code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
from __future__ import print_function
from __future__ import absolute_import
#
# LinkedIn Sales Module
#
import requests
from bs4 import BeautifulSoup
import logging
from plugins.base import PageGrabber
from plugins.colors import BodyColors as bc
import json
try:
import __builtin__ as bi
except:
import builtins... | normal | {
"blob_id": "570e0d46aa1ea88d1784447e8f693199e3c3b6ad",
"index": 9488,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass LinkedInGrabber(PageGrabber):\n\n def get_info(self, email):\n client = requests.Session()\n print('[' + bc.CPRP + '?' + bc.CEND + '] ' + bc.CCYN + 'LinkedIn' +... | [
0,
2,
3,
4,
5
] |
#!/usr/bin/env python
# coding: utf-8
import sys
sys.path.insert(0, "/code/huggingface/transformers-fair-wmt/src")
import logging
logging.disable(logging.INFO) # disable INFO and DEBUG logger everywhere
from transformers.tokenization_fsmt import FSMTTokenizer
from transformers.modeling_fsmt import FSMTForConditional... | normal | {
"blob_id": "7864138459caf469a0148420718b2282598141de",
"index": 6674,
"step-1": "<mask token>\n\n\ndef translate(src, tgt, text):\n mname = f'stas/wmt19-{src}-{tgt}'\n tokenizer = FSMTTokenizer.from_pretrained(mname)\n model = FSMTForConditionalGeneration.from_pretrained(mname)\n encoded = tokenizer... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class BuildDataset(data.Dataset):
<|reserved_special_token_0|>
def __init__(self, imgs_path, labels, extra_info=None, transform=None):
"""
The constructor gets the images path and their respectively labels and extra information (if it exists).
In addition,... | flexible | {
"blob_id": "4e31c2a80bec77a1f5aafc8a91617fb4b2941788",
"index": 432,
"step-1": "<mask token>\n\n\nclass BuildDataset(data.Dataset):\n <mask token>\n\n def __init__(self, imgs_path, labels, extra_info=None, transform=None):\n \"\"\"\n The constructor gets the images path and their respectivel... | [
3,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class trinet(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def generate_image_left(self, img, disp):
return bilinear_sampler_1d_h(img, -disp)
def generate_image_right(self, img... | flexible | {
"blob_id": "fbd8af4ab3e4ebdcb07509db776d38f9c26fd06a",
"index": 9446,
"step-1": "<mask token>\n\n\nclass trinet(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def generate_image_left(self, img, disp):\n return bilinear_sampler_1d_h(img, -disp)\n\n def generate_... | [
8,
14,
15,
17,
18
] |
from django.views.generic import ListView
class ExperimentList(ListView):
pass
| normal | {
"blob_id": "10990282c8aa0b9b26a69e451132ff37257acbc6",
"index": 3331,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ExperimentList(ListView):\n pass\n",
"step-3": "from django.views.generic import ListView\n\n\nclass ExperimentList(ListView):\n pass\n",
"step-4": null,
"step-5": n... | [
0,
1,
2
] |
import xlrd
from django.shortcuts import redirect
from django.contrib import messages
from django.utils.translation import ugettext_lazy as _
from django.core import validators
from utils.views import render_to
from accounts.models import Account
from .models import ExternalSubscriber
from .forms import ExternalSubsc... | normal | {
"blob_id": "2ec41e02c95a270455c096e85829b7220eeda0c7",
"index": 1317,
"step-1": "<mask token>\n\n\ndef validate_email(value, row_number):\n error_message = _(u'Invalid e-mail address on \"%d\" line.')\n return validators.EmailValidator(validators.email_re, unicode(\n error_message % row_number), 'i... | [
2,
3,
4,
5,
6
] |
a = ['a', 'b', 'c', 'd', 'e']
print(';'.join(a))
| normal | {
"blob_id": "a10403d7809b97c1bcdfa73224b8c365519cc456",
"index": 7275,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(';'.join(a))\n",
"step-3": "a = ['a', 'b', 'c', 'd', 'e']\nprint(';'.join(a))\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
import sys
def main():
lines = [line.strip() for line in sys.stdin.readlines()]
h = lines.index("")
w = len(lines[0].split()[0])
start = 0
grids = set()
while start < len(lines):
grid = tuple(x.split()[0] for x in lines[start:start + h])
if len(grid) == h:
grids.add(grid)
start += h + 1
... | normal | {
"blob_id": "6ef8a174dcce633b526ce7d6fdb6ceb11089b177",
"index": 3652,
"step-1": "import sys\n\ndef main():\n lines = [line.strip() for line in sys.stdin.readlines()]\n h = lines.index(\"\")\n w = len(lines[0].split()[0])\n start = 0\n grids = set()\n while start < len(lines):\n grid = tuple(x.split()[0... | [
0
] |
<|reserved_special_token_0|>
class AtomExtensionGrammar(extension.ExtensionGrammar):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class AtomExtensionGrammar(extension.ExtensionGrammar):
<|reserved_... | flexible | {
"blob_id": "ac5c6a534d5131438d9590b070e6b392d4ebed0c",
"index": 9764,
"step-1": "<mask token>\n\n\nclass AtomExtensionGrammar(extension.ExtensionGrammar):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass AtomExtensionGrammar(extension.ExtensionGrammar):\n <mask t... | [
1,
2,
3,
4
] |
import numpy as np
import cPickle as pkl
data_l = []
data_path = "/home/marc/data/"
with open(data_path+'covtype.data') as fp:
for line in fp:
tmp_l = [ int(elem) for elem in line.split(',') ]
data_l.append(tmp_l)
data = np.array(data_l)
np.random.shuffle(data)
quintil = data.shape[0]/5
train_x = data[:qu... | normal | {
"blob_id": "c8975306473dda49be6c5f19f6663214ec7e7105",
"index": 7655,
"step-1": "import numpy as np\nimport cPickle as pkl\n\n\n\ndata_l = []\ndata_path = \"/home/marc/data/\"\nwith open(data_path+'covtype.data') as fp:\n for line in fp:\n\t\ttmp_l = [ int(elem) for elem in line.split(',') ]\n\t\tdata_l.appe... | [
0
] |
<|reserved_special_token_0|>
class TopoPlot(object):
<|reserved_special_token_0|>
def __init__(self, data=None, axes=None):
"""Setup defaults.
Parameters
----------
data : Pandas.Series or dict
Pandas Series with values indexed by electrodes.
