code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
import os
import log
import core
import time
__description__ = 'OS X Auditor'
__author__ = 'Atarimaster & @Jipe_'
__version__ = '0.5.0'
ROOT_PATH = '/'
Euid = str(os.geteuid())
Egid = str(os.getegid())
def generate_header():
header = {}
# Description(Audited By)
description = "Report generated by " + _... | normal | {
"blob_id": "547d67bce7eb05e55e02c73a22342ca572e89f39",
"index": 9959,
"step-1": "<mask token>\n\n\ndef GetAuditedSystemVersion():\n global OSX_VERSION\n SysVersion = 'Unknown system version'\n SystemVersionPlist = False\n SystemVersionPlist = core.UniversalReadPlist(\n '/System/Library/CoreSe... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def pixels_generator(w, h):
i = 0
while i < w * h:
yield divmod(i, w)
i = i + 1
<|reserved_special_token_1|>
def pixels_generator(w, h):
i = 0
while i < (w * h):
yield divmod(i, w)
i = i + 1
| flexible | {
"blob_id": "bb481fa038835abc6d61a4985b1e30c7c00bff96",
"index": 158,
"step-1": "<mask token>\n",
"step-2": "def pixels_generator(w, h):\n i = 0\n while i < w * h:\n yield divmod(i, w)\n i = i + 1\n",
"step-3": "def pixels_generator(w, h):\n i = 0\n while i < (w * h):\n yield... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
FILE = 'Luke'
NAME = 'Luke Walker'
NATIONALITY = 'American'
CLASS = 'Manipulator'
WEAPON = ''
BIRTH = ''
BIRTH_LOCATION = ''
LETTER = 'W'
RECRUITMENT_ORDER = 10
SUMMARY = ''
ABILITIES = ''
BACKSTORY = ''
HIGHLIGHTS = ''
SUMMONS = 'Tonberry', 'Grimnir', 'Griev... | flexible | {
"blob_id": "fa3ab879541c04e278317b11dd79e6e1b4319536",
"index": 7586,
"step-1": "<mask token>\n",
"step-2": "FILE = 'Luke'\nNAME = 'Luke Walker'\nNATIONALITY = 'American'\nCLASS = 'Manipulator'\nWEAPON = ''\nBIRTH = ''\nBIRTH_LOCATION = ''\nLETTER = 'W'\nRECRUITMENT_ORDER = 10\nSUMMARY = ''\nABILITIES = ''\nB... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def less(i1, i2):
return i1[0] * i2[1] < i2[0] * i1[1]
def equal(i1, i2):
return i1[0] * i2[1] == i2[0] * i1[1]
def more(i1, i2):
return i1[0] * i2[1] > i2[0] * i1[1]
def partition(x, l, r, pivot):
il = l
... | flexible | {
"blob_id": "a5e693a79211570f2d27575657496992f8fee164",
"index": 9075,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef less(i1, i2):\n return i1[0] * i2[1] < i2[0] * i1[1]\n\n\ndef equal(i1, i2):\n return i1[0] * i2[1] == i2[0] * i1[1]\n\n\ndef more(i1, i2):\n return i1[0] * i2[1] > i2[0]... | [
0,
5,
7,
8,
9
] |
<|reserved_special_token_0|>
class PrescriptionForm(forms.ModelForm):
class Meta:
model = Prescription
exclude = ['doctor']
widgets = {'prescription': forms.Textarea(attrs={'rows': 4})}
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.field... | flexible | {
"blob_id": "d3425017d4e604a8940997afd0c35a4f7eac1170",
"index": 6944,
"step-1": "<mask token>\n\n\nclass PrescriptionForm(forms.ModelForm):\n\n\n class Meta:\n model = Prescription\n exclude = ['doctor']\n widgets = {'prescription': forms.Textarea(attrs={'rows': 4})}\n\n def __init__(... | [
2,
3,
4,
5,
6
] |
v1 = 3 + 4 * 2
print(v1)
v2 = (2 + 6) * 2
print(v2)
v3 = 2 ** 3 ** 2
print(v3)
v4 = 20 + 80 / 2
print(v4)
| normal | {
"blob_id": "e6694403eecf2c4511c1fce959b5939f5f457bb8",
"index": 9384,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(v1)\n<mask token>\nprint(v2)\n<mask token>\nprint(v3)\n<mask token>\nprint(v4)\n",
"step-3": "v1 = 3 + 4 * 2\nprint(v1)\nv2 = (2 + 6) * 2\nprint(v2)\nv3 = 2 ** 3 ** 2\nprint(v3)\n... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
BINDINGS_DIRECTORY = os.path.join(os.path.dirname(os.path.abspath(__file__)
), 'bindings')
TMUX_CONFIG_DIRECTORY = os.path.join(BINDINGS_DIRECTORY, 'tmux')
DEFAULT_SYSTEM_CONFIG_DIR = None
<|reserved_special_token_1|>
from ... | flexible | {
"blob_id": "c435b0f162512bb2bc0c35e1817f64c5ef9ff7bc",
"index": 1871,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nBINDINGS_DIRECTORY = os.path.join(os.path.dirname(os.path.abspath(__file__)\n ), 'bindings')\nTMUX_CONFIG_DIRECTORY = os.path.join(BINDINGS_DIRECTORY, 'tmux')\nDEFAULT_SYSTEM_CONFIG_DI... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Video_Server(threading.Thread):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def run(self):
detector, predictor = face_capture_edit.face_init(self.
face_shape_predictor)
print('face_capture_init is ready')
print('VIDEO se... | flexible | {
"blob_id": "6b138dabf57166ec971052fff7df89ae0346e083",
"index": 1582,
"step-1": "<mask token>\n\n\nclass Video_Server(threading.Thread):\n <mask token>\n <mask token>\n\n def run(self):\n detector, predictor = face_capture_edit.face_init(self.\n face_shape_predictor)\n print('f... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def __prepare_train_data(df, feature):
groups = df.groupby(['event', 'start'])
data = []
labels = []
for id, group in groups:
values = group['CylinderBorePressure'].values
data.append(np.reshape(values, (len(values), 1)))
labels.append(id[0])
re... | flexible | {
"blob_id": "55030648a6b76636e456990c1d2b02baa35a695d",
"index": 9221,
"step-1": "<mask token>\n\n\ndef __prepare_train_data(df, feature):\n groups = df.groupby(['event', 'start'])\n data = []\n labels = []\n for id, group in groups:\n values = group['CylinderBorePressure'].values\n dat... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class QuadraticEquationsSolverConfig(AppConfig):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class QuadraticEquationsSolverConfig(AppConfig):
name = 'quadratic_equations_so... | flexible | {
"blob_id": "730fc527f3d2805559e8917e846b0b13f4a9f6ee",
"index": 2316,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass QuadraticEquationsSolverConfig(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass QuadraticEquationsSolverConfig(AppConfig):\n name = 'quadratic_equations... | [
0,
1,
2,
3
] |
"""
问题描述
玛莎（Marsha）和比尔（Bill）拥有一系列大理石。他们希望将藏品分开，以使两者获得相等的份额。如果所有的大理石都具有相同的价值，这将很容易，因为那样他们就可以将收藏品分成两半。
但不幸的是，有些大理石比其他大理石更大或更漂亮。因此，玛莎（Marsha）和比尔（Bill）首先为每个大理石分配一个值，即一个介于1到6之间的自然数。
现在，他们希望对大理石进行分割，以使每个大理石都获得相同的总价值。不幸的是，他们意识到以这种方式分割大理石可能是不可能的（即使所有大理石的总价值是均匀的）。
例如，如果存在一个值为1的大理石，值为3的一个，值为4的两个，则不能将它们拆分为相等值的集合。因此，他们要求您编写一个程序来检查... | normal | {
"blob_id": "0d20b75bcc87db8f3e4bdd9d6448cc44c979de1d",
"index": 137,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('输入:')\nwhile True:\n s = input()\n if s == '0 0 0 0 0 0':\n break\n S.append(s)\nprint('\\n输出:')\n<mask token>\nfor k in range(len(S)):\n p = [int(i) for i in S[k... | [
0,
1,
2,
3
] |
import random
def patternToNumber(pattern):
if len(pattern) == 0:
