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|>
<|reserved_special_token_0|>
print(' Début du projet covid-19 !! ')
print(' test repository distant')
<|reserved_special_token_1|>
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 1 11:06:35 2020
@author: fitec
"""
# version 1
pri... | flexible | {
"blob_id": "3657d02271a27c150f4c67d67a2a25886b00c593",
"index": 3306,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(' Début du projet covid-19 !! ')\nprint(' test repository distant')\n",
"step-3": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Nov 1 11:06:35 2020\n\n... | [
0,
1,
2
] |
# Generated by Django 3.1.3 on 2020-11-18 13:26
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
... | normal | {
"blob_id": "fa09937ce64952795ae27cb91bf2c52dfb3ef4da",
"index": 4532,
"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 = [migrations.sw... | [
0,
1,
2,
3,
4
] |
import importlib
if __name__ == '__main__':
module = importlib.import_module('UserFile')
print(module.if_new_message)
print(module.ID)
| normal | {
"blob_id": "8a773448383a26610f4798e12fb514248e71dc4b",
"index": 698,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n module = importlib.import_module('UserFile')\n print(module.if_new_message)\n print(module.ID)\n",
"step-3": "import importlib\nif __name__ == '__ma... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassi... | flexible | {
"blob_id": "759ff4cc123e85bdc8c1457bb521cd35841956cd",
"index": 482,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncv2.imshow('img', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n",
"step-3": "<mask token>\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\neye_cascade = cv... | [
0,
1,
2,
3,
4
] |
"""Tasks for managing Debug Information Files from Apple App Store Connect.
Users can instruct Sentry to download dSYM from App Store Connect and put them into Sentry's
debug files. These tasks enable this functionality.
"""
import logging
import pathlib
import tempfile
from typing import List, Mapping, Tuple
impor... | normal | {
"blob_id": "51bc2668a9f9f4425166f9e6da72b7a1c37baa01",
"index": 9628,
"step-1": "<mask token>\n\n\ndef inner_dsym_download(project_id: int, config_id: str) ->None:\n \"\"\"Downloads the dSYMs from App Store Connect and stores them in the Project's debug files.\"\"\"\n with sdk.configure_scope() as scope:\... | [
3,
5,
7,
9,
10
] |
# coding=utf-8
# http://rate.tmall.com/list_detail_rate.htm?itemId=41464129793&sellerId=1652490016¤tPage=1
import requests, re
from Tkinter import *
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import random
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei'] ... | normal | {
"blob_id": "123d3906ce040a4daa5309eae555bad5509f805e",
"index": 671,
"step-1": "# coding=utf-8\n# http://rate.tmall.com/list_detail_rate.htm?itemId=41464129793&sellerId=1652490016¤tPage=1\nimport requests, re\nfrom Tkinter import *\nimport numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as... | [
0
] |
import qrcode
def generate_qr(query):
img = qrcode.make(query)
| normal | {
"blob_id": "e97bcf31657317f33f4a138ede80bb9171337f52",
"index": 4730,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef generate_qr(query):\n img = qrcode.make(query)\n",
"step-3": "import qrcode\n\n\ndef generate_qr(query):\n img = qrcode.make(query)\n",
"step-4": null,
"step-5": null,... | [
0,
1,
2
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from __future__ import annotations
from typing import List, Dict, NamedTuple, Union, Optional
import codecs
import collections
import enum
import json
import re
import struct
from refinery.lib.structures import StructReader
from refinery.units.formats.office... | normal | {
"blob_id": "566dab589cdb04332a92138b1a1faf53cd0f58b8",
"index": 5419,
"step-1": "<mask token>\n\n\nclass MSITableColumnInfo(NamedTuple):\n <mask token>\n number: int\n attributes: int\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n @property\n def length(self) ->int:\n... | [
14,
21,
22,
26,
27
] |
import random
responses = ['Seems so','Never','Untrue','Always no matter what','You decide your fate','Not sure','Yep','Nope','Maybe','Nein','Qui','Ask the person next to you','That question is not for me']
def answer():
question = input('Ask me anything: ')
print(random.choice(responses))
answer()
secondQues... | normal | {
"blob_id": "41eef711c79fb084c9780b6d2638d863266e569d",
"index": 837,
"step-1": "<mask token>\n\n\ndef answer():\n question = input('Ask me anything: ')\n print(random.choice(responses))\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef answer():\n question = input('Ask me anything: ')\n print... | [
1,
2,
3,
4,
5
] |
def simple_formatter(zipcode: str, address: str) ->str:
return f'{zipcode}は「{address}」です'
| normal | {
"blob_id": "b1dce573e6da81c688b338277af214838bbab9dd",
"index": 8649,
"step-1": "<mask token>\n",
"step-2": "def simple_formatter(zipcode: str, address: str) ->str:\n return f'{zipcode}は「{address}」です'\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
from fgpio import GPIO
import boards
| normal | {
"blob_id": "f66f79cd4132b23c082149a3a1d887f661fd7ee5",
"index": 7247,
"step-1": "<mask token>\n",
"step-2": "from fgpio import GPIO\nimport boards\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
# 1
def transform_data(fn):
print(fn(10))
# 2
transform_data(lambda data: data / 5)
# 3
def transform_data2(fn, *args):
for arg in args:
print(fn(arg))
transform_data2(lambda data: data / 5, 10, 15, 22, 30)
# 4
def transform_data2(fn, *args):
for arg in args:
print('Resu... | normal | {
"blob_id": "c87e6f8780bf8d9097f200c7f2f0faf55beb480c",
"index": 52,
"step-1": "<mask token>\n\n\ndef transform_data2(fn, *args):\n for arg in args:\n print(fn(arg))\n\n\n<mask token>\n",
"step-2": "def transform_data(fn):\n print(fn(10))\n\n\n<mask token>\n\n\ndef transform_data2(fn, *args):\n ... | [
1,
2,
3,
4,
5
] |
import onmt
import torch.nn as nn
import torch.nn.functional as F
import torch
import torch.cuda
from torch.autograd import Variable
class CopyGenerator(nn.Module):
"""
Generator module that additionally considers copying
words directly from the source.
