code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
def fonct(valeur, a= None):
if type(a) is list:
a.append(valeur)
# a+= valeur
elif type(a) is tuple:
a += tuple((valeur,))
elif type(a) is str:
a += str(valeur)
elif type(a) is set:
a.add(valeur)
el... | normal | {
"blob_id": "2a13fffa105a5dd546c30c892e59888eb6ead996",
"index": 4645,
"step-1": "<mask token>\n",
"step-2": "def fonct(valeur, a=None):\n if type(a) is list:\n a.append(valeur)\n elif type(a) is tuple:\n a += tuple((valeur,))\n elif type(a) is str:\n a += str(valeur)\n elif ty... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def main(batch_size, crop_size, learning_rate, segmentation_task_ratio,
weight_decay, save_folder, epochs, alpha):
print(learning_rate)
print(alpha)
print(weight_decay)
train_dataset = ClfSegDataset(subset=[0, 1])
train_loader = get_mixup_loader(train_dataset, batc... | flexible | {
"blob_id": "94b3fa700d7da0ca913adeb0ad5324d1fec0be50",
"index": 7104,
"step-1": "<mask token>\n\n\ndef main(batch_size, crop_size, learning_rate, segmentation_task_ratio,\n weight_decay, save_folder, epochs, alpha):\n print(learning_rate)\n print(alpha)\n print(weight_decay)\n train_dataset = Clf... | [
1,
2,
3,
4,
5
] |
import html
import logging
import re
import pyarabic.araby as araby
ACCEPTED_MODELS = [
"bert-base-arabertv01",
"bert-base-arabert",
"bert-base-arabertv02",
"bert-base-arabertv2",
"bert-large-arabertv02",
"bert-large-arabertv2",
"araelectra-base",
"araelectra-base-discriminator",
"... | normal | {
"blob_id": "6c3f60f05adbebe521ba08d7a7e9fc10b1cc914f",
"index": 2907,
"step-1": "<mask token>\n\n\nclass ArbertmoPreprocessor:\n <mask token>\n\n def __init__(self, model_name, keep_emojis=False, remove_html_markup=\n True, replace_urls_emails_mentions=True, strip_tashkeel=True,\n strip_tatw... | [
12,
13,
14,
15,
16
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
root.title('Attendance')
root.geometry('+450+250')
<|reserved_special_token_0|>
with open(fileName, newline='') as file:
reader = csv.reader(file)
r = 0
for col in reader:
c = 0
for row in col:
... | flexible | {
"blob_id": "2343a9d3e253b5a0347b5890a5d7b9c3be777669",
"index": 5958,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nroot.title('Attendance')\nroot.geometry('+450+250')\n<mask token>\nwith open(fileName, newline='') as file:\n reader = csv.reader(file)\n r = 0\n for col in reader:\n c = ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
input = """
t(Z) :- t0(Z).
t(Z) :- g(X,Y,Z), t(X), not t(Y).
t0(2).
g(5,1,3).
g(1,2,4).
g(3,4,5).
"""
output = """
t(Z) :- t0(Z).
t(Z) :- g(X,Y,Z), t(X), not t(Y).
t0(2).
g(5,1,3).
g(1,2,4).
g(3,4,5).
"""
| flexible | {
"blob_id": "df5c79c79d827b6b3de7ceb4b1e3c652c8956346",
"index": 2620,
"step-1": "<mask token>\n",
"step-2": "input = \"\"\"\nt(Z) :- t0(Z).\nt(Z) :- g(X,Y,Z), t(X), not t(Y).\n\nt0(2).\ng(5,1,3).\ng(1,2,4).\ng(3,4,5).\n\n\"\"\"\noutput = \"\"\"\nt(Z) :- t0(Z).\nt(Z) :- g(X,Y,Z), t(X), not t(Y).\n\nt0(2).\ng(5... | [
0,
1
] |
# This file imports all files for this module for easy inclusions around the game.
from viewController import *
from navigationController import *
from noticer import *
from Images import *
from fancyButton import *
from constants import *
from textObject import *
from UIButton import *
# from spriteFromRect import ... | normal | {
"blob_id": "7168a8eb401478aa26ee9033262bb5c8fe33f186",
"index": 7011,
"step-1": "<mask token>\n",
"step-2": "from viewController import *\nfrom navigationController import *\nfrom noticer import *\nfrom Images import *\nfrom fancyButton import *\nfrom constants import *\nfrom textObject import *\nfrom UIButto... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class BoardingPass:
<|reserved_special_token_0|>
def export(self):
fileName = 'reservations/data_reservation/boarding_passes'
file = open(fileName, 'a')
flights = self.reservation.getFlights()
... | flexible | {
"blob_id": "a3662b4b9569046e67c39c1002234c1fbd85c650",
"index": 8102,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass BoardingPass:\n <mask token>\n\n def export(self):\n fileName = 'reservations/data_reservation/boarding_passes'\n file = open(fileName, 'a')\n flights... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for train_index, test_index in kf.split(x):
xtr = x.iloc[train_index]
ytr = y[train_index]
<|reserved_special_token_0|>
if k % 2 == 0:
k = k + 1
else:
k = k
<|reserved_special_token_0|>
print('Skor KNN: ', round(cr... | flexible | {
"blob_id": "84db1803a352e0ed8c01b7166f522d46ec89b6f5",
"index": 2487,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor train_index, test_index in kf.split(x):\n xtr = x.iloc[train_index]\n ytr = y[train_index]\n<mask token>\nif k % 2 == 0:\n k = k + 1\nelse:\n k = k\n<mask token>\nprint('S... | [
0,
1,
2,
3,
4
] |
import csv
from pprint import pprint as pp
with open('nodes_tags.csv', 'r') as f:
tags = csv.DictReader(f)
for row in tags:
if row['key'] == 'FIXME':
pp(row)
| normal | {
"blob_id": "d0981d279f7090d5309aa564252dba731a34a66b",
"index": 1424,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('nodes_tags.csv', 'r') as f:\n tags = csv.DictReader(f)\n for row in tags:\n if row['key'] == 'FIXME':\n pp(row)\n",