return 0
return 4 * patternToNumber(pattern[0:-1]) + symbolToNumber(pattern[-1:])
def symbolToNumber(symbol):
if symbol == "A":
return 0
if symbol == "C":
return 1
if symbol == "G":
return 2
if sy... | normal | {
"blob_id": "51848a64102f7fe8272fcf56a9792ed50c430538",
"index": 9115,
"step-1": "<mask token>\n\n\ndef patternToNumber(pattern):\n if len(pattern) == 0:\n return 0\n return 4 * patternToNumber(pattern[0:-1]) + symbolToNumber(pattern[-1:])\n\n\ndef symbolToNumber(symbol):\n if symbol == 'A':\n ... | [
8,
9,
11,
13,
15
] |
<|reserved_special_token_0|>
class CampaignPerformance:
<|reserved_special_token_0|>
def __init__(self, campaign, start):
self.campaign = campaign
self.start = start
self.BUDGETS_NAME = 'Budgets'
self.required_ran = False
<|reserved_special_token_0|>
def _get_start_da... | flexible | {
"blob_id": "a860e6670719a733e75c7580cf2e07765b0777eb",
"index": 2806,
"step-1": "<mask token>\n\n\nclass CampaignPerformance:\n <mask token>\n\n def __init__(self, campaign, start):\n self.campaign = campaign\n self.start = start\n self.BUDGETS_NAME = 'Budgets'\n self.required_... | [
9,
12,
13,
15,
16
] |
<|reserved_special_token_0|>
class TicketshopATLayer(PloneSandboxLayer):
defaultBases = PLONE_FIXTURE,
def setUpZope(self, app, configurationContext):
import Products.ATContentTypes
self.loadZCML(package=Products.ATContentTypes, context=
configurationContext)
import bda.pl... | flexible | {
"blob_id": "5d7080f2778133d1938853512ca038edcf7c0dc4",
"index": 1002,
"step-1": "<mask token>\n\n\nclass TicketshopATLayer(PloneSandboxLayer):\n defaultBases = PLONE_FIXTURE,\n\n def setUpZope(self, app, configurationContext):\n import Products.ATContentTypes\n self.loadZCML(package=Products... | [
4,
7,
10,
11,
14
] |
# Mostra entre as 7 pessoas, quantas pessoas são maiores de idade.
num1 = 0
for c in range(0,7):
pe1 = int(input('Digite o ano de nascimento: '))
pe1 = 2019 - pe1
if pe1 >= 21:
num1 = num1 + 1
print(f'Entre as 7 pessoas, {num1} pessoas são maiores de idade.') | normal | {
"blob_id": "251d589a5815d77d2bc375d8d4a7d41e79a2a5cd",
"index": 5303,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor c in range(0, 7):\n pe1 = int(input('Digite o ano de nascimento: '))\n pe1 = 2019 - pe1\n if pe1 >= 21:\n num1 = num1 + 1\nprint(f'Entre as 7 pessoas, {num1} pessoas s... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def partition3(array, left, right):
pivot = array[right]
begin = left - 1
end = left - 1
for j in range(left, right):
if array[j] < pivot:
begin += 1
array[begin], array[j] = array... | flexible | {
"blob_id": "a2fc9d947c75eaaaeafcd92750c99f4cfcdb9d7d",
"index": 4517,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef partition3(array, left, right):\n pivot = array[right]\n begin = left - 1\n end = left - 1\n for j in range(left, right):\n if array[j] < pivot:\n be... | [
0,
2,
3,
4,
5
] |
def summation(numbers):
positive_numbers = []
normalized_numbers = []
numbers_list = numbers.split()
for idx, arg in enumerate(numbers_list):
int_arg = int(arg)
if int_arg < 0:
new_arg = abs(int_arg) * 2
else:
new_arg = int_arg
positive_numbers.app... | normal | {
"blob_id": "791df87235f5da634fc62ebc3a3741cea6e2deca",
"index": 3841,
"step-1": "<mask token>\n",
"step-2": "def summation(numbers):\n positive_numbers = []\n normalized_numbers = []\n numbers_list = numbers.split()\n for idx, arg in enumerate(numbers_list):\n int_arg = int(arg)\n if... | [
0,
1
] |
# Generated by Django 3.2.6 on 2021-08-15 05:17
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('website', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='tasks',
name='cleanlinessLevel',
... | normal | {
"blob_id": "6f9f204cbd6817d5e40f57e71614ad03b64d9003",
"index": 3152,
"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 = [('website', '... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@attach_common
class TalkBotThread(QThread):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def run(self):
self.start_databas... | flexible | {
"blob_id": "77763f501c6776969d2594f987e5d7ab7d4377fb",
"index": 317,
"step-1": "<mask token>\n\n\n@attach_common\nclass TalkBotThread(QThread):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def run(self):\n self.start_database()\n ... | [
6,
7,
9,
11,
12
] |
<|reserved_special_token_0|>
class SpeciesSerializer(TranslatedModelSerializer, PictogramSerializerMixin):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class Meta:
model = sensitivity_models.Species
fields = ['id', 'name', 'practices', 'url',... | flexible | {
"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
] |
from abc import ABCMeta, abstractmethod
__author__ = 'Alexiy'
class Protocol:
"""base protocol class"""
__metaclass__ = ABCMeta
FAIL = 'Failed'
@abstractmethod
def execute(self, command):
""""execute command method"""
class LocalProtocol(Protocol):
"""simple protocol for using bots ... | normal | {
"blob_id": "8d1067a9bb0629276ef27de91f63cf2370a44e24",
"index": 1369,
"step-1": "<mask token>\n\n\nclass Protocol:\n <mask token>\n <mask token>\n <mask token>\n\n @abstractmethod\n def execute(self, command):\n \"\"\"\"execute command method\"\"\"\n\n\nclass LocalProtocol(Protocol):\n ... | [
6,
7,
8,
11
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def main():
if len(sys.argv) < 3:
sys.stderr.write(
'Usage: %s CONFIG_URI {bootstrap | ALEMBIC_OPTS}\n' % sys.argv[0])
sys.exit(1)
config_uri = sys.argv.pop(1)
if sys.argv[1] == 'bootstrap... | flexible | {
"blob_id": "7b459cf321f351e1485a9aef0ca23067f411e430",
"index": 7446,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n if len(sys.argv) < 3:\n sys.stderr.write(\n 'Usage: %s CONFIG_URI {bootstrap | ALEMBIC_OPTS}\\n' % sys.argv[0])\n sys.exit(1)\n config_uri... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def parseSeq(lines, seqName):
"""splits each column"""
data = []
for line in lines:
data.append(line.split(' '))
"""removes any spaces"""
for i in range(len(data)):
for j in range(data[i].count('')):
data[i].remove('')
"""deletes the num... | flexible | {
"blob_id": "19e387cb731dad21e5ee50b0a9812df984c13f3b",
"index": 7890,
"step-1": "<mask token>\n\n\ndef parseSeq(lines, seqName):\n \"\"\"splits each column\"\"\"\n data = []\n for line in lines:\n data.append(line.split(' '))\n \"\"\"removes any spaces\"\"\"\n for i in range(len(data)):\n ... | [
1,
2,
3,
4,
5
] |
'''
Created on 3 Jul 2009
@author: charanpal
An abstract base class which represents a graph generator. The graph generator
takes an existing empty graph and produces edges over it.
