code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def area(a, b):
resultado = a * b
return resultado
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def area(a, b):
resultado = a * b
return resultado
def main():
num1 = float(input('INTRODUCE LA BASE: '))
num2 = float(... | flexible | {
"blob_id": "282dbdb3a8d9ed914e8ca5c7fa74d2873920e18c",
"index": 7308,
"step-1": "<mask token>\n",
"step-2": "def area(a, b):\n resultado = a * b\n return resultado\n\n\n<mask token>\n",
"step-3": "def area(a, b):\n resultado = a * b\n return resultado\n\n\ndef main():\n num1 = float(input('IN... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class TestKOrderStatistic(unittest.TestCase):
def test_find(self):
for a, k, ans in test_case_find:
self.assertEqual(k_order_statistic(a, k), ans)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TestKOrderStati... | flexible | {
"blob_id": "b93cd5ad957da37b1a4cca1d465a67723110e926",
"index": 2813,
"step-1": "<mask token>\n\n\nclass TestKOrderStatistic(unittest.TestCase):\n\n def test_find(self):\n for a, k, ans in test_case_find:\n self.assertEqual(k_order_statistic(a, k), ans)\n <mask token>\n",
"step-2": "<m... | [
2,
3,
4,
5
] |
# Named Entity Recognition on Medical Data (BIO Tagging)
# Bio-Word2Vec Embeddings Source and Reference: https://github.com/ncbi-nlp/BioWordVec
import os
import re
import torch
import pickle
from torch import nn
from torch import optim
import torch.nn.functional as F
import numpy as np
import random
from DNC.dnc imp... | normal | {
"blob_id": "eb99def75404bc3b674bcb633714009149f2d50d",
"index": 5097,
"step-1": "<mask token>\n\n\nclass task_NER:\n\n def __init__(self):\n self.name = 'NER_task_bio'\n self.controller_size = 128\n self.controller_layers = 1\n self.num_read_heads = 1\n self.num_write_heads... | [
12,
20,
26,
27,
29
] |
__author__ = 'matthias'
from tcp import *
from data import *
#SERVER = "131.225.237.31"
#PORT = 33487
data = LaserData()
#server = TCP(SERVER, PORT)
server = TCP()
server.start_server()
for i in range(100):
data = server.recv_server()
print data
| normal | {
"blob_id": "1e4d18909b72ceef729efdd7b2ab996ace45f1bd",
"index": 6367,
"step-1": "__author__ = 'matthias'\n\nfrom tcp import *\nfrom data import *\n\n#SERVER = \"131.225.237.31\"\n#PORT = 33487\n\ndata = LaserData()\n#server = TCP(SERVER, PORT)\nserver = TCP()\nserver.start_server()\nfor i in range(100):\n da... | [
0
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# This file is part of CbM (https://github.com/ec-jrc/cbm).
# Author : Konstantinos Anastasakis
# Credits : GTCAP Team
# Copyright : 2021 European Commission, Joint Research Centre
# License : 3-Clause BSD
from ipywidgets import (Text, VBox, HBox, Label, Password... | normal | {
"blob_id": "22afc6b9df87ef1eba284da20a807366278c24d4",
"index": 1343,
"step-1": "<mask token>\n\n\ndef rest_api(mode=None):\n \"\"\"\"\"\"\n values = config.read()\n wt_url = Text(value=values['api']['url'], placeholder='Add URL',\n description='API URL:', disabled=False)\n wt_user = Text(val... | [
2,
3,
4,
5,
6
] |
import unittest
import shapely.geometry as gm
from alphaBetaLab.abRectangularGridBuilder import abRectangularGridBuilder
class testAbRectangularGridBuilder(unittest.TestCase):
def getMockHiResAlphaMtxAndCstCellDet(self, posCellCentroids = None):
class _mockClass:
def __init__(self, posCellCentroids):
... | normal | {
"blob_id": "6175ce6534d44d703df6cdef94fc2b1285e25f49",
"index": 2202,
"step-1": "<mask token>\n\n\nclass testAbRectangularGridBuilder(unittest.TestCase):\n\n def getMockHiResAlphaMtxAndCstCellDet(self, posCellCentroids=None):\n\n\n class _mockClass:\n\n def __init__(self, posCellCentroids):... | [
6,
7,
8,
9,
10
] |
from flask import Flask, request, jsonify
import sqlite3
from database import Database
app = Flask(__name__)
db = Database()
@app.route('/')
def homepage():
argslist = request.args
faciltype = argslist.get('facil')
facils = []
try:
facils = db.getFacilitiesFromFacilityType(facilty... | normal | {
"blob_id": "2424d667e1bb4ee75b5053eb6f9b002787a5317f",
"index": 6391,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef homepage():\n argslist = request.args\n faciltype = argslist.get('facil')\n facils = []\n try:\n facils = db.getFacilitiesFromFacilityType(faciltype)\n facils = map(l... | [
2,
6,
7,
8,
9
] |
#n-repeated element
class Solution:
def repeatedNTimes(self, A):
freq = {}
for i in A:
if i in freq.keys():
freq[i] += 1
else:
freq[i] = 1
key = list(freq.keys())
val = list(freq.values())
m = max(val)
return key... | normal | {
"blob_id": "d50618f7784e69b46cb665ec1a9c56f7a2867785",
"index": 5033,
"step-1": "class Solution:\n <mask token>\n\n\n<mask token>\n",
"step-2": "class Solution:\n\n def repeatedNTimes(self, A):\n freq = {}\n for i in A:\n if i in freq.keys():\n freq[i] += 1\n ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
scheme = 'http'
hostname = 'localhost'
port = 9000
routes = ['/available/2', '/available/4']
<|reserved_special_token_1|>
# -*- coding: utf-8 -*-
scheme = 'http'
hostname = 'localhost'
port = 9000
routes = [
'/available/2',
'/available/4'
]
| flexible | {
"blob_id": "d1402469232b5e3c3b09339849f6899e009fd74b",
"index": 3323,
"step-1": "<mask token>\n",
"step-2": "scheme = 'http'\nhostname = 'localhost'\nport = 9000\nroutes = ['/available/2', '/available/4']\n",
"step-3": "# -*- coding: utf-8 -*-\n\n\nscheme = 'http'\n\nhostname = 'localhost'\n\nport = 9000\n\... | [
0,
1,
2
] |
data = " Ramya , Deepa,LIRIL ,amma, dad, Kiran, 12321 , Suresh, Jayesh, Ramesh,Balu"
lst = data.split(",")
for name in lst:
name = name.strip().upper()
rname = name[::-1]
if name == rname:
print(name)
girlsdata = "Tanvi,Dhatri,Haadya,Deepthi,Deepa,Ramya"
# Name which start with DEE get those name... | normal | {
"blob_id": "622b388beb56eba85bbb08510c2bcea55f23da9a",
"index": 721,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor name in lst:\n name = name.strip().upper()\n rname = name[::-1]\n if name == rname:\n print(name)\n<mask token>\nprint('-' * 20)\n<mask token>\nfor name in names:\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def import_csv_from_aws():
client = boto3.client('s3', aws_access_key_id=AWS_ACCESS_KEY_ID,
aws_secret_access_key=AWS_SECRET_ACCESS_KEY)
client.download_file('ergast-csv', 'filtered_laptimes.csv',
'filter... | flexible | {
"blob_id": "b573db8ea0845fb947636b8d82ed462904c6005d",
"index": 5519,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef import_csv_from_aws():\n client = boto3.client('s3', aws_access_key_id=AWS_ACCESS_KEY_ID,\n aws_secret_access_key=AWS_SECRET_ACCESS_KEY)\n client.download_file('ergas... | [
0,
1,
2,
3
] |
"""Ex026 Faça um programa que leia uma frase pelo teclado e mostre:
Quantas vezes aparece a letra "A".
Em que posição ela aparece a primeira vez.