axes : matplo... | flexible | {
"blob_id": "5bd7160b6b2e283e221aeb0a6913e6d13511c1db",
"index": 7073,
"step-1": "<mask token>\n\n\nclass TopoPlot(object):\n <mask token>\n\n def __init__(self, data=None, axes=None):\n \"\"\"Setup defaults.\n\n Parameters\n ----------\n data : Pandas.Series or dict\n ... | [
19,
22,
23,
25,
30
] |
def quick_sort(arr):
q_sort(arr, 0, len(arr) - 1)
def q_sort(arr, left, right):
if left < right:
pivot_index = partition(arr, left, right)
q_sort(arr, left, pivot_index - 1)
q_sort(arr, pivot_index + 1, right)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def quick_sor... | flexible | {
"blob_id": "09a5c96b7f496aca6b34d7f0a83d5b1e182ca409",
"index": 1627,
"step-1": "def quick_sort(arr):\n q_sort(arr, 0, len(arr) - 1)\n\n\ndef q_sort(arr, left, right):\n if left < right:\n pivot_index = partition(arr, left, right)\n q_sort(arr, left, pivot_index - 1)\n q_sort(arr, piv... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def change_label():
var.set(random.choice(ch))
<|reserved_special_token_0|>
def slove():
expr.set(eval(expr.get()))
<|reserved_special_token_0|>
def clear():
expr.set('')
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
var.se... | flexible | {
"blob_id": "33938a28aad29e996255827825a0cdb1db6b70b7",
"index": 5842,
"step-1": "<mask token>\n\n\ndef change_label():\n var.set(random.choice(ch))\n\n\n<mask token>\n\n\ndef slove():\n expr.set(eval(expr.get()))\n\n\n<mask token>\n\n\ndef clear():\n expr.set('')\n\n\n<mask token>\n",
"step-2": "<mas... | [
3,
4,
5,
6,
7
] |
numero_uno=int(input("ingresa el primer numero "))
numero_dos=int(input("ingresa el segundo numero "))
print(numero_uno)
print(numero_dos)
total=numero_uno +numero_dos
print("el total de la suma de : "+str(numero_uno)+" + "+str(numero_dos)+" es = a "+str(total)) | normal | {
"blob_id": "5685befae923fc336a2a5e0eb5e382c2e7d82d04",
"index": 9613,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(numero_uno)\nprint(numero_dos)\n<mask token>\nprint('el total de la suma de : ' + str(numero_uno) + ' + ' + str(\n numero_dos) + ' es = a ' + str(total))\n",
"step-3": "numero... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
_base_ = '../model.py'
model = dict(type='ImageClassifier', task='classification', pretrained=None,
backbone=dict(), head=dict(in_channels=-1, loss=dict(type=
'CrossEntropyLoss', loss_weight=1.0), topk=(1, 5)))
checkpoint_config = dict(type='Checkpoin... | flexible | {
"blob_id": "8bd5eff12e68f7145676f5e089b51376a82ab489",
"index": 3231,
"step-1": "<mask token>\n",
"step-2": "_base_ = '../model.py'\nmodel = dict(type='ImageClassifier', task='classification', pretrained=None,\n backbone=dict(), head=dict(in_channels=-1, loss=dict(type=\n 'CrossEntropyLoss', loss_weight... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
DATABASES = {'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME':
':memory:'}}
INSTALLED_APPS = ('django.contrib.auth', 'django.contrib.admin',
'django.contrib.sessions', 'django.contrib.contenttypes',
'django.c... | flexible | {
"blob_id": "34ecf2bd9bc72a98aba4584880a198dd24899dbe",
"index": 6218,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nDATABASES = {'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME':\n ':memory:'}}\nINSTALLED_APPS = ('django.contrib.auth', 'django.contrib.admin',\n 'django.contrib.sessions'... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def on_action(relay_option, number):
"""To turn on the chosen relay"""
relay_option.on()
print(f'relay {number} is turning on')
<|reserved_special_token_0|>
def toggle_action(relay_option, number):
"""To toggle the chosen relay"""
print(f'relay {number} is toggling... | flexible | {
"blob_id": "d82412055affc96d634957c953a35ea69b7e702f",
"index": 403,
"step-1": "<mask token>\n\n\ndef on_action(relay_option, number):\n \"\"\"To turn on the chosen relay\"\"\"\n relay_option.on()\n print(f'relay {number} is turning on')\n\n\n<mask token>\n\n\ndef toggle_action(relay_option, number):\n... | [
6,
7,
9,
10,
11
] |
#GUIcal.py
from tkinter import *
from tkinter import ttk
import math
GUI=Tk()
GUI.title('My Cal Program')
GUI.geometry('500x500')
def calc():
height=v_height.get()
base=v_base.get()#ดึงค่ามาจากv_base
print(f'height is {height}')
print(f'Basal length is {base}')
length= math.isqrt((height*height)+(b... | normal | {
"blob_id": "77d7fb49ed4c3e78b148cd446e9a5c6a0e6fac8b",
"index": 835,
"step-1": "<mask token>\n\n\ndef calc():\n height = v_height.get()\n base = v_base.get()\n print(f'height is {height}')\n print(f'Basal length is {base}')\n length = math.isqrt(height * height + base * base)\n print('Lenght i... | [
1,
2,
3,
4,
5
] |
# This is main file where we create the instances of Movie class
# and run the file to view the movie website page
# we have to import media where class Movie is defined and
# fresh_tomatoes python files
import fresh_tomatoes
import media
# Each instance has 8 arguments: Title, story line, poster image,
# trailer url... | normal | {
"blob_id": "9dfc8414628a8b09de3c24c504dd4163efdd3d35",
"index": 6010,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfresh_tomatoes.open_movies_page(movies)\n",
"step-3": "<mask token>\nalien_covenant = media.Movie('Alien: Covenant',\n 'The crew of a colony ship, bound for a remote planet, discover... | [
0,
1,
2,
3,
4
] |
from .factories import *
| normal | {
"blob_id": "c036e6a0a9f06b08ee3eb43655dd833b46fd1e76",
"index": 3690,
"step-1": "<mask token>\n",
"step-2": "from .factories 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|>
<|reserved_special_token_0|>
krait.mvc.set_init_ctrl(ws.WsPageController())
<|reserved_special_token_1|>
import krait
from ctrl import ws
krait.mvc.set_init_ctrl(ws.WsPageController())
| flexible | {
"blob_id": "da2b946238b429188fe3fa50286658d4b5cdbf41",
"index": 5752,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nkrait.mvc.set_init_ctrl(ws.WsPageController())\n",
"step-3": "import krait\nfrom ctrl import ws\nkrait.mvc.set_init_ctrl(ws.WsPageController())\n",
"step-4": null,
"step-5": null,
... | [
0,
1,
2
] |
"""
Package for haasplugin.
"""
| normal | {
"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
] |
<|reserved_special_token_0|>
class Netonix:
<|reserved_special_token_0|>
def _get(self, url, params=None, timeout=15, **kwargs):
full_url = 'https://' + self.ip + self.url[url]
return self.s.get(full_url, params=params, timeout=timeout, **kwargs)
<|reserved_special_token_0|>
@staticm... | flexible | {
"blob_id": "743d261052e4532c1304647501719ad897224b4e",
"index": 8991,
"step-1": "<mask token>\n\n\nclass Netonix:\n <mask token>\n\n def _get(self, url, params=None, timeout=15, **kwargs):\n full_url = 'https://' + self.ip + self.url[url]\n return self.s.get(full_url, params=params, timeout=... | [
9,
11,
13,
14,
20
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.20 on 2019-04-11 03:58
from __future__ import unicode_literals
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('produksi', '0055_auto_20190409_1316'),
]
operations = [
migrations.R... | normal | {
"blob_id": "1eb5df463bbd39002c5dbc3f88459e2f26d4b465",
"index": 8505,
"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 = [('produksi', ... | [
0,
1,
2,
3,
4
] |
#@@---------------------------@@
# Author: Chamil Jayasundara
# Date: 5/18/17
# Description: Extract SFLOW data from slow logs
#@@---------------------------@@
import itertools
from collections import defaultdict
"""Flow Sample and Datagram Objects"""
class Container(object):
def __init__(self, id):
... | normal | {
"blob_id": "395ff2e7c052b57548151fc71fad971c94ebceea",
"index": 3974,