"step-3": "import csv\nfrom pprint... | [
0,
1,
2
] |
# Copyright (c) 2021 Cisco and/or its affiliates.
#
# SPDX-License-Identifier: Apache-2.0 OR GPL-2.0-or-later
#
# Licensed under the Apache License 2.0 or
# GNU General Public License v2.0 or later; you may not use this file
# except in compliance with one of these Licenses. You
# may obtain a copy of the Licenses at:... | normal | {
"blob_id": "ea6d726e8163ed0f93b8078323fa5f4e9115ad73",
"index": 1639,
"step-1": "<mask token>\n\n\nclass TrafficScriptArg:\n <mask token>\n <mask token>\n\n def get_arg(self, arg_name):\n \"\"\"Get argument value.\n\n :param arg_name: Argument name.\n :type arg_name: str\n :... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def generateNoise():
caveMap = []
column = 1
row = 1
while column <= mapWidth:
while row <= mapHeight:
if (column == 1 or column == mapWidth or row == 1 or row ==
mapHeight):
caveMap.append([column, row, 1])
e... | flexible | {
"blob_id": "7feac838f17ef1e4338190c0e8c284ed99369693",
"index": 1628,
"step-1": "<mask token>\n\n\ndef generateNoise():\n caveMap = []\n column = 1\n row = 1\n while column <= mapWidth:\n while row <= mapHeight:\n if (column == 1 or column == mapWidth or row == 1 or row ==\n ... | [
5,
6,
8,
9,
11
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
plt.figure()
plt.plot(r, g_r, color='black')
plt.xlabel('r')
plt.ylabel('g(r)')
plt.xlim((0, rmax))
plt.ylim((0, 1.05 * g_r.max()))
plt.show()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
filename = 'C:\\Users\\Max... | flexible | {
"blob_id": "516d9790f40c021d45302948b7fba0cf3e00da0a",
"index": 6322,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.figure()\nplt.plot(r, g_r, color='black')\nplt.xlabel('r')\nplt.ylabel('g(r)')\nplt.xlim((0, rmax))\nplt.ylim((0, 1.05 * g_r.max()))\nplt.show()\n",
"step-3": "<mask token>\nfilenam... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python
def sumbelow(n):
multiples_of_3 = set(range(0,n,3))
multiples_of_5 = set(range(0,n,5))
return sum(multiples_of_3.union(multiples_of_5))
#one linear:
# return sum(set(range(0,n,3)).union(set(range(0,n,5)))),
# or rather,
# return sum(set(range(0,n,3) + range(0,n,5)))
if __name__ == '__ma... | normal | {
"blob_id": "8dbc0b9b80aae4cb5c4101007afc50ac54f7a7e7",
"index": 5873,
"step-1": "#!/usr/bin/python\n\ndef sumbelow(n):\n multiples_of_3 = set(range(0,n,3))\n multiples_of_5 = set(range(0,n,5))\n return sum(multiples_of_3.union(multiples_of_5))\n\n#one linear:\n# return sum(set(range(0,n,3)).union(set(r... | [
0
] |
from __future__ import absolute_import
import unittest
import yaml
import os
from bok_choy.web_app_test import WebAppTest
from .pages.job_config_history_subpage import JobConfigHistorySubPage
class TestJobConfigHistory(WebAppTest):
def setUp(self):
super(TestJobConfigHistory, self).setUp()
config_... | normal | {
"blob_id": "51bdbec732bebd73a84b52c6d1d39eead047d29e",
"index": 5349,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TestJobConfigHistory(WebAppTest):\n\n def setUp(self):\n super(TestJobConfigHistory, self).setUp()\n config_path = os.getenv('CONFIG_PATH')\n try:\n ... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class UserinfoSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = Userinfo
fields = ('fname', 'lname', 'address', 'city', 'state', 'zipcode',
'dob', 'phone', 'email', 'author... | flexible | {
"blob_id": "124ece8f2f4ecc53d19657e2463cc608befb1ce7",
"index": 3722,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass UserinfoSerializer(serializers.HyperlinkedModelSerializer):\n\n\n class Meta:\n model = Userinfo\n fields = ('fname', 'lname', 'address', 'city', 'state', 'zipc... | [
0,
1,
2,
3,
4
] |
import time
import datetime
import math
import os
import random
import logzero
import logging
from logzero import logger
from sense_hat import SenseHat
import ephem
anyException = False
# program Time is here for easy acces (in minutes)
programTime = 175
# 2:55 min of runtime
# _________________________... | normal | {
"blob_id": "05e468c2f64e33d6b390f681314ed7961bd4def7",
"index": 2684,
"step-1": "<mask token>\n\n\ndef setLoggingFile():\n \"\"\"\n This function will setup a logger and logfile\n \"\"\"\n try:\n dirPath = os.path.dirname(os.path.realpath(__file__))\n dirFiles = os.listdir(dirPath)\n ... | [
10,
12,
14,
15,
16
] |
<|reserved_special_token_0|>
class TestRandomSelectNode(unittest.TestCase):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class TestRandomSample(unittest.TestCase):
def setUp(self):
np.r... | flexible | {
"blob_id": "3a88ff479e3b01518d79e9930c29514863f96f9b",
"index": 1568,
"step-1": "<mask token>\n\n\nclass TestRandomSelectNode(unittest.TestCase):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n<mask token>\n\n\nclass TestRandomSample(unittest.TestCase):\n\n def setUp(self):\n ... | [
5,
8,
9,
10,
13
] |
<|reserved_special_token_0|>
@base.ReleaseTracks(base.ReleaseTrack.ALPHA)
class Describe(base.DescribeCommand):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def Run(self, args):
guest_policy_ref = args.CONCEPTS.guest_policy.Parse()
release_track = self.ReleaseTrack()
... | flexible | {
"blob_id": "d6a677ed537f6493bb43bd893f3096dc058e27da",
"index": 507,
"step-1": "<mask token>\n\n\n@base.ReleaseTracks(base.ReleaseTrack.ALPHA)\nclass Describe(base.DescribeCommand):\n <mask token>\n <mask token>\n\n def Run(self, args):\n guest_policy_ref = args.CONCEPTS.guest_policy.Parse()\n ... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class Post(db.Model):
post_id = db.Column(db.Integer, primary_key=True, nullable=False)
title = db.Column(db.String(50))
body = db.Column(db.String(200))
timestamp = db.Column(db.DateTime, index=True, default=datetime.utcnow)
user_id = db.relationship(db.Integer, db.Fo... | flexible | {
"blob_id": "5cfdb1f6b99f59a83a9bd42b7daf3e016eee94a8",
"index": 2898,
"step-1": "<mask token>\n\n\nclass Post(db.Model):\n post_id = db.Column(db.Integer, primary_key=True, nullable=False)\n title = db.Column(db.String(50))\n body = db.Column(db.String(200))\n timestamp = db.Column(db.DateTime, inde... | [
5,
8,
10,
11,
13
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if len(sys.argv) <= 3:
print('Not enough args usage: anova.py <*.csv> <rv1,rv2> <target to beat>')
print('ex: best-mean.py testdata.csv nicdrop 95000')
print('<rv> is response variable')
exit()
<|reserved_special_t... | flexible | {
"blob_id": "b9e78629fe094d933fdc0ffa2f9d9d1880e78c12",
"index": 9078,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif len(sys.argv) <= 3:\n print('Not enough args usage: anova.py <*.csv> <rv1,rv2> <target to beat>')\n print('ex: best-mean.py testdata.csv nicdrop 95000')\n print('<rv> is respo... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Solution:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Solution:
def twoSum(self, nums, target):
"""
:type nums: ... | flexible | {
"blob_id": "b3f62c331ff4ae9f909fc90cc7303997b32daceb",
"index": 1876,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n <mask token>\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass Solution:\n\n def twoSum(self, nums, target):\n \"\"\"\n :type nums: List... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print("We're going to speak anything you type in a different accent")
<|reserved_special_token_0|>
print(language_code)
<|reserved_special_token_0|>
myobj.save('texty.mp3')
os.system('mpg321 texty.mp3')
<|reserved_special_token_... | flexible | {
"blob_id": "545053bc2b7c8687622d747673f2ad37b978014c",
"index": 3403,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(\"We're going to speak anything you type in a different accent\")\n<mask token>\nprint(language_code)\n<mask token>\nmyobj.save('texty.mp3')\nos.system('mpg321 texty.mp3')\n",
"st... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
print('I am main!')