Em que posição ela aparece pela última vez."""
frase = str(input('Digite uma frase: ')).strip().lower()
n_a = frase.count('a')
f_a = frase.find('a')+1
l_a= frase.rfind('a')-1... | normal | {
"blob_id": "58f3b8c5470c765c81f27d39d9c28751a8c2b719",
"index": 277,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(f'Sua frase tem {n_a} letras a')\nprint(f'A letra A aparece pela primeira vez na {f_a}° posição')\nprint(f'A letra A apaerece pela ultima vez na {l_a}° posição')\n",
"step-3": "<ma... | [
0,
1,
2,
3
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import json
import codecs
import Levenshtein
import logging
import random
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import time
from sklearn.model_selection import KFold
import numpy as np
import s... | normal | {
"blob_id": "37804c92b69d366cc1774335b6a2295dfd5b98f3",
"index": 6592,
"step-1": "<mask token>\n\n\ndef gen_label(uid1, uid2):\n if same_line_dict[uid1].__contains__(uid2) and same_line_dict[uid2\n ].__contains__(uid1):\n return '1'\n else:\n return '-1'\n\n\n<mask token>\n\n\ndef gen_... | [
2,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def generatetest(n=100, filename='test_data'):
ids = []
names_list = []
for _ in range(n):
ids.append(''.join(random.choices(string.ascii_letters + string.
digits, k=9)))
names_list.append... | flexible | {
"blob_id": "aa913fd40a710cfd7288fd59c4039c4b6a5745cc",
"index": 4569,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef generatetest(n=100, filename='test_data'):\n ids = []\n names_list = []\n for _ in range(n):\n ids.append(''.join(random.choices(string.ascii_letters + string.\n ... | [
0,
1,
2,
3,
4
] |
import math
from historia.utils import unique_id, position_in_range
from historia.pops.models.inventory import Inventory
from historia.economy.enums.resource import Good, NaturalResource
from historia.economy.enums.order_type import OrderType
from historia.economy.models.price_range import PriceRange
from historia.econ... | normal | {
"blob_id": "887a39f1eeb81e6472938c2451e57866d3ac4a45",
"index": 661,
"step-1": "<mask token>\n\n\nclass Pop(object):\n <mask token>\n\n def __init__(self, province, pop_job, population):\n \"\"\"\n Creates a new Pop.\n manager (Historia)\n province (SecondaryDivision)\n ... | [
15,
26,
28,
32,
33
] |
# import necessary modules
import cv2
import xlsxwriter
import statistics
from matplotlib import pyplot as plt
import math
import tqdm
import numpy as np
import datetime
def getDepths(imgs, img_names, intersectionCoords, stakeValidity, templateIntersections,
upperBorder, tensors, actualTensors, intersectionDist, b... | normal | {
"blob_id": "24a538dcc885b37eb0147a1ee089189f11b20f8a",
"index": 7945,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef getDepths(imgs, img_names, intersectionCoords, stakeValidity,\n templateIntersections, upperBorder, tensors, actualTensors,\n intersectionDist, blobDistTemplate, debug, debu... | [
0,
1,
2,
3
] |
import discord
class Leveling:
__slots__ = ('sid', 'channelID', 'message', 'noxpchannelIDs',
'noxproleID', 'remove', 'bot', 'roles')
sid: int
channelID: int
message: str
noxpchannelIDs: list[int]
noxproleID: int
remove: bool
roles: list[list]
def __init__(self, bot, sid, r... | normal | {
"blob_id": "346df9706dc222f43a77928964cd54e7d999a585",
"index": 8052,
"step-1": "<mask token>\n\n\nclass Leveling:\n <mask token>\n sid: int\n channelID: int\n message: str\n noxpchannelIDs: list[int]\n noxproleID: int\n remove: bool\n roles: list[list]\n <mask token>\n\n @property... | [
2,
3,
4,
5
] |
#!/usr/local/bin/python3
from sys import stdin
import argparse
# Default values
alignment = 'l'
border = 'none'
stretch_factor = '1.0'
toprule = ''
# Default options
custom_header = False
standalone = False
stretch = False
booktabs = False
# Parsing command-line options
parser = argparse.ArgumentParser('<stdin> | cs... | normal | {
"blob_id": "591ac07e735e08bcafa8274eb1a1547a01261f55",
"index": 8430,
"step-1": "<mask token>\n\n\ndef rule(type):\n if booktabs:\n if type == 'top':\n return '\\\\toprule'\n if type == 'mid':\n return '\\\\midrule'\n if type == 'bottom':\n return '\\\\bo... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class Individual:
<|reserved_special_token_0|>
@property
def dir(self):
"""Get the unitary vector of direction.
Returns:
numpy.ndarray: The unitary vector of direction.
"""
return unit_vector(normalize_angle(self.angle))
<|res... | flexible | {
"blob_id": "386e491f6b10ca27f513d678c632571c29093ad2",
"index": 5825,
"step-1": "<mask token>\n\n\nclass Individual:\n <mask token>\n\n @property\n def dir(self):\n \"\"\"Get the unitary vector of direction.\n\n Returns:\n numpy.ndarray: The unitary vector of direction.\n\n ... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for line in f:
line = line.strip()
if len(line) == 0:
continue
name, *marks = line.split(',')
if len(marks) == 0:
continue
marks = filter(str.isdigit, marks)
total = sum(map(int, marks))
... | flexible | {
"blob_id": "00587de133ee68415f31649f147fbff7e9bf65d5",
"index": 3337,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor line in f:\n line = line.strip()\n if len(line) == 0:\n continue\n name, *marks = line.split(',')\n if len(marks) == 0:\n continue\n marks = filter(str.is... | [
0,
1,
2,
3
] |
# Python program to count number of digits in a number.
# print len(str(input('Enter No.: ')))
num = input("Enter no.: ")
i = 1
while num / 10:
num = num / 10
i += 1
if num < 10:
break
print i
| normal | {
"blob_id": "37748e3dd17f2bdf05bb28b4dfded12de97e37e4",
"index": 9619,
"step-1": "# Python program to count number of digits in a number.\n\n# print len(str(input('Enter No.: ')))\n\nnum = input(\"Enter no.: \")\n\ni = 1\nwhile num / 10:\n num = num / 10\n i += 1\n if num < 10:\n break\nprint i\n... | [
0
] |
import requests
import tkinter as tk
from tkinter.font import Font
from time import strptime
class Window(tk.Tk):
def __init__(self):
super().__init__()
#取得網路上的資料
res = requests.get('https://flask-robert.herokuapp.com/youbike')
jsonObj = res.json()
areas = jsonObj['areas']
... | normal | {
"blob_id": "f9becdb48583423e7bd3730d1cd74a6a016663dc",
"index": 1768,
"step-1": "<mask token>\n\n\nclass Window(tk.Tk):\n\n def __init__(self):\n super().__init__()\n res = requests.get('https://flask-robert.herokuapp.com/youbike')\n jsonObj = res.json()\n areas = jsonObj['areas']... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
def print_all_models():
return models.Sample.objects.all()
@sync_to_async
def _create_record(name):
return models.Sample.objects.create(name=name)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
os.environ.setdefault('DJANGO_SETTINGS_M... | flexible | {
"blob_id": "4afb556ceca89eb90ba800db4f383afad1cd42a5",
"index": 3765,
"step-1": "<mask token>\n\n\ndef print_all_models():\n return models.Sample.objects.all()\n\n\n@sync_to_async\ndef _create_record(name):\n return models.Sample.objects.create(name=name)\n\n\n<mask token>\n",
"step-2": "<mask token>\no... | [
2,
3,
4,
5,
6
] |
def test_corr_callable_method(self, datetime_series):
my_corr = (lambda a, b: (1.0 if (a == b).all() else 0.0))
s1 = Series([1, 2, 3, 4, 5])
s2 = Series([5, 4, 3, 2, 1])
expected = 0
tm.assert_almost_equal(s1.corr(s2, method=my_corr), expected)
tm.assert_almost_equal(datetime_series.corr(datetim... | normal | {
"blob_id": "5e68233fde741c0d2a94bf099afb6a91c08e2a29",
"index": 6071,
"step-1": "<mask token>\n",