"""
def __init__(self, opt, src_dict, tgt_d... | normal | {
"blob_id": "704b3c57ca080862bed7a4caa65d1c8d5a32fa0b",
"index": 168,
"step-1": "<mask token>\n\n\nclass CopyGenerator(nn.Module):\n <mask token>\n\n def __init__(self, opt, src_dict, tgt_dict):\n super(CopyGenerator, self).__init__()\n self.linear = nn.Linear(opt.rnn_size, tgt_dict.size())\n... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class TreeDrawer:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def draw_tree(self, filename):
tree_file = open('dnaml.tree')
x = tree_file.read()
tree = Phylo.read(StringIO(x[:-2]), 'newick')
Phylo.draw(tree, do_show=False)
... | flexible | {
"blob_id": "5adb16c654a4e747f803590c42328fa6ba642e95",
"index": 7599,
"step-1": "<mask token>\n\n\nclass TreeDrawer:\n <mask token>\n <mask token>\n\n def draw_tree(self, filename):\n tree_file = open('dnaml.tree')\n x = tree_file.read()\n tree = Phylo.read(StringIO(x[:-2]), 'newic... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def filter_by_session(schedule_table, time_entity):
subjects_of_day = filter_by_weekday(schedule_table, time_entity)
start_session_hour = parser.parse(time_entity['value']['from']).hour
schedule = []
for subject in subjects_of_day:
subject_start_time = int(subject[... | flexible | {
"blob_id": "6339f5c980ab0c0fb778870196493ddd83963ae7",
"index": 9203,
"step-1": "<mask token>\n\n\ndef filter_by_session(schedule_table, time_entity):\n subjects_of_day = filter_by_weekday(schedule_table, time_entity)\n start_session_hour = parser.parse(time_entity['value']['from']).hour\n schedule = [... | [
5,
6,
7,
10,
11
] |
from django.http import HttpResponse
from rest_framework.decorators import api_view
@api_view(['GET'])
def get_status(request):
if request.method == 'GET':
return HttpResponse(content='Service is OK!')
| normal | {
"blob_id": "f021940c16b7ed7fdf1088f2137d3ef724719c80",
"index": 1726,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@api_view(['GET'])\ndef get_status(request):\n if request.method == 'GET':\n return HttpResponse(content='Service is OK!')\n",
"step-3": "from django.http import HttpRespo... | [
0,
1,
2
] |
from django.shortcuts import render
from django.views.generic import View
from django.http import JsonResponse
from django_redis import get_redis_connection
from django.contrib.auth.mixins import LoginRequiredMixin
from good.models import GoodsSKU
class CartAddView(View):
'''添加购物车'''
def post(self,request):
... | normal | {
"blob_id": "5feea24d269409306338f772f01b0ee1d2736e2e",
"index": 9246,
"step-1": "<mask token>\n\n\nclass UpdateCartView(View):\n <mask token>\n\n def post(self, request):\n user = request.user\n if not user.is_authenticated:\n return JsonResponse({'res': 0, 'errmsg': '请先登录'})\n ... | [
11,
14,
17,
19,
20
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def solution(people, limit):
people.sort()
cnt = 0
left_idx = 0
right_idx = len(people) - 1
while left_idx <= right_idx:
if people[left_idx] + people[right_idx] <= limit:
cnt += 1
... | flexible | {
"blob_id": "b0dbc4e8a2ce41dc9d2040890e3df4d078680fa1",
"index": 5444,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef solution(people, limit):\n people.sort()\n cnt = 0\n left_idx = 0\n right_idx = len(people) - 1\n while left_idx <= right_idx:\n if people[left_idx] + people... | [
0,
1,
2
] |
"""
losettings.py
Contains a class for profiles and methods to save and load them from xml files.
Author: Stonepaw
Version 2.0
Rewrote pretty much everything. Much more modular and requires no maintence when a new attribute is added.
No longer fully supports profiles from 1.6 and earlier.
Copy... | normal | {
"blob_id": "b29c11b11fd357c7c4f774c3c6a857297ff0d021",
"index": 3144,
"step-1": "\"\"\"\r\nlosettings.py\r\n\r\nContains a class for profiles and methods to save and load them from xml files.\r\n\r\nAuthor: Stonepaw\r\n\r\nVersion 2.0\r\n\r\n Rewrote pretty much everything. Much more modular and requires no... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from plprofiler_tool import main
from plprofiler import plprofiler
| flexible | {
"blob_id": "6b616f5ee0a301b76ad3f7284b47f225a694d33c",
"index": 1281,
"step-1": "<mask token>\n",
"step-2": "from plprofiler_tool import main\nfrom plprofiler import plprofiler\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 9 20:06:32 2020
@author: Supriyo
"""
import networkx as nx
import matplotlib.pyplot as plt
g=nx.Graph()
#l=[1,2,3]
# g.add_node(1)
# g.add_node(2)
# g.add_node(3)
# g.add_nodes_from(l)
# g.add_edge(1,2)
# g.add_edge(2,3)
#... | normal | {
"blob_id": "3bfa9d42e3fd61cf6b7ffaac687f66c2f4bc073e",
"index": 3906,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nnx.draw(g)\nnx.draw(h)\nplt.show()\nnx.write_gexf(g, 'test.gexf')\n",
"step-3": "<mask token>\ng = nx.Graph()\ng = nx.complete_graph(10)\nh = nx.gnp_random_graph(10, 0.5)\nnx.draw(g)\nn... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class DeepModel:
<|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|>
@deep_model('trivial')
class DummyModel... | flexible | {
"blob_id": "36257340ebbc6bd2c7fa5995511b2c859f58f8e5",
"index": 3232,
"step-1": "<mask token>\n\n\nclass DeepModel:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n@deep_model('trivial')\nclass DummyModel(DeepModel):\n\n def b... | [
7,
12,
13,
14,
18
] |
from django.db import models
from orders.constants import OrderStatus
from subscriptions.models import Subscription
class Order(models.Model):
subscription = models.OneToOneField(
Subscription,
on_delete=models.CASCADE,
related_name='order',
)
order_status = models.CharField(
... | normal | {
"blob_id": "78ddae64cc576ebaf7f2cfaa4553bddbabe474b7",
"index": 6918,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Order(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Order(m... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def hard_negative_loss_mining(c_loss, negative_mask, k):
"""Hard negative mining in classification loss."""
k = tf.maximum(k, 1)
k = tf.minimum(k, c_loss.shape[-1])
neg_c_loss = c_loss * negative_mask
neg_c_loss = tf.nn.top_k(neg_c_loss, k)[0]
return tf.reduce_sum(... | flexible | {
"blob_id": "6e17fef4507c72190a77976e4a8b2f56880f2d6f",
"index": 4895,
"step-1": "<mask token>\n\n\ndef hard_negative_loss_mining(c_loss, negative_mask, k):\n \"\"\"Hard negative mining in classification loss.\"\"\"\n k = tf.maximum(k, 1)\n k = tf.minimum(k, c_loss.shape[-1])\n neg_c_loss = c_loss * ... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# you can use print for debugging purposes, e.g.