else:
print(__name__)
for i in range(0, 6):
print(i)
<|reserved_special_token_0|>
print(mylist)
<|reserved_special_token_0|>
while value not in range(0, 6):
try:
... | flexible | {
"blob_id": "f218f47acfb078877645de26c64e57f92dbcd953",
"index": 8003,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n print('I am main!')\nelse:\n print(__name__)\nfor i in range(0, 6):\n print(i)\n<mask token>\nprint(mylist)\n<mask token>\nwhile value not in range(0... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def parse_cr(cr):
binary = cr.value
string = binary.decode('utf-8')
return string.split(',')
def get_title(cr):
get = parse_cr(cr)[2]
head = get[5:9]
if head == 'data':
trunc = get[12:]
return trunc.split('/')[0]
else:
trunc = get[10:]... | flexible | {
"blob_id": "374fbb986524f28cc86f6e579f504eeb8ddc9701",
"index": 1122,
"step-1": "<mask token>\n\n\ndef parse_cr(cr):\n binary = cr.value\n string = binary.decode('utf-8')\n return string.split(',')\n\n\ndef get_title(cr):\n get = parse_cr(cr)[2]\n head = get[5:9]\n if head == 'data':\n ... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class myPickle:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class myPickle:
def make(self, obj, fileName):
print('myPickle make file', fileName)
pickle.dump(obj, open(fileName, 'wb'))... | flexible | {
"blob_id": "e50feccd583d7e33877d5fcc377a1d79dc247d3a",
"index": 3117,
"step-1": "<mask token>\n\n\nclass myPickle:\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass myPickle:\n\n def make(self, obj, fileName):\n print('myPickle make file', fileName)\n pickle.dump(obj,... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class Trainer:
def __init__(self, model=None, opt=Config()):
self.model = model
self.opt = opt
self.criterion = opt.criterion
self.pred_id = self.opt.predictor_id
self.optimizer = opt.optimizer(self.model.parameters(), lr=opt.lr)
self.l... | flexible | {
"blob_id": "8b7894e274647e48e3a1fe12473937bd6c62e943",
"index": 8741,
"step-1": "<mask token>\n\n\nclass Trainer:\n\n def __init__(self, model=None, opt=Config()):\n self.model = model\n self.opt = opt\n self.criterion = opt.criterion\n self.pred_id = self.opt.predictor_id\n ... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
try:
import tidylib
def tidy(html):
html, errors = tidylib.tidy_document(html, options={'force-output':
True, 'output-xhtml': True, 'tidy-mark': False})
return html
except ImportError:
def... | flexible | {
"blob_id": "33ec822f6149a57244edf6d8d99a5b3726600c2e",
"index": 3236,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n import tidylib\n\n def tidy(html):\n html, errors = tidylib.tidy_document(html, options={'force-output':\n True, 'output-xhtml': True, 'tidy-mark': False})\... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class Stack:
def __init__(self):
self.q1 = Queue()
self.q2 = Queue()
def empty(self):
return self.q1.empty()
def push(self, element):
if self.empty():
self.q1.enqueue(element)
else:
self.q2.enqueue(element)
... | flexible | {
"blob_id": "4f5f4aadfeabb13790b417b334c5f73c6d0345a7",
"index": 9256,
"step-1": "<mask token>\n\n\nclass Stack:\n\n def __init__(self):\n self.q1 = Queue()\n self.q2 = Queue()\n\n def empty(self):\n return self.q1.empty()\n\n def push(self, element):\n if self.empty():\n ... | [
5,
7,
8,
9,
11
] |
#!/usr/bin/env python3
# Licensed under the Apache License, Version 2.0 or the MIT License.
# SPDX-License-Identifier: Apache-2.0 OR MIT
# Copyright Tock Contributors 2023.
# Prints out the source locations of panics in a Tock kernel ELF
#
# This tool attempts to trace all panic locations in a Tock kernel ELF by
# tr... | normal | {
"blob_id": "8c0a4d5a86d9ebd38ea05efb5b5b570368ce1449",
"index": 1336,
"step-1": "<mask token>\n\n\ndef matches_panic_funcs(name):\n \"\"\"If the passed name contains one of the known panic_functions,\n return the match\n \"\"\"\n for func in panic_functions:\n if func in name:\n re... | [
7,
10,
11,
12,
13
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
cv2.imshow('image1', img[0:int(img_height / 2), 0:int(img_width / 2)])
cv2.imshow('image2', img[int(img_height / 2):img_height, 0:int(img_width / 2)])
cv2.imshow('image3', img[0:int(img_height / 2), int(img_width / 2):img_width])
... | flexible | {
"blob_id": "8c6f890631e9696a7907975b5d0bb71d03b380da",
"index": 839,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncv2.imshow('image1', img[0:int(img_height / 2), 0:int(img_width / 2)])\ncv2.imshow('image2', img[int(img_height / 2):img_height, 0:int(img_width / 2)])\ncv2.imshow('image3', img[0:int(img_... | [
0,
1,
2,
3
] |
import pygame
class SpriteObject(pygame.sprite.Sprite):
def __init__(self, x, y, w, h, color):
pygame.sprite.Sprite.__init__(self)
self.angle = 0
self.original_image = pygame.Surface([w, h], pygame.SRCALPHA)
self.original_image.fill(color)
self.image = self.original_... | normal | {
"blob_id": "b90c6a3f8fe084bc2acc0b733750124a1387527c",
"index": 1712,
"step-1": "<mask token>\n\n\nclass SpriteObject(pygame.sprite.Sprite):\n <mask token>\n\n def update(self):\n self.rotate()\n\n def rotate(self):\n self.angle += 0.3\n self.image = pygame.transform.rotate(self.or... | [
3,
4,
5,
6,
8
] |
<|reserved_special_token_0|>
class GroupParticipation(models.Model):
account = models.ForeignKey(Account, related_name='groups')
parts = models.FloatField(default=1.0)
group = models.ForeignKey(Group, related_name='participants')
def __str__(self):
return out.substitute(account=self.account, ... | flexible | {
"blob_id": "11337f6f9cf22ba6fbed68dfcb7a07fb6368e94e",
"index": 6350,
"step-1": "<mask token>\n\n\nclass GroupParticipation(models.Model):\n account = models.ForeignKey(Account, related_name='groups')\n parts = models.FloatField(default=1.0)\n group = models.ForeignKey(Group, related_name='participants... | [
3,
4,
6,
7,
8
] |
# Copyright (c) 2020, Galois, Inc.
#
# All Rights Reserved
#
# This material is based upon work supported by the Defense Advanced Research
# Projects Agency (DARPA) under Contract No. FA8750-20-C-0203.
#
# Any opinions, findings and conclusions or recommendations expressed in this
# material are those of the author(s) ... | normal | {
"blob_id": "41294c803cf42611fa003f21b74a49dd5576a8e8",
"index": 5973,
"step-1": "<mask token>\n\n\nclass MigrationVisitor(semtk.DefaultSemTKVisitor):\n\n def __init__(self, data: RemoveIsATypeOf):\n self.data = data\n",
"step-2": "<mask token>\n\n\n@dataclass\nclass RemoveIsATypeOf(OntologyChange):\... | [
2,
5,
6,
7,
8
] |
import pytesseract
from PIL import Image
img = Image.open("flag.png")
text = pytesseract.image_to_string(img)
def rot(*symbols):
def _rot(n):
encoded = ''.join(sy[n:] + sy[:n] for sy in symbols)
lookup = str.maketrans(''.join(symbols), encoded)
return lambda s: s.translate(lookup)
re... | normal | {
"blob_id": "b7a60322b4a0fcb6de16cd12be33db265a2b8746",
"index": 2735,
"step-1": "<mask token>\n\n\ndef rot(*symbols):\n\n def _rot(n):\n encoded = ''.join(sy[n:] + sy[:n] for sy in symbols)\n lookup = str.maketrans(''.join(symbols), encoded)\n return lambda s: s.translate(lookup)\n re... | [
1,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class Process:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def send_neighbours(self, data, exceptions=[]):
for i in [x for x in self.neighbours if x not in exceptions]:
self.send(i, data)
<|reserved_specia... | flexible | {
"blob_id": "c5a2c00d53111d62df413907d4ff4ca5a02d4035",
"index": 7005,
"step-1": "<mask token>\n\n\nclass Process:\n <mask token>\n <mask token>\n <mask token>\n\n def send_neighbours(self, data, exceptions=[]):\n for i in [x for x in self.neighbours if x not in exceptions]:\n self.... | [
2,
8,
9,
11,
12
] |
#!/usr/bin/env python
import argparse
import pymssql
import json
#get the lcmMediaId from DB.