"step-2": "def test_corr_callable_method(self, datetime_series):\n my_corr = lambda a, b: 1.0 if (a == b).all() else 0.0\n s1 = Series([1, 2, 3, 4, 5])\n s2 = Series([5, 4, 3, 2, 1])\n expected = 0\n ... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [url('^$', SprintListView.as_view(), name='sprint_list'),
path('create/', view=CreateSprintView.as_view(), name='create_sprint'),
path('modificar/<int:sprint_pk>/', view=UpdateSprintView.as_view(),
name='... | flexible | {
"blob_id": "2b1ec422a42af59a048c708f86b686eb0564b51f",
"index": 2456,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [url('^$', SprintListView.as_view(), name='sprint_list'),\n path('create/', view=CreateSprintView.as_view(), name='create_sprint'),\n path('modificar/<int:sprint_pk>/'... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class FlowerIdentify(tornado.web.RequestHandler):
def get(self):
self.render('flower_identify.html')
class IdentifyHandler(tornado.websocket.WebSocketHandler):
def post(self):
dataUrl = self.get_body_argument('image')
Orientation = self.get_body_argumen... | flexible | {
"blob_id": "1c3b1776f14a085bec90be11028c87dc47f00293",
"index": 1722,
"step-1": "<mask token>\n\n\nclass FlowerIdentify(tornado.web.RequestHandler):\n\n def get(self):\n self.render('flower_identify.html')\n\n\nclass IdentifyHandler(tornado.websocket.WebSocketHandler):\n\n def post(self):\n ... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class GenomicArray:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __init__(self, data_table: Optional[Union[Sequence, pd.DataFrame]],
meta_dict: Optional[Mapping]=None):
if data_table is None or isinstance(data_... | flexible | {
"blob_id": "0b833276ca10118f2d60e229ff03400b03915958",
"index": 2429,
"step-1": "<mask token>\n\n\nclass GenomicArray:\n <mask token>\n <mask token>\n <mask token>\n\n def __init__(self, data_table: Optional[Union[Sequence, pd.DataFrame]],\n meta_dict: Optional[Mapping]=None):\n if dat... | [
36,
38,
48,
49,
55
] |
from flask import logging
from flask_sqlalchemy import SQLAlchemy
from passlib.apps import custom_app_context as pwd_context
logger = logging.getLogger(__name__)
db = SQLAlchemy() # flask-sqlalchemy
class User(db.Model):
__tablename__ = 'users'
id = db.Column(db.Integer, primary_key=True)
username = db... | normal | {
"blob_id": "e976f7e423d75f7fc8a3d5cd597bdd9358ae317e",
"index": 5243,
"step-1": "<mask token>\n\n\nclass User(db.Model):\n __tablename__ = 'users'\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String(32), index=True)\n password_hash = db.Column(db.String(128))\n\n def h... | [
11,
13,
15,
16,
17
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
warnings.filterwarnings('ignore')
<|reserved_special_token_0|>
os.chdir(lib_path)
<|reserved_special_token_0|>
print(res.summary())
<|reserved_special_token_0|>
X0
<|reserved_special_token_0|>
b
<|reserved_special_token_0|>
covid_... | flexible | {
"blob_id": "2060f57cfd910a308d60ad35ebbbf9ffd5678b9c",
"index": 3519,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwarnings.filterwarnings('ignore')\n<mask token>\nos.chdir(lib_path)\n<mask token>\nprint(res.summary())\n<mask token>\nX0\n<mask token>\nb\n<mask token>\ncovid_actual.loc[:, 'Date':'human... | [
0,
1,
2,
3,
4
] |
from django.db.models import manager
from django.shortcuts import render
from django.http import JsonResponse
from rest_framework.response import Response
from rest_framework.utils import serializer_helpers
from rest_framework.views import APIView
from rest_framework.pagination import PageNumberPagination
from rest_fr... | normal | {
"blob_id": "34536e3112c8791c8f8d48bb6ffd059c1af38e2f",
"index": 8978,
"step-1": "<mask token>\n\n\nclass StockPagination(PageNumberPagination):\n page_size = 20\n page_size_query_param = 'page_size'\n max_page_size = 500\n\n\nclass StockView(APIView):\n\n def get(self, request, *args, **kwargs):\n ... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
@service_marker
class TestTrainingDebuggerJob:
def _wait_sagemaker_training_rule_eval_status(self, training_job_name,
rule_type: str, expected_status: str, wait_periods: int=30,
period_length: int=30):
return wait_for_status(expected_status, wait_periods, peri... | flexible | {
"blob_id": "6f107d0d0328c2445c0e1d0dd10e51227da58129",
"index": 3900,
"step-1": "<mask token>\n\n\n@service_marker\nclass TestTrainingDebuggerJob:\n\n def _wait_sagemaker_training_rule_eval_status(self, training_job_name,\n rule_type: str, expected_status: str, wait_periods: int=30,\n period_le... | [
4,
7,
8,
9,
11
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TestComparisonExpression:
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TestComparisonExpression:
def test_cmp(self):
assert exp.parse('CMP(1, 2)') == {'... | flexible | {
"blob_id": "91959f6621f05b1b814a025f0b95c55cf683ded3",
"index": 5856,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TestComparisonExpression:\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass TestComparisonExpression:\n\n def test_cmp(self):\n assert exp.parse('CMP(1, 2)') ... | [
0,
1,
2,
3
] |
from tensorflow import keras
class SkippableSeq(keras.utils.Sequence):
def __init__(self, seq):
super(SkippableSeq, self).__init__()
self.start = 0
self.seq = seq
def __iter__(self):
return self
def __next__(self):
res = self.seq[self.start]
self.start = (self.start + 1) % len(self)
... | normal | {
"blob_id": "2417dd4f3787742832fec53fec4592165d0fccfc",
"index": 9513,
"step-1": "<mask token>\n\n\nclass SkippableSeq(keras.utils.Sequence):\n\n def __init__(self, seq):\n super(SkippableSeq, self).__init__()\n self.start = 0\n self.seq = seq\n\n def __iter__(self):\n return se... | [
9,
10,
11,
12,
13
] |
def find_max(a, b):
if a > b:
return a
return b
def find_max_three(a, b, c):
return find_max(a, find_max(b, c))
| normal | {
"blob_id": "71dc429033b159f6ed806358f2286b4315e842d9",
"index": 9617,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef find_max_three(a, b, c):\n return find_max(a, find_max(b, c))\n",
"step-3": "def find_max(a, b):\n if a > b:\n return a\n return b\n\n\ndef find_max_three(a, b, ... | [
0,
1,
2
] |
# binary search
# iterative
def Iter_BinarySearch(array,b,e,value):
while(b<=e):#pay attention to the judgement!
mid=(b+e)/2#floor
if (array[mid]<value):#value in [mid,e]
b=mid+1
elif (array[mid]>value):#value in [b,mid]
e=mid-1
else:
print "find ... | normal | {
"blob_id": "f2d7f0b0d27bd43223d0eb6a6279b67968461dad",
"index": 9499,
"step-1": "# binary search\n\n# iterative\ndef Iter_BinarySearch(array,b,e,value):\n while(b<=e):#pay attention to the judgement!\n mid=(b+e)/2#floor\n if (array[mid]<value):#value in [mid,e]\n b=mid+1\n eli... | [
0
] |
variable_1 = 100
variable_2 = 500
variable_3 = 222.5
variable_4 = 'Hello'
variable_5 = 'world'
print(variable_1, variable_2, variable_3, sep=', ')
print(variable_4, variable_5, sep=', ', end='!\n')
user_age = input('Введите ваш возраст: ')
user_name = input('Введите ваше имя: ')
print(variable_4 + ', ' + user_name + '!... | normal | {
"blob_id": "12ca9a81574d34d1004ac9ebcb2ee4b31d7171e2",
"index": 5623,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(variable_1, variable_2, variable_3, sep=', ')\nprint(variable_4, variable_5, sep=', ', end='!\\n')\n<mask token>\nprint(variable_4 + ', ' + user_name + '! ' + 'Ваш возраст: ' + user... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class CopoChunkedUploadCompleteView(ChunkedUploadCompleteView):
do_md5_check = False
def get_response_data(self, chunked_upload, request):
"""
Data for the response. Should return a dictionary-like object.