# print "this is a debug message"
def solution(A):
N = len (A)
#min_avg = min( (A[0] + A[1]) / 2, (A[0] + A[1] + A[2]) / 3)
min_avg = (A[0] + A[1]) / 2.0
min_idx = 0
now_avg = 0.0
for i in xra... | normal | {
"blob_id": "caa92eb5582135f60a6034cb83d364501361d00e",
"index": 7726,
"step-1": "<mask token>\n",
"step-2": "def solution(A):\n N = len(A)\n min_avg = (A[0] + A[1]) / 2.0\n min_idx = 0\n now_avg = 0.0\n for i in xrange(1, N - 1):\n now_avg = (A[i] + A[i + 1]) / 2.0\n if now_avg < ... | [
0,
1,
2
] |
from Get2Gether.api_routes.schedule import schedule_router
from Get2Gether.api_routes.auth import auth_router
from Get2Gether.api_routes.event import event_router
| normal | {
"blob_id": "cd9d10a3ee3956762d88e76a951023dd77023942",
"index": 6411,
"step-1": "<mask token>\n",
"step-2": "from Get2Gether.api_routes.schedule import schedule_router\nfrom Get2Gether.api_routes.auth import auth_router\nfrom Get2Gether.api_routes.event import event_router\n",
"step-3": null,
"step-4": nu... | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def createRandomPhoneNumber():
phoneNumberFront = ['130', '131', '132', '133', '134', '135', '136',
'137', '138', '139', '150', '151', '152', '153', '158', '159',
'177', '180', '181', '182', '183', '186', '18... | flexible | {
"blob_id": "5e8f9a222fb2c35b4720e48f0277481e410aee47",
"index": 2791,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef createRandomPhoneNumber():\n phoneNumberFront = ['130', '131', '132', '133', '134', '135', '136',\n '137', '138', '139', '150', '151', '152', '153', '158', '159',\n ... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class Task(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>... | flexible | {
"blob_id": "e59bd92a94399d4a81687fc5e52e9ae04b9de768",
"index": 7472,
"step-1": "<mask token>\n\n\nclass Task(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>\n <m... | [
7,
8,
9,
10,
11
] |
from src.basepages.BreadCrumbTicketInfoBasePage import *
class BreadCrumbHomeBasePage:
def __init__(self):
""
def gotoTicketInfoBasePage(self,ticketInfoPage):
self.driver.get(ticketInfoPage)
breadCrumbTicketInfoBasePage = BreadCrumbTicketInfoBasePage()
breadCrumbTicketInfoBasePage.driver = self.driver
r... | normal | {
"blob_id": "47c1746c2edfe4018decd59efbacc8be89a1f49e",
"index": 3653,
"step-1": "<mask token>\n\n\nclass BreadCrumbHomeBasePage:\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass BreadCrumbHomeBasePage:\n <mask token>\n\n def gotoTicketInfoBasePage(self, ticketInfoPage):\n ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class Client(Base):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class TakenBook(Base):
__tablename__ = 'taken_books'
id = Col... | flexible | {
"blob_id": "a288e66e64d386afd13bfc7b5b13d4a47d15cd6d",
"index": 1316,
"step-1": "<mask token>\n\n\nclass Client(Base):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass TakenBook(Base):\n __tablename__ = 'taken_books'\n id = Column(Integ... | [
3,
7,
8,
10,
12
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while True:
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
file.write('\ntimestamp: %s ' % st)
print('\ntimestamp: %s ' % st)
print('Temperature: %0.1f C' % bme680.tempe... | flexible | {
"blob_id": "ae7fc034249b7dde6d6bca33e2e6c8f464284cfc",
"index": 9718,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n ts = time.time()\n st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n file.write('\\ntimestamp: %s ' % st)\n print('\\ntimestamp: %s ' % st... | [
0,
1,
2,
3,
4
] |
import os
# Set a single thread per process for numpy with MKL/BLAS
os.environ['MKL_NUM_THREADS'] = '1'
os.environ['OPENBLAS_NUM_THREADS'] = '1'
os.environ['MKL_DEBUG_CPU_TYPE'] = '5'
import numpy as np
from matplotlib import pyplot as plt
from copy import deepcopy
from kadal.optim_tools.MOBO import MOBO
from kadal.s... | normal | {
"blob_id": "ba289bcdc0aa7c2ad70dba7fac541900d0b55387",
"index": 7585,
"step-1": "<mask token>\n\n\ndef generate_kriging():\n nsample = 20\n nvar = 2\n nobj = 2\n lb = -1 * np.ones(shape=[nvar])\n ub = 1 * np.ones(shape=[nvar])\n sampoption = 'halton'\n samplenorm, sample = sampling(sampopti... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class BaseService:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class BaseService:
<|reserved_special_token_0|>
def post(self, path, body):
result = self._context.http.post(path, body)
... | flexible | {
"blob_id": "5000663e3cde9c1a1100c9022707ccae13db0034",
"index": 1426,
"step-1": "<mask token>\n",
"step-2": "class BaseService:\n <mask token>\n <mask token>\n",
"step-3": "class BaseService:\n <mask token>\n\n def post(self, path, body):\n result = self._context.http.post(path, body)\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class EditUserProfileView(LoginRequiredMixin, UpdateView):
model = Profile
form_class = UserProfileForm
template_name = 'profile.html'
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@login_required
def home(request):
return render(request, 'home.html')
<|... | flexible | {
"blob_id": "21d261dec6668a24030f37b7dcb87c0132e63528",
"index": 1365,
"step-1": "<mask token>\n\n\nclass EditUserProfileView(LoginRequiredMixin, UpdateView):\n model = Profile\n form_class = UserProfileForm\n template_name = 'profile.html'\n",
"step-2": "<mask token>\n\n\n@login_required\ndef home(re... | [
2,
3,
4,
5,
6
] |
"""Main application for FastAPI"""
from typing import Dict
from fastapi import FastAPI
from fastapi.openapi.utils import get_openapi
from cool_seq_tool.routers import default, mane, mappings, SERVICE_NAME
from cool_seq_tool.version import __version__
app = FastAPI(
docs_url=f"/{SERVICE_NAME}",
openapi_url=... | normal | {
"blob_id": "c6fa8c33630fc2f7ffb08aace1a260e6805ddfa2",
"index": 7670,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp.include_router(default.router)\napp.include_router(mane.router)\napp.include_router(mappings.router)\n\n\ndef custom_openapi() ->Dict:\n \"\"\"Generate custom fields for OpenAPI re... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