def getMediaId(contentProviderMediaName):
#test db
conn = pymssql.connect(host='CHELLSSSQL23.karmalab.net', user='TravCatalog', password='travel', database='LodgingCatalogMaster_Phoenix')
#prod db
#conn = pyms... | normal | {
"blob_id": "a5b7f565a1797e5f326bcf26ff7c8ad2469dca70",
"index": 7442,
"step-1": "<mask token>\n\n\ndef getMediaId(contentProviderMediaName):\n conn = pymssql.connect(host='CHELLSSSQL23.karmalab.net', user=\n 'TravCatalog', password='travel', database=\n 'LodgingCatalogMaster_Phoenix')\n cur ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
__version__ = '18.07.0'
| flexible | {
"blob_id": "3cac7829cf0c07ddc704a25ec3c781c9510a8e0c",
"index": 3613,
"step-1": "<mask token>\n",
"step-2": "__version__ = '18.07.0'\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
from django.conf.urls import url
from myapp import views
urlpatterns = [
url(r'^$', views.homepage, name='homepage'),
url(r'^search/', views.my_search_view, name = 'article_detail')
] | normal | {
"blob_id": "388e43850a2e114cfe7869293ee814831a088b3e",
"index": 8468,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [url('^$', views.homepage, name='homepage'), url('^search/',\n views.my_search_view, name='article_detail')]\n",
"step-3": "from django.conf.urls import url\nfrom myapp... | [
0,
1,
2,
3
] |
import random #importing the random library from python
answers = ["It is certain", "Without a doubt", "Yes, definitely",
"You may rely on it", "As I see it, yes", "Most likely",
"Outlook good", "Yes", "Signs point to yes", "Reply hazy, try again",
"Ask again later", "Better not tell yo... | normal | {
"blob_id": "b5e9af166f3b55e44d9273077e5acd05b1fd68fa",
"index": 2335,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile ans:\n ans = input('Ask the magic 8 ball a question. (Press enter to leave): \\n')\n print(random.choice(answers))\n",
"step-3": "<mask token>\nanswers = ['It is certain', '... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_rosters_from_excel(django_file):
workbook = xlrd.open_workbook(file_contents=django_file.read())
worksheet = workbook.sheet_by_name('Match_Rosters')
num_rows = worksheet.nrows - 1
cur_row = -1
rosters... | flexible | {
"blob_id": "a7a219e9ea5cdec004ef936958994ed1f5a96103",
"index": 3244,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_rosters_from_excel(django_file):\n workbook = xlrd.open_workbook(file_contents=django_file.read())\n worksheet = workbook.sheet_by_name('Match_Rosters')\n num_rows = ... | [
0,
1,
2,
3
] |
# #writing a file
# fout = open('Session14/output.txt', 'w')
# line1 = "How many roads must a man walk down\n"
# fout.write(line1)
# line2 = "Before you call him a man?\n"
# fout.write(line2)
# #when you are done writing, you should close the file.
# fout.close()
# #if you dont close the file, it gets closed for you wh... | normal | {
"blob_id": "de1262da699a18266ad8673597391f625783a44d",
"index": 5721,
"step-1": "<mask token>\n\n\ndef walk2(dirname):\n \"\"\"Prints the names of all files in \n dirname and its subdirectories.\n\n dirname: string name of directory\n \"\"\"\n for root, dirs, files in os.walk(dirname):\n f... | [
1,
2,
3,
4,
5
] |
a = 2
while a == 1:
b = source()
c = function(b)
| normal | {
"blob_id": "56cae7b7a0338bd4a405cdc3cdcd9945a9df8823",
"index": 5839,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile a == 1:\n b = source()\n<mask token>\n",
"step-3": "a = 2\nwhile a == 1:\n b = source()\nc = function(b)\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def cgroup_mount_option(command: Callable[..., None]) ->Callable[..., None]:
"""
Option for choosing to mount `/sys/fs/cgroup` into the container.
"""
function = click.option('--mount-sys-fs-cgroup/--no-mount-sys... | flexible | {
"blob_id": "237f5e2e37187e26b5628032e37d3a525ef72b9a",
"index": 7261,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef cgroup_mount_option(command: Callable[..., None]) ->Callable[..., None]:\n \"\"\"\n Option for choosing to mount `/sys/fs/cgroup` into the container.\n \"\"\"\n functi... | [
0,
1,
2,
3
] |
####################################################################
# a COM client coded in Python: talk to MS-Word via its COM object
# model; uses either dynamic dispatch (run-time lookup/binding),
# or the static and faster type-library dispatch if makepy.py has
# been run; install the windows win32all extensions... | normal | {
"blob_id": "df19aa720993c2385a6d025cf7ec8f3935ee4191",
"index": 9343,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif len(argv) == 2:\n docdir = argv[1]\n<mask token>\nspot.InsertBefore('Hello COM client world!')\nnewdoc.SaveAs(docdir + 'pycom.doc')\nnewdoc.SaveAs(docdir + 'copy.doc')\nnewdoc.Close... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
import sys, re
window = 2
for line in sys.stdin:
line = line.strip()
twits = line.split()
i = 0
while i <len(twits):
j = 0
while j <len(twits):
if i!= j:
print("%s%s\t%d" % (twits[i]+' ', twits[j], 1))
j+=1
i+=1 | normal | {
"blob_id": "e884825325ceb401142cab0618d9d4e70e475cf5",
"index": 893,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor line in sys.stdin:\n line = line.strip()\n twits = line.split()\n i = 0\n while i < len(twits):\n j = 0\n while j < len(twits):\n if i != j:\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class ModelEvaluator(Evaluator):
def __init__(self, dataset: Dataset, batch_size: int, num_workers: int,
mixed_precision: bool=True):
self.dataset = dataset
self.mixed_precision = mixed_precision
self.loader = DataLoader(dataset, batch_size, shuffle=Fa... | flexible | {
"blob_id": "493dbf85069f2115896a5f5f5d593c8d95b85cff",
"index": 4594,
"step-1": "<mask token>\n\n\nclass ModelEvaluator(Evaluator):\n\n def __init__(self, dataset: Dataset, batch_size: int, num_workers: int,\n mixed_precision: bool=True):\n self.dataset = dataset\n self.mixed_precision =... | [
