( index )
return indicies
| normal | {
"blob_id": "23c75840efd9a8fd68ac22d004bfe3b390fbe612",
"index": 2314,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef getAllIndiciesNames():\n indicies = set()\n for index in connect_to_elasticsearch().indices.get_alias('*'):\n indicies.add(index)\n print(index)\n return in... | [
0,
1,
2,
3
] |
import urllib2
import urllib
import json
import gzip
from StringIO import StringIO
service_url = 'https://babelfy.io/v1/disambiguate'
lang = 'EN'
key = ''
filehandle = open('triples/triples2.tsv') # the triples and the sentences where the triples were extracted
filehandle_write = open('triples/disambiguated_triples... | normal | {
"blob_id": "cd9f94d55eb13f5fc9959546e89a0af8ab2ea0db",
"index": 6147,
"step-1": "import urllib2\nimport urllib\nimport json\nimport gzip\n\nfrom StringIO import StringIO\n\nservice_url = 'https://babelfy.io/v1/disambiguate'\nlang = 'EN'\nkey = ''\n\nfilehandle = open('triples/triples2.tsv') # the triples and t... | [
0
] |
import scrapy
from kingfisher_scrapy.base_spiders import BigFileSpider
from kingfisher_scrapy.util import components, handle_http_error
class France(BigFileSpider):
"""
Domain
France
Swagger API documentation
https://doc.data.gouv.fr/api/reference/
"""
name = 'france'
# SimpleSpi... | normal | {
"blob_id": "369bffa21b5b8c0ca1d93da3aa30a38e2f4c82cc",
"index": 9451,
"step-1": "<mask token>\n\n\nclass France(BigFileSpider):\n <mask token>\n <mask token>\n <mask token>\n\n def start_requests(self):\n url = (\n 'https://www.data.gouv.fr/api/1/datasets/donnees-essentielles-de-la... | [
3,
4,
5,
6,
7
] |
# coding: utf-8
# 2021/5/29 @ tongshiwei
import logging
def get_logger():
_logger = logging.getLogger("EduNLP")
_logger.setLevel(logging.INFO)
_logger.propagate = False
ch = logging.StreamHandler()
ch.setFormatter(logging.Formatter('[%(name)s, %(levelname)s] %(message)s'))
ch.setLevel(logging.... | normal | {
"blob_id": "41f71589d3fb9f5df218d8ffa0f608a890c73ad2",
"index": 8486,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_logger():\n _logger = logging.getLogger('EduNLP')\n _logger.setLevel(logging.INFO)\n _logger.propagate = False\n ch = logging.StreamHandler()\n ch.setFormatter(... | [
0,
1,
2,
3,
4
] |
from flask import Flask
app = Flask(__name__)
import orderapi, views, models, processing
if __name__=="__main__":
orderapi.app.debug = True
orderapi.app.run(host='0.0.0.0', port=34203)
views.app.debug = True
views.app.run(host='0.0.0.0', port=42720)
| normal | {
"blob_id": "3218a9e82cd19bab1680079aee5f09a97992629e",
"index": 6038,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n orderapi.app.debug = True\n orderapi.app.run(host='0.0.0.0', port=34203)\n views.app.debug = True\n views.app.run(host='0.0.0.0', port=42720)\n",
... | [
0,
1,
2,
3,