'''
from apgl.util.Util import Util
class AbstractGraphGenerator(object):
def generate(self, graph):
Util.abst... | normal | {
"blob_id": "e37e468d8a41b8711fb0eb4ddec7db67691f9156",
"index": 488,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass AbstractGraphGenerator(object):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass AbstractGraphGenerator(object):\n\n def generate(self, graph):\n Util.abstrac... | [
0,
1,
2,
3,
4
] |
#配置我们文件所在目录的搜寻环境
import os,sys
#第一步先拿到当前文件的路径
file_path = os.path.abspath(__file__)
#第二步 根据这个路径去拿到这个文件所在目录的路径
dir_path = os.path.dirname(file_path)
#第三步：讲这个目录的路径添加到我们的搜寻环境当中
sys.path.append(dir_path)
#第四步，动态设置我们的setting文件
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "gulishop.settings")
#第五步，让设置好的环境初始化生效
... | normal | {
"blob_id": "35ae9c86594b50bbe4a67d2cc6b20efc6f6fdc64",
"index": 295,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append(dir_path)\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'gulishop.settings')\n<mask token>\ndjango.setup()\n<mask token>\nfor lev1 in row_data:\n cat1 = GoodsCategory... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def main(arguments):
docker = [('Dockerfile.ubuntu1804', 'ubuntu1804_ansible_testinfra'), (
'Dockerfile.ubuntu1604', 'ubuntu1604_ansible_testinfra')]
docker_username = os.environ.get('DOCKER_USERNAME', None)
if docker_username is None:
docker_username = input('... | flexible | {
"blob_id": "1ad40ef3aa7c81b6eee4fe0b98bcdd2f1110ef8d",
"index": 5990,
"step-1": "<mask token>\n\n\ndef main(arguments):\n docker = [('Dockerfile.ubuntu1804', 'ubuntu1804_ansible_testinfra'), (\n 'Dockerfile.ubuntu1604', 'ubuntu1604_ansible_testinfra')]\n docker_username = os.environ.get('DOCKER_USE... | [
2,
3,
4,
5,
6
] |
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 4 15:19:49 2018
@author: haoyu
"""
import numpy as np
def train_test_split(X, y, test_ratio = 0.2, seed = None):
'''将数据X和y按照test_ratio分割成X_train,X_test,y_train,y_test'''
assert X.shape[0] == y.shape[0], \
'the size of X must be equal to the size of y'
... | normal | {
"blob_id": "beda3d13e3dc12f7527f5c5ba8a0eb05c2734fd9",
"index": 6133,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef train_test_split(X, y, test_ratio=0.2, seed=None):\n \"\"\"将数据X和y按照test_ratio分割成X_train,X_test,y_train,y_test\"\"\"\n assert X.shape[0] == y.shape[0\n ], 'the size of... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class recommendationsys:
def __init__(self, nyear):
self.activityyear = 10
self.debug = 0
self.nremd = 3
PROJECT_DIRECTORY = 'output/project/' + project_name
self.f_titles = PROJECT_DIRECTORY + '/cleantitles_target.txt'
self.f_authors =... | flexible | {
"blob_id": "4a8a733a965e25ad7ef53600fad6dd47343655b0",
"index": 8677,
"step-1": "<mask token>\n\n\nclass recommendationsys:\n\n def __init__(self, nyear):\n self.activityyear = 10\n self.debug = 0\n self.nremd = 3\n PROJECT_DIRECTORY = 'output/project/' + project_name\n sel... | [
21,
25,
35,
43,
47
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('Массив случайных чисел:\n', array)
<|reserved_special_token_0|>
for el in range(SIZE):
if array[el] > max_el:
max_el = array[el]
max_el_inx = el
if array[el] < min_el:
min_el = array[el]
... | flexible | {
"blob_id": "6027836b1b5d3cb8b842b1a1b77f5c9777269896",
"index": 7177,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Массив случайных чисел:\\n', array)\n<mask token>\nfor el in range(SIZE):\n if array[el] > max_el:\n max_el = array[el]\n max_el_inx = el\n if array[el] < min_e... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'day66.settings')
import django
django.setup()
from applistions.models import MyClass, Student, Teacher, Employee
from django.db.models... | flexible | {
"blob_id": "ee72262fb29b46784fb357269dd5160192968c1b",
"index": 1713,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'day66.settings')\n import django\n django.setup()\n from applistions.models import MyClass, Stude... | [
0,
1,
2,
3
] |
from collections import defaultdict
from django.shortcuts import render
from django.views.decorators.cache import cache_control
from peterbecom.plog.models import BlogItem, Category
from peterbecom.plog.utils import utc_now
from peterbecom.plog.views import json_view
ONE_MONTH = 60 * 60 * 24 * 30
@cache_control(pu... | normal | {
"blob_id": "e90fb3b6009dd4fb780649c04398b361fa1ae195",
"index": 8489,
"step-1": "<mask token>\n\n\n@cache_control(public=True, max_age=ONE_MONTH)\ndef index(request):\n return render(request, 'ajaxornot/index.html')\n\n\n<mask token>\n\n\n@cache_control(public=True, max_age=ONE_MONTH)\ndef view1(request):\n ... | [
7,
9,
14,
15,
17
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Course(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Course(models.Model):
cid = ... | flexible | {
"blob_id": "226fc85dc8b6d549fddef0ca43ad629875ac0717",
"index": 3080,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Course(models.Model):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Course(models.Model):\n cid = models.CharField(max_length... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def check(t, d, c):
if t == 1:
if m1[2] != m2[-2] and not c:
check(t + 1, d * -1, 1)
if d == 1:
m1.appendleft(m1.pop())
else:
m1.append(m1.popleft())
elif t == 4:
if m4[-2] != m3[2] and not c:
check(t ... | flexible | {
"blob_id": "7e3a5e1f19683b1716f3c988dcc1e65fee1cae13",
"index": 8956,
"step-1": "<mask token>\n\n\ndef check(t, d, c):\n if t == 1:\n if m1[2] != m2[-2] and not c:\n check(t + 1, d * -1, 1)\n if d == 1:\n m1.appendleft(m1.pop())\n else:\n m1.append(m1.pop... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def load_data():
"""
Helper function for loading in the data
------
# of training samples: 419
# of testing samples: 150
------
"""
df = pd.read_csv('../../Data/breast_cancer_data/data.csv')
cols = df.columns
X = df[cols[2:-1]].to_numpy()
y = d... | flexible | {
"blob_id": "cf65966f5daf88bdefc7a8aa2ff80835cff0d0b6",
"index": 4627,
"step-1": "<mask token>\n\n\ndef load_data():\n \"\"\"\n Helper function for loading in the data\n\n ------\n # of training samples: 419\n # of testing samples: 150\n ------\n \"\"\"\n df = pd.read_csv('../../Data/brea... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/python
import sys
class Generator:
def __init__(self, seed, factor, multiple):
self.value = seed
self.factor = factor
self.multiple = multiple
def iterate(self):
self.value = ( self.value * self.factor ) % 2147483647
# Repeat if this isn't an exact multiple
... | normal | {
"blob_id": "4bb006e2e457f5b11157dacb43fe94c8b400f146",
"index": 5105,
"step-1": "#!/usr/bin/python\n\nimport sys\n\nclass Generator:\n def __init__(self, seed, factor, multiple):\n self.value = seed\n self.factor = factor\n self.multiple = multiple\n\n def iterate(self):\n self... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def visualize_batch(pointclouds, pred_labels, labels, categories):
batch_size = len(pointclouds)
fig = plt.figure(figsize=(8, batch_size / 2))
ncols = 5
nrows = max(1, batch_size // 5)
for idx, pc in enumerat... | flexible | {
"blob_id": "0ced42c8bfaad32fc2b397326150e6c7bc5cedab",