5,
6,
7,
8,
9
] |
# This package will contain the spiders of your Scrapy project
#
# Please refer to the documentation for information on how to create and manage
# your spiders.
import datetime
import scrapy
from ScrapyProject.items import ScrapyItem
class ThalesSpider(scrapy.Spider):
#item_id = ScrapyItem()
name = 'thales'
allo... | normal | {
"blob_id": "fd1b871c5cf79874acf8d5c4f1f73f7a381e23f7",
"index": 8278,
"step-1": "<mask token>\n\n\nclass ThalesSpider(scrapy.Spider):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass ThalesSpider(scrapy.Spider):\n <mask token>\n <mask token>\... | [
1,
2,
3,
4,
5
] |
import os
import shutil
def flatCopyWithExt(srcDir, dstDir, ext):
if not os.path.exists(dstDir):
os.makedirs(dstDir)
for basename in os.listdir(srcDir):
if basename.endswith(ext):
pathname = os.path.join(srcDir, basename)
if os.path.isfile(pathname):
shutil.copy2(pathname, dstDir)
def move... | normal | {
"blob_id": "649c0c0f170b50fe51f5eaf11908e968f66625c9",
"index": 5925,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef moveSDLIncludes():\n flatCopyWithExt('./ext/SDL2/core/code/include/',\n './ext/SDL2/core/include/', '.h')\n flatCopyWithExt('./ext/SDL2/SDL2-image/code/',\n '.... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Contact(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_... | flexible | {
"blob_id": "514a3fc312d36e6f9b601ede7f7a3940c138d39a",
"index": 2000,
"step-1": "<mask token>\n\n\nclass Contact(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __unicode__... | [
5,
8,
9,
10,
11
] |
import ssl
import sys
import psycopg2 #conectarte python con postresql
import paho.mqtt.client #pip install paho-mqtt
import json
conn = psycopg2.connect(host = 'raja.db.elephantsql.com', user= 'oyoqynnr', password ='myHVlpJkEO21o29GKYSvMCGI3g4y05bh', dbname= 'oyoqynnr')
def on_connect(client, userdata, flags, r... | normal | {
"blob_id": "f1b36e3ce3189c8dca2e41664ac1a6d632d23f79",
"index": 5078,
"step-1": "<mask token>\n\n\ndef on_connect(client, userdata, flags, rc):\n print('Conectado (%s)' % client._client_id)\n client.subscribe(topic='unimet/#', qos=0)\n\n\ndef ventasTIENDA(client, userdata, message):\n a = json.loads(me... | [
3,
4,
5,
6,
7
] |
import os
from pathlib import Path
from sphinx_testing import with_app
@with_app(buildername="html", srcdir="doc_test/doc_role_need_max_title_length_unlimited")
def test_max_title_length_unlimited(app, status, warning):
os.environ["MAX_TITLE_LENGTH"] = "-1"
app.build()
html = Path(app.outdir, "index.htm... | normal | {
"blob_id": "3346ca7cdcfe9d9627bfe08be2b282897b3c319c",
"index": 6943,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@with_app(buildername='html', srcdir=\n 'doc_test/doc_role_need_max_title_length_unlimited')\ndef test_max_title_length_unlimited(app, status, warning):\n os.environ['MAX_TITLE_... | [
0,
1,
2,
3,
4
] |
import numpy as np
import cv2 as cv
import methods as meth
from numpy.fft import fft2, fftshift, ifft2, ifftshift
import pandas
import os
import noGPU as h
import matplotlib.pyplot as plt
class fullSys():
def __init__(self, dir, file, size, line):
csv_reader = pandas.read_csv(file, index_col='Objective')
... | normal | {
"blob_id": "e3c9487f3221ca89b9014b2e6470ca9d4dbc925a",
"index": 2239,
"step-1": "<mask token>\n\n\nclass section:\n\n def __init__(self, i0, j0, subImg, Params):\n self.Params = Params\n self.subParams = {}\n self.subParams['wLen'] = [6.3e-07, 5.3e-07, 4.3e-07]\n self.subParams['s... | [
9,
11,
13,
15,
19
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('{0:f} {1:f}'.format(r * r * math.pi, 2 * r * math.pi))
<|reserved_special_token_1|>
<|reserved_special_token_0|>
r = float(input())
print('{0:f} {1:f}'.format(r * r * math.pi, 2 * r * math.pi))
<|reserved_special_token... | flexible | {
"blob_id": "e28cca2273e1c3ad4b8a955843e7dfb45c00694c",
"index": 3246,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('{0:f} {1:f}'.format(r * r * math.pi, 2 * r * math.pi))\n",
"step-3": "<mask token>\nr = float(input())\nprint('{0:f} {1:f}'.format(r * r * math.pi, 2 * r * math.pi))\n",
"step-... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
import optparse
import os
import shutil
import sys
from AutoCrab.AutoCrab2 import core
def main():
parser = optparse.OptionParser()
parser.add_option("-r", "--recursive", dest="recursive", action="store_true", help="Recursively look for CRAB job files and directories.")
(opts, args) = parser... | normal | {
"blob_id": "d1fe06766958e8532c49d33e887d6c4996573c22",
"index": 4964,
"step-1": "#!/usr/bin/env python\n\nimport optparse\nimport os\nimport shutil\nimport sys\n\nfrom AutoCrab.AutoCrab2 import core\n\ndef main():\n\tparser = optparse.OptionParser()\n\tparser.add_option(\"-r\", \"--recursive\", dest=\"recursive... | [
0
] |
import os
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import cv2
import color_to_gray_operations
VIZ_PATH = '../output_data/visualizations/gray_intensities/'
def visualize_grayscale_intensities(img, out_path):
img_x, img_y = np.mgrid[0: img.shape[0], 0: img.shape... | normal | {
"blob_id": "21fec6d307b928a295f2ffbf267456f9cd9ea722",
"index": 9105,
"step-1": "<mask token>\n\n\ndef visualize_grayscale_intensities(img, out_path):\n img_x, img_y = np.mgrid[0:img.shape[0], 0:img.shape[1]]\n fig = plt.figure()\n ax = fig.gca(projection='3d')\n ax.plot_surface(img_x, img_y, img, r... | [
4,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
class FormControllerApi(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def submit_form_with_http_info(self, **kwargs):
"""Submit a form to be parsed and sent as an email to an address determined by the form fields ... | flexible | {
"blob_id": "a4ccf373695b7df60039bc8f6440a6ad43d265c1",
"index": 3750,
"step-1": "<mask token>\n\n\nclass FormControllerApi(object):\n <mask token>\n <mask token>\n <mask token>\n\n def submit_form_with_http_info(self, **kwargs):\n \"\"\"Submit a form to be parsed and sent as an email to an ad... | [
2,
3,
4,
5,
7
] |
<|reserved_special_token_0|>
def main():
args, ipython_args = parser.parse_known_args()
lines = ['from diofant import *', 'init_printing()',
"a, b, c, d, t, x, y, z = symbols('a:d t x:z')",
"k, m, n = symbols('k m n', integer=True)",
"f, g, h = symbols('f g h', cls=Function)",
... | flexible | {
"blob_id": "80e395715d3ae216beb17e7caed1d8d03c5c56de",
"index": 9943,
"step-1": "<mask token>\n\n\ndef main():\n args, ipython_args = parser.parse_known_args()\n lines = ['from diofant import *', 'init_printing()',\n \"a, b, c, d, t, x, y, z = symbols('a:d t x:z')\",\n \"k, m, n = symbols('k... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
@handler_define
class HelloWorld(BaseHandler):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@handler_define
class HelloWorld(BaseHandler):
@api_define('HelloWorld', '/', [], description='HelloWorld')
def get(self):
self.w... | flexible | {
"blob_id": "3c738a07d71338ab838e4f1d683e631252d50a30",
"index": 4085,
"step-1": "<mask token>\n\n\n@handler_define\nclass HelloWorld(BaseHandler):\n <mask token>\n",
"step-2": "<mask token>\n\n\n@handler_define\nclass HelloWorld(BaseHandler):\n\n @api_define('HelloWorld', '/', [], description='HelloWorl... | [
1,
2,
3,
4,
5
] |
import csv
from functools import reduce
class Csvread:
def __init__(self, fpath):
self._path=fpath
with open (fpath) as file:
read_f=csv.reader(file)
print(read_f) #<_csv.reader object at 0x000002A53144DF40>
self._sheet = list(read_f)[1:] #utworzenie listy
... | normal | {
"blob_id": "67793c8851e7107c6566da4e0ca5d5ffcf6341ad",
"index": 8867,
"step-1": "<mask token>\n\n\nclass Csvcalc:\n\n def __init__(self, cont):\n self._cont = cont\n\n def row_count(self):\n return len(self._cont)\n\n def get_row(self, row_no):\n return self._cont[row_no]\n\n de... | [
7,
10,
11,
13,
15
] |
# help from https://stackoverflow.com/questions/19007383/compare-two-different-files-line-by-line-in-python
with open('Book1.txt', 'r') as file1:
with open('20k.txt', 'r') as file2:
same = set(file1).intersection(file2)
same.discard('\n')
with open('notin20kforBook1.txt', 'w') as file_out:
for line i... | normal | {