"step-1": "<mask token>\n\n\nclass WithinDatagram(object):\n\n def __init__(self, traceObj):\n self.Trace = traceObj\n self.current_datagram = None\n <mask token>\n\n\nclass WithinFlowsample(object):\n\n def __init__(se... | [
9,
12,
14,
16,
23
] |
<|reserved_special_token_0|>
class GenericPower(Entity):
<|reserved_special_token_0|>
def __init__(self, unique_id, entity_type=EntityType.find(100), name=
'Unnamed entity', state=STATE_UNKNOWN, state_value=None, last_checkin=0
):
Entity.__init__(self, unique_id, entity_type, name=nam... | flexible | {
"blob_id": "18e76df1693d4fc27620a0cf491c33197caa5d15",
"index": 4055,
"step-1": "<mask token>\n\n\nclass GenericPower(Entity):\n <mask token>\n\n def __init__(self, unique_id, entity_type=EntityType.find(100), name=\n 'Unnamed entity', state=STATE_UNKNOWN, state_value=None, last_checkin=0\n ... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def test_duration():
ins = main.convert()
names = ins.multiconvert()
for name in names:
induration, outduration = ins.ffprobe(name[0], name[1])
assert induration == approx(outduration)
indurat... | flexible | {
"blob_id": "92c247b827d2ca4dce9b631a2c09f2800aabe216",
"index": 6129,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_duration():\n ins = main.convert()\n names = ins.multiconvert()\n for name in names:\n induration, outduration = ins.ffprobe(name[0], name[1])\n assert... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Ui_Dialog(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Ui_Dialog(object):
def setupUi(self, Dialog):
Dialog.se... | flexible | {
"blob_id": "3222dd7c2d19d86f2e085cb489ab4a48307ba132",
"index": 7458,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Ui_Dialog(object):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Ui_Dialog(object):\n\n def setupUi(self, Dialog):\n Dialog.setObjectNam... | [
0,
1,
2,
4,
5
] |
from math import pi
from root_regula_falsi import *
r = 1.0
ρs = 200.0
ρw = 1000.0
def f(h):
Vw = 4*pi*r**3/3 - pi*h**2/3*(3*r - h) # displaced volume of water
Vs = 4*pi*r**3/3
return ρw*Vw - ρs*Vs
xr = root_regula_falsi(f, 0.0, 2*r) | normal | {
"blob_id": "3e7d2bacb15c39658ef5044685b73068deb1c145",
"index": 6060,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef f(h):\n Vw = 4 * pi * r ** 3 / 3 - pi * h ** 2 / 3 * (3 * r - h)\n Vs = 4 * pi * r ** 3 / 3\n return ρw * Vw - ρs * Vs\n\n\n<mask token>\n",
"step-3": "<mask token>\nr ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def loadDataFrame(fileName, fileSchema):
return spark.read.format('csv').schema(fileSchema).option('header', 'true'
).option('mode', 'DROPMALFORMED').csv('/FileStore/tables/%s' % fileName
)
<|reserved_special_token_0|>
def top_movies(user_id, n):
"""
This f... | flexible | {
"blob_id": "d22ebe24605065452ae35c44367ee21a726ae7a1",
"index": 1892,
"step-1": "<mask token>\n\n\ndef loadDataFrame(fileName, fileSchema):\n return spark.read.format('csv').schema(fileSchema).option('header', 'true'\n ).option('mode', 'DROPMALFORMED').csv('/FileStore/tables/%s' % fileName\n )\... | [
2,
3,
4,
5,
6
] |
def progress_format(user):
json = dict()
json["progres_id"] = user[0]
json["percentage"] = user[1]
json["user_id"] = user[2]
json["technology"] = user[3]
return json
def progresses_format(users):
json = dict()
json["users_progresses"] = list()
for user in users:
... | normal | {
"blob_id": "6ebf6bdfc6a4a1fe49f4eed1a2c1802f8adeef08",
"index": 1195,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef progresses_format(users):\n json = dict()\n json['users_progresses'] = list()\n for user in users:\n json['users_progresses'].append(progress_format(user))\n re... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class Solution:
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class Solution:
def addBinary(self, a: str, b: str) ->str:
if len(a) < len(b):
a = '0' * (len(b) - len(a)) + a
else:
b = '0' * (len(a... | flexible | {
"blob_id": "227a56c970a74d515ab694d2c0924885e2209cfe",
"index": 7089,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def addBinary(self, a: str, b: str) ->str:\n if len(a) < len(b):\n a = '0' * (len(b) - len(a)) + a\n e... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .BLWecc import curve, setCurve, getPublicKey, getPrivateKey, getAddress as getAddressByCode, pub2add as getAddressByPublicKey, sign, verifyTx as ... | flexible | {
"blob_id": "25ee13314c7cf828b8805d9f483bd5ee12073228",
"index": 8004,
"step-1": "<mask token>\n",
"step-2": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom .BLWecc import curve, setCurve, getPublicKey, getPrivateKey, getAddress as getAddres... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TimezoneMiddleware(object):
<|reserved_special_token_0|>
def process_request(self, request):
user = request.user
if hasattr(user, 'profile'):
user_tz = user.profile.timezone
... | flexible | {
"blob_id": "839d4182663983a03975465d3909631bd6db1d83",
"index": 9919,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TimezoneMiddleware(object):\n <mask token>\n\n def process_request(self, request):\n user = request.user\n if hasattr(user, 'profile'):\n user_tz ... | [
0,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def minimize_neldermead(func, x0, args=(), callback=None, maxiter=None,
maxfev=None, disp=False, return_all=False, initial_simplex=None, xatol=
0.0001, fatol=0.0001, **unknown_options):
"""
Minimization of scalar... | flexible | {
"blob_id": "35921b081e8e8c4da2b16afc20b27b636e9a6676",
"index": 4761,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef minimize_neldermead(func, x0, args=(), callback=None, maxiter=None,\n maxfev=None, disp=False, return_all=False, initial_simplex=None, xatol=\n 0.0001, fatol=0.0001, **unkno... | [
0,
1,
2,
3,
4
] |
# Generated by Django 3.1.7 on 2021-03-25 00:33
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('restaurante', '0003_auto_20210324_1932'),
]
operations = [
migrations.AlterModelOptions(
name='comprobantemodel',
options={'... | normal | {
"blob_id": "f76a3fac75e7e2b156f4bff5094f11009b65b599",
"index": 8822,
"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 = [('restaurante... | [
0,
1,
2,
3,
4
] |
import cv2
import numpy as np
from math import *
def appendimages(im1,im2):
""" Return a new image that appends the two images side-by-side. """
# select the image with the fewest rows and fill in enough empty rows
rows1 = im1.shape[0]
rows2 = im2.shape[0]
if rows1 < rows2:
im1 = np.concat... | normal | {
"blob_id": "c3e313805c6f91f9aac77922edfd09650143f905",
"index": 4862,
"step-1": "<mask token>\n\n\ndef appendimages(im1, im2):\n \"\"\" Return a new image that appends the two images side-by-side. \"\"\"\n rows1 = im1.shape[0]\n rows2 = im2.shape[0]\n if rows1 < rows2:\n im1 = np.concatenate(... | [
9,
11,
12,
15,
18
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.10.5 on 2017-03-15 15:20
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('challenges', '0019_auto_20170310_1114'),
]
operations = [
migrations.AddFie... | normal | {
"blob_id": "6b7ff00eb9a5d0837def5b245ba2d4a0acec972e",
"index": 3466,
"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 = [('challenges'... | [
0,
1,
2,
3,
4
] |
import unittest
from HTMLTestRunner import HTMLTestRunner
discover = unittest.defaultTestLoader.discover(start_dir='./',
pattern='test*.py',
top_level_dir=None)
f = open('report.html', 'wb+')
runner = HTMLTestRunner(stream=f,... | normal | {
"blob_id": "051062a78d3f8b0caefd15f7a57a8500ddc019a6",
"index": 9290,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nrunner.run(discover)\nf.close()\n",
"step-3": "<mask token>\ndiscover = unittest.defaultTestLoader.discover(start_dir='./', pattern=\n 'test*.py', top_level_dir=None)\nf = open('repo... | [
0,
1,
2,
3,
4
] |
"""Settings module for test app."""