DEBUG = True
SECRET_KEY = os.environ['SECRET_KEY']
ROOT_URLCONF = 'floweryroad.urls.docker_production'
ALLOWED_HOSTS = [os.environ['WEB_HOST']]
CORS_ORIGIN_WHITELIST = [os.environ['CORS']]
DATABASES = {'default': {'ENGINE': 'djang... | flexible | {
"blob_id": "ab35684166f07a3ab9e64f2ff98980e25a3fc576",
"index": 1643,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nDEBUG = True\nSECRET_KEY = os.environ['SECRET_KEY']\nROOT_URLCONF = 'floweryroad.urls.docker_production'\nALLOWED_HOSTS = [os.environ['WEB_HOST']]\nCORS_ORIGIN_WHITELIST = [os.environ['CO... | [
0,
1,
2
] |
# Feito por Kelvin Schneider
#12
numero = input("Digite um numero de telefone: ")
numero = numero.replace("-","")
if (len(numero) < 8):
while len(numero) < 8:
numero = "3" + numero
numero = numero[:4] + "-" + numero[4:]
print("Numero: ", numero)
elif (len(numero) > 8):
print("Numero invalido... | normal | {
"blob_id": "6297256bce1954f041915a1ce0aa0546689850f3",
"index": 2256,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif len(numero) < 8:\n while len(numero) < 8:\n numero = '3' + numero\n numero = numero[:4] + '-' + numero[4:]\n print('Numero: ', numero)\nelif len(numero) > 8:\n print... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
"""
openapi.schematics
~~~~~~~~~~~~~~~~~~
Schematics plugin for apispec based on ext.MarshmallowPlugin
"""
import warnings
from apispec import BasePlugin
from .common import resolve_schema_instance, make_schema_key
from .openapi import OpenAPIConverter
def resolver(schema):
"... | normal | {
"blob_id": "1c5655563d05498f016fb2d41a07331b9e8de5e8",
"index": 2019,
"step-1": "<mask token>\n\n\nclass SchematicsPlugin(BasePlugin):\n <mask token>\n\n def __init__(self, schema_name_resolver=None):\n super().__init__()\n self.schema_name_resolver = schema_name_resolver or resolver\n ... | [
9,
12,
13,
14,
16
] |
<|reserved_special_token_0|>
class GridData:
def __init__(self, datafile, labelfile):
f = open(datafile, 'rb')
f2 = open(labelfile, 'r')
self.samples = []
self.labels = []
self.label_names = []
self.data_size = 30
self.source = datafile
sample_size ... | flexible | {
"blob_id": "8475792cc2d55f030f0bd9e7d0240e3b59ed996b",
"index": 7774,
"step-1": "<mask token>\n\n\nclass GridData:\n\n def __init__(self, datafile, labelfile):\n f = open(datafile, 'rb')\n f2 = open(labelfile, 'r')\n self.samples = []\n self.labels = []\n self.label_names =... | [
2,
4,
5,
7,
8
] |
# stdlib
from typing import Any
# third party
import numpy as np
# syft absolute
import syft as sy
from syft.core.common.uid import UID
from syft.core.node.new.action_object import ActionObject
from syft.core.node.new.action_store import DictActionStore
from syft.core.node.new.context import AuthedServiceContext
from... | normal | {
"blob_id": "b76d3b6a4c15833ee2b25fede5923e1fe1dc4dd7",
"index": 5422,
"step-1": "<mask token>\n\n\ndef test_signing_key() ->None:\n test_signing_key = SyftSigningKey.from_string(test_signing_key_string)\n assert isinstance(test_signing_key, SyftSigningKey)\n assert str(test_signing_key) == test_signing... | [
3,
7,
10,
11,
12
] |
<|reserved_special_token_0|>
def test_inject_at_timestep():
with tf.Graph().as_default():
with tf.Session() as sess:
in_seq = tf.constant(np.array([[[1, 2, 3, 4], [5, 6, 7, 8]], [[
9, 10, 11, 12], [13, 14, 15, 16]], [[17, 18, 19, 20], [21,
22, 23, 24]]], dtype=... | flexible | {
"blob_id": "958f6e539f9f68892d77b6becc387581c6adfa16",
"index": 3366,
"step-1": "<mask token>\n\n\ndef test_inject_at_timestep():\n with tf.Graph().as_default():\n with tf.Session() as sess:\n in_seq = tf.constant(np.array([[[1, 2, 3, 4], [5, 6, 7, 8]], [[\n 9, 10, 11, 12], [... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class TankDriveResetEncoders(Command):
<|reserved_special_token_0|>
def execute(self):
subsystems.driveline.resetEncoders()
print('CMD TankDriveResetEncoders: Reset Completed')
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_tok... | flexible | {
"blob_id": "f73faabe955e3ae05039e58ebabe5c012e080f38",
"index": 9906,
"step-1": "<mask token>\n\n\nclass TankDriveResetEncoders(Command):\n <mask token>\n\n def execute(self):\n subsystems.driveline.resetEncoders()\n print('CMD TankDriveResetEncoders: Reset Completed')\n <mask token>\n",
... | [
2,
3,
4,
5,
6
] |
#! /usr/bin/python2.7
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import pylab as pl
x = range(1, 19)
d = pd.read_csv('data.csv')
pl.clf()
pl.plot(x, d['reelection'], 'o-', label='reelection')
pl.plot(x, d['rerun'], 'o-', label='rerun')
pl.plot(x, d['ratio'], 'o-', label='incumbent ratio')
pl.fill... | normal | {
"blob_id": "156b3e09a65402d4f964c2886b8f5519168eb13a",
"index": 2894,
"step-1": "<mask token>\n",
"step-2": "<mask token>\npl.clf()\npl.plot(x, d['reelection'], 'o-', label='reelection')\npl.plot(x, d['rerun'], 'o-', label='rerun')\npl.plot(x, d['ratio'], 'o-', label='incumbent ratio')\npl.fill_between(x, d['... | [
0,
1,
2,
3,
4
] |
import itertools
def sevens_in_a_row(arr,n):
in_a_row={}
for iteration in arr:
if arr[iteration]==arr[iteration+1]:
print blaaa
def main():
n=3
arr=['1','1','1','2','3','-4']
print (sevens_in_a_row(arr,n))
if __name__== '__main__':
main() | normal | {
"blob_id": "a2626b384d0b7320ee9bf7cd75b11925ccc00666",
"index": 9399,
"step-1": "import itertools\ndef sevens_in_a_row(arr,n):\n\tin_a_row={}\n\tfor iteration in arr:\n\t\tif arr[iteration]==arr[iteration+1]:\n\t\t\tprint blaaa\n\t\t\t\n\ndef main():\n\tn=3\n\tarr=['1','1','1','2','3','-4']\n\tprint (sevens_in_... | [
0
] |
#!/usr/bin/env python
# coding:utf-8
"""
200. 岛屿数量
难度
中等
给定一个由 '1'（陆地）和 '0'（水）组成的的二维网格，计算岛屿的数量。一个岛被水包围，并且它是通过水平方向或垂直方向上相邻的陆地连接而成的。你可以假设网格的四个边均被水包围。
示例 1:
输入:
11110
11010
11000
00000
输出: 1
示例 2:
输入:
11000
11000
00100
00011
输出: 3
"""
# ===============================================================================... | normal | {
"blob_id": "b46f19708e9e2a1be2bbd001ca6341ee7468a60d",