"blob_id": "21a41356fcedb36223498db0fe783e4a9e8e1ba6",
"index": 210,
"step-1": "<mask token>\n",
"step-2": "with open('Book1.txt', 'r') as file1:\n with open('20k.txt', 'r') as file2:\n same = set(file1).intersection(file2)\nsame.discard('\\n')\nwith open('notin20kforBook1.txt', 'w') as file_out:\n ... | [
0,
1,
2
] |
<|reserved_special_token_0|>
def MostTeams(OffAndDef):
most = []
count = 0
for playerid, playerdata in OffAndDef.items():
if playerdata['name'] != '':
if len(playerdata['Teams']) > count:
count = len(playerdata['Teams'])
most = [[playerdata['name'], len(... | flexible | {
"blob_id": "2a4f57cd0fc1c50cba06c285849432c6f71f28e2",
"index": 2642,
"step-1": "<mask token>\n\n\ndef MostTeams(OffAndDef):\n most = []\n count = 0\n for playerid, playerdata in OffAndDef.items():\n if playerdata['name'] != '':\n if len(playerdata['Teams']) > count:\n ... | [
8,
9,
10,
15,
17
] |
import xmlrpclib
import socket
import time
import math
import re
from roundup.exceptions import Reject
REVPAT = re.compile(r'(r[0-9]+\b|rev(ision)? [0-9]+\b)')
def extract_classinfo(db, klass, nodeid, newvalues):
if None == nodeid:
node = newvalues
content = newvalues['content']
else:
... | normal | {
"blob_id": "3ec0c20fb2dfed9930885885288cc5d47f4f5ee5",
"index": 6196,
"step-1": "\nimport xmlrpclib\nimport socket\nimport time\nimport math\nimport re\n\nfrom roundup.exceptions import Reject\n\nREVPAT = re.compile(r'(r[0-9]+\\b|rev(ision)? [0-9]+\\b)')\n\ndef extract_classinfo(db, klass, nodeid, newvalues):\n... | [
0
] |
# coding=utf-8
"""
@Author: Freshield
@Contact: yangyufresh@163.com
@File: a1_test_call.py
@Time: 2021-01-20 17:40
@Last_update: 2021-01-20 17:40
@Desc: None
@==============================================@
@ _____ _ _ _ _ @
@ | __|___ ___ ___| |_|_|___| |_| | @
@ | __| ... | normal | {
"blob_id": "325770130473153d092d3058587e9666625e12d0",
"index": 5670,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(r.url)\nprint(r.text)\n<mask token>\nprint(r.text)\nprint(r.url)\n<mask token>\nprint(r.text)\nprint(r)\n",
"step-3": "<mask token>\nurl = 'https://www.baidu.com'\nurl = 'http://w... | [
0,
1,
2,
3,
4
] |
import os
import csv
import re
totWords = 0
wordLen = 0
totSentWithPunctuation = 0
sourceFile = os.path.join('Resources', 'paragraph_2.txt')
with open(sourceFile, 'r') as paragraph:
paragraph = paragraph.read().split("\n\n")
for sentence in paragraph:
# Remove punctuation from sentences
sentWithPunctua... | normal | {
"blob_id": "3cd7abf9659fe1db0ef3aa58df8dd7fd959e10a6",
"index": 386,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(sourceFile, 'r') as paragraph:\n paragraph = paragraph.read().split('\\n\\n')\nfor sentence in paragraph:\n sentWithPunctuation = sentence\n sentNoPunctuation = re.sub('... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@outputSchema('word:chararray')
def reverse(word):
"""
Return the reverse text of the provided word
"""
return word[::-1]
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
... | flexible | {
"blob_id": "94560d8f6528a222e771ca6aa60349d9682e8f4b",
"index": 6558,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@outputSchema('word:chararray')\ndef reverse(word):\n \"\"\"\n Return the reverse text of the provided word\n \"\"\"\n return word[::-1]\n\n\n<mask token>\n",
"step-3": "<... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def list(request):
techniques = Technique.objects.annotate(num_images=Count('images')
).order_by('-num_images')
return render_to_response('technique/list.html', {'techniques':
techniques}, RequestContext(request))
<|reserved_special_token_0|>
<|reserved_special... | flexible | {
"blob_id": "565e994576a57f8bbdcb201f2439bd7e595fa53e",
"index": 9679,
"step-1": "<mask token>\n\n\ndef list(request):\n techniques = Technique.objects.annotate(num_images=Count('images')\n ).order_by('-num_images')\n return render_to_response('technique/list.html', {'techniques':\n technique... | [
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_rep_name(string):
return string[-1:]
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_rep_name(string):
return string[-1:]
<|reserved_special_token_0|>
for n... | flexible | {
"blob_id": "a3588a521a87765d215fd2048407e5e54fb87e94",
"index": 4276,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_rep_name(string):\n return string[-1:]\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef get_rep_name(string):\n return string[-1:]\n\n\n<mask token>\nfor name in... | [
0,
1,
2,
3,
5
] |
#this apps is open
#Let's start with introduction
print "Hi, I am x0x. Could we introduce ourselves? (yes/no)"
answer = raw_input()
if answer.lower() == 'yes':
print "Okay, what is your name?"
name = raw_input()
print "Hi", name
print "Nice to meet you."
print "What are you going to do?"
print... | normal | {
"blob_id": "a28c62a18d793fb285353902d01801c720bcb454",
"index": 1653,
"step-1": "#this apps is open\n\n#Let's start with introduction\n\nprint \"Hi, I am x0x. Could we introduce ourselves? (yes/no)\"\nanswer = raw_input()\nif answer.lower() == 'yes':\n print \"Okay, what is your name?\"\n name = raw_input... | [
0
] |
# from django.contrib.auth.models import User
from django.db.models.signals import post_save
from django.contrib.auth.models import AbstractBaseUser, BaseUserManager
from django.db import models
# from applications.models import ApplicationReview
# from profiles.models import Restaurant, Program, Courier
# Enum f... | normal | {
"blob_id": "8a1f024be00200218782c919b21161bf48fc817e",
"index": 7805,
"step-1": "<mask token>\n\n\nclass UserClass(AbstractBaseUser):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token... | [
20,
31,
32,
36,
37
] |
<|reserved_special_token_0|>
class User(mongoengine.Document):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_t... | flexible | {
"blob_id": "51cdb41836415c08609ee6a6bcc3adbaf2533da4",
"index": 3697,
"step-1": "<mask token>\n\n\nclass User(mongoengine.Document):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>... | [
2,
3,
4,
5,
6
] |
# -*- coding: utf-8 -*-
#imports
from math import sqrt, pi, exp
from csv import reader
from random import seed,randrange
"""
Helper functions
"""
#calculate probability
def probability(x,avg,standev):
exponent = exp(-((x-avg)**2 / (2 * standev**2)))
return (1/(sqrt(2*pi) *standev)) * exponent
#mean
def avg(... | normal | {
"blob_id": "f92a1398a27541557ec5bbf752d44ce40d1df94a",
"index": 4131,
"step-1": "<mask token>\n\n\ndef standev(vals):\n mean = avg(vals)\n var = sum([((x - mean) ** 2) for x in vals]) / float(len(vals) - 1)\n return sqrt(var)\n\n\n<mask token>\n\n\ndef read_csv(file_name):\n data = list()\n with ... | [
9,
11,
12,
18,
19
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class EasyTechConfig(AppConfig):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class EasyTechConfig(AppConfig):
name = 'easy_tech'
<|reserved_special_token_1|>
from django... | flexible | {
"blob_id": "0ef172ced411213c0f7daccd632f8d5ec97379c3",
"index": 5604,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass EasyTechConfig(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass EasyTechConfig(AppConfig):\n name = 'easy_tech'\n",
"step-4": "from django.apps import... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = [(... | flexible | {
"blob_id": "45b2b611a80b93c9a7d8ec8a09e5838147e1ea76",
"index": 8626,
"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 = [('info', '001... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if silva == True:
print('Existe Silva nesse nome')
else:
print('Não há Silva nesse nome')
<|reserved_special_token_1|>
nome = str(input('Digite um nome completo: ')).lower()
silva = 'silva' in nome
if silva == True:
... | flexible | {
"blob_id": "faebefcadbc184fab29deb2988089223a8f09e7e",
"index": 8219,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif silva == True:\n print('Existe Silva nesse nome')\nelse:\n print('Não há Silva nesse nome')\n",
"step-3": "nome = str(input('Digite um nome completo: ')).lower()\nsilva = 'silv... | [
0,
1,
2
] |
<|reserved_special_token_0|>
def delete_devices():
"""."""