ENV = "development"
TESTING = True
SQLALCHEMY_DATABASE_URI = "sqlite://"
SECRET_KEY = "not-so-secret-in-tests"
DEBUG_TB_ENABLED = False
SQLALCHEMY_TRACK_MODIFICATIONS = False
APP_ENV = "testing"
JWT_SECRET_KEY = (
"-----BEGIN RSA PRIVATE KEY-----\n"
"MIICWwIBAAKBgQDdlatRjR... | normal | {
"blob_id": "909ea7b9335a858662f83abc71b4d58578bd0850",
"index": 8261,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nENV = 'development'\nTESTING = True\nSQLALCHEMY_DATABASE_URI = 'sqlite://'\nSECRET_KEY = 'not-so-secret-in-tests'\nDEBUG_TB_ENABLED = False\nSQLALCHEMY_TRACK_MODIFICATIONS = False\nAPP_EN... | [
0,
1,
2
] |
from django import forms
from django.core import validators
class NameSearch(forms.Form):
name = forms.CharField(label='Search By Name')
| normal | {
"blob_id": "7620ff333422d0354cc41c2a66444c3e8a0c011f",
"index": 1606,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass NameSearch(forms.Form):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass NameSearch(forms.Form):\n name = forms.CharField(label='Search By Name')\n",
"step-4": "f... | [
0,
1,
2,
3
] |
"""
Auxiliary functions for calculating the utility of achieving a certain data rate (for a UE).
Attention: The absolute reward that's achieved with different utilities cannot be compared directly (diff ranges)!
"""
import numpy as np
from deepcomp.util.constants import MIN_UTILITY, MAX_UTILITY
def linear_clipped_ut... | normal | {
"blob_id": "e3de072d6bce2ecc105306c06b9a9aa0362130ff",
"index": 6234,
"step-1": "<mask token>\n\n\ndef log_utility(curr_dr):\n \"\"\"\n More data rate increases the utility following a log function: High initial increase, then flattens.\n\n :param curr_dr: Current data rate\n :param factor: Factor t... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while True:
speech.say('I am a DALEK - EXTERMINATE', speed=120, pitch=100, throat=
100, mouth=200)
<|reserved_special_token_1|>
from microbit import *
import speech
while True:
speech.say('I am a DALEK - EXTERMI... | flexible | {
"blob_id": "dad78d7948fb1038f9cf66732f39c18a18f2a3c8",
"index": 5233,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n speech.say('I am a DALEK - EXTERMINATE', speed=120, pitch=100, throat=\n 100, mouth=200)\n",
"step-3": "from microbit import *\nimport speech\nwhile True:\n s... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [path('admin/', admin.site.urls), path('', views.index, name=
'index')]
<|reserved_special_token_1|>
from django.contrib import admin
from django.urls import path
from django.conf.urls import url
from . import... | flexible | {
"blob_id": "b0fad3847519bb18365a8cd4226d06e9d96a8308",
"index": 1258,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('admin/', admin.site.urls), path('', views.index, name=\n 'index')]\n",
"step-3": "from django.contrib import admin\nfrom django.urls import path\nfrom django.con... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class FizzBuzzTest(unittest.TestCase):
def check_fizz_buzz(self, value, expected):
result = fizz_buzz(value)
self.assertEqual(expected, result)
<|reserved_special_token_0|>
def test_fizz_buzz__fizz_buzz_2_2(self):
self.check_fizz_buzz(2, '2')
def... | flexible | {
"blob_id": "59d543ed443c156ac65f9c806ba5bada6bcd0c21",
"index": 6891,
"step-1": "<mask token>\n\n\nclass FizzBuzzTest(unittest.TestCase):\n\n def check_fizz_buzz(self, value, expected):\n result = fizz_buzz(value)\n self.assertEqual(expected, result)\n <mask token>\n\n def test_fizz_buzz_... | [
7,
10,
11,
12,
14
] |
#coding=utf-8
import yaml
import os
import os.path
import shutil
import json
import subprocess
import sys
sys.path.append(os.path.split(os.path.realpath(__file__))[0])
import rtool.taskplugin.plugin.MultiProcessRunner as MultiProcessRunner
import rtool.utils as utils
logger = utils.getLogger('CopyRes')
def run():
lo... | normal | {
"blob_id": "364150d6f37329c43bead0d18da90f0f6ce9cd1b",
"index": 4886,
"step-1": "<mask token>\n\n\nclass CopyResAction:\n <mask token>\n default_option = None\n res_root = None\n packing_root = None\n ignore_list = []\n\n def setResRoot(self, root):\n self.res_root = root\n pass\... | [
6,
8,
13,
14,
15
] |
#listas
lista=[]
print(lista)
#lista semana
listasemana=["Lunes","Martes","Miercoles","Jueves","Viernes"]
print(listasemana[0])
#lista semana
listasemana=["Lunes","Martes","Miercoles","Jueves","Viernes"]
print(listasemana[-1])
#lista semana
listasemana=["Lunes","Martes","Miercoles","Jueves","Viernes"]
print(listase... | normal | {
"blob_id": "37b23dc520abc7cbb6798f41063696916065626f",
"index": 2203,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(lista)\n<mask token>\nprint(listasemana[0])\n<mask token>\nprint(listasemana[-1])\n<mask token>\nprint(listasemana[0, 3])\n<mask token>\nprint(conjunto)\n<mask token>\nprint(lista1p... | [
0,
1,
2,
3
] |
# coding: utf-8
'''
Created on 2013-7-8
@author: huqiming
'''
import json
import re
import urllib2
'''
图说内容
'''
class ts_content:
'''
图说标题
'''
title = ''
'''
图说日期
'''
date = ''
'''
图说段落
'''
parts = []
def __str__(self):
return 'parts: ... | normal | {
"blob_id": "094f482ec6d36dfaed7e908bc445e6e015ec409d",
"index": 2718,
"step-1": "# coding: utf-8\r\n'''\r\nCreated on 2013-7-8\r\n@author: huqiming\r\n'''\r\nimport json\r\nimport re\r\nimport urllib2\r\n'''\r\n图说内容\r\n'''\r\nclass ts_content:\r\n '''\r\n 图说标题\r\n '''\r\n title = ''\r\n '''\r\n ... | [
0
] |
from __future__ import absolute_import, division, print_function
import time
from flytekit.sdk.tasks import python_task, dynamic_task, inputs, outputs
from flytekit.sdk.types import Types
from flytekit.sdk.workflow import workflow_class, Input
from six.moves import range
@inputs(value1=Types.Integer)
@outputs(out=T... | normal | {
"blob_id": "c30b0db220bdacd31ab23aa1227ce88affb79daa",
"index": 2322,
"step-1": "<mask token>\n\n\n@workflow_class\nclass FlyteDJOLoadTestWorkflow(object):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\n@workflow_class\nclass FlyteDJOLoadTestWorkflow(object):\n tasks_count = Input(Typ... | [
1,
2,
4,
5,
6
] |
import pymongo
import redis
import json
from time import time
user_timeline_mongodb = "mongodb://user-timeline-mongodb.sdc-socialnetwork-db.svc.cluster.local:27017/"
user_timeline_redis = "user-timeline-redis.sdc-socialnetwork-db.svc.cluster.local"
def handle(req):
"""handle a request to the function
Args:
... | normal | {
"blob_id": "37969899aa646f4cdd7a5513f17d26b334870f1b",
"index": 6598,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef handle(req):\n \"\"\"handle a request to the function\n Args:\n req (str): request body\n \"\"\"\n start = time()\n event = json.loads(req)\n user_id = ev... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def test_fibonacci_zero():
actual = func.fibonacci(0)
expected = 0
assert actual == expected
def test_fibonacci_one():
actual = func.fibonacci(1)
expected = 1
assert actual == expected
def test_fibonacci_negative():
actual = func.fibonacci(-5)
expected ... | flexible | {
"blob_id": "49722f640eec02029865fd702e13e485eda6391b",
"index": 8126,
"step-1": "<mask token>\n\n\ndef test_fibonacci_zero():\n actual = func.fibonacci(0)\n expected = 0\n assert actual == expected\n\n\ndef test_fibonacci_one():\n actual = func.fibonacci(1)\n expected = 1\n assert actual == ex... | [