"index": 7147,
"step-1": "<mask token>\n\n\nclass Solution(object):\n <mask token>\n directions = [(-1, 0), (0, 1), (1, 0), (0, -1)]\n\n def numIslands(self, grid):\n \"\"\"\n :type grid: List[List[str]]\n :rtype: int\n ... | [
3,
5,
7,
10,
12
] |
"""
对自定义的类进行排序
"""
import operator
class User:
def __init__(self, name, id):
self.name = name
self.id = id
def __repr__(self):
return 'User({},{})'.format(self.name, self.id)
def run():
users = [User('wang', 1), User('zhao', 4), User('chen', 3), User('wang', 2)]
# 这种方式相对速度快... | normal | {
"blob_id": "e8ef3a5e41e68b4d219aa1403be392c51cc010e6",
"index": 7302,
"step-1": "<mask token>\n\n\nclass User:\n\n def __init__(self, name, id):\n self.name = name\n self.id = id\n\n def __repr__(self):\n return 'User({},{})'.format(self.name, self.id)\n\n\n<mask token>\n",
"step-2"... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
app.config.from_object('config')
api.init_app(app)
if __name__ == '__main__':
app.run()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
app = Flask(__name__)
app.config.from_object('config')
api.init_app(app)
if _... | flexible | {
"blob_id": "f4ea36c3154f65c85647da19cfcd8a058c507fe1",
"index": 4992,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp.config.from_object('config')\napi.init_app(app)\nif __name__ == '__main__':\n app.run()\n",
"step-3": "<mask token>\napp = Flask(__name__)\napp.config.from_object('config')\napi.... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def get_last_argument(words):
return ' '.join(words)[:-1]
<|reserved_special_token_0|>
def parse_what_is_the(words):
question_number = None
arg = None
if words[3] == POPULATION_KEY:
question_number = 3
arg = get_last_argument(words[5:])
elif words[3... | flexible | {
"blob_id": "18dce1ce683b15201dbb5436cbd4288a0df99c28",
"index": 938,
"step-1": "<mask token>\n\n\ndef get_last_argument(words):\n return ' '.join(words)[:-1]\n\n\n<mask token>\n\n\ndef parse_what_is_the(words):\n question_number = None\n arg = None\n if words[3] == POPULATION_KEY:\n question_... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def engparallelb2(MU, NU, b1, b2, x1, x2, y1, y2, eta, a):
b1x = b1[0]
b1y = b1[1]
b1z = b1[2]
b2x = b2[0]
b2y = b2[1]
b2z = b2[2]
Rab = Rp(x2, y2, eta, a) - Rp(x2, y1, eta, a) - Rp(x1, y2, eta, a) + ... | flexible | {
"blob_id": "2611d7dd364f6a027da29c005754ac2465faa8be",
"index": 8667,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef engparallelb2(MU, NU, b1, b2, x1, x2, y1, y2, eta, a):\n b1x = b1[0]\n b1y = b1[1]\n b1z = b1[2]\n b2x = b2[0]\n b2y = b2[1]\n b2z = b2[2]\n Rab = Rp(x2, y2, ... | [
0,
2,
3,
4,
5
] |
from setuptools import setup
setup(name='gym_asset_allocation',
version='0.0.1',
install_requires=['gym','numpy','pandas','quandl'] # And any other dependencies
) | normal | {
"blob_id": "952f8341f0fcbe6f3f3d1075ce345e61967a4336",
"index": 4381,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='gym_asset_allocation', version='0.0.1', install_requires=['gym',\n 'numpy', 'pandas', 'quandl'])\n",
"step-3": "from setuptools import setup\nsetup(name='gym_asset_alloca... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('%.2f' % ukupanPut)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
r = float(input())
p = int(input())
obim = 2 * r * math.pi
ukupanPut = p * obim
ukupanPut = ukupanPut * 0.01
print('%.2f' % ukupanPut)
<|rese... | flexible | {
"blob_id": "1f27b697985c7417e6d8d978703175a415c6c57d",
"index": 327,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('%.2f' % ukupanPut)\n",
"step-3": "<mask token>\nr = float(input())\np = int(input())\nobim = 2 * r * math.pi\nukupanPut = p * obim\nukupanPut = ukupanPut * 0.01\nprint('%.2f' % uk... | [
0,
1,
2,
3,
4
] |
import sys, warnings
if sys.version_info[0] < 3:
warnings.warn("At least Python 3.0 is required to run this program", RuntimeWarning)
else:
print('Normal continuation')
| normal | {
"blob_id": "a6d5552fa0648fcf9484a1498e4132eb80ecfc86",
"index": 2304,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif sys.version_info[0] < 3:\n warnings.warn('At least Python 3.0 is required to run this program',\n RuntimeWarning)\nelse:\n print('Normal continuation')\n",
"step-3": "im... | [
0,
1,
2,
3
] |
from os import path
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.pipeline import Pipeline
from sta211.datasets import load_train_dataset, load_test_dataset, find_best_train_dataset
from sklearn.model_selection import GridSearchCV
from sta211.selection import get_naive_bayes, get_mlp, get_svm,... | normal | {
"blob_id": "c99878dbd5610c8a58f00912e111b1eef9d3893e",
"index": 7782,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ngrid.fit(X, y)\n<mask token>\nprint('Result for {} configurations'.format(len(parameters)))\nfor p in parameters:\n print('{};{:.2f}%;{:.4f}%;±{:.4f}%'.format(', '.join(map(lambda k:\n... | [
0,
1,
2,
3,
4
] |
import tensorflow as tf
import settings
import numpy as np
slim = tf.contrib.slim
class Model:
def __init__(self, training = True):
self.classes = settings.classes_name
self.num_classes = len(settings.classes_name)
self.image_size = settings.image_size
self.cell_size = setting... | normal | {
"blob_id": "8ccec24e1a7060269ffbb376ba0c480da9eabe0a",
"index": 819,
"step-1": "<mask token>\n\n\nclass Model:\n\n def __init__(self, training=True):\n self.classes = settings.classes_name\n self.num_classes = len(settings.classes_name)\n self.image_size = settings.image_size\n se... | [
6,
7,
8,
9,
10
] |
<|reserved_special_token_0|>
class DatasetLoader(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 DatasetLoader(object):
<|reserved_special_token_0|>
... | flexible | {
"blob_id": "b668945820abe893b92fdf26ccd8563ccff804ee",
"index": 1981,
"step-1": "<mask token>\n\n\nclass DatasetLoader(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass DatasetLoader(object):\n <mask token>\n\n def __init__(self, ds_i... | [