4
] |
import requests
import os
from bs4 import BeautifulSoup
from urllib.parse import urljoin
CURRENT_DIR = os.getcwd()
DOWNLOAD_DIR = os.path.join(CURRENT_DIR, 'malware_album')
os.makedirs(DOWNLOAD_DIR, exist_ok=True)
url = 'http://old.vision.ece.ucsb.edu/~lakshman/malware_images/album/'
class Extractor(object):
"... | normal | {
"blob_id": "a53d7b4c93fa49fb0162138d4a262fe7a5546148",
"index": 5215,
"step-1": "<mask token>\n\n\nclass Extractor(object):\n \"\"\"docstring for Parser\"\"\"\n\n def __init__(self, html, base_url):\n self.soup = BeautifulSoup(html, 'html5lib')\n self.base_url = base_url\n\n def get_album... | [
9,
10,
11,
13,
14
] |
#ERP PROJECT
import pyrebase
import smtplib
config = {
"apiKey": "apiKey",
"authDomain": "erproject-dd24e-default-rtdb.firebaseapp.com",
"databaseURL": "https://erproject-dd24e-default-rtdb.firebaseio.com",
"storageBucket": "erproject-dd24e-default-rtdb.appspot.com"
}
firebase = pyrebase.initialize_app(conf... | normal | {
"blob_id": "3e7e6d7a0137d91dc7437ff91a39d7f8faad675e",
"index": 7075,
"step-1": "<mask token>\n\n\ndef j():\n global i\n import pandas as pd\n st1.update({i: st})\n data = pd.DataFrame(st1)\n print(data)\n data.to_csv('student.csv')\n fa1.update({i: fa})\n data1 = pd.DataFrame(fa1)\n ... | [
1,
2,
3,
4,
5
] |
import pandas as pd
import numpy as np
df = pd.DataFrame([['Hospital1', '2019-10-01'], ['Hospital2', '2019-10-01'],
['Hospital3', '2019-10-01'], ['Hospital1', '2019-10-01'], ['Hospital2',
'2019-10-02'], ['Hospital3', '2019-10-02'], ['Hospital2', '2019-10-03'],
['Hospital2', '2019-10-04'], ['Hospital3', '201... | normal | {
"blob_id": "8d8f1f0dbb76b5c536bd1a2142bb61c51dd75075",
"index": 9573,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(pd.pivot_table(df, values='Date', index='Hospital_Name', aggfunc=np.size)\n )\nprint(df2.sum())\n",
"step-3": "<mask token>\ndf = pd.DataFrame([['Hospital1', '2019-10-01'], ['H... | [
0,
1,
2,
3
] |
no_list = {"tor:", "getblocktemplate", " ping ", " pong "}
for i in range(1, 5):
with open("Desktop/"+str(i)+".log", "r") as r:
with open("Desktop/"+str(i)+"-clean.log", "a+") as w:
for line in r:
if not any(s in line for s in no_list):
w.write(line)
| normal | {
"blob_id": "f14a8d0d51f0baefe20b2699ffa82112dad9c38f",
"index": 6582,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(1, 5):\n with open('Desktop/' + str(i) + '.log', 'r') as r:\n with open('Desktop/' + str(i) + '-clean.log', 'a+') as w:\n for line in r:\n ... | [
0,
1,
2,
3
] |
from floppy.node import Node, Input, Output, Tag, abstractNode
@abstractNode
class StringNode(Node):
Tag('StringOperations')
class StringAppend(StringNode):
"""
Creates a new node which combines two strings. These can be seperated by a delimiter.
:param nodeClass: subclass object of 'Node'.