8,
9,
11,
13,
14
] |
<|reserved_special_token_0|>
def get_tweets(filename):
""" Process a json formatted file with tweets using pandas read_json """
try:
tweets = []
pd_tweets = pd.read_json(filename, lines=True)
pd_tweets = pd_tweets[pd_tweets.text.notnull()]['text']
tweets = pd_tweets.to_list()
... | flexible | {
"blob_id": "acd2d84529e197d6f9d134e8d7e25a51a442f3ae",
"index": 8615,
"step-1": "<mask token>\n\n\ndef get_tweets(filename):\n \"\"\" Process a json formatted file with tweets using pandas read_json \"\"\"\n try:\n tweets = []\n pd_tweets = pd.read_json(filename, lines=True)\n pd_twee... | [
2,
3,
4,
5,
6
] |
"""
Given a list of partitioned and sentiment-analyzed tweets, run several trials
to guess who won the election
"""
import json
import math
import sys
import pprint
import feature_vector
def positive_volume(f):
return f['relative_volume'] * f['positive_percent']
def inv_negative_volume(f):
return 1.0 - f['r... | normal | {
"blob_id": "d508cb0a8d4291f1c8e76d9d720be352c05ef146",
"index": 8651,
"step-1": "<mask token>\n\n\ndef positive_volume(f):\n return f['relative_volume'] * f['positive_percent']\n\n\n<mask token>\n\n\ndef normalized_sentiment(f):\n return (f['average_sentiment'] + 1) / 2\n\n\ndef normalized_square_sentimen... | [
6,
7,
10,
12,
13
] |
from django.test import TestCase, Client
from accounts.models import Account
from .data import account
from rest_framework import status
class TestAccountRequests(TestCase):
def setUp(self):
self.client = Client()
self.superuser = Account.objects.create_superuser(**account)
def test_register... | normal | {
"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
] |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""BatchNorm (BN) utility functions and custom batch-size BN implementations"""
from functools import partial
import torch
import torch.nn as nn
from pytorchvideo.layers.batch_norm import (
NaiveSyncBatchNorm1d,
Na... | normal | {
"blob_id": "4e5e1be289b32655736d8c6c02d354a85d4268b7",
"index": 3027,
"step-1": "<mask token>\n\n\nclass SubBatchNorm3d(nn.Module):\n <mask token>\n\n def __init__(self, num_splits, **args):\n \"\"\"\n Args:\n num_splits (int): number of splits.\n args (list): other arg... | [
5,
6,
7,
8,
9
] |
# Generated by Django 3.0.3 on 2020-02-09 06:29
import datetime
from django.db import migrations, models
from django.utils.timezone import utc
class Migration(migrations.Migration):
dependencies = [
('devices_collect', '0004_auto_20200209_1304'),
]
operations = [
migrations.AlterField(
... | normal | {
"blob_id": "b07d042c61e9e6647822989444e72db2e01c64d0",
"index": 5751,
"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 = [('devices_col... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def delete(request, pk):
if delete_a_model(pk):
return redirect('myapp:home')
else:
return HttpResponse('Error Occured')
def search(request):
if request.method == 'POST':
pk = request.POST.get('pk')
try:
content = read_a_model(pk)
... | flexible | {
"blob_id": "dc4de382ab16f036c6174e711f5c9fe52868ccc9",
"index": 8445,
"step-1": "<mask token>\n\n\ndef delete(request, pk):\n if delete_a_model(pk):\n return redirect('myapp:home')\n else:\n return HttpResponse('Error Occured')\n\n\ndef search(request):\n if request.method == 'POST':\n ... | [
2,
4,
5,
6,
7
] |
from __future__ import division # floating point division
import csv
import random
import math
import numpy as np
import dataloader as dtl
import classalgorithms as algs
def getaccuracy(ytest, predictions):
correct = 0
for i in range(len(ytest)):
if ytest[i] == predictions[i]:
correct ... | normal | {
"blob_id": "c8ab53c77ff3646a30ca49eaafc275afeadd2ca6",
"index": 9545,
"step-1": "from __future__ import division # floating point division\nimport csv\nimport random\nimport math\nimport numpy as np\n\nimport dataloader as dtl\nimport classalgorithms as algs\n \n \ndef getaccuracy(ytest, predictions):\n cor... | [
0
] |
<|reserved_special_token_0|>
def setup_to_transfer_learn(model):
"""Freeze all layers and compile the model"""
for layer in model.layers:
layer.trainable = False
def add_new_last_layer(base_model, nb_classes):
x = base_model.output
x = Dropout(0.5, name='drop9')(x)
x = Convolution2D(nb_c... | flexible | {
"blob_id": "39b9106a3b0305db8cc7316be3b76e58e5577b92",
"index": 4980,
"step-1": "<mask token>\n\n\ndef setup_to_transfer_learn(model):\n \"\"\"Freeze all layers and compile the model\"\"\"\n for layer in model.layers:\n layer.trainable = False\n\n\ndef add_new_last_layer(base_model, nb_classes):\n ... | [
5,
6,
7,
9,
10
] |
<|reserved_special_token_0|>
class GoogleTTS:
<|reserved_special_token_0|>
def check_google_connection(self):
try:
message = 'Hallo'
filename = 'temp_voice.mp3'
tts = gTTS(text=message, lang='de')
tts.save(filename)
os.remove(filename)
... | flexible | {
"blob_id": "9ed674513bebe65ece538e9ce2b3945bb0c532cc",
"index": 1357,
"step-1": "<mask token>\n\n\nclass GoogleTTS:\n <mask token>\n\n def check_google_connection(self):\n try:\n message = 'Hallo'\n filename = 'temp_voice.mp3'\n tts = gTTS(text=message, lang='de')\n... | [
5,
7,
8,
9,
10
] |
import os
import json
from .utils import *
def _unique_predict(solve_list):
valid_solve_list = filter(lambda x: x[0] is not None, solve_list)
valid_solve_list = sorted(valid_solve_list, key=lambda x: x[0])
unique_solve_list = list()
current_no = -1
for e in valid_solve_list:
if current_no ... | normal | {
"blob_id": "00a1b5f20f15994a659eda56201ba7c45d49a4db",
"index": 4186,
"step-1": "<mask token>\n\n\ndef _unique_predict(solve_list):\n valid_solve_list = filter(lambda x: x[0] is not None, solve_list)\n valid_solve_list = sorted(valid_solve_list, key=lambda x: x[0])\n unique_solve_list = list()\n cur... | [
3,
4,
5,
6,
7
] |
{
# Theme information
'name' : 'Clarico CMS Blocks',
'category' : 'Website',
'version' : '1.0',
'summary': '13 CMS Building Blocks',
'description': """""",
# Dependencies
'depends': [
'snippet_style_1',
'snippet_style_2',
'snippet_style_3',
'snippet_style_4'... | normal | {
"blob_id": "34f98d4a6a15c9a7b42f237cab204b736dc97136",
"index": 1372,
"step-1": "<mask token>\n",
"step-2": "{'name': 'Clarico CMS Blocks', 'category': 'Website', 'version': '1.0',\n 'summary': '13 CMS Building Blocks', 'description': '', 'depends': [\n 'snippet_style_1', 'snippet_style_2', 'snippet_sty... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class GlobalTestOpenAcademySession(TransactionCase):
<|reserved_special_token_0|>
def setUp(self):
super(GlobalTestOpenAcademySession, self).setUp()
self.session = self.env['openacademy.session']
... | flexible | {
"blob_id": "7edd833103e1de92e57559c8a75379c26266963b",
"index": 7835,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass GlobalTestOpenAcademySession(TransactionCase):\n <mask token>\n\n def setUp(self):\n super(GlobalTestOpenAcademySession, self).setUp()\n self.session = self.... | [
0,
4,
5,
6,
7
] |
import os
def is_admin():
"""
The function ``is_admin`` detects whether the calling process is running
with administrator/superuser privileges. It works cross-platform on
either Windows NT systems or Unix-based systems.