db = tango.Database()
class_list = db.get_class_list('*')
print('class list = ', class_list)
server_list = db.get_server_list('*')
print('server list = ', server_list)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|re... | flexible | {
"blob_id": "f3dad6a474d5882beaac7d98f8f60c347730ee55",
"index": 8428,
"step-1": "<mask token>\n\n\ndef delete_devices():\n \"\"\".\"\"\"\n db = tango.Database()\n class_list = db.get_class_list('*')\n print('class list = ', class_list)\n server_list = db.get_server_list('*')\n print('server li... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def process_mile(price, use_time, mile):
"""
mile处理
"""
mile_per_month = mile / use_time
if mile_per_month < gl.MILE_THRESHOLD_2_5:
return price + 0.035 * (1 - mile_per_month / gl.MILE_THRESHOLD_2_5
) * price
elif gl.MILE_THRESHOLD_2_5 <= mile_p... | flexible | {
"blob_id": "1f01989f10be5404d415d4abd1ef9ab6c8695aba",
"index": 6069,
"step-1": "<mask token>\n\n\ndef process_mile(price, use_time, mile):\n \"\"\"\n mile处理\n \"\"\"\n mile_per_month = mile / use_time\n if mile_per_month < gl.MILE_THRESHOLD_2_5:\n return price + 0.035 * (1 - mile_per_mont... | [
12,
15,
16,
18,
25
] |
import torch, torchvision
import torch.nn.functional as F
import transformers
from transformers import BertTokenizer, BertModel
from transformers.models.bert.modeling_bert import BertPreTrainingHeads
from utils import construct_bert_input, EvaluationDataset, save_json
from fashionbert_evaluator_parser import Evaluation... | normal | {
"blob_id": "7a01bffa5d7f0d5ecff57c97478f2cf5e9a27538",
"index": 1210,
"step-1": "<mask token>\n\n\nclass FashionbertEvaluator(transformers.BertPreTrainedModel):\n\n def __init__(self, config):\n super().__init__(config)\n self.bert = BertModel(config)\n self.im_to_embedding = torch.nn.Li... | [
8,
10,
12,
13,
14
] |
# -*- coding: utf-8 -*-
import scrapy
import os
from topdb.items import BiqugeItem
class NovelsSpider(scrapy.Spider):
name = 'novels'
allowed_domains = ['xbiquge.la']
start_urls = ['http://www.xbiquge.la/xiaoshuodaquan/']
def parse(self, response):
# 小说分类
path = '/Users/qx/Documents... | normal | {
"blob_id": "af668751074df6f182c7121821587270734ea5af",
"index": 1075,
"step-1": "<mask token>\n\n\nclass NovelsSpider(scrapy.Spider):\n <mask token>\n <mask token>\n <mask token>\n\n def parse(self, response):\n path = '/Users/qx/Documents/小说/new/'\n all = response.xpath(\".//div[@clas... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
def join_game(request):
if request.method != 'POST':
return HttpResponseRedirect('/game')
form_data = json.loads(request.body.decode('utf-8'))
form = JoinForm(form_data)
if form.is_valid():
code = int(form.cleaned_data['code'])
input_name = form.cle... | flexible | {
"blob_id": "d650f578ea30772489625ee26f3e4bf04131964b",
"index": 6140,
"step-1": "<mask token>\n\n\ndef join_game(request):\n if request.method != 'POST':\n return HttpResponseRedirect('/game')\n form_data = json.loads(request.body.decode('utf-8'))\n form = JoinForm(form_data)\n if form.is_val... | [
3,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def basicRegex(strings):
if not isinstance(strings, list):
return []
ans = []
for string in strings:
pattern = re.compile(BASICPATTERN % string.strip())
ans.append(pattern)
return ans
<|... | flexible | {
"blob_id": "1a28aea824752d18cbd462693f8f8980dba4974e",
"index": 9387,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef basicRegex(strings):\n if not isinstance(strings, list):\n return []\n ans = []\n for string in strings:\n pattern = re.compile(BASICPATTERN % string.strip(... | [
0,
1,
2,
3,
4
] |
try:
from zcrmsdk.src.com.zoho.crm.api.dc.data_center import DataCenter
except Exception as e:
from .data_center import DataCenter
class EUDataCenter(DataCenter):
"""
This class represents the properties of Zoho CRM in EU Domain.
"""
@classmethod
def PRODUCTION(cls):
"""
... | normal | {
"blob_id": "27c364ccf4a6703f74c95ebb386f8ced38b1eafd",
"index": 4960,
"step-1": "<mask token>\n\n\nclass EUDataCenter(DataCenter):\n <mask token>\n\n @classmethod\n def PRODUCTION(cls):\n \"\"\"\n This method represents the Zoho CRM Production environment in EU domain\n :return: An... | [
4,
5,
7,
8,
9
] |
##
#Author: Stephen
##
import socket
import select
import sys, os
from contextlib import contextmanager
hostip = 'localhost'
hostport = 8089
def connect(hostip=hostip,hostport=hostport):
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
IP_address = hostip
Port = hostport
server.connect((IP_a... | normal | {
"blob_id": "5cdf8cd4bfebb9aab2e8f421047fc1ba3190d566",
"index": 3451,
"step-1": "<mask token>\n\n\ndef connect(hostip=hostip, hostport=hostport):\n server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n IP_address = hostip\n Port = hostport\n server.connect((IP_address, Port))\n return serv... | [
5,
6,
7,
8,
9
] |
class TrieNode:
def __init__(self):
self.children = [None for i in range(26)]
self.isEndOfWord = 0
class Trie:
def __init__(self):
self.root = self.getNode()
def getNode(self):
return TrieNode()
def insert(self, key):
root = self.root
length = len(key)
for level in range(length):
index = ord(key[le... | normal | {
"blob_id": "5c7c90717f2e98c26675fec6390b4ea9797d6a4e",
"index": 2240,
"step-1": "class TrieNode:\n\tdef __init__(self):\n\t\tself.children = [None for i in range(26)]\n\t\tself.isEndOfWord = 0\nclass Trie:\n\tdef __init__(self):\n\t\tself.root = self.getNode()\n\tdef getNode(self):\n\t\treturn TrieNode()\n\tdef... | [
0
] |
# -*- coding: utf-8 -*-
import graphviz
import fa_util
class Graph:
def draw(self, directory, filename, rules, start_state, accept_states):
g = graphviz.Digraph(format="svg", graph_attr={'rankdir': 'LR'})
self.add_start_edge(g, start_state)
edges = {}
for rule in rules:
... | normal | {
"blob_id": "c0e94a0d20397ebbbdddf726307b19b6c5c85ae6",
"index": 9082,
"step-1": "<mask token>\n\n\nclass Graph:\n\n def draw(self, directory, filename, rules, start_state, accept_states):\n g = graphviz.Digraph(format='svg', graph_attr={'rankdir': 'LR'})\n self.add_start_edge(g, start_state)\n ... | [
6,
7,
9,
10,
12
] |
<|reserved_special_token_0|>
class TestNukeBoxDB(unittest.TestCase):
<|reserved_special_token_0|>
def setUp(self):
"""
B{Test} Data
- 2 User dict obj.