1,
4,
5,
6,
7
] |
from binary_search_tree.gen_unique_bst import gen_unique_bst
# The maximum depth is the number of nodes along the longest path from the root node down to the farthest leaf node.
def max_depth(root):
if not root:
return 0
return max(max_depth(root.left), max_depth(root.right)) + 1
# The minimum depth... | normal | {
"blob_id": "3e54d2ddddf6f8186137e5801ca4ba40d1061987",
"index": 2801,
"step-1": "from binary_search_tree.gen_unique_bst import gen_unique_bst\n\n\n# The maximum depth is the number of nodes along the longest path from the root node down to the farthest leaf node.\ndef max_depth(root):\n if not root:\n ... | [
0
] |
<|reserved_special_token_0|>
class TestObs(unittest.TestCase):
<|reserved_special_token_0|>
def setUp(self):
self.validator = Validator()
self.validator.adata = examples.adata.copy()
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reser... | flexible | {
"blob_id": "f4306f80330850415b74d729384f360489644e39",
"index": 354,
"step-1": "<mask token>\n\n\nclass TestObs(unittest.TestCase):\n <mask token>\n\n def setUp(self):\n self.validator = Validator()\n self.validator.adata = examples.adata.copy()\n <mask token>\n <mask token>\n <mask... | [
43,
45,
50,
57,
75
] |
# Copyright 2016 Osvaldo Santana Neto
#
# 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 applicable law or agreed to in ... | normal | {
"blob_id": "09284a96467b09c2ad7b65530c015fdb64b198a4",
"index": 2638,
"step-1": "<mask token>\n\n\nclass ModelBuilder:\n <mask token>\n <mask token>\n <mask token>\n\n def build_user(self, user_data):\n user = User(name=user_data.nome, federal_tax_number=\n FederalTaxNumber(user_da... | [
29,
30,
33,
37,
42
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
admin.site.register(Persona)
<|reserved_special_token_1|>
from django.contrib import admin
from pharma_models.personas.models import Persona
admin.site.register(Persona)
| flexible | {
"blob_id": "59d04ebd9a45c6a179a2da1f88f728ba2af91c05",
"index": 590,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nadmin.site.register(Persona)\n",
"step-3": "from django.contrib import admin\nfrom pharma_models.personas.models import Persona\nadmin.site.register(Persona)\n",
"step-4": null,
"ste... | [
0,
1,
2
] |
from tkinter import *
from tkinter.scrolledtext import ScrolledText
def load():
with open(filename.get()) as file:
# delete every between line 1 char 0 to END
# INSERT is the current insertion point
contents.delete('1.0', END)
contents.insert(INSERT, file.read())
def save():
with open(filename.g... | normal | {
"blob_id": "fcf4cb5c47e4aa51d97b633ecdfec65246e82bd8",
"index": 9011,
"step-1": "<mask token>\n\n\ndef load():\n with open(filename.get()) as file:\n contents.delete('1.0', END)\n contents.insert(INSERT, file.read())\n\n\ndef save():\n with open(filename.get(), 'w') as file:\n file.wr... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class TestMacAddress(configure.NetTestCase, TestField):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def test_insert(self):
self.base('08-00-2b-01-02-03')
val = self.orm.new().insert(self.base)
val = getattr(val, self.base.field_name)
... | flexible | {
"blob_id": "b5dba7c1566721f8bb4ec99bc2f13cae4ade4f0a",
"index": 8713,
"step-1": "<mask token>\n\n\nclass TestMacAddress(configure.NetTestCase, TestField):\n <mask token>\n <mask token>\n\n def test_insert(self):\n self.base('08-00-2b-01-02-03')\n val = self.orm.new().insert(self.base)\n ... | [
8,
9,
11,
14,
15
] |
text = "I love Python Programming"
for word in text.split():
print(word) | normal | {
"blob_id": "fdc8f9ff9a0e2cd8ad1990948036d9e420fdc074",
"index": 4216,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor word in text.split():\n print(word)\n",
"step-3": "text = 'I love Python Programming'\nfor word in text.split():\n print(word)\n",
"step-4": "text = \"I love Python Programm... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
"""
Default organizer for bioinfoinformatics project directiories - RNA-Seq based model
"""
import os
import sys
#main path
curr_path = os.getcwd()
print("\nYour current directory is: " + curr_path + "\n\nIt contains the following files and directories:\n\n" + str(os.listdir("."))) # displays... | normal | {
"blob_id": "0131657a7675904ee2743448f514a9f11e0dc0ad",
"index": 7561,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(\"\"\"\nYour current directory is: \"\"\" + curr_path +\n \"\"\"\n\nIt contains the following files and directories:\n\n\"\"\" + str(os.\n listdir('.')))\n<mask token>\nos.mkd... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def test_parse_redis_key(config_helper, ingestion_manager):
im = ingestion_manager
job_name = config_helper.nodes_ingestion_operation
operation = config_helper.nodes_ingestion_operation
labels = config_helper.tes... | flexible | {
"blob_id": "13451352e8dcdfe64771f9fc188b13a31b8109f5",
"index": 4555,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_parse_redis_key(config_helper, ingestion_manager):\n im = ingestion_manager\n job_name = config_helper.nodes_ingestion_operation\n operation = config_helper.nodes_in... | [
0,
2,
3,
4,
5
] |
<|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_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.... | flexible | {
"blob_id": "89d0d5d13c5106c504c6727c7784f048a30495dc",
"index": 5560,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n initial = T... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@pytest.mark.parametrize('data, parsed', REDIS_PARSE)
def test_parse_redis_data(data, parsed):
"""Test to see if data dict in bytes is parsed."""
assert redis_data.parse_redis_data(data) == parsed
def test_parse_redis_data_error():
"""Test to see if parse redis raises value ... | flexible | {
"blob_id": "7f4a5779564efde7eaf08741d00254dd4aa37569",
"index": 4218,
"step-1": "<mask token>\n\n\n@pytest.mark.parametrize('data, parsed', REDIS_PARSE)\ndef test_parse_redis_data(data, parsed):\n \"\"\"Test to see if data dict in bytes is parsed.\"\"\"\n assert redis_data.parse_redis_data(data) == parsed... | [
7,
8,
10,
11,
12
] |
import os
import sqlite3
from typing import Any
from direct_geocoder import get_table_columns
from reverse_geocoder import is_point_in_polygon
from utils import zip_table_columns_with_table_rows, get_average_point
def get_organizations_by_address_border(city: str,
nodes: list[... | normal | {
"blob_id": "79f945694f853e5886b590020bb661ecd418510d",
"index": 4567,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_organizations_by_address_border(city: str, nodes: list[tuple[float,\n float]]) ->list[dict[str, Any]]:\n result = []\n radius = 0.0025\n with sqlite3.connect(os.pa... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def nums(phrase, morph=pymorphy2.MorphAnalyzer()):
""" согласование существительных с числительными, стоящими перед ними """
phrase = phrase.replace(' ', ' ').replace(',', ' ,')
numeral = ''
new_phrase = []
for word in phrase.split(' '):
if 'NUMB' in morph.par... | flexible | {
"blob_id": "a98be930058269a6adbc9a28d1c0ad5d9abba136",
"index": 35,
"step-1": "<mask token>\n\n\ndef nums(phrase, morph=pymorphy2.MorphAnalyzer()):\n \"\"\" согласование существительных с числительными, стоящими перед ними \"\"\"\n phrase = phrase.replace(' ', ' ').replace(',', ' ,')\n numeral = ''\n ... | [
10,
11,
13,
17,
18
] |
import os
import tkinter as tk
from tkinter import messagebox
from os.path import join
from pynput import keyboard
from src.save_count import SaveCount
class MainApplication(tk.Frame):
def __init__(self, root: tk.Tk):
super().__init__(root)
self.root = root
self.pack(padx=32, pady=32, e... | normal | {
"blob_id": "7e2bf898eb1c0118205042797e6dac535342979b",
"index": 185,
"step-1": "<mask token>\n\n\nclass MainApplication(tk.Frame):\n <mask token>\n\n def count_up(self):\n if self.var_count.get() == 0 and self.lst_counts.index(tk.END) == 0:\n SaveCount(tk.Toplevel(), self.save_to_listbox... | [
6,
9,
14,
15,
16
] |
# -*- coding:utf-8 -*-
# author:Kyseng
# file: cRandomString.py
# time: 2018/11/8 11:41 PM
# functhion:
import random
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
class cRandomString():
@staticmethod
def RandomTitle(name):
# name = name.decode('utf8')
# print name
platform =... | normal | {
"blob_id": "ed02cbf3ebef307d6209004e1e388312bfda0b50",
"index": 2027,
"step-1": "<mask token>\n\n\nclass cRandomString:\n\n @staticmethod\n def RandomTitle(name):\n platform = ['PS4', 'XBOX', 'PC', 'NS', 'IOS']\n random.shuffle(platform)\n platform = '/'.join(platform)\n firstW... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class Post(db.Model):
id = db.Column(db.Integer(), primary_key=True)
title = db.Column(db.String(255))
text = db.Column(db.Text())
date = db.Column(db.DateTime())
user_id = db.Column(db.Integer(), db.ForeignKey('user.id'))
comments = db.relationship('Comment', back... | flexible | {
"blob_id": "dd0e96a1f93cbffedc11262a883dda285f5c224c",
"index": 9703,
"step-1": "<mask token>\n\n\nclass Post(db.Model):\n id = db.Column(db.Integer(), primary_key=True)\n title = db.Column(db.String(255))\n text = db.Column(db.Text())\n date = db.Column(db.DateTime())\n user_id = db.Column(db.In... | [
12,
17,
20,
21
] |
<|reserved_special_token_0|>
def csv_parser(statement):
import psycopg2
return_ls = []
try:
connection = psycopg2.connect(user='icu_bot', password=
'5B2xwP8h4Ln4Y8Xs', host='85.214.150.208', port='5432',
database='ICU')
cursor = connection.cursor()
sql_Query... | flexible | {
"blob_id": "516ea681a55255e4c98e7106393180f9ad2e0250",
"index": 8455,
"step-1": "<mask token>\n\n\ndef csv_parser(statement):\n import psycopg2\n return_ls = []\n try:\n connection = psycopg2.connect(user='icu_bot', password=\n '5B2xwP8h4Ln4Y8Xs', host='85.214.150.208', port='5432',\n... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@admin.register(AuxiliaryTicket)
class AuxiliaryTicketAdmin(admin.ModelAdmin):
pass
@admin.register(UserTicket)
class UserTicketAdmin(admin.ModelAdmin):
pass
<|reserved_special_token_1|>
<|reserved_special_token_0|>... | flexible | {
"blob_id": "c73a199d1c1c1867f3d53ceebf614bc9b65c0d5e",
"index": 280,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@admin.register(AuxiliaryTicket)\nclass AuxiliaryTicketAdmin(admin.ModelAdmin):\n pass\n\n\n@admin.register(UserTicket)\nclass UserTicketAdmin(admin.ModelAdmin):\n pass\n",