Called *only* if POST is successful.
"""
... | flexible | {
"blob_id": "2b7415d86f9157ae55228efdd61c9a9e9920bc5c",
"index": 7716,
"step-1": "<mask token>\n\n\nclass CopoChunkedUploadCompleteView(ChunkedUploadCompleteView):\n do_md5_check = False\n\n def get_response_data(self, chunked_upload, request):\n \"\"\"\n Data for the response. Should return ... | [
12,
13,
14,
15,
18
] |
#!/usr/bin/python
# encoding: utf-8
#
# In case of reuse of this source code please do not remove this copyright.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the Licen... | normal | {
"blob_id": "a7218971b831e2cfda9a035eddb350ecf1cdf938",
"index": 17,
"step-1": "#!/usr/bin/python\n# encoding: utf-8\n#\n# In case of reuse of this source code please do not remove this copyright.\n#\n#\tThis program is free software: you can redistribute it and/or modify\n#\tit under the terms of the GNU Gener... | [
0
] |
import unittest
import json
import os
import copy
from nested.nested_dict import NestedDict
from pprint import pprint
class TestNestedDict(unittest.TestCase):
@classmethod
def setUpClass(cls):
path = os.path.dirname(__file__)
cls.afile = os.path.join(path, '../nested/data/food_nested_dict.jso... | normal | {
"blob_id": "f9a255a464b5f48a1a8be2e2887db721a92e7f4e",
"index": 1474,
"step-1": "<mask token>\n\n\nclass TestNestedDict(unittest.TestCase):\n <mask token>\n <mask token>\n\n def test_dfood(self):\n self.assertEqual(self.dfood.keys(), [u'0001', u'0002', u'0003'])\n <mask token>\n <mask toke... | [
3,
6,
8,
10,
12
] |
from collections import namedtuple
from os import getenv
from pathlib import Path
TMP = getenv("TMP", "/tmp")
PYBITES_FAKER_DIR = Path(getenv("PYBITES_FAKER_DIR", TMP))
CACHE_FILENAME = "pybites-fake-data.pkl"
FAKE_DATA_CACHE = PYBITES_FAKER_DIR / CACHE_FILENAME
BITE_FEED = "https://codechalleng.es/api/bites/"
BLOG_FE... | normal | {
"blob_id": "7336b8dec95d23cbcebbff2a813bbbd5575ba58f",
"index": 2327,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nTMP = getenv('TMP', '/tmp')\nPYBITES_FAKER_DIR = Path(getenv('PYBITES_FAKER_DIR', TMP))\nCACHE_FILENAME = 'pybites-fake-data.pkl'\nFAKE_DATA_CACHE = PYBITES_FAKER_DIR / CACHE_FILENAME\nBI... | [
0,
1,
2,
3
] |
v1=int(input("Introdu virsta primei persoane"))
v2=int(input("Introdu virsta persoanei a doua"))
v3=int(input("Introdu virsta persoanei a treia"))
if ((v1>18)and(v1<60)):
print(v1)
elif((v2>18)and(v2<60)):
print(v2)
elif((v3>18)and(v3<60)):
print(v3) | normal | {
"blob_id": "b8c749052af0061373808addea3ad419c35e1a29",
"index": 3324,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif v1 > 18 and v1 < 60:\n print(v1)\nelif v2 > 18 and v2 < 60:\n print(v2)\nelif v3 > 18 and v3 < 60:\n print(v3)\n",
"step-3": "v1 = int(input('Introdu virsta primei persoane'... | [
0,
1,
2,
3
] |
from .personal_questions import *
from .survey_questions import *
| normal | {
"blob_id": "a8f2d527e9824d3986f4bb49c3cc75fd0d999bf7",
"index": 3290,
"step-1": "<mask token>\n",
"step-2": "from .personal_questions import *\nfrom .survey_questions import *\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
<|reserved_special_token_0|>
def load_norm_file(fname):
"""Parse the norm file and return the mean system norms"""
try:
with open(fname, 'r') as fh:
lines = fh.readlines()
norms = [float(ll.strip().split()[0]) for ll in lines]
return norms
except:
return... | flexible | {
"blob_id": "d03669924233edf33fcb6645f5ed7ab118f54a95",
"index": 7610,
"step-1": "<mask token>\n\n\ndef load_norm_file(fname):\n \"\"\"Parse the norm file and return the mean system norms\"\"\"\n try:\n with open(fname, 'r') as fh:\n lines = fh.readlines()\n norms = [float(ll.s... | [
5,
6,
7,
8,
9
] |
import random
import numpy as np
import torch
from utils import print_result, set_random_seed, get_dataset, get_extra_args
from cogdl.tasks import build_task
from cogdl.datasets import build_dataset
from cogdl.utils import build_args_from_dict
DATASET_REGISTRY = {}
def build_default_args_for_node_classification(da... | normal | {
"blob_id": "2396f7acab95260253c367c62002392760157705",
"index": 1236,
"step-1": "<mask token>\n\n\ndef build_default_args_for_node_classification(dataset):\n cpu = not torch.cuda.is_available()\n args = {'lr': 0.01, 'weight_decay': 0.0005, 'max_epoch': 1000,\n 'max_epochs': 1000, 'patience': 100, '... | [
5,
6,
7,
8,
10
] |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import tempfile
from functools import partial
import numpy as np
import torch
from ax.benchmark.benchmark_pr... | normal | {
"blob_id": "52eec56f7f5da8356f61301994f846ef7769f73b",
"index": 6189,
"step-1": "<mask token>\n\n\nclass JSONStoreTest(TestCase):\n\n def setUp(self):\n self.experiment = get_experiment_with_batch_and_single_trial()\n\n def testJSONEncodeFailure(self):\n self.assertRaises(JSONEncodeError, ob... | [
7,
10,
12,
14,
18
] |
<|reserved_special_token_0|>
def get_datas():
filename = None
while True:
filename = input('Please enter filename:')
if not filename.strip():
print('Filename is empty!')
continue
if not os.path.exists(filename):
print('File is not exists!')