"""
if os.name == 'nt':
try:
# Only Windows users wit... | normal | {
"blob_id": "f1601d3d820b93631f9b1358627a5716016ad135",
"index": 5473,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef is_admin():\n \"\"\"\n The function ``is_admin`` detects whether the calling process is running\n with administrator/superuser privileges. It works cross-platform on \n ... | [
0,
1,
2,
3
] |
'''
Temperature Container
'''
class TempHolder:
range_start = 0
range_end = 0
star_count_lst = [0,0,0,0,0,0]
counter = 0
def __init__(self, in_range_start, in_range_end):
self.range_start = in_range_start
self.range_end = in_range_end
self.counter = 0
self.s... | normal | {
"blob_id": "330b843501e0fdaff21cc4eff1ef930d54ab6e8d",
"index": 747,
"step-1": "<mask token>\n\n\nclass FRSHTTHolder:\n frshtt_code = ''\n star_count_lst = [0, 0, 0, 0, 0, 0]\n counter = 0\n\n def __init__(self, in_frshtt_code):\n self.frshtt_code = in_frshtt_code\n self.counter = 0\n ... | [
11,
13,
15,
19,
23
] |
<|reserved_special_token_0|>
class LicenseChecker(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class LicenseChecker(object):
<|reserved_special_token_0|>
... | flexible | {
"blob_id": "c70aa1a373530ac73553753e62d3989f5bc79287",
"index": 687,
"step-1": "<mask token>\n\n\nclass LicenseChecker(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass LicenseChecker(object):\n <mask token>\n <mask token>\n\n def ... | [
1,
3,
5,
6,
7
] |
# Copyright 2013-2022 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack.package import *
class RDt(RPackage):
"""A Wrapper of the JavaScript Library 'DataTables'.
Data obje... | normal | {
"blob_id": "c88e2336432f93d95b4e2285aa532b673a4a410b",
"index": 1095,
"step-1": "<mask token>\n\n\nclass RDt(RPackage):\n <mask token>\n <mask token>\n version('0.23', sha256=\n '360ae2fcb1141125a1b16448570fc37d14c4dd3f78a872c26df4fda1787cdc70')\n version('0.20', sha256=\n 'c66d7f49ec1... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def collect_env():
"""Collect the information of the running environments.
Returns:
dict: The environment information. The following fields are contained.
- sys.platform: The variable of ``sys.platf... | flexible | {
"blob_id": "ee489c2e313a96671db79398218f8604f7ae1bf3",
"index": 3569,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef collect_env():\n \"\"\"Collect the information of the running environments.\n\n Returns:\n dict: The environment information. The following fields are contained.\n\n ... | [
0,
1,
2,
3
] |
"""
CONVERT HOURS INTO SECONDS
Write a function that converts hours into seconds.
Examples:
- how_many_seconds(2) -> 7200
- how_many_seconds(10) -> 36000
- how_many_seconds(24) -> 86400
Notes:
- 60 seconds in a minute; 60 minutes in a hour.
- Don't forget to return your answer.
"""
"""
U.P.E... | normal | {
"blob_id": "34c7e6b6bc687bc641b7e3b9c70fd0844af8e340",
"index": 8969,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef how_many_seconds(hrs_int):\n secs_int = None\n if hrs_int > 0 and hrs_int is not None:\n secs_int = hrs_int * 60 * 60\n return secs_int\n else:\n rai... | [
0,
1,
2
] |
# Generated by Django 2.2.10 on 2020-03-13 14:02
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('system', '0005_location'),
]
operations = [
migrations.AddField(
model_name='setting',
name='runned_locations_initi... | normal | {
"blob_id": "211ef4c64e42c54423ac8dab2128952874a2cf5a",
"index": 7694,
"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 = [('system', '0... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Desc(Descriptive):
name = 'desc'
attrs = ()
class Metadata(Descriptive):
name = 'metadata'
attrs = ()
class Title(Descriptive):
name = 'title'
attrs = ()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Descriptive(Element):
<|rese... | flexible | {
"blob_id": "178570047458eb3eeda00f9153ef2159eb4cbef3",
"index": 9188,
"step-1": "<mask token>\n\n\nclass Desc(Descriptive):\n name = 'desc'\n attrs = ()\n\n\nclass Metadata(Descriptive):\n name = 'metadata'\n attrs = ()\n\n\nclass Title(Descriptive):\n name = 'title'\n attrs = ()\n",
"step-2... | [
6,
7,
8,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class RNN_instruction_encoder(nn.Module):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class RNN_instruction_encoder(nn.Module):
def __init... | flexible | {
"blob_id": "16106250548ef60b475b009116cfeb7a25101637",
"index": 7727,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass RNN_instruction_encoder(nn.Module):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass RNN_instruction_encoder(nn.Module):\n\n def __init__(self, vo... | [
0,
1,
3,
4
] |
from aiogram import Dispatcher
from create_bot import bot
from data_base import sqlite_db
# new user in group
async def new_member(message):
new_user = message.new_chat_members[0]
user_id = new_user['id']
if new_user['username']:
user_name = new_user['username']
elif new_user['first_name']:
... | normal | {
"blob_id": "dfcfa4fa036fe8c058d66fc0b9ea73ddb9d4446e",
"index": 7524,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef register_handlers_for_other(dp: Dispatcher):\n dp.register_message_handler(new_member, content_types=['new_chat_members'])\n dp.register_message_handler(left_member, content... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def compare(a):
if a > 11:
print('big')
elif a == 10:
print('reallybig')
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def drive(carspeed):
if carspeed > 200:
print('very fast... | flexible | {
"blob_id": "de3eaa5823fb396050527c148273c30bed6ce8ca",
"index": 2644,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef compare(a):\n if a > 11:\n print('big')\n elif a == 10:\n print('reallybig')\n\n\n<mask token>\n",
"step-3": "def drive(carspeed):\n if carspeed > 200:\n ... | [
0,
1,
2,
3,
4
] |
import pandas as pd
df1 = pd.read_csv('Tweets1.csv', names=['tweet'])
df2 = pd.read_csv('Tweets2.csv', names=['tweet'])
df3 = pd.read_csv('Tweets3.csv', names=['tweet'])
df = pd.concat([df1, df2, df3], axis=0, join='outer', ignore_index=False,
keys=None, levels=None, names=None, verify_integrity=False, copy=True)
d... | normal | {
"blob_id": "7d6196268b85861e76efaa53e14976f2eae09405",
"index": 3226,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndf.to_csv('Tweets.csv', index=None, header=None)\n",
"step-3": "<mask token>\ndf1 = pd.read_csv('Tweets1.csv', names=['tweet'])\ndf2 = pd.read_csv('Tweets2.csv', names=['tweet'])\ndf3 =... | [
0,
1,
2,
3
] |
#include os
#include math
output_file = 'output/mvnt'
def file_writeout(srvN, pos);
with open(output_file, 'a') as f:
f.write(srvN, ' to ', pos)
return 0
class leg(legN):
def __init__(legN):
srvHY = 'srv' + legN + 'HY'
srvHX = 'srv' + legN + 'HX'
srvEY = 'srv' + le... | normal | {
"blob_id": "901f87752026673c41a70655e987ecc2d5cb369f",
"index": 7273,
"step-1": "#include os\n#include math\n\noutput_file = 'output/mvnt'\n\ndef file_writeout(srvN, pos);\n with open(output_file, 'a') as f:\n f.write(srvN, ' to ', pos)\n return 0\n \nclass leg(legN):\n def __init__(legN)... | [
0
] |
<|reserved_special_token_0|>
def get_choice(attempt):
"""
return an integer input from the user
"""
try:
user_text = ''
if attempt == 1:
user_text = 'Guess a number between 0 and 99:'
choice = int(input(user_text))
except ValueError:
return get_choice()
... | flexible | {
"blob_id": "f7d487ec99e2fa901677ab9aec0760a396722e12",
"index": 8245,
"step-1": "<mask token>\n\n\ndef get_choice(attempt):\n \"\"\"\n return an integer input from the user\n \"\"\"\n try:\n user_text = ''\n if attempt == 1:\n user_text = 'Guess a number between 0 and 99:'\n... | [
2,
3,
4,
5,
6
] |
import numpy as np
import dxchange
import ptychotomo
if __name__ == "__main__":
# read object
u = dxchange.read_tiff('data/init_object.tiff')
u = u+1j*u/2
nz, n, _ = u.shape
# parameters
center = n/2
ntheta = 384
ne = 3*n//2
ngpus = 1
pnz = nz//2
theta = np.linspace(0... | normal | {
"blob_id": "4ed6f4db4c9c3319d6289ba402f81bbd8accf915",
"index": 9782,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n u = dxchange.read_tiff('data/init_object.tiff')\n u = u + 1.0j * u / 2\n nz, n, _ = u.shape\n center = n / 2\n ntheta = 384\n ne = 3 * n // ... | [
0,
1,
2,
3
] |
#!/usr/bin/env python3
#
# nextskeleton - An assembler skeleton for the ZX Spectrum Next
#
# Copyright (C) 2020 Richard "Shred" Körber
# https://github.com/shred/nextskeleton
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You ma... | normal | {
"blob_id": "0744ec646e7b9303c67c25dff2997568c6171b91",
"index": 108,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nparser.add_argument('nex', help='path of the .nex file to be launched')\nparser.add_argument('file', help='autoexec.bas file to be generated')\n<mask token>\ncontents += bytearray((0, 10))... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def geoDistance(p1, p2):
return Geodesic.WGS84.Inverse(p1.y, p1.x, p2.y, p2.x)['s12']
<|reserved_special_token_0|>
def compare(f):
return geoDistance(f.getLocation(), melbourne)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def g... | flexible | {
"blob_id": "920f00632599945397364dd0f52f21234e17f9ef",
"index": 9445,
"step-1": "<mask token>\n\n\ndef geoDistance(p1, p2):\n return Geodesic.WGS84.Inverse(p1.y, p1.x, p2.y, p2.x)['s12']\n\n\n<mask token>\n\n\ndef compare(f):\n return geoDistance(f.getLocation(), melbourne)\n\n\n<mask token>\n",
"step-2... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if settings.DEBUG:
urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT
)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [path('user', include('user.urls')), path('ord... | flexible | {
"blob_id": "97cc29e0d54e5d5e05dff16c92ecc4046363185f",
"index": 344,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif settings.DEBUG:\n urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT\n )\n",
"step-3": "<mask token>\nurlpatterns = [path('user', include('user.urls... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def test_mongo_logging_client_persists_log():
"""
Test to see if the mongodb client logger
can persist a log entry to the database
"""
error_message = 'This is a test message.'
logger = LoggingService(console_output=True)
result = logger.log(LogEntry(LogLevel.E... | flexible | {
"blob_id": "a29cf9e7006d52cea8f5ccdcbc2087983ffa3ef3",
"index": 2973,
"step-1": "<mask token>\n\n\ndef test_mongo_logging_client_persists_log():\n \"\"\"\n Test to see if the mongodb client logger\n can persist a log entry to the database\n \"\"\"\n error_message = 'This is a test message.'\n ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class AdmiravelMundoNovo(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def transicao_estado(self, acao):
if self._valor_estado == 2 and acao == 0:
self._estado_6()
elif self._valor_estado == 2 and acao == 1:
self._e... | flexible | {
"blob_id": "38ffbb6a66837e975a611a57579bb365ab69a32c",
"index": 9504,
"step-1": "<mask token>\n\n\nclass AdmiravelMundoNovo(object):\n <mask token>\n <mask token>\n\n def transicao_estado(self, acao):\n if self._valor_estado == 2 and acao == 0:\n self._estado_6()\n elif self._v... | [
18,
21,
23,
25,
28
] |
#!/usr/bin/env python
# encoding: utf-8
"""
@author: swensun
@github:https://github.com/yunshuipiao
@software: python
@file: encode_decode.py
@desc: 字符串编解码
@hint:
"""
def encode(strs):
"""Encodes a list of strings to a single string.
:type strs: List[str]
:rtype: str
"""
res = ''
... | normal | {
"blob_id": "2561db1264fe399db85460e9f32213b70ddf03ff",
"index": 1864,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef encode(strs):\n \"\"\"Encodes a list of strings to a single string.\n :type strs: List[str]\n :rtype: str\n \"\"\"\n res = ''\n for string in strs.split(... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""Tests for the Spotlight Volume configuration plist plugin."""
import unittest
# pylint: disable=unused-import
from plaso.formatters import plist as plist_formatter
from plaso.parsers import plist
from plaso.parsers.plist_plugins import spotlight_volume
from tests.parsers.... | normal | {
"blob_id": "6d1b882af2a027f2eecaa3a881dbcab1e3a3b92b",
"index": 9608,
"step-1": "<mask token>\n\n\nclass SpotlightVolumePluginTest(test_lib.PlistPluginTestCase):\n <mask token>\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass SpotlightVolumePluginTest(test_lib.Pl... | [
1,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
parser.add_argument('infile', help='the file list to be processed')
parser.add_argument('-d', '--directory', default='./', help=
'directory where files are located')
parser.add_argument('-s', '--suffix', default='_EmsRawEvent.... | flexible | {
"blob_id": "58c7b405096a5fdc5eeacb5e5f314f2d1bb85af6",
"index": 6229,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nparser.add_argument('infile', help='the file list to be processed')\nparser.add_argument('-d', '--directory', default='./', help=\n 'directory where files are located')\nparser.add_arg... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ActivityConfig(AppConfig):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ActivityConfig(AppConfig):
name = 'apps.activity'
<|reserved_special_token_1|>
from dj... | flexible | {
"blob_id": "2a69aa0cd9d0e39ad82d6a354e956bdad0648797",
"index": 2252,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ActivityConfig(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass ActivityConfig(AppConfig):\n name = 'apps.activity'\n",
"step-4": "from django.apps im... | [
0,
1,
2,
3
] |
from . import *
from ..utils.constants import NUM_SEARCH_RESULT
def get_course_by_id(course_id):
return Course.query.filter_by(id=course_id).first()
def get_course_by_subject_and_course_num(subject_code, course_num):
return Course.query.filter_by(subject_code=subject_code, course_num=course_num).first()
d... | normal | {
"blob_id": "b3f0aae91c885d0e15ff3e456b5cab43fca65b67",
"index": 4184,
"step-1": "<mask token>\n\n\ndef get_course_by_id(course_id):\n return Course.query.filter_by(id=course_id).first()\n\n\n<mask token>\n\n\ndef create_course(subject_code, course_num, title):\n optional_course = get_course_by_subject_and... | [
4,
5,
6,
7,
8
] |
import requests
from bs4 import BeautifulSoup
from urllib.request import urlretrieve
import json
import time
#功能一：下载单一歌曲、歌词
def single_song(song_id,path,song_name): #下载单一歌曲，输入为歌曲id，保存路径，歌曲名称