- contains basic data required by the MongoDB collection "Users"
- indexes exist on "mac_id" and "files" en... | flexible | {
"blob_id": "bf63ceca2347f750cdf38dce620eaa3c73b556f1",
"index": 1733,
"step-1": "<mask token>\n\n\nclass TestNukeBoxDB(unittest.TestCase):\n <mask token>\n\n def setUp(self):\n \"\"\"\n B{Test} Data\n\n - 2 User dict obj.\n - contains basic data required by the MongoDB co... | [
5,
7,
9,
11,
12
] |
#import cvxopt
from cvxopt import matrix, spmatrix, solvers
#import scipy
from scipy.special import expit
import numpy as np
import sys
import pandas as pd
import time
class KernelNC():
"""
distance based classifier for spectrum kernels
"""
def __init__(self, classes):
self.classes = class... | normal | {
"blob_id": "6f35c29f6f2dcc6c1dae3e9c1ddf595225748041",
"index": 3018,
"step-1": "<mask token>\n\n\nclass KernelNC:\n <mask token>\n\n def __init__(self, classes):\n self.classes = classes\n\n def compute_dist(self, X, Y):\n K_x = np.dot(X, X.T).toarray()\n K_y = np.dot(Y, Y.T).toar... | [
16,
17,
19,
20,
21
] |
from __future__ import print_function
from __future__ import division
import os
import sys
import time
import datetime
import os.path as osp
from collections import defaultdict
import numpy as np
import math
from functools import partial
from tqdm import tqdm
import glog as log
import torch
import torch.nn as nn
impo... | normal | {
"blob_id": "0ad529298f321d2f3a63cde8179a50cf2881ee00",
"index": 2162,
"step-1": "<mask token>\n\n\ndef main():\n global args\n torch.manual_seed(args.seed)\n if not args.use_avai_gpus:\n os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices\n use_gpu = torch.cuda.is_available()\n if args.u... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
if __name__ == '__main__':
print('--------------------------------------')
query = 'user=pilgrim&database=master&password=PapayaWhip'
a_list = query.split('&')
print(a_list)
print('--------------------------------------')
a_list_of_lis... | flexible | {
"blob_id": "5c3bf49f88dec429ec85cceb8130cccf2691363b",
"index": 1538,
"step-1": "<mask token>\n",
"step-2": "if __name__ == '__main__':\n print('--------------------------------------')\n query = 'user=pilgrim&database=master&password=PapayaWhip'\n a_list = query.split('&')\n print(a_list)\n pr... | [
0,
1
] |
class Node(object):
def __init__(self,data):
self.data = data
self.left = None
self.right = None
self.parent = None
class tree(object):
def __init__(self):
self.root = None
def insert(self,root,value):
if self.root == None:
self.root = No... | normal | {
"blob_id": "64c32b3ada7fff51a7c4b07872b7688e100897d8",
"index": 81,
"step-1": "class Node(object):\n <mask token>\n\n\nclass tree(object):\n\n def __init__(self):\n self.root = None\n\n def insert(self, root, value):\n if self.root == None:\n self.root = Node(value)\n el... | [
7,
8,
9,
10,
11
] |
<|reserved_special_token_0|>
def convertTime(et):
"""'2017-06-01 11:41:53 AM' to '2017-06-01 11:41:53' """
hour = int(et[11:13])
if et.find('PM') != -1 and hour != 12:
dateString = et[:10]
hour = hour + 12
et = dateString + ' ' + str(hour) + et[13:19]
elif et.find('AM') != -1 a... | flexible | {
"blob_id": "9b8b196e1ad845ab745dabe5abe3be7bea0d5695",
"index": 4835,
"step-1": "<mask token>\n\n\ndef convertTime(et):\n \"\"\"'2017-06-01 11:41:53 AM' to '2017-06-01 11:41:53' \"\"\"\n hour = int(et[11:13])\n if et.find('PM') != -1 and hour != 12:\n dateString = et[:10]\n hour = hour + ... | [
4,
7,
10,
12,
13
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = [(... | flexible | {
"blob_id": "b27913d2cd29f174d79652af6da2846e397373fc",
"index": 1549,
"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 = [('lists', '00... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def extract_field_from_request(request: Request, field_name: str
) ->typing.Optional[int]:
"""
Extracts attribte from request
if attribute is present in data it has precedence over query parameters
"""
try:
value = request.data.get(field_name)
except At... | flexible | {
"blob_id": "0b7523035fdad74454e51dc9da9fc4e9bea2f6bf",
"index": 6904,
"step-1": "<mask token>\n\n\ndef extract_field_from_request(request: Request, field_name: str\n ) ->typing.Optional[int]:\n \"\"\"\n Extracts attribte from request\n if attribute is present in data it has precedence over query par... | [
1,
2,
3,
4,
5
] |
import re
match = re.search(r'pi+', 'piiig')
print 'found', match.group() == "piii"
| normal | {
"blob_id": "82083f16c18db35193fa2aa45bc28c5201962f90",
"index": 6704,
"step-1": "\n\nimport re\n\n\nmatch = re.search(r'pi+', 'piiig')\nprint 'found', match.group() == \"piii\"\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def memory(count: int, start_numbers: list):
numbers = defaultdict(lambda : tuple(2 * [None]), {el: (idx, None) for
idx, el in enumerate(start_numbers)})
last = start_numbers[-1]
for idx in range(len(numbers... | flexible | {
"blob_id": "0f0adde7241898d2efe7e2b5cc218e42ed7b73d8",
"index": 5475,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef memory(count: int, start_numbers: list):\n numbers = defaultdict(lambda : tuple(2 * [None]), {el: (idx, None) for \n idx, el in enumerate(start_numbers)})\n last = st... | [
0,
1,
2,
3,
4
] |
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
import random
from utils.misc import *
from utils.adapt_helpers import *
from utils.rotation import rotate_batch, rotate_single_with_label
from utils.model import resnet18
from utils.train_helpers impor... | normal | {
"blob_id": "1f345a20343eb859cb37bf406623c0fc10722357",
"index": 4826,
"step-1": "<mask token>\n\n\ndef gn_helper(planes):\n return nn.GroupNorm(args.group_norm, planes)\n\n\n<mask token>\n",
"step-2": "<mask token>\nparser.add_argument('--dataroot', default='data/CIFAR-10-C/')\nparser.add_argument('--share... | [
1,
2,
3,
4,
5
] |
/home/runner/.cache/pip/pool/f6/0b/37/37d1907955d15568c921a952a47d6e8fcc905cf4f36ab6f99f5fc7315a | normal | {
"blob_id": "002b795f61645ba2023cdb359167d2a65535d768",
"index": 5710,
"step-1": "/home/runner/.cache/pip/pool/f6/0b/37/37d1907955d15568c921a952a47d6e8fcc905cf4f36ab6f99f5fc7315a",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
<|reserved_special_token_0|>
def getDependenceStr(ins1, ins2, reg):
return f'{ins1} -> {ins2}: {reg}'
def getInstructionStr(ins, reg1, reg2, reg3):
return f'{ins} {reg1} {reg2} {reg3}'
<|reserved_special_token_0|>
def validateInput(str):
if str.strip() == '':
return True
return len(str.s... | flexible | {
"blob_id": "e045dc348fb2e9de51dbeada1d1826211cf89eae",
"index": 3114,
"step-1": "<mask token>\n\n\ndef getDependenceStr(ins1, ins2, reg):\n return f'{ins1} -> {ins2}: {reg}'\n\n\ndef getInstructionStr(ins, reg1, reg2, reg3):\n return f'{ins} {reg1} {reg2} {reg3}'\n\n\n<mask token>\n\n\ndef validateInput(s... | [
7,
10,
11,
13,
16
] |
from django.shortcuts import render,redirect
from .forms import UserRegisterForm, IsEmri ,TestForm,PDF_Rapor
from django.contrib import messages
from django.contrib.auth import authenticate, login ,logout
from django.http import HttpResponseRedirect, HttpResponse ,JsonResponse
from django.urls import reverse
from djang... | normal | {
"blob_id": "74dd9151195fef41862c2793621172518f1f486d",
"index": 5248,
"step-1": "<mask token>\n\n\n@login_required\ndef index(request):\n grup = request.user.grup\n birim = request.user.birim\n emirler = Emir.objects.filter(durum='Aktif')\n l = list()\n for e in emirler.values():\n data = ... | [
10,
25,
26,
33,
35
] |
/Users/linhly/anaconda/lib/python3.6/reprlib.py | normal | {
"blob_id": "127ca34d3fae3af4506258388a28c539ccc7c33b",
"index": 4120,
"step-1": "/Users/linhly/anaconda/lib/python3.6/reprlib.py",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
<|reserved_special_token_0|>
def plot_grad_flow(named_parameters):
"""Plots the gradients flowing through different layers in the net during training.
Can be used for checking for possible gradient vanishing / exploding problems.
Usage: Plug this function in Trainer class after loss.backwards() as
"p... | flexible | {
"blob_id": "0fb424dafaac184882ea56f36265e0b19b5a4c50",
"index": 9758,
"step-1": "<mask token>\n\n\ndef plot_grad_flow(named_parameters):\n \"\"\"Plots the gradients flowing through different layers in the net during training.\n Can be used for checking for possible gradient vanishing / exploding problems.... | [
1,
2,
3,
4,
5
] |
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