... | flexible | {
"blob_id": "6829f7bcbc1b12500795eec19829ff077502e270",
"index": 3260,
"step-1": "<mask token>\n\n\ndef get_datas():\n filename = None\n while True:\n filename = input('Please enter filename:')\n if not filename.strip():\n print('Filename is empty!')\n continue\n ... | [
6,
7,
8,
10,
12
] |
<|reserved_special_token_0|>
class RoomView(View):
def get(self, request, room_id):
try:
room = Room.objects.get(id=room_id)
rating_list = [field.name for field in Review._meta.get_fields(
) if field.name not in ['id', 'review_user', 'review_room',
... | flexible | {
"blob_id": "cc5b22a0246fcc9feaed6a0663095a6003e6cef1",
"index": 6685,
"step-1": "<mask token>\n\n\nclass RoomView(View):\n\n def get(self, request, room_id):\n try:\n room = Room.objects.get(id=room_id)\n rating_list = [field.name for field in Review._meta.get_fields(\n ... | [
6,
7,
8,
9,
10
] |
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func, inspect
from flask import Flask, jsonify, render_template, redirect
from flask_pymongo import PyMongo
from config import mongo_password, mongo_username, sql_username, sql_pass... | normal | {
"blob_id": "15e1ce95398ff155fe594c3b39936d82d71ab9e2",
"index": 5015,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef index():\n pokemon_data = mongo.db.pokemon.find_one()\n return render_template('index.html', pokemon_data=pokemon_data)\n\n\n@app.route('/stats')\ndef stats():\n session = Session(eng... | [
3,
4,
5,
6,
7
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2015 RAPP
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#http://www.apache.org/licenses/LICENSE-2.0
# Unless required by app... | normal | {
"blob_id": "f4e287f5fce05e039c54f1108f6e73020b8d3d8f",
"index": 9346,
"step-1": "<mask token>\n\n\nclass AsyncHandler(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass AsyncHandler(object):\n <mask token>\n\n def __init__(self, future... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class ncbDB(Pconfig):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def ncb_getQuery(self, querySQL):
result = []
try:
with self.connect_... | flexible | {
"blob_id": "257a4d0b0c713624ea8452dbfd6c5a96c9a426ad",
"index": 8344,
"step-1": "<mask token>\n\n\nclass ncbDB(Pconfig):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def ncb_getQuery(self, querySQL):\n result = []\n try:\n with self.co... | [
4,
7,
8,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
sys.path.append('./Pytorch-UNet/')
<|reserved_special_token_0|>
if __name__ == '__main__':
logger = Logger()
torch.backends.cudnn.benchmark = True
args = parse_args()
logger.update_args(args)
if not os.path.exi... | flexible | {
"blob_id": "fbd5c7fa335d6bde112e41a55d15aee31e3ebaf7",
"index": 2759,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append('./Pytorch-UNet/')\n<mask token>\nif __name__ == '__main__':\n logger = Logger()\n torch.backends.cudnn.benchmark = True\n args = parse_args()\n logger.update_... | [
0,
1,
2,
3
] |
import sys
from collections import deque
t = int(sys.stdin.readline().rstrip())
for _ in range(t):
n, m = map(int, sys.stdin.readline().split())
q = deque(map(int, sys.stdin.readline().split()))
count = 0
while q:
highest = max(q)
doc = q.popleft()
m -= 1
if doc != highes... | normal | {
"blob_id": "a571abd88184c8d8bb05245e9c3ce2e4dabb4c09",
"index": 615,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor _ in range(t):\n n, m = map(int, sys.stdin.readline().split())\n q = deque(map(int, sys.stdin.readline().split()))\n count = 0\n while q:\n highest = max(q)\n ... | [
0,
1,
2,
3
] |
from platypush.message.response import Response
class CameraResponse(Response):
pass
# vim:sw=4:ts=4:et:
| normal | {
"blob_id": "4c38d0487f99cdc91cbce50079906f7336e51482",
"index": 5462,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass CameraResponse(Response):\n pass\n",
"step-3": "from platypush.message.response import Response\n\n\nclass CameraResponse(Response):\n pass\n",
"step-4": "from platypu... | [
0,
1,
2,
3
] |
from core.models import Atom
from core.models.vector3d import cVector3D
from fractions import Fraction
class SpaceGroup(object):
def __init__(self,
index=None,
name=None,
lattice_system=None,
lattice_centering=None,
inversion=Non... | normal | {
"blob_id": "88731049227629ed84ff56922d7ac11d4a137984",
"index": 5376,
"step-1": "<mask token>\n\n\nclass Centering(object):\n\n def __init__(self, letter, additional_lattice_points):\n self.letter = letter\n self.additional_lattice_points = additional_lattice_points\n\n def transform(self, o... | [
15,
28,
33,
34,
44
] |
import os
import h5py
import numpy as np
import torch
from datasets.hdf5 import get_test_datasets
from unet3d import utils
from unet3d.config import load_config
from unet3d.model import get_model
logger = utils.get_logger('UNet3DPredictor')
def predict(model, hdf5_dataset, config):
"""
Return prediction ma... | normal | {
"blob_id": "6fba773025268d724283e510a03d0592282adb0a",
"index": 1780,
"step-1": "<mask token>\n\n\ndef save_predictions(prediction_maps, output_file, dataset_names):\n \"\"\"\n Saving probability maps to a given output H5 file. If 'average_channels'\n is set to True average the probability_maps across ... | [
2,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
class CompanyInfo(object):
def __init__(self):
self._alter_list = None
self._basic_info = None
self._case_info_list = None
self._entinv_list = None
self._fr_position_list = None
self._frinv_list = None
self._person_list = None
... | flexible | {
"blob_id": "6743a4f3c9118e790e52b586a36d71a735101702",
"index": 1901,
"step-1": "<mask token>\n\n\nclass CompanyInfo(object):\n\n def __init__(self):\n self._alter_list = None\n self._basic_info = None\n self._case_info_list = None\n self._entinv_list = None\n self._fr_posi... | [
14,
18,
19,
20,
22
] |
import pandas as pd
import csv
import numpy as np
import matplotlib.pyplot as plt
#import csv file with recorded left, right servo angles and their corresponding roll and pitch values
df = pd.read_csv('C:/Users/yuyan.shi/Desktop/work/head-neck/kinematics/tabblepeggy reference tables/mid_servo_angle_2deg_3.csv') ... | normal | {
"blob_id": "fd7961d3a94b53ae791da696bb2024165db8b8fc",
"index": 5354,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.scatter(df['left_rel_angle'], df['right_rel_angle'])\nplt.xlabel('Left servo angle(deg)')\nplt.ylabel('Right servo angle(deg)')\nplt.title('Plot of left and right servo values')\nplt.... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class HomePageView(TemplateView):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class HomePageView(TemplateView):
template_name = 'base.html'
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def index(request):
contex... | flexible | {
"blob_id": "f0a54feaa165a393c4e87cbac2a38347633acf5a",
"index": 1425,
"step-1": "<mask token>\n\n\nclass HomePageView(TemplateView):\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass HomePageView(TemplateView):\n template_name = 'base.html'\n",
"step-3": "<mask token>\n\n\ndef index(request):\n ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('app.root_path===', app.root_path)
print('app.static_url_path===', app.static_url_path)
app.secret_key('uaremyhero')
<|reserved_special_token_0|>
Session(app)
app.register_blueprint(login.login)
app.register_blueprint()
<|... | flexible | {
"blob_id": "9d2fdf47b5c4b56cc0177a9c0a86b1ed57c88d49",
"index": 4151,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('app.root_path===', app.root_path)\nprint('app.static_url_path===', app.static_url_path)\napp.secret_key('uaremyhero')\n<mask token>\nSession(app)\napp.register_blueprint(login.logi... | [
0,
1,
2,
3,
4
] |
from page_parsing import get_item_info_from,url_list,item_info,get_links_from
# ================================================= < <链接去重 > > =====================================================
# 设计思路：
# 1.分两个数据库，第一个用于只用于存放抓取下来的 url (ulr_list)；第二个则储存 url 对应的物品详情信息(item_info)
# 2.在抓取过程中在第二个数据库中写... | normal | {
"blob_id": "4f2017632d905c80c35fbaead83ecb7e1ac95760",
"index": 9868,
"step-1": " from page_parsing import get_item_info_from,url_list,item_info,get_links_from\n\n\n # ================================================= < <链接去重 > > =====================================================\n\n # 设计思路：\n # ... | [
0
] |
import pymongo
client = pymongo.MongoClient("mongodb://localhost:27017/")