"index": 4991,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef visualize_batch(pointclouds, pred_labels, labels, categories):\n batch_size = len(pointclouds)\n fig = plt.figure(figsize=(8, batch_size / 2))\n ncols = 5\n nrows = ma... | [
0,
1,
2,
3,
4
] |
def climb_ways(n, k): | normal | {
"blob_id": "05144338cc9c0c65010e0b8a3dd6fb50f6343214",
"index": 6641,
"step-1": "def climb_ways(n, k):",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
# Copyright (c) 2019 NVIDIA Corporation
from nemo.backends.pytorch.nm import DataLayerNM
from nemo.core.neural_types import *
from nemo.core import DeviceType
import torch
from .datasets import BertPretrainingDataset
class BertPretrainingDataLayer(DataLayerNM):
@staticmethod
def create_ports():
input... | normal | {
"blob_id": "a47ffd5df49ec627442a491f81a117b3e68ff50b",
"index": 2326,
"step-1": "<mask token>\n\n\nclass BertPretrainingDataLayer(DataLayerNM):\n <mask token>\n\n def __init__(self, *, tokenizer, dataset, name, max_seq_length,\n sentence_indices_filename=None, mask_probability=0.15, **kwargs):\n ... | [
4,
5,
6,
7,
8
] |
import tensorflow as tf
from vgg16 import vgg16
def content_loss(content_layer, generated_layer):
# sess.run(vgg_net.image.assign(generated_image))
# now we define the loss as the difference between the reference activations and
# the generated image activations in the specified layer
# return 1/2 * ... | normal | {
"blob_id": "f92b939bf9813e5c78bc450ff270d5fb6171792a",
"index": 4810,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef content_loss(content_layer, generated_layer):\n return tf.scalar_mul(0.5, tf.nn.l2_loss(content_layer - generated_layer))\n\n\n<mask token>\n\n\ndef get_gram_matrix(matrix, num... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def clearConsole():
os.system('cls' if os.name == 'nt' else 'clear')
def main():
checkArgs()
rfile = open(sys.argv[1], 'r')
wfile = open(output_name, 'w')
parseAndStrip(rfile, wfile)
rfile.close()
wfile.close()
def checkArgs():
if len(sys.argv) < 2 or l... | flexible | {
"blob_id": "9c09309d23510aee4409a6d9021c2991afd2d349",
"index": 521,
"step-1": "<mask token>\n\n\ndef clearConsole():\n os.system('cls' if os.name == 'nt' else 'clear')\n\n\ndef main():\n checkArgs()\n rfile = open(sys.argv[1], 'r')\n wfile = open(output_name, 'w')\n parseAndStrip(rfile, wfile)\n... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@jit
def resolve():
N = int(input())
ans = 0
for n in range(1, N + 1):
for m in range(n, N + 1, n):
ans += m
print(ans)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserve... | flexible | {
"blob_id": "8d8df517ca5486e62cc1b5ac23bbcfa65ed9c1ff",
"index": 6611,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@jit\ndef resolve():\n N = int(input())\n ans = 0\n for n in range(1, N + 1):\n for m in range(n, N + 1, n):\n ans += m\n print(ans)\n\n\n<mask token>\n"... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(12):
col = []
for j in range(12):
col.append(float(input()))
v.append(col)
<|reserved_special_token_0|>
for i in range(1, 12):
for j in range(a):
s += v[i][j]
a += 1
if o == 'S':
... | flexible | {
"blob_id": "0df20722fba6223c9d4fc9f72bfb399b479db6ac",
"index": 7917,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(12):\n col = []\n for j in range(12):\n col.append(float(input()))\n v.append(col)\n<mask token>\nfor i in range(1, 12):\n for j in range(a):\n s ... | [
0,
1,
2,
3
] |
import user
# or from user import User
from post import Post
app_user_one = user.User("rr@gg.com", "Riks R", "ppp1", "student")
app_user_one.get_user_info()
app_user_one.change_status("in job market")
app_user_one.get_user_info()
app_user_two = user.User("z43@gg.com", "Bobby L", "zz1", "student")
app_user_two.get_us... | normal | {
"blob_id": "f59db28b669a41051cc6d0d4b8e14d1c7b0edd11",
"index": 2555,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp_user_one.get_user_info()\napp_user_one.change_status('in job market')\napp_user_one.get_user_info()\n<mask token>\napp_user_two.get_user_info()\n<mask token>\nnew_post.get_post_info()... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
print(sum([int(d) for d in str(pow(2, 1000))]))
| flexible | {
"blob_id": "fc0c8deb3a5a57934c9e707911c352af55100c3c",
"index": 3533,
"step-1": "<mask token>\n",
"step-2": "print(sum([int(d) for d in str(pow(2, 1000))]))\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
import random
def Fun_hiraganas():
hiraganas = ['a', 'i', 'u', 'e', 'o', 'ka', 'ki', 'ku', 'ke', 'ko', 'sa', 'shi', 'su', 'se',
'so', 'ta', 'chi', 'tsu', 'te', 'to', 'na', 'ni', 'nu', 'ne', 'no', 'ha', 'hi', 'fu', 'he', 'ho']
print("escriba el hiragana", hiraganas[random.randint(0, len(hiraganas)-1)])
print("Hell... | normal | {
"blob_id": "1fe7d5db1b47ba082301d07d010c6796fbd7edb7",
"index": 6859,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef Fun_hiraganas():\n hiraganas = ['a', 'i', 'u', 'e', 'o', 'ka', 'ki', 'ku', 'ke', 'ko',\n 'sa', 'shi', 'su', 'se', 'so', 'ta', 'chi', 'tsu', 'te', 'to', 'na',\n 'n... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class Message(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_1|... | flexible | {
"blob_id": "1476d4f488e6c55234a34dc5b6182e3b8ad4f702",
"index": 6201,
"step-1": "<mask token>\n\n\nclass Message(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Message(models.Mo... | [
1,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class Entity_list_user(db.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_t... | flexible | {
"blob_id": "6928ff58ddb97883a43dfd867ff9a89db72ae348",
"index": 6567,
"step-1": "<mask token>\n\n\nclass Entity_list_user(db.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 <mask token>... | [
3,
4,
5,
6,
7
] |
# module: order functionality
# HW2: complete this func
def process_option(food, option):
# print(food.keys())
food_name = list(food.keys())[option-1]
food_price = food[food_name]
print(food_price)
print("You have chosen: ", option, food_name, "!", " For unit price: ", food_price)
... | normal | {
"blob_id": "07bd3c7cacbf8d0e39d06b21456258ad92cb2294",
"index": 676,
"step-1": "<mask token>\n",
"step-2": "def process_option(food, option):\n food_name = list(food.keys())[option - 1]\n food_price = food[food_name]\n print(food_price)\n print('You have chosen: ', option, food_name, '!', ' For u... | [
0,
1,
2,
3,
4
] |
from flask import Flask, render_template, send_from_directory
from flask import request, send_file
from flask_cors import CORS
import os
import json
from crossdomain import crossdomain
import constants
import generation_tools
from music_theory import name_chords_in_tracks
import midi_tools
from client_logging import Cl... | normal | {
"blob_id": "471cab65aac29f5b47de0ffef8f032dbbadf8dd0",
"index": 1877,
"step-1": "<mask token>\n\n\ndef add_logs_to_response(response):\n response['logs'] = ClientLogger.get_logs()\n ClientLogger.clear_logs()\n return response\n\n\n@app.route('/generate/melody', methods=['POST', 'OPTIONS'])\n@crossdomai... | [
6,
8,
9,
11,
12
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def backup_cron():
if settings.DBBACKUP_STORAGE is not '':
management.call_command('dbbackup')
<|reserved_special_token_1|>
from django.core import management
from django.conf import settings
def backup_cron():
... | flexible | {
"blob_id": "ae9f1c4f70801dace0455c051ba4d4bfb7f3fe67",