"st... | [
0,
2,
3,
4
] |
# Head start.
# ask me for this solution: 6cb9ce6024b5fd41aebb86ccd40d8080
# this line is not needed, just for better output:
from pprint import pprint
# just remove the top line
def count_or_add_trigrams(trigram, trigrams_so_far):
'''
Takes a trigram, and a list of previously seen trigrams
and ... | normal | {
"blob_id": "753cc532e4d049bacff33c97de4d80bb9ab8ece8",
"index": 2655,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef count_or_add_trigrams(trigram, trigrams_so_far):\n \"\"\"\n Takes a trigram, and a list of previously seen trigrams\n and yields the same list with all discovered and cou... | [
0,
2,
3,
4,
5
] |
import luigi
import numpy as np
import tqdm
import os
from scipy import spatial
from kq import wordmat_distance
class QuestionVectorTask(luigi.Task):
resources = {'cpu': 1}
dataset = luigi.Parameter()
def requires(self):
#yield wordmat_distance.WeightedSentenceVecs()
yield wordmat_distan... | normal | {
"blob_id": "ae6a6f7622bf98c094879efb1b9362a915a051b8",
"index": 1175,
"step-1": "<mask token>\n\n\nclass QuestionVectorTask(luigi.Task):\n <mask token>\n <mask token>\n <mask token>\n\n def output(self):\n return luigi.LocalTarget('./cache/question_distance/%s.npy' % self.\n datase... | [
7,
8,
11,
12,
13
] |
import random
import sys
import math
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, Activation
from snake_game import Snake
from snake_game import Fruit
import pygame
from pygame.locals import *
# Neural Network glo... | normal | {
"blob_id": "fc1b9ab1fb1ae71d70b3bf5c879a5f604ddef997",
"index": 9969,
"step-1": "<mask token>\n\n\ndef save_pool():\n for i in range(total_models):\n current_pool[i].save_weights(save_location + str(i) + '.keras')\n print('Pool saved')\n\n\ndef create_model():\n \"\"\"\n Create Neural Network... | [
13,
15,
16,
20,
21
] |
<|reserved_special_token_0|>
class Solution:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Solution:
"""
@param: start: start value.
@param: end: end value.
@return: The root of Segment Tree.
"""
def buil... | flexible | {
"blob_id": "5e20a517131f7a372d701548e4f370766a84ba52",
"index": 6134,
"step-1": "<mask token>\n\n\nclass Solution:\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n \"\"\"\n @param: start: start value.\n @param: end: end value.\n @return: The root of Segment Tr... | [
1,
3,
4,
5,
6
] |
class Solution:
def maxSideLength(self, mat: List[List[int]], threshold: int) -> int:
def squareSum(r1: int, c1: int, r2: int, c2: int) -> int:
return prefixSum[r2 + 1][c2 + 1] - prefixSum[r1][c2 + 1] - prefixSum[r2 + 1][c1] + prefixSum[r1][c1]
m = len(mat)
n = len(mat[0])
... | normal | {
"blob_id": "c8f2df1471a9581d245d52437470b6c67b341ece",
"index": 7297,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def maxSideLength(self, mat: List[List[int]], threshold: int) ->int:\n\n def squareSum(r1: int, c1: int, r2: int, c2: in... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Post(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class Meta:
ordering = ['parent_id', 'created_at']
<|reserved_special_token_0|>
... | flexible | {
"blob_id": "5c4a48de94cf5bfe67e6a74c33a317fa1da8d2fa",
"index": 7330,
"step-1": "<mask token>\n\n\nclass Post(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n class Meta:\n ordering = ['parent_id', 'created_at']\n <mask token>\n <mask... | [
3,
7,
8,
9,
10
] |
import pytest
from moa.primitives import NDArray, UnaryOperation, BinaryOperation, Function
from moa.yaccer import build_parser
@pytest.mark.parametrize("expression,result", [
("< 1 2 3>", NDArray(shape=(3,), data=[1, 2, 3], constant=False)),
])
def test_parse_vector(expression, result):
parser = build_parse... | normal | {
"blob_id": "a8b5cf45e5f75ae4b493f5fc9bb4555319f1a725",
"index": 5294,
"step-1": "<mask token>\n\n\n@pytest.mark.parametrize('expression,result', [('< 1 2 3>', NDArray(shape=(\n 3,), data=[1, 2, 3], constant=False))])\ndef test_parse_vector(expression, result):\n parser = build_parser(start='vector')\n ... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(
'Bienvenido a este programa para que introduzcas una frase y un carácter, y decirte cuántas veces aparece ese carácter en tu frase.'