song_url = "http://music.163.com/song/media/outer/url?id=%s" % song_id
down_path = path +'... | normal | {
"blob_id": "3b11d514b15775e4c818a7a2adf9a80e89dca968",
"index": 5801,
"step-1": "<mask token>\n\n\ndef save2txt(songname, lyric, path):\n print('歌词下载完成：' + songname)\n lyric_path = path + '\\\\' + songname + '.txt'\n with open(lyric_path, 'a', encoding='utf-8') as f:\n f.write(lyric)\n\n\n<mask ... | [
17,
20,
21,
24,
33
] |
<|reserved_special_token_0|>
def save():
Names = Name.get()
Ages = Age.get()
Genders = Gender.get()
Heights = height.get()
weights = weight.get()
rollnos = StudentId.get()
Sports = Sport.get()
cursor.execute(
"""
INSERT INTO Students(Name, Age, Gender, Height,_weight,Studen... | flexible | {
"blob_id": "8058ff209af03b7365ffad2a9ce2e2805b548f53",
"index": 9927,
"step-1": "<mask token>\n\n\ndef save():\n Names = Name.get()\n Ages = Age.get()\n Genders = Gender.get()\n Heights = height.get()\n weights = weight.get()\n rollnos = StudentId.get()\n Sports = Sport.get()\n cursor.ex... | [
4,
5,
6,
7,
8
] |
from __future__ import division
import numpy as np
table = open("Tables\\table1.txt", "w")
table.write("\\begin{tabular}{|c|c|c|c|} \\hline\n")
table.write("Hidden Neurons & Loss & Training Acc. & Valid. Acc. \\\\ \\hline\n")
H = [1,5,10,11,12,20,40]
for h in H:
file = open("Out\\out-h"+str(h)+".txt", "r")
line = ... | normal | {
"blob_id": "3cace66ddf8484d285c2b2a8fabbb83778a2c4af",
"index": 4352,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntable.write('\\\\begin{tabular}{|c|c|c|c|} \\\\hline\\n')\ntable.write(\n 'Hidden Neurons & Loss & Training Acc. & Valid. Acc. \\\\\\\\ \\\\hline\\n')\n<mask token>\nfor h in H:\n f... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class MoneyFst(GraphFst):
<|reserved_special_token_0|>
def __init__(self, decimal: GraphFst, deterministic: bool=True):
super().__init__(name='money', kind='verbalize', deterministic=
deterministic)
maj_singular_masc = pynutil.delete('currency_maj: "')... | flexible | {
"blob_id": "dccdca65cce2959b07657636e23e7c9ab8a4f96c",
"index": 1382,
"step-1": "<mask token>\n\n\nclass MoneyFst(GraphFst):\n <mask token>\n\n def __init__(self, decimal: GraphFst, deterministic: bool=True):\n super().__init__(name='money', kind='verbalize', deterministic=\n determinist... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def query_parse(GIVEN_QUERY):
try:
countryIds_query = list(map(lambda x: x['country_id'], GIVEN_QUERY[
'countries']))
except:
countryIds_query = None
try:
days_query = GIVEN_QUERY[... | flexible | {
"blob_id": "b52807a15cef8f07f685f8761a470d4a24d9c3dc",
"index": 6603,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef query_parse(GIVEN_QUERY):\n try:\n countryIds_query = list(map(lambda x: x['country_id'], GIVEN_QUERY[\n 'countries']))\n except:\n countryIds_query... | [
0,
1,
2,
3
] |
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph
from reportlab.lib.styles import getSampleStyleSheet
def paragraph_spacing():
doc = SimpleDocTemplate("paragraph_spacing.pdf", pagesize=letter)
styles = getSampleStyleSheet()
#Mengahasilkan spasi antar ... | normal | {
"blob_id": "d79e65b7aa09066230dec1a472f4535dff4123b5",
"index": 4217,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef paragraph_spacing():\n doc = SimpleDocTemplate('paragraph_spacing.pdf', pagesize=letter)\n styles = getSampleStyleSheet()\n styles['Normal'].spaceBefore = 10\n styles[... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(img.shape)
print(img[257][400])
<|reserved_special_token_0|>
cv2.imshow('Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
img = cv2.imread('assets/logo.jpg', -1... | flexible | {
"blob_id": "35e66e5e154f5cd70f187a1cde33cef71102e1a6",
"index": 6829,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(img.shape)\nprint(img[257][400])\n<mask token>\ncv2.imshow('Image', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n",
"step-3": "<mask token>\nimg = cv2.imread('assets/logo.jpg', ... | [
0,
1,
2,
3,
4
] |
import pylab,numpy as np
from numpy import sin
from matplotlib.patches import FancyArrowPatch
fig=pylab.figure()
w=1
h=1
th=3.14159/25.
x=np.r_[0,0,w,w,0]
y=np.r_[0,h,h-w*sin(th),0-w*sin(th),0]
pylab.plot(x,y)
x=np.r_[0,0,w/2.0,w/2.0,0]
y=np.r_[0,h/6.0,h/6.0-w/2.0*sin(th),0-w/2.0*sin(th),0]
pylab.plot(x... | normal | {
"blob_id": "c485466a736fa0a4f183092e561a27005c01316d",
"index": 8616,
"step-1": "<mask token>\n",
"step-2": "<mask token>\npylab.plot(x, y)\n<mask token>\npylab.plot(x, y, '--')\npylab.text(w / 4.0, h / 12.0 - w / 4.0 * sin(th) - h / 30.0,\n '$A_{a,subcool}$', ha='center', va='center')\n<mask token>\npylab... | [
0,
1,
2,
3,
4
] |
"""
This module takes care of starting the API Server, Loading the DB and Adding the endpoints
"""
import os
from flask import Flask, request, jsonify, url_for
from flask_migrate import Migrate
from flask_swagger import swagger
from flask_cors import CORS
from flask_jwt_extended import (
JWTManager, jwt_required, c... | normal | {
"blob_id": "36d596c1019dbaaf8dc394633ca464421517dc21",
"index": 3381,
"step-1": "\"\"\"\nThis module takes care of starting the API Server, Loading the DB and Adding the endpoints\n\"\"\"\nimport os\nfrom flask import Flask, request, jsonify, url_for\nfrom flask_migrate import Migrate\nfrom flask_swagger import... | [
0
] |
<|reserved_special_token_0|>
def remove_posts(data, index_list):
data = data.drop(index_list)
return data.reset_index(drop=True)
<|reserved_special_token_0|>
def preprocess(text):
text = text.lower()
text = text.replace('$', ' ')
text = text.replace('-', ' ')
text = text.replace('/', ' ')
... | flexible | {
"blob_id": "341fb4442ba1d1bb13dbbe123e1051e1ceeb91e7",
"index": 4431,
"step-1": "<mask token>\n\n\ndef remove_posts(data, index_list):\n data = data.drop(index_list)\n return data.reset_index(drop=True)\n\n\n<mask token>\n\n\ndef preprocess(text):\n text = text.lower()\n text = text.replace('$', ' '... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for line in f:
l = line.strip()
l = l.split(',')
l = map(float, l)
data.append(l)
f.close()
for i in range(100):
shuffle(data)
for l in data:
train_data.append(l[0:-1])
train_class.append(int(l[-1]))
<|... | flexible | {
"blob_id": "b8b20d6c977a6c1df6a592188c6e799f12da6a23",
"index": 9734,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor line in f:\n l = line.strip()\n l = l.split(',')\n l = map(float, l)\n data.append(l)\nf.close()\nfor i in range(100):\n shuffle(data)\nfor l in data:\n train_data.a... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for `feat` package."""
from feat.detector import Detector
from feat.data import Fex
from feat.utils import get_resource_path
from .utils import get_test_data_path
import pandas as pd
import feat
import os
import wget
# def test_models():
# print("Downloading... | normal | {
"blob_id": "753bdbf080e7a8652c39e40beeae51f74382d606",
"index": 1300,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_detector():\n detector = Detector(n_jobs=1)\n assert detector['n_jobs'] == 1\n assert type(detector) == Detector\n inputFname = os.path.join(get_test_data_path(),... | [
0,
1,
2,
3
] |
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