# Database Name
db = client["Test"]
# Collection Name
col = db["C100"]
x = col.find_one()
print(x) | normal | {
"blob_id": "7d10fb58aa5213516c656c05966fcaad6868ae81",
"index": 1548,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(x)\n",
"step-3": "<mask token>\nclient = pymongo.MongoClient('mongodb://localhost:27017/')\ndb = client['Test']\ncol = db['C100']\nx = col.find_one()\nprint(x)\n",
"step-4": "im... | [
0,
1,
2,
3,
4
] |
# Databricks notebook source
#import and create sparksession object
from pyspark.sql import SparkSession
spark=SparkSession.builder.appName('rc').getOrCreate()
# COMMAND ----------
#import the required functions and libraries
from pyspark.sql.functions import *
# COMMAND ----------
# Convert csv file to Spark Data... | normal | {
"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
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for group in groups:
allananswers = set(list('abcdefghijklmnopqrstuvwxyz'))
answers = set()
people = group.split('\n')
for person in people:
allananswers = allananswers & set(list(person))
for answe... | flexible | {
"blob_id": "8f1ec65ca60605747f46f596e0b5848922bcd0b5",
"index": 2127,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor group in groups:\n allananswers = set(list('abcdefghijklmnopqrstuvwxyz'))\n answers = set()\n people = group.split('\\n')\n for person in people:\n allananswers = a... | [
0,
1,
2,
3,
4
] |
import os
import io
import yaml
from collections import OrderedDict
from rich.console import Console
from malwarebazaar.platform import get_config_path, get_config_dir
class Config(OrderedDict):
instance = None
def __init__(self):
ec = Console(stderr=True, style="bold red")
Config.ensure_pa... | normal | {
"blob_id": "5a9e0b220d2c94aea7e3d67338771cf48c3aec8f",
"index": 6439,
"step-1": "<mask token>\n\n\nclass Config(OrderedDict):\n <mask token>\n\n def __init__(self):\n ec = Console(stderr=True, style='bold red')\n Config.ensure_path(ec)\n config_file = get_config_path()\n if not... | [
4,
5,
6,
7,
8
] |
from .ctoybox import Game, State as FrameState, Input
import numpy as np
from PIL import Image
import json
from typing import Dict, Any, List, Tuple, Union, Optional
def json_str(js: Union[Dict[str, Any], Input, str]) -> str:
"""
Turn an object into a JSON string -- handles dictionaries, the Input class, and... | normal | {
"blob_id": "c77e320cee90e8210e4c13d854649b15f6e24180",
"index": 2798,
"step-1": "<mask token>\n\n\nclass Toybox(object):\n <mask token>\n\n def __init__(self, game_name: str, grayscale: bool=True, frameskip: int\n =0, seed: Optional[int]=None, withstate: Optional[dict]=None):\n \"\"\"\n ... | [
27,
30,
39,
59,
65
] |
import requests
url = 'https://item.jd.com/100008348550.html'
try:
r = requests.get(url)
r.raise_for_status()
print(r.encoding)
r.encoding = r.apparent_encoding
print(r.text[:1000])
print(r.apparent_encoding)
except:
print('error')
| normal | {
"blob_id": "0271c45a21047b948946dd76f147692bb16b8bcf",
"index": 5378,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n r = requests.get(url)\n r.raise_for_status()\n print(r.encoding)\n r.encoding = r.apparent_encoding\n print(r.text[:1000])\n print(r.apparent_encoding)\nexcept:\n... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
os.chdir(main_dir)
<|reserved_special_token_0|>
for col in loan_seller_cols:
cmbs.drop(columns=col, axis=1, inplace=True)
<|reserved_special_token_0|>
for key, value in regex_dict.items():
cmbs.columns = [re.sub(key, value... | flexible | {
"blob_id": "eb890c68885cbab032ce9d6f3be3fd7013a2788b",
"index": 2140,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nos.chdir(main_dir)\n<mask token>\nfor col in loan_seller_cols:\n cmbs.drop(columns=col, axis=1, inplace=True)\n<mask token>\nfor key, value in regex_dict.items():\n cmbs.columns = [... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def aitoff_projection(theta, phi):
import numpy as np
theta = theta - np.pi
cos_phi = np.cos(phi)
denom = np.sqrt(1 + cos_phi * np.cos(theta / 2))
x = 180 * cos_phi * np.sin(theta / 2) / denom
x = x + 180
y = 90 * np.sin(phi) / den... | flexible | {
"blob_id": "0dcf90514543a1ca801e82cd402b3e1002b1f5d0",
"index": 9262,
"step-1": "<mask token>\n",
"step-2": "def aitoff_projection(theta, phi):\n import numpy as np\n theta = theta - np.pi\n cos_phi = np.cos(phi)\n denom = np.sqrt(1 + cos_phi * np.cos(theta / 2))\n x = 180 * cos_phi * np.sin(th... | [
0,
1,
2
] |
from django.shortcuts import redirect, render
from users.models import CustomUser
from .models import Profile
def profile_page_view(request, username):
current_user = request.user
user = CustomUser.objects.get(username=username)
profile = Profile.objects.get(user=user)
if current_user in profile.follow... | normal | {
"blob_id": "3caaa455cda0567b79ae063c777846157839d64f",
"index": 8548,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef profile_page_view(request, username):\n current_user = request.user\n user = CustomUser.objects.get(username=username)\n profile = Profile.objects.get(user=user)\n if ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class event_ticket(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_t... | flexible | {
"blob_id": "bddba2fd710829db17c6419878ce535df0aba01c",
"index": 2760,
"step-1": "<mask token>\n\n\nclass event_ticket(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>... | [
7,
12,
14,
19,
20
] |
<|reserved_special_token_0|>
def mutate_operator(root, nodes, path):
candidates = [node for node in nodes.keys() if type(node) in OP_TYPES.
keys() and _check_parent_type(node, nodes, OP_PARENT_TYPES)]
if len(candidates) == 0:
return -1
mut_node = random.choice(candidates)
type_idx = OP... | flexible | {
"blob_id": "c0524301a79788aa34a039fc46799021fb45362c",
"index": 7141,
"step-1": "<mask token>\n\n\ndef mutate_operator(root, nodes, path):\n candidates = [node for node in nodes.keys() if type(node) in OP_TYPES.\n keys() and _check_parent_type(node, nodes, OP_PARENT_TYPES)]\n if len(candidates) == ... | [
3,
4,
5,
6,
7
] |
__author__ = 'asistente'
#from __future__ import absolute_import
from unittest import TestCase
from selenium import webdriver
from selenium.webdriver.common.by import By
class FunctionalTest(TestCase):
def setUp(self):
self.browser = webdriver.Chrome("C:\\chromedriver\\chromedriver.exe")
self.b... | normal | {
"blob_id": "fc4cf800c663abf20bfba7fcc1032e09a992641b",
"index": 5334,
"step-1": "<mask token>\n\n\nclass FunctionalTest(TestCase):\n\n def setUp(self):\n self.browser = webdriver.Chrome('C:\\\\chromedriver\\\\chromedriver.exe')\n self.browser.implicitly_wait(2)\n\n def tearDown(self):\n ... | [
6,
7,
9,
13,
14
] |
from PyQt5.QtWidgets import QWidget, QHBoxLayout, QGraphicsOpacityEffect, \
QPushButton
from PyQt5.QtCore import Qt
class ToolBar(QWidget):
"""
Window for entering parameters
"""
def __init__(self, parent):
super().__init__(parent)
self._main_wnd = parent
self.setAttribut... | normal | {
"blob_id": "772e2e0a442c1b63330e9b526b76d767646b0c7c",
"index": 7819,
"step-1": "<mask token>\n\n\nclass ToolBar(QWidget):\n <mask token>\n\n def __init__(self, parent):\n super().__init__(parent)\n self._main_wnd = parent\n self.setAttribute(Qt.WA_StyledBackground, True)\n sel... | [
3,
5,
6,
9,
10
] |
config_prefix = "<"
config_suported_types = ["PNG", "GIF", "JPEG"]
config_pattern = "^[A-Za-z0-9_]*$"
config_max_storage = int(1E9)
config_max_name_length = 20
config_message_by_line = 2
config_max_message_length = 2000
config_max_emote_length = 8*int(1E6)
config_pong = """
,;;;!!!!!;;.
:!!!!!!!!!!!!!!;
... | normal | {
"blob_id": "dc2deb7d4c9cc126a6d80435fe9dbc16d6ac8941",
"index": 9397,
"step-1": "<mask token>\n",
"step-2": "config_prefix = '<'\nconfig_suported_types = ['PNG', 'GIF', 'JPEG']\nconfig_pattern = '^[A-Za-z0-9_]*$'\nconfig_max_storage = int(1000000000.0)\nconfig_max_name_length = 20\nconfig_message_by_line = 2\... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def main():
with open('./src/test/predictions.json', 'r') as f:
data = json.load(f)
total = len(data['label'])
google = 0
sphinx = 0
for i in range(len(data['label'])):
label = data['label'][i... | flexible | {
"blob_id": "9fc184fe3aa498138138403bef719c59b85b3a80",
"index": 4392,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n with open('./src/test/predictions.json', 'r') as f:\n data = json.load(f)\n total = len(data['label'])\n google = 0\n sphinx = 0\n for i in range(l... | [