"index": 4813,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef backup_cron():\n if settings.DBBACKUP_STORAGE is not '':\n management.call_command('dbbackup')\n",
"step-3": "from django.core import management\nfrom django.conf impo... | [
0,
1,
2
] |
from typing import Tuple
#Creating a trie structure and it's node
class TrieNode(object):
def __init__(self, char: str):
self.char = char
self.children = []
#the last character of the word.`
self.word_finished = False
#counter for this character
self.counter = 1
... | normal | {
"blob_id": "dcda8f26a06145579a9be6e5fbfdaed83d4908da",
"index": 2459,
"step-1": "<mask token>\n\n\nclass TrieNode(object):\n\n def __init__(self, char: str):\n self.char = char\n self.children = []\n self.word_finished = False\n self.counter = 1\n self.OccurrenceList = {}\n... | [
3,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(drugs)
<|reserved_special_token_0|>
for drug in drugs:
with open('/Users/sandeep.dey/Downloads/2020-02-06_scrape/%s' % drug
) as json_file:
for record in json.load(json_file):
output_records.a... | flexible | {
"blob_id": "e7f511b97f316157a768203afe9f36ea834ebb6c",
"index": 5493,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(drugs)\n<mask token>\nfor drug in drugs:\n with open('/Users/sandeep.dey/Downloads/2020-02-06_scrape/%s' % drug\n ) as json_file:\n for record in json.load(json_fil... | [
0,
1,
2,
3,
4
] |
import torch
import numpy as np
from torch.autograd import Variable
from util import helpers
from util.metrics import ECELoss, ece_score
import sklearn.metrics as skm
import os
import pandas as pd
import pickle
def eval(path_in, path_out, net, testloader, oodloader, use_cuda=True, save_dir=None):
f1 = open(path_... | normal | {
"blob_id": "edd2b7b453d7fa33e6cca3b5dbc895f034a9e22a",
"index": 2746,
"step-1": "<mask token>\n\n\ndef eval_cifar10(path_in, path_out, net, testloader, oodloader, use_cuda=\n True, save_dir=None):\n f1 = open(path_in, 'w')\n f2 = open(path_out, 'w')\n ece_criterion = ECELoss().cuda()\n net.eval()... | [
1,
2,
3,
4,
5
] |
# -*- python -*-
# ex: set syntax=python:
# Copyright (c) 2012 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
# See master.experimental/slaves.cfg for documentation.
slaves = [
#########################################... | normal | {
"blob_id": "e807cef534226f3efb4a8df471598727fa068f02",
"index": 3805,
"step-1": "<mask token>\n",
"step-2": "slaves = []\n",
"step-3": "# -*- python -*-\n# ex: set syntax=python:\n\n# Copyright (c) 2012 The Chromium Authors. All rights reserved.\n# Use of this source code is governed by a BSD-style license ... | [
0,
1,
2
] |
# Generated by Django 2.2.1 on 2019-05-23 14:07
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('presentes', '0015_caso_lugar_del_hecho'),
]
operations = [
migrations.AddField(
model_name='organizacion',
name='des... | normal | {
"blob_id": "5cd767564e8a261561e141abeebb5221cb3ef2c2",
"index": 6919,
"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 = [('presentes',... | [
0,
1,
2,
3,
4
] |
"""Write a program that asks the user to enter a word and then
capitalizes every other letter of that word. So if the user enters "rhinoceros",
the program should print "rHiNoCeRoS"""
word=str(input("please enter the word\n"))
count=0
for char in word:
if count==0:
print(char.upper(),end="")
count=1
... | normal | {
"blob_id": "bc837d95ef22bd376f8b095e7aeb1f7d15c0e22e",
"index": 941,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor char in word:\n if count == 0:\n print(char.upper(), end='')\n count = 1\n else:\n print(char.lower(), end='')\n count = 0\n",
"step-3": "<mask toke... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class TestAccountRequests(TestCase):
def setUp(self):
self.client = Client()
self.superuser = Account.objects.create_superuser(**account)
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
c... | flexible | {
"blob_id": "3d43bf0d0ca1df06b3647a33f88cee067eeff9f4",
"index": 2605,
"step-1": "<mask token>\n\n\nclass TestAccountRequests(TestCase):\n\n def setUp(self):\n self.client = Client()\n self.superuser = Account.objects.create_superuser(**account)\n <mask token>\n <mask token>\n",
"step-2"... | [
2,
3,
4,
5,
6
] |
#(C)Inspire Search 2020/5/31 Coded by Tsubasa Kato (@_stingraze)
#Last edited on 2020/6/1 11:36AM JST
import sys
import spacy
import re
#gets query from argv[1]
text = sys.argv[1]
nlp = spacy.load('en_core_web_sm')
doc = nlp(text)
ahref = "<a href=\""
ahref2 = "\"\>"
#arrays for storing subject and object types
sub... | normal | {
"blob_id": "ecc001394c1f3bba78559cba7eeb216dd3a942d8",
"index": 4711,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor d in doc:\n word = d.text\n pos = d.pos_\n dep = d.dep_\n if re.search('subj', dep):\n word2 = (ahref + 'http://www.superai.online/solr/search.php?query=' +\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(n):
x = rng.integers(low=0, high=1920)
y = rng.integers(low=0, high=1080)
if i == 0:
flock.append(Boid(x, y, width, height, infected=True, curado=False,
alive=True))
else:
... | flexible | {
"blob_id": "78c4e14e5afdf857082b60bf4020f0f785d93a0d",
"index": 9704,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(n):\n x = rng.integers(low=0, high=1920)\n y = rng.integers(low=0, high=1080)\n if i == 0:\n flock.append(Boid(x, y, width, height, infected=True, curado=Fa... | [
0,
3,
4,
5,
6
] |
import re
def find_all_links(text):
result = []
iterator = re.finditer(r"https?\:\/\/(www)?\.?\w+\.\w+", text)
for match in iterator:
result.append(match.group())
return result | normal | {
"blob_id": "b8c7aa5ff7387eacb45d996fa47186d193b44782",
"index": 4823,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef find_all_links(text):\n result = []\n iterator = re.finditer('https?\\\\:\\\\/\\\\/(www)?\\\\.?\\\\w+\\\\.\\\\w+', text)\n for match in iterator:\n result.append(m... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
"""The app module, containing the app factory function."""
from flask import Flask, render_template
from flask_cors import CORS
from flask_misaka import Misaka
from flask_mailman import Mail
from flask_talisman import Talisman
from werkzeug.middleware.proxy_fix import ProxyFix
from micawber.pro... | normal | {
"blob_id": "2257f73a290dfd428a874e963c26e51f1c1f1efa",
"index": 927,
"step-1": "<mask token>\n\n\ndef register_extensions(app):\n \"\"\"Register Flask extensions.\"\"\"\n assets.init_app(app)\n hashing.init_app(app)\n cache.init_app(app)\n db.init_app(app)\n login_manager.init_app(app)\n mi... | [
5,
9,
10,
12,
14
] |
<|reserved_special_token_0|>
class TCRPowerCalculator:
<|reserved_special_token_0|>
def predict_detection_probability_2step(self, tcr_frequency, num_reads,
num_cells, detect_thresh=1):
"""
2-step detection probability model where
1) Num_cells_TCR is sampled first from the blood (Poiss... | flexible | {
"blob_id": "d327151c9659078e12e4aca46631de33e7ca4dcf",
"index": 167,
"step-1": "<mask token>\n\n\nclass TCRPowerCalculator:\n <mask token>\n\n def predict_detection_probability_2step(self, tcr_frequency, num_reads,\n num_cells, detect_thresh=1):\n \"\"\"\n\t\t2-step detection probability mod... | [
3,
4,
5,
6,
7
] |
"""
Simulator contains the tools needed to set up a multilayer antireflection
coating simulation.