:ret... | normal | {
"blob_id": "1bb151171bbbb899456324056be3634e87b5c8fb",
"index": 3494,
"step-1": "<mask token>\n\n\nclass StringAppend(StringNode):\n <mask token>\n Input('First', str)\n Input('Second', str)\n Input('Delimiter', str, optional=True, default='')\n Output('Joined', str)\n <mask token>\n\n\nclass ... | [
4,
5,
6,
8
] |
class Point:
def __init__(self,x,y):
self.x=x
self.y=y
def __str__(self):
return "({0},{1})".format(self.x,self.y)
def __add__(self, other):
self.x=self.x+other.x
self.y=self.y+other.y
return Point(self.x,self.y)
p1=Point(1,2)
p2=Point(3,4)
p... | normal | {
"blob_id": "1bebd3c18742f5362d2e5f22c539f6b13ad58d2a",
"index": 2873,
"step-1": "class Point:\n <mask token>\n\n def __str__(self):\n return '({0},{1})'.format(self.x, self.y)\n\n def __add__(self, other):\n self.x = self.x + other.x\n self.y = self.y + other.y\n return Poin... | [
3,
4,
5,
6,
7
] |
#! /usr/bin/env python3
import common, os, shutil, sys
def main():
os.chdir(common.root)
shutil.rmtree('shared/target', ignore_errors = True)
shutil.rmtree('platform/build', ignore_errors = True)
shutil.rmtree('platform/target', ignore_errors = True)
shutil.rmtree('tests/target', ignore_errors = True)
shut... | normal | {
"blob_id": "2305d0b7ec0d9e08e3f1c0cedaafa6ed60786e50",
"index": 7359,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n os.chdir(common.root)\n shutil.rmtree('shared/target', ignore_errors=True)\n shutil.rmtree('platform/build', ignore_errors=True)\n shutil.rmtree('platform/ta... | [
0,
1,
2,
3,
4
] |
total = totmil = cont = menor = 0
barato = ' '
print('-' * 40)
print('LOJA SUPER BARATÃO')
print('-' * 40)
while True:
produto = str(input('Nome do Produto: '))
preco = float(input('Preço: '))
cont += 1
total += preco
if preco > 1000:
totmil += 1
if cont == 1 or preco < menor:
ba... | normal | {
"blob_id": "35b24ffa14f8b3c2040d5becc8a35721e86d8b3d",
"index": 345,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('-' * 40)\nprint('LOJA SUPER BARATÃO')\nprint('-' * 40)\nwhile True:\n produto = str(input('Nome do Produto: '))\n preco = float(input('Preço: '))\n cont += 1\n total += ... | [
0,
1,
2
] |
import json
from flask import Flask, request, jsonify
from lib.chess_utils import run_game
def create_app():
app = Flask(__name__)
@app.route('/')
def hello_world():
return 'Hello, World!'
@app.route('/process_game', methods=['POST'])
def process_game():
move_sequence = json.load... | normal | {
"blob_id": "60ca8b1d7307a9d8183e3617f238efcfb9d707dd",
"index": 1950,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef create_app():\n app = Flask(__name__)\n\n @app.route('/')\n def hello_world():\n return 'Hello, World!'\n\n @app.route('/process_game', methods=['POST'])\n d... | [
0,
1,
2,
3
] |
from django.core.mail import send_mail
from django.template.loader import render_to_string
from django.utils.html import strip_tags
from datetime import datetime, timedelta
def sendmail(subject, template, to, context):
template_str = 'app/' + template + '.html'
html_msg = render_to_string(template_str, {'data... | normal | {
"blob_id": "0349a8a4841b024afd77d20ae18810645fad41cd",
"index": 4883,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef sendmail(subject, template, to, context):\n template_str = 'app/' + template + '.html'\n html_msg = render_to_string(template_str, {'data': context})\n plain_msg = strip_... | [
0,
1,
2
] |
from rest_framework import filters
from rest_framework.generics import ListAPIView
from rest_framework.permissions import IsAuthenticated
from rest_framework.viewsets import ModelViewSet
from apis.models import Contact, Address, InvoicePosition, Country, Invoice
from apis.serializers import ContactSerializer, AddressS... | normal | {
"blob_id": "43bad38d209b5c326cb9f17ba1ae135d06320e97",
"index": 145,
"step-1": "<mask token>\n\n\nclass InvoicePositionViewSet(ModelViewSet):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass CountryListView(ListAPIView):\n queryset = Country.objects.all()\n serializer_class = CountrySerial... | [
5,
6,
9,
11,
12
] |
from django.shortcuts import render
from .models import Recipe, Author
def index(request):
recipes_list = Recipe.objects.all()
return render(request, "index.html",
{"data": recipes_list, "title": "Recipe Box"})
def recipeDetail(request, recipe_id):
recipe_detail = Recipe.objects.filter... | normal | {
"blob_id": "f0f8ad7b65707bcf691847ccb387e4d026b405b5",
"index": 6395,
"step-1": "<mask token>\n\n\ndef authorDetail(request, author_id):\n author = Author.objects.filter(id=author_id).first()\n recipes = Recipe.objects.filter(author=author_id)\n return render(request, 'author_detail.html', {'recipes': ... | [
1,
2,
3,
4,
5
] |
from urllib.parse import quote
from top_model import db
from top_model.ext.flask import FlaskTopModel
from top_model.filesystem import ProductPhotoCIP
from top_model.webstore import Product, Labo
from unrest import UnRest
class Hydra(FlaskTopModel):
def __init__(self, *args, **kwargs):
super().__init__(*... | normal | {
"blob_id": "de3a4053b5b0d4d2d5c2dcd317e64cf9b4faeb75",
"index": 562,
"step-1": "<mask token>\n\n\nclass Hydra(FlaskTopModel):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.config['CLIENT_ID'] = 4\n self.config['BASE_IMAGE_URL'\n ] = 'https... | [