0,
1,
2,
3,
4
] |
# Задание 1
# Выучите основные стандартные исключения, которые перечислены в данном уроке.
# Задание 2
# Напишите программу-калькулятор, которая поддерживает следующие операции: сложение, вычитание,
# умножение, деление и возведение в степень. Программа должна выдавать сообщения об ошибке и
# продолжать работу при ввод... | normal | {
"blob_id": "a8341bf422a4d31a83ff412c6aac75e5cb8c5e0f",
"index": 5876,
"step-1": "<mask token>\n\n\ndef adding(user_list):\n sumnum = 0\n for item in user_list:\n sumnum += item\n return sumnum\n\n\ndef subtraction(user_list):\n subtractnum = user_list[0]\n for item in user_list[1:]:\n ... | [
3,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [path('', views.artifact, name='artifacts'), path(
'<int:artifact_id>', views.detail, name='detail'), path('register/',
views.register, name='register')]
<|reserved_special_token_1|>
from django.contrib im... | flexible | {
"blob_id": "9b73037e8af7d4f91261cebf895b68650182fcd5",
"index": 2780,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('', views.artifact, name='artifacts'), path(\n '<int:artifact_id>', views.detail, name='detail'), path('register/',\n views.register, name='register')]\n",
"st... | [
0,
1,
2,
3
] |
from xgboost import XGBRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import pandas as pd
import numpy as np
from ghg import GHGPredictor
predictor = GHGPredictor()
dataset_df = pd.read_csv("db-wheat.csv", index_col=0)
# print(dataset_df.iloc[1])
dataset_d... | normal | {
"blob_id": "0ebd3ca5fd29b0f2f2149dd162b37f39668f1c58",
"index": 7397,
"step-1": "<mask token>\n\n\ndef predict(model, row):\n preds = []\n for perc in range(-10, 11):\n new_row = row.copy()\n row_copy = row.copy()\n new_row = new_row.drop(labels=['Area', 'Year', 'Crop',\n '... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
api_id = '2168275'
api_hash = 'e011a9cb95b7e7e153aa5840985fc883'
<|reserved_special_token_1|>
api_id = "2168275"
api_hash = "e011a9cb95b7e7e153aa5840985fc883"
| flexible | {
"blob_id": "c6d6fcc242e1b63104a3f3eb788880635257ff4c",
"index": 7503,
"step-1": "<mask token>\n",
"step-2": "api_id = '2168275'\napi_hash = 'e011a9cb95b7e7e153aa5840985fc883'\n",
"step-3": "api_id = \"2168275\"\napi_hash = \"e011a9cb95b7e7e153aa5840985fc883\"\n",
"step-4": null,
"step-5": null,
"step-... | [
0,
1,
2
] |
while True:
print("Light Levels:" + input.light_level())
if input.light_level() < 6:
light.set_all(light.rgb(255, 0, 255))
elif input.light_level() < 13:
light.set_all(light.rgb(255, 0, 0))
else:
light.clear()
| normal | {
"blob_id": "7277b045f85d58383f26ab0d3299feb166f45e36",
"index": 2575,
"step-1": "<mask token>\n",
"step-2": "while True:\n print('Light Levels:' + input.light_level())\n if input.light_level() < 6:\n light.set_all(light.rgb(255, 0, 255))\n elif input.light_level() < 13:\n light.set_all(... | [
0,
1,
2
] |
"""empty message
Revision ID: 3e4ee9eaaeaa
Revises: 6d58871d74a0
Create Date: 2016-07-25 15:30:38.008238
"""
# revision identifiers, used by Alembic.
revision = '3e4ee9eaaeaa'
down_revision = '6d58871d74a0'
from alembic import op
import sqlalchemy as sa
def upgrade():
### commands auto generated by Alembic - ... | normal | {
"blob_id": "db49313d2bc8b9f0be0dfd48c6065ea0ab3294cb",
"index": 4032,
"step-1": "<mask token>\n\n\ndef downgrade():\n op.drop_index(op.f('ix_account_sub_int'), table_name='account')\n op.drop_index(op.f('ix_account_mac'), table_name='account')\n op.drop_index(op.f('ix_account_interface'), table_name='a... | [
1,
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
# @Time : 2019/3/5 上午9:55
# @Author : yidxue
from src.handler.base.base_handler import BaseHandler
from src.utils.tools import read_model
from tornado.options import options
import os
module_path = os.path.abspath(os.path.join(os.curdir))
model_path = os.path.join(module_path, 'model')
cl... | normal | {
"blob_id": "a8ae59bb525c52ef852655f0ef1e32d96c8914d6",
"index": 1356,
"step-1": "<mask token>\n\n\nclass ReloadModelHandler(BaseHandler):\n\n def __init__(self, application, request, **kwargs):\n super(ReloadModelHandler, self).__init__(application, request, **kwargs\n )\n <mask token>\n... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def process_resources(_res_iter):
for rows in _res_iter:
def process_rows(_rows):
for row in _rows:
for column in columns:
if column in row:
del row[column]
yield row
yield process... | flexible | {
"blob_id": "17b3fb44d9e7a09fe3b807b47bdc0248b6960634",
"index": 4022,
"step-1": "<mask token>\n\n\ndef process_resources(_res_iter):\n for rows in _res_iter:\n\n def process_rows(_rows):\n for row in _rows:\n for column in columns:\n if column in row:\n ... | [
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def partition_benthic(reach, runoff, runoff_mass, erosion_mass):
from .parameters import soil, stream_channel, benthic
try:
reach = self.region.flow_file.fetch(reach)
q, v, l = reach.q, reach.v, reach.l
except AttributeError:
return None, None, (None, N... | flexible | {
"blob_id": "5890525b16b42578ac06e7ab2170c5613feea0a5",
"index": 6494,
"step-1": "<mask token>\n\n\ndef partition_benthic(reach, runoff, runoff_mass, erosion_mass):\n from .parameters import soil, stream_channel, benthic\n try:\n reach = self.region.flow_file.fetch(reach)\n q, v, l = reach.q,... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class TestComponents(unittest.TestCase):
def setUp(self):
self.cpu = CPU(cycles=5)
self.disk = DiskIO(cycles=5)
self.network = Network(cycles=5)
def test_cpu_length(self):
cpu_data = self.cpu.get_data(start=0, stop=1, noise=0.01)
self.asse... | flexible | {
"blob_id": "4f54f3e306df3b861124adb4fe544089446e8021",
"index": 3453,
"step-1": "<mask token>\n\n\nclass TestComponents(unittest.TestCase):\n\n def setUp(self):\n self.cpu = CPU(cycles=5)\n self.disk = DiskIO(cycles=5)\n self.network = Network(cycles=5)\n\n def test_cpu_length(self):\... | [
4,
5,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def density(arr, ax=None, logx=False, logy=False, bins=25, mode='density',
extent=None, contours=[], percentiles=True, relim=True, cmap=
DEFAULT_CONT_COLORMAP, shading='auto', vmin=0.0, colorbar=False, **kwargs):
"""... | flexible | {
"blob_id": "ae475dc95c6a099270cf65d4b471b4b430f02303",
"index": 8840,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef density(arr, ax=None, logx=False, logy=False, bins=25, mode='density',\n extent=None, contours=[], percentiles=True, relim=True, cmap=\n DEFAULT_CONT_COLORMAP, shading='auto... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class DynamicVPTree:
<|reserved_special_token_0|>
def __init__(self, dist_fn, min_tree_size=4):
"""
:param dist_fn: Metric distance function used for vp-trees
:param min_tree_size: Minimum number of nodes to form a tree (extra nodes are stored in a pool un... | flexible | {
"blob_id": "22e6616fb98ecfb256587c3767c7c289decc6bf6",
"index": 3049,
"step-1": "<mask token>\n\n\nclass DynamicVPTree:\n <mask token>\n\n def __init__(self, dist_fn, min_tree_size=4):\n \"\"\"\n :param dist_fn: Metric distance function used for vp-trees\n :param min_tree_size: Minimu... | [
4,
6,
9,
11,
12
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def IsContinuous(numbers):
if not numbers or len(numbers) < 1:
return False
numbers.sort()