Based on transfer matrix method outlined in Hou, H.S. 1974.
"""
# Author: Andrew Nadolski (with lots of help from previous work by Colin Merkel,
# Steve Byrnes, and Aritoki Suzuki)
# Filename: simulator.py
impo... | normal | {
"blob_id": "a2292bc9cee57c5d4a7d36c66510ce4b4f3e20da",
"index": 3687,
"step-1": "<mask token>\n\n\nclass SubstrateLayer(Layer):\n <mask token>\n <mask token>\n\n def __repr__(self):\n return '{} (substrate)'.format(self.name)\n\n\nclass TerminatorLayer(Layer):\n \"\"\"A special case of ``Laye... | [
45,
51,
53,
54,
60
] |
import requests
import json
r = requests.get('http://pythonspot.com/')
jsondata = str(r.headers).replace("'", '"')
print(jsondata)
#headerObj = json.loads(jsondata)
#ERROR >> json.decoder.JSONDecodeError: Expecting ',' delimiter: line 1 column 556 (char 555)
#print(headerObj)["server"]
#print(headerObj)['content-leng... | normal | {
"blob_id": "7404dd324d54bb072e56985716bbae746b4dd219",
"index": 1395,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(jsondata)\n",
"step-3": "<mask token>\nr = requests.get('http://pythonspot.com/')\njsondata = str(r.headers).replace(\"'\", '\"')\nprint(jsondata)\n",
"step-4": "import requests... | [
0,
1,
2,
3,
4
] |
def fibonacci(n):
'''returns the nth number of the Fibonacci
sequence. where the first position is indexed at 0.
n must be an iteger greater than or equal to 0'''
#these are the first two numbers in the sequence.
fib = [0,1]
#If the users enters a number less than 2 then just get that number fr... | normal | {
"blob_id": "ca75e23d91eef8a5c5b78c0ea7c903b80640af25",
"index": 7957,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef sum_series(n, x=0, y=1):\n \"\"\"sum_series returns the nth number of the Fibonacci, the Lucas sequence\n or the Foo sequence where the first position is indexed at 0. ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def times2(x):
return x * 2
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(df.head())
<|reserved_special_token_0|>
print('========================')
print(newdf)
def times2(x):
return x * 2
print('========================')
pr... | flexible | {
"blob_id": "422a4945ebf453d3e09e9e7e76dd32b30488680e",
"index": 3011,
"step-1": "<mask token>\n\n\ndef times2(x):\n return x * 2\n\n\n<mask token>\n",
"step-2": "<mask token>\nprint(df.head())\n<mask token>\nprint('========================')\nprint(newdf)\n\n\ndef times2(x):\n return x * 2\n\n\nprint('=... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def min_hacks(d, p):
shots = [0]
damage = 0
for c in p:
if c == 'S':
shots[-1] += 1
damage += 2 ** (len(shots) - 1)
else:
shots.append(0)
hacks = 0
while damage > d:
hacked = Fals... | flexible | {
"blob_id": "607700faebc2018327d66939419cc24a563c3900",
"index": 6515,
"step-1": "<mask token>\n",
"step-2": "def min_hacks(d, p):\n shots = [0]\n damage = 0\n for c in p:\n if c == 'S':\n shots[-1] += 1\n damage += 2 ** (len(shots) - 1)\n else:\n shots.a... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Resource:
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Resource:
def __init__(self, row: tuple):
self.video_path = row[0]
self.pic_path = row[1]... | flexible | {
"blob_id": "65aa27addaec6014fe5fd66df2c0d3632231a314",
"index": 3124,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Resource:\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Resource:\n\n def __init__(self, row: tuple):\n self.video_path = row[0]\n self.pic_path = ... | [
0,
1,
2,
3,
4
] |
import unittest
import sys
from tests.jep_pipe import jep_pipe
from tests.jep_pipe import build_java_process_cmd
import jep
@unittest.skipIf(sys.platform.startswith("win"), "subprocess complications on Windows")
class TestSharedModules(unittest.TestCase):
def setUp(self):
pass
def test_shared_module... | normal | {
"blob_id": "39bc90f34cccebe9a8b1475e396caa1c14f6b2df",
"index": 9004,
"step-1": "<mask token>\n\n\n@unittest.skipIf(sys.platform.startswith('win'),\n 'subprocess complications on Windows')\nclass TestSharedModules(unittest.TestCase):\n\n def setUp(self):\n pass\n\n def test_shared_modules(self):... | [
3,
4,
5,
6,
7
] |
# -*- coding: utf-8 -*-
# DATE 2018-08-21
# AUTHER = tongzz
#
import MySQLdb
from Elements.LoginElements import *
import datetime
import sys
class Tradepasswd():
def __init__(self):
self.db_config={
'host': '172.28.38.59',
'usr': 'mysqladmin',
'passwd': '12... | normal | {
"blob_id": "ed66e8028d653cf6b7ea4703fef5a658665c48db",
"index": 1034,
"step-1": "# -*- coding: utf-8 -*-\r\n# DATE 2018-08-21\r\n# AUTHER = tongzz\r\n#\r\n\r\nimport MySQLdb\r\nfrom Elements.LoginElements import *\r\nimport datetime\r\nimport sys\r\nclass Tradepasswd():\r\n def __init__(self):\r\n sel... | [
0
] |
<|reserved_special_token_0|>
def home(request):
return render(request, 'home.html')
<|reserved_special_token_0|>
def docs(request):
return render(request, 'docs.html')
<|reserved_special_token_0|>
def publications(request):
return render(request, 'publications.html')
<|reserved_special_token_0|>... | flexible | {
"blob_id": "f7a493ab8e9845d0e9da33a0ee45d7c3ef66deb5",
"index": 7507,
"step-1": "<mask token>\n\n\ndef home(request):\n return render(request, 'home.html')\n\n\n<mask token>\n\n\ndef docs(request):\n return render(request, 'docs.html')\n\n\n<mask token>\n\n\ndef publications(request):\n return render(r... | [
3,
4,
5,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from Get2Gether.api_routes.schedule import schedule_router
from Get2Gether.api_routes.auth import auth_router
from Get2Gether.api_routes.event import event_router
| flexible | {
"blob_id": "cd9d10a3ee3956762d88e76a951023dd77023942",
"index": 6411,
"step-1": "<mask token>\n",
"step-2": "from Get2Gether.api_routes.schedule import schedule_router\nfrom Get2Gether.api_routes.auth import auth_router\nfrom Get2Gether.api_routes.event import event_router\n",
"step-3": null,
"step-4": nu... | [
0,
1
] |
from MultisizerReader import MultiSizerReader
import os
import matplotlib.pyplot as plt
#Get all spread sheet files in fodler and create multisizer files for each
folder = "./Data_Organised/DilutionTestingLowOD"
allFiles = os.listdir(folder)
multiSizerFiles = [allFiles[i] for i in range(len(allFiles)) if allFiles[i].e... | normal | {
"blob_id": "2f0aa1f294f34a4f3ffb47c15ab74fc792765f10",
"index": 9195,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor files in multiSizerFiles:\n data.append(MultiSizerReader(path=os.path.join(folder, files)))\n<mask token>\nfor d in data:\n OD = d.name.split('_')[4] + '.' + d.name.split('_')[5... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
from __future__ import print_function, unicode_literals
from eight import *
from whoosh.fields import TEXT, ID, Schema
bw2_schema = Schema(
name=TEXT(stored=True, sortable=True),
comment=TEXT(stored=True),
product=TEXT(stored=True, sortable=True),
categories=TEXT(stored=True),
... | normal | {
"blob_id": "07aafcb3db9c57ad09a29a827d72744ef0d22247",
"index": 3319,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nbw2_schema = Schema(name=TEXT(stored=True, sortable=True), comment=TEXT(\n stored=True), product=TEXT(stored=True, sortable=True), categories=TEXT\n (stored=True), location=TEXT(sto... | [
0,
1,
2,
3
] |
x = int(input("Enter number:"))
y = x/2
print(y)
for i in
| normal | {
"blob_id": "79c6b7c3d23248f249b55af1d097a66a78a2c22f",
"index": 9164,
"step-1": "x = int(input(\"Enter number:\"))\ny = x/2\nprint(y)\n\nfor i in \n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
# -*- coding: utf-8 -*-
"""
A customised logger for this project for logging to the file and console
Created on 29/07/2022
@author: PNimbhore
"""
# imports
import os
import logging
class Logger:
"""
A custom logger which will take care
of logging to console and file.