3,
6,
7,
8,
9
] |
#!/usr/bin/env python
#-------------------------------------------------------------------------------
#
# Circle finder.
#
# Rowan Leeder
#
#-------------------------------------------------------------------------------
#
# Listens on the 'scan' and 'base_scan' topics. These are the pioneers SICK
# topic and Stage's ... | normal | {
"blob_id": "3ac02308959749b8cd264e660c3d6334fd385fd4",
"index": 1114,
"step-1": "#!/usr/bin/env python\n#-------------------------------------------------------------------------------\n#\n# Circle finder.\n#\n# Rowan Leeder\n#\n#-------------------------------------------------------------------------------\n#... | [
0
] |
"""
Pattern matching problem
Boyer Moore algorithm
First is my attempt, below is the code provided in the book
Idea:
Optimize brute force approach using 2 heuristics:
- Looking-Glass: start searches from last character of the
pattern and work backwards
- Character-Jump: During testing of a pattern P, a mismatch
in T[i... | normal | {
"blob_id": "c418b9b6903ebdad204a3a55f2384a94a3be0d09",
"index": 5561,
"step-1": "<mask token>\n\n\ndef find_boyer_moore2(T, P):\n \"\"\" return lowest index of T at which the substring P begins or -1\"\"\"\n n, m = len(T), len(P)\n if m == 0:\n return 0\n last = {}\n for k in range(m):\n ... | [
1,
2,
3,
4,
5
] |
#usage:
#crawl raw weibo text data from sina weibo users(my followees)
#in total, there are 20080 weibo tweets, because there is uplimit for crawler
# -*- coding: utf-8 -*-
import weibo
APP_KEY = 'your app_key'
APP_SECRET = 'your app_secret'
CALL_BACK = 'your call back url'
def run():
token = "your access token got... | normal | {
"blob_id": "8a04166e091e2da348928598b2356c8ad75dd831",
"index": 5889,
"step-1": "#usage:\n#crawl raw weibo text data from sina weibo users(my followees)\n#in total, there are 20080 weibo tweets, because there is uplimit for crawler\n\n# -*- coding: utf-8 -*-\nimport weibo\n\nAPP_KEY = 'your app_key'\nAPP_SECRET... | [
0
] |
alunos = list()
while True:
nome = str(input('Nome: '))
nota1 = float(input('Nota 1: '))
nota2 = float(input('Nota 2: '))
media = (nota1+nota2)/2
alunos.append([nome, [nota1, nota2], media])
pergunta = str(input('Quer continuar [S/N]? ')).upper()[0]
if pergunta == 'N':
break
print('-... | normal | {
"blob_id": "8dcd4914c58a7ecafdfdd70b698ef3b7141386a6",
"index": 2632,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n nome = str(input('Nome: '))\n nota1 = float(input('Nota 1: '))\n nota2 = float(input('Nota 2: '))\n media = (nota1 + nota2) / 2\n alunos.append([nome, [nota1,... | [
0,
1,
2,
3
] |
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