number_of_zero = 0
number_of_gap = 0
for i in range(len(numbers)):
if numbers[i] == 0:
number_of_zero += 1
small = n... | flexible | {
"blob_id": "68a776d7fccc8d8496a944baff51d2a862fc7d31",
"index": 1259,
"step-1": "<mask token>\n",
"step-2": "def IsContinuous(numbers):\n if not numbers or len(numbers) < 1:\n return False\n numbers.sort()\n number_of_zero = 0\n number_of_gap = 0\n for i in range(len(numbers)):\n ... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def merge(p, n):
global vet
global aux
if n <= 1:
return 0
c = merge(p, n // 2) + merge(p + n // 2, n - n // 2)
d, a, b = 0, 0, n // 2
while d < n:
if a != n // 2 and (b == n or vet[p + a]... | flexible | {
"blob_id": "fe081a422db6b7f10c89179beab852c6b74ec687",
"index": 2795,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef merge(p, n):\n global vet\n global aux\n if n <= 1:\n return 0\n c = merge(p, n // 2) + merge(p + n // 2, n - n // 2)\n d, a, b = 0, 0, n // 2\n while d <... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def readimg(dirs, imgname):
img = cv2.imread(dirs + imgname)
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
return img
def readimg_color(dirs, imgname):
img = cv2.imread(dirs + imgname)
img = cv2.normalize(img.astype('float'), None, 0.0, 1.0, cv2.NORM_MINMAX)
return... | flexible | {
"blob_id": "e08ab06be0957e5e173df798742abc493eac84d0",
"index": 6006,
"step-1": "<mask token>\n\n\ndef readimg(dirs, imgname):\n img = cv2.imread(dirs + imgname)\n img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n return img\n\n\ndef readimg_color(dirs, imgname):\n img = cv2.imread(dirs + imgname)\n ... | [
9,
11,
14,
15,
16
] |
import pickle
import time
DECAY = 0.95
DEPTH = 2
def init_cache(g):
'''
Initialize simrank cache for graph g
'''
g.cache = {}
def return_and_cache(g, element, val):
'''
Code (and function name) is pretty self explainatory here
'''
g.cache[element] = val
return val
def simrank_impl(g, node1, node2, t, is_wei... | normal | {
"blob_id": "535ee547475fbc2e1c0ee59e3e300beda1489d47",
"index": 4215,
"step-1": "import pickle\nimport time\nDECAY = 0.95\nDEPTH = 2\n\ndef init_cache(g):\n\t'''\n\tInitialize simrank cache for graph g\n\t'''\n\tg.cache = {}\n\ndef return_and_cache(g, element, val):\n\t'''\n\tCode (and function name) is pretty ... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def make_single_prediction(wav_file, model, is_game):
""" Predictions with model that is locally saved
:param wav_file: wav-file we want to predict
:param model: Trained model for our predictions
:return: None "... | flexible | {
"blob_id": "a17c448b068b28881f9d0c89be6037503eca3974",
"index": 5700,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef make_single_prediction(wav_file, model, is_game):\n \"\"\" Predictions with model that is locally saved\n\n :param wav_file: wav-file we want to predict\n :param model: T... | [
0,
1,
2,
3,
4
] |
print("2 + 3 * 4 =")
print(2 + 3 * 4)
print("2 + (3 * 4) = ")
print(2 + (3 * 4))
| normal | {
"blob_id": "58d137d614a0d5c11bf4325c1ade13f4f4f89f52",
"index": 3184,
"step-1": "<mask token>\n",
"step-2": "print('2 + 3 * 4 =')\nprint(2 + 3 * 4)\nprint('2 + (3 * 4) = ')\nprint(2 + 3 * 4)\n",
"step-3": "print(\"2 + 3 * 4 =\")\nprint(2 + 3 * 4)\n\nprint(\"2 + (3 * 4) = \")\nprint(2 + (3 * 4))\n",
"step-... | [
0,
1,
2
] |
from opengever.propertysheets.assignment import get_document_assignment_slots
from opengever.propertysheets.assignment import get_dossier_assignment_slots
from opengever.propertysheets.storage import PropertySheetSchemaStorage
from plone.restapi.services import Service
LISTING_TO_SLOTS = {
u'dossiers': get_dossie... | normal | {
"blob_id": "ab352c9431fda19bc21a9f7ffa075303641cca45",
"index": 155,
"step-1": "<mask token>\n\n\nclass ListingCustomFieldsGet(Service):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass ListingCustomFieldsGet(Service):\n <mask token>\n\n def reply(self):\n solr_fields = ... | [
1,
2,
3,
5,
6
] |
class Pinnwand:
def __init__(self):
self.__zettel = []
def hefteAn(self, notiz):
prio = notiz.count('!')
self.__zettel.append((prio, notiz))
<|reserved_special_token_0|>
def __str__(self):
ausgabe = 'Notizen\n'
zettelListe = self.__zettel[:]
zettelListe... | flexible | {
"blob_id": "382a3b8bcd07c7098cecf2b770e46dfff50eeb98",
"index": 2695,
"step-1": "class Pinnwand:\n\n def __init__(self):\n self.__zettel = []\n\n def hefteAn(self, notiz):\n prio = notiz.count('!')\n self.__zettel.append((prio, notiz))\n <mask token>\n\n def __str__(self):\n ... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def parse(query):
print('parsing the query...')
query = dnf_converter.convert(query)
cp_clause_list = []
clause_list = []
for cp in query['$or']:
clauses = []
if '$and' in cp:
for ... | flexible | {
"blob_id": "999de0965efa3c1fe021142a105dcf28184cd5ba",
"index": 43,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef parse(query):\n print('parsing the query...')\n query = dnf_converter.convert(query)\n cp_clause_list = []\n clause_list = []\n for cp in query['$or']:\n claus... | [
0,
1,
2,
3
] |
#!/usr/bin/python
import serial
import time
import sys
senderId="\x01"
receiverId="\x00"
#openSerial just opens the serial connection
def openSerial(port):
#Some configuration for the serial port
ser = serial.Serial()
ser.baudrate = 300
ser.port = port
ser.bytesize = 8
ser.stopbits = 2
ser.open()
return ser
... | normal | {
"blob_id": "bf1d54015a9ae529f4fda4fa9b9f7c874ec3b240",
"index": 4514,
"step-1": "#!/usr/bin/python\n\nimport serial\nimport time\nimport sys\n\nsenderId=\"\\x01\"\nreceiverId=\"\\x00\"\n\n#openSerial just opens the serial connection\ndef openSerial(port):\n\t#Some configuration for the serial port\n\tser = seri... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = [(... | flexible | {
"blob_id": "ab12468b1da20c896e3578091fd9ba245dcfa0a4",
"index": 1350,
"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 = [('core', '000... | [
0,
1,
2,
3,
4
] |
# Copyright (c) 2021, Omid Erfanmanesh, All rights reserved.
import math
import numpy as np
import pandas as pd
from data.based.based_dataset import BasedDataset
from data.based.file_types import FileTypes
class DengueInfection(BasedDataset):
def __init__(self, cfg, development):
super(DengueInfectio... | normal | {
"blob_id": "93ac8a1f795f7809a3e88b56ce90bf1d31706554",
"index": 1139,
"step-1": "<mask token>\n\n\nclass DengueInfection(BasedDataset):\n <mask token>\n\n def cyclic_encoder(self, col, max_val):\n self.df[col + '_sin'] = np.sin(2 * np.pi * self.df[col] / max_val)\n self.df[col + '_cos'] = np... | [
16,
18,
22,
25,
33
] |
#!/usr/bin/env python
import rospy
import numpy as np
import time
import RPi.GPIO as GPIO
from ccn_raspicar_ros.msg import RaspiCarWheel
from ccn_raspicar_ros.msg import RaspiCarWheelControl
from ccn_raspicar_ros.srv import RaspiCarMotorControl
class MotorControl(object):
def __init__(self, control_pin=[16, 18,... | normal | {
"blob_id": "2985360c1e2d03c619ea2994c609fdf8c033bebd",
"index": 9177,
"step-1": "<mask token>\n\n\nclass MotorControl(object):\n <mask token>\n <mask token>\n\n def forward(self, speed=1.0, t=None):\n self.pwm_r1.ChangeDutyCycle(self.r_level * speed)\n self.pwm_r2.ChangeDutyCycle(0)\n ... | [
5,
11,
13,
15,
18
] |
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