"""
def __init__(self, filepat... | normal | {
"blob_id": "45d57f8392b89776f9349c32b4bb2fa71a4aaa83",
"index": 8610,
"step-1": "<mask token>\n\n\nclass Logger:\n <mask token>\n\n def __init__(self, filepath):\n \"\"\"\n Constructor\n :param filepath:\n \"\"\"\n self.filepath = filepath\n self.logger = logging.... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for line in f:
line = line.rstrip('\n')
line = line.replace('[', '')
splitted = line.split(']')
stringTime = splitted[0]
stringTask = splitted[1]
datetimeTime = datetime.strptime(stringTime, '%Y-%m-%d %H:%M... | flexible | {
"blob_id": "293533d07b530be9e8f97f1720619bf6c3113cca",
"index": 9447,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor line in f:\n line = line.rstrip('\\n')\n line = line.replace('[', '')\n splitted = line.split(']')\n stringTime = splitted[0]\n stringTask = splitted[1]\n datetimeTi... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class TestFabric(unittest.TestCase):
def setUp(self):
env.test_home = os.path.join(env.localroot, 'deploy', 'test')
user_config = yaml.load(open(os.path.join(env.localroot, 'deploy',
'test', 'machines_user.yml')))
env.update(user_config['default'])... | flexible | {
"blob_id": "7700e3c4061f0e81a1dea8fa8b27a0380fc26e71",
"index": 7171,
"step-1": "<mask token>\n\n\nclass TestFabric(unittest.TestCase):\n\n def setUp(self):\n env.test_home = os.path.join(env.localroot, 'deploy', 'test')\n user_config = yaml.load(open(os.path.join(env.localroot, 'deploy',\n ... | [
12,
14,
16,
19,
20
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
capture.release()
cv2.destroyAllWindows()
<|reserved_special_token_0|>
if len(faces) >= 1:
sys.stdout.write('1')
else:
sys.stdout.write('0')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
face_cascade = cv2.C... | flexible | {
"blob_id": "4d707e23f66e8b6bea05a5901d3d8e459247c6c1",
"index": 3840,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncapture.release()\ncv2.destroyAllWindows()\n<mask token>\nif len(faces) >= 1:\n sys.stdout.write('1')\nelse:\n sys.stdout.write('0')\n",
"step-3": "<mask token>\nface_cascade = cv... | [
0,
1,
2,
3,
4
] |
n,k = map(int,raw_input().split())
nums = list(map(int,raw_input().split()))
if k==1:
print min(nums)
elif k==2:
print max(nums[0],nums[-1])
else:
print max(nums)
| normal | {
"blob_id": "041a5bf205c1b3b3029623aa93835e99104464b2",
"index": 2361,
"step-1": "n,k = map(int,raw_input().split())\nnums = list(map(int,raw_input().split()))\nif k==1:\n print min(nums)\nelif k==2:\n print max(nums[0],nums[-1])\nelse:\n print max(nums)\n",
"step-2": null,
"step-3": null,
"step-4": nul... | [
0
] |
<|reserved_special_token_0|>
class Euler(BaseEuler):
def solve(self):
fp = path.join(getcwd(), 'euler/resources/names.txt')
with open(fp, 'r') as f:
names = sorted([name for name in f.read().replace('"', '').
split(',')])
return sum([get_name_score(names, name)... | flexible | {
"blob_id": "40d08bfa3286aa30b612ed83b5e9c7a29e9de809",
"index": 6540,
"step-1": "<mask token>\n\n\nclass Euler(BaseEuler):\n\n def solve(self):\n fp = path.join(getcwd(), 'euler/resources/names.txt')\n with open(fp, 'r') as f:\n names = sorted([name for name in f.read().replace('\"',... | [
3,
4,
5,
6,
7
] |
# Example 15-5. Using a BookDict, but not quite as intended
>>> from books import BookDict
>>> pp = BookDict(title='Programming Pearls',
... authors='Jon Bentley',
... isbn='0201657880',
... pagecount=256)
>>> pp
{'title': 'Programming Pearls', 'authors': 'Jon Bentley', 'isbn'... | normal | {
"blob_id": "ab9d8e36518c4d42f1e29fbc5552078a5a338508",
"index": 7010,
"step-1": "# Example 15-5. Using a BookDict, but not quite as intended\n\n>>> from books import BookDict\n>>> pp = BookDict(title='Programming Pearls',\n... authors='Jon Bentley',\n... isbn='0201657880',\n... ... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def main():
a = int(input(
'Informe a sua opção (Aleatório = 0, Você escolhe = outro numero): '))
if a != 0:
linhas = int(input('Informe o número de linhas da matriz: '))
colunas = int(input('Info... | flexible | {
"blob_id": "c80ecb97c8863b724169715b766024ce824b9225",
"index": 5572,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n a = int(input(\n 'Informe a sua opção (Aleatório = 0, Você escolhe = outro numero): '))\n if a != 0:\n linhas = int(input('Informe o número de linhas... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(studentMarks[3])
studentMarks.append(95)
for i in studentMarks:
print(i)
<|reserved_special_token_0|>
while i < len(studentMarks):
print(studentMarks[i])
i += 1
<|reserved_special_token_1|>
<|reserved_special_... | flexible | {
"blob_id": "d442d5c7afd32dd149bb47fc9c4355409c53dab8",
"index": 6719,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(studentMarks[3])\nstudentMarks.append(95)\nfor i in studentMarks:\n print(i)\n<mask token>\nwhile i < len(studentMarks):\n print(studentMarks[i])\n i += 1\n",
"step-3": "... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def drawing_plt():
thisImg = os.listdir(caltech_dir)
row = 4
cols = int(math.ceil(len(thisImg) / 4))
fig = plt.figure()
i = 1
for image in glob.glob('C:/cnnTest/*.jpg'):
img = cv2.imread(image)
subplot = fig.add_subplot(row, cols, i)
subplot... | flexible | {
"blob_id": "1255a9df2fbe11d92991f3f0f7054b92cb017628",
"index": 2941,
"step-1": "<mask token>\n\n\ndef drawing_plt():\n thisImg = os.listdir(caltech_dir)\n row = 4\n cols = int(math.ceil(len(thisImg) / 4))\n fig = plt.figure()\n i = 1\n for image in glob.glob('C:/cnnTest/*.jpg'):\n img ... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while i == 0:
try:
print("Let's divide some numbers!")
a1 = input('Enter numerator: ')
b1 = input('Enter denominator: ')
a = int(a1)
b = int(b1)
print(a1 + ' divied by ' + b1 + '... | flexible | {
"blob_id": "dcc1b0decf2fca6309dbb60faebd3f0a6944cd7d",
"index": 9130,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile i == 0:\n try:\n print(\"Let's divide some numbers!\")\n a1 = input('Enter numerator: ')\n b1 = input('Enter denominator: ')\n a = int(a1)\n b ... | [
0,
1,
2,
3
] |
"""empty message
Revision ID: 6374505f9e6e
Revises: 9dc91bb7d2ba
Create Date: 2016-11-14 10:55:08.418923
"""
# revision identifiers, used by Alembic.
revision = '6374505f9e6e'
down_revision = '9dc91bb7d2ba'
from alembic import op
import sqlalchemy as sa
import sqlalchemy.types as ty
def upgrade():
### command... | normal | {
"blob_id": "7badb7c9f1e00dfc379468b7bd73a3f09bffe6de",
"index": 1191,
"step-1": "<mask token>\n\n\ndef downgrade():\n op.alter_column('run', 'polarion_id', type_=ty.String(1024))\n op.alter_column('auto_result', 'skip', type_=ty.String(65535))\n op.alter_column('auto_result', 'failure', type_=ty.String... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class TestDataEmptyArray(object):
@staticmethod
def get_array():
return []
class TestDataUniqueValues(object):
@staticmethod
def get_array():
return [5, 3, 2]
@staticmethod
def get_expected_result():
return 2
class TestDataExactlyTwoD... | flexible | {
"blob_id": "8fdc9a52b00686e10c97fa61e43ddbbccb64741b",
"index": 8946,
"step-1": "<mask token>\n\n\nclass TestDataEmptyArray(object):\n\n @staticmethod\n def get_array():\n return []\n\n\nclass TestDataUniqueValues(object):\n\n @staticmethod\n def get_array():\n return [5, 3, 2]\n\n ... | [
9,
10,
11,
13,
14
] |
"Unit tests for reverse URL lookup"
from django.core.urlresolvers import reverse_helper, NoReverseMatch
import re, unittest
test_data = (
('^places/(\d+)/$', 'places/3/', [3], {}),
('^places/(\d+)/$', 'places/3/', ['3'], {}),
('^places/(\d+)/$', NoReverseMatch, ['a'], {}),
('^places/(\d+)/$', NoRevers... | normal | {
"blob_id": "b7ccb41c43a0db6f1bf9e6ba5cef1b9b1417e297",
"index": 633,
"step-1": "\"Unit tests for reverse URL lookup\"\n\nfrom django.core.urlresolvers import reverse_helper, NoReverseMatch\nimport re, unittest\n\ntest_data = (\n ('^places/(\\d+)/$', 'places/3/', [3], {}),\n ('^places/(\\d+)/$', 'places/3/... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--header-mutate-level', type=int, choices=range(11
), nargs='?', help=
'Set the mutation level for the headers (0-10). Defa... | flexible | {
"blob_id": "350a79d6cead6814ad48292b14a204e753dc938c",
"index": 4363,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--header-mutate-level', type=int, choices=range(11\n ), nargs='?', help=\n 'Set ... | [
0,
1,
2,
3,
4
] |
import numpy as np
import cv2
FRAME_WIDTH = 320
FRAME_HEIGHT = 240
cv2.namedWindow('Measure Angle with centerline')
# WebCam Initialize
vidCapture = cv2.VideoCapture(1)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('webcam_record.avi', fourcc, 20.0, (640, 480))
while True:
# ke... | normal | {
"blob_id": "500d6f473f07b35bf2d075d3061ac2e54eab702a",
"index": 4156,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncv2.namedWindow('Measure Angle with centerline')\n<mask token>\nwhile True:\n ret, frame = vidCapture.read()\n if ret == True:\n out.write(frame)\n cv2.imshow('frame',... | [
0,
1,
2,
3,
4
] |
import urllib.request
import urllib.parse
import json
content = input("请输入需要翻译的内容：")
url = 'http://fanyi.youdao.com/translate?smartresult=dict&smartresult=rule'
data = {}
data['action'] = 'FY_BY_CLICKBUTTION'
data['bv'] = '1ca13a5465c2ab126e616ee8d6720cc3'
data['client'] = 'fanyideskweb'
data['doctype'] = 'json'
dat... | normal | {
"blob_id": "e01b1f57a572571619d6c0981370030dc6105fd2",
"index": 8636,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('翻译结果：%s' % target['translateResult'][0][0]['tgt'])\n",
"step-3": "<mask token>\ncontent = input('请输入需要翻译的内容：')\nurl = 'http://fanyi.youdao.com/translate?smartresult=dict&smartres... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 4 20:28:44 2019
@author: nicholustintzaw
"""
####################################################################################################
####################################################################################################... | normal | {
"blob_id": "5a2716fc7b4c0a56fbd0de5d45d71fb33320adf0",
"index": 2889,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nos.chdir(masterdir)\nexec(open('01_newqs_directory.py', 'r', encoding='utf8').read())\nexec(open('02_new_register.py', 'r', encoding='utf8').read())\nexec(open('03_moved_in.py', 'r', enco... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class TestDocument(unittest.TestCase):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def test_get_texts(self):
texts = self.doc.get_texts()
self.assertEqual(2, len(texts))
def test_get_term_data(self):
term_data = self.doc.get_term_dat... | flexible | {
"blob_id": "f86d01c4b980ac44dcdb1b0008493e1dbda25971",
"index": 4544,
"step-1": "<mask token>\n\n\nclass TestDocument(unittest.TestCase):\n <mask token>\n <mask token>\n\n def test_get_texts(self):\n texts = self.doc.get_texts()\n self.assertEqual(2, len(texts))\n\n def test_get_term_d... | [
3,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
"""
Package for haasplugin.
"""
| flexible | {
"blob_id": "20518302b6a67f8f1ac01f1adf4fe06ab2eaf280",
"index": 3098,
"step-1": "<mask token>\n",
"step-2": "\"\"\"\nPackage for haasplugin.\n\"\"\"\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
'''Lab01 ex4
E/16/319 Rathnayake R.P.V.N'''
from dataclasses import asdict
from json import dumps
from dataclasses import dataclass
from typing import List, Dict
import json
import ex1 #import the ex1 to get the lord_course_registraion function
s1=ex1.load_course_registrations("data.txt") #lord the list of Student ... | normal | {
"blob_id": "8a5ade450485f9114fa91c00c7588535ccbaf0e6",
"index": 1923,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('student_registrations.json', 'w') as f:\n f.write(e)\n",
"step-3": "<mask token>\ns1 = ex1.load_course_registrations('data.txt')\ns1 = map(asdict, s1)\ne = json.dumps(list... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from .net import *
| flexible | {
"blob_id": "73337246bd54df53842360510148f3a6f4763ace",
"index": 6251,
"step-1": "<mask token>\n",
"step-2": "from .net import *\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from packer.utils import hello_world
| flexible | {
"blob_id": "d549303228e860ae278a5a9497a4a3a68989aeca",
"index": 6097,
"step-1": "<mask token>\n",
"step-2": "from packer.utils import hello_world\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class YumiConstants:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|res... | flexible | {
"blob_id": "34c81b9318d978305748d413c869a86ee6709e2c",
"index": 996,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass YumiConstants:\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... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for letter in input_string:
if letter in input_string:
output_string += ALPHABET[(ALPHABET.index(letter) + key) % 26]
else:
output_string += letter
print(f'Encoded String is {output_string}')
<|reserved_s... | flexible | {
"blob_id": "b2db622596d0dff970e44759d25360a62f5fea83",
"index": 4725,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor letter in input_string:\n if letter in input_string:\n output_string += ALPHABET[(ALPHABET.index(letter) + key) % 26]\n else:\n output_string += letter\nprint(f'En... | [
0,
1,
2,
3
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.