)
print(
"""------------------------------------------------------------------... | flexible | {
"blob_id": "65301be73bb56147609a103a932266013c3c0bd6",
"index": 1148,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(\n 'Bienvenido a este programa para que introduzcas una frase y un carácter, y decirte cuántas veces aparece ese carácter en tu frase.'\n )\nprint(\n \"\"\"----------------... | [
0,
1,
2,
3
] |
#
# PySNMP MIB module CISCO-L2NAT-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/CISCO-L2NAT-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 17:47:06 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27... | normal | {
"blob_id": "2fb95fa2b7062085f31c6b1dbb8c1336c3871e93",
"index": 3271,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nciscoL2natMIB.setRevisions(('2013-04-16 00:00',))\nif mibBuilder.loadTexts:\n ciscoL2natMIB.setLastUpdated('201304160000Z')\nif mibBuilder.loadTexts:\n ciscoL2natMIB.setOrganization... | [
0,
1,
2,
3
] |
"""
generalised behaviour for actors and vacancies
"""
from mesa import Agent
from random import shuffle
import numpy as np
class Entity(Agent):
"""
superclass for vacancy and actor agents
not intended to be used on its own, but to inherit its methods to multiple other agents
"""
def __init__(sel... | normal | {
"blob_id": "68b967ecf18d576758cf05e889919944cfc34dcd",
"index": 250,
"step-1": "<mask token>\n\n\nclass Entity(Agent):\n <mask token>\n\n def __init__(self, unique_id, model):\n super().__init__(unique_id, model)\n self.type = ''\n self.position = ''\n self.log = []\n se... | [
5,
6,
7,
9,
10
] |
#!/bin/usr/python
'''
Author: SaiKumar Immadi
Basic DBSCAN clustering algorithm written in python
5th Semester @ IIIT Guwahati
'''
# You can use this code for free. Just don't plagiarise it for your lab assignments
import sys
from math import sqrt
from random import randint
import matplotlib.pyplot as plt
def main(a... | normal | {
"blob_id": "624ecf743d5be1acc33df14bd721b3103d232f0e",
"index": 2444,
"step-1": "#!/bin/usr/python\n'''\nAuthor: SaiKumar Immadi\nBasic DBSCAN clustering algorithm written in python\n5th Semester @ IIIT Guwahati\n'''\n\n# You can use this code for free. Just don't plagiarise it for your lab assignments\n\nimpor... | [
0
] |
from Bio import SeqIO
def flatten(l):
return [j for i in l for j in i]
def filter_sequences_by_len_from_fasta(file, max_len):
with open(file) as handle:
return [str(record.seq) for record in SeqIO.parse(handle, 'fasta') if
len(record.seq) <= max_len]
| normal | {
"blob_id": "1fdb9db4c1c8b83c72eeb34f10ef9d289b43b79f",
"index": 3166,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef flatten(l):\n return [j for i in l for j in i]\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef flatten(l):\n return [j for i in l for j in i]\n\n\ndef filter_sequen... | [
0,
1,
2,
3
] |
#!/usr/bin/python
#_*_ coding: utf-8 _*_
import MySQLdb as mdb
import sys
con = mdb.connect("localhost","testuser","testdB","testdb")
with con:
cur = con.cursor()
cur.execute("UPDATE Writers SET Name = %s WHERE Id = %s ",
("Guy de manupassant", "4"))
print "Number of rows updated: %d "% cur.... | normal | {
"blob_id": "94a84c7143763c6b7ccea1049cdec8b7011798cd",
"index": 6569,
"step-1": "#!/usr/bin/python\n#_*_ coding: utf-8 _*_\n\nimport MySQLdb as mdb\nimport sys\n\ncon = mdb.connect(\"localhost\",\"testuser\",\"testdB\",\"testdb\")\n\nwith con:\n cur = con.cursor()\n\n cur.execute(\"UPDATE Writers SET Name... | [
0
] |
# Generated by Django 3.1.7 on 2021-03-29 18:50
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
("core", "0052_add_more_tags"),
]
operations = [
migrations.RenameField(
model_name="reporter",
old_name="auth0_role_name",
... | normal | {
"blob_id": "c0cabf2b6f7190aefbaefa197a9008de3a344147",
"index": 2082,
"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', '005... | [
0,
1,
2,
3,
4
] |
import numpy as np
import h5py
def rotate_z(theta, x):
theta = np.expand_dims(theta, 1)
outz = np.expand_dims(x[:, :, 2], 2)
sin_t = np.sin(theta)
cos_t = np.cos(theta)
xx = np.expand_dims(x[:, :, 0], 2)
yy = np.expand_dims(x[:, :, 1], 2)
outx = cos_t * xx - sin_t * yy
outy = sin_t * x... | normal | {
"blob_id": "855bfc9420a5d5031cc673231cc7993ac67df076",
"index": 5515,
"step-1": "<mask token>\n\n\nclass ModelFetcher(object):\n <mask token>\n\n def train_data(self):\n rng_state = np.random.get_state()\n np.random.shuffle(self._train_data)\n np.random.set_state(rng_state)\n n... | [
3,
6,
8,
10,
11
] |
columns = ['account', 'name', 'Death', 'archetype', 'profession', 'elite',
'phases.All.actual_boss.dps', 'phases.All.actual.dps',
'phases.All.actual_boss.flanking', 'phases.All.actual_boss.scholar',
'phases.All.actual_boss.condi_dps', 'phases.All.actual_boss.power_dps',
'phases.All.buffs.aegis', 'phases... | normal | {
"blob_id": "fa948838b5c2d688fe8c748166f23ffc8e677f93",
"index": 9265,
"step-1": "<mask token>\n",
"step-2": "columns = ['account', 'name', 'Death', 'archetype', 'profession', 'elite',\n 'phases.All.actual_boss.dps', 'phases.All.actual.dps',\n 'phases.All.actual_boss.flanking', 'phases.All.actual_boss.sc... | [
0,
1
] |
from utilidades import moeda
p = float(input('Digite o preço: R$'))
print(f'Metade de {moeda.moeda(p)} é {moeda.metade(p, show=True)}')
print(f'O dobro de {moeda.moeda(p)} é {moeda.dobro(p, show=True)}')
print(f'Aumentando 10%, temos {moeda.aumentar(p, 10, show=True)}')
print(f'Reduzindo 13%, temos {moeda.diminuir(p, 1... | normal | {
"blob_id": "5a50ca64810c391231a00c6bfe5ae925ffe5ca7d",
"index": 6332,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(f'Metade de {moeda.moeda(p)} é {moeda.metade(p, show=True)}')\nprint(f'O dobro de {moeda.moeda(p)} é {moeda.dobro(p, show=True)}')\nprint(f'Aumentando 10%, temos {moeda.aumentar(p, ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class ParticleSwarmOptimization:
<|reserved_special_token_0|>
def __init__(self, hyperparams, lower_bound, upper_bound):
self.lower_bound = lower_bound
self.upper_bound = upper_bound
self.num_particles = hyperparams.num_particles
self.w = hyperpara... | flexible | {
"blob_id": "096df1db4d8673ae7886a1b2022148c92f64a23e",
"index": 1725,
"step-1": "<mask token>\n\n\nclass ParticleSwarmOptimization:\n <mask token>\n\n def __init__(self, hyperparams, lower_bound, upper_bound):\n self.lower_bound = lower_bound\n self.upper_bound = upper_bound\n self.nu... | [
5,
7,
9,
12,
13
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(a, b + 1):
true_prime = True
for num in range(2, i):
if i % num == 0:
true_prime = False
if true_prime:
count += 1
print(count)
<|reserved_special_token_1|>
<|reserved_spec... | flexible | {
"blob_id": "ed4c97913a9dba5cf6be56050a8d2ce24dbd6033",
"index": 1870,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(a, b + 1):\n true_prime = True\n for num in range(2, i):\n if i % num == 0:\n true_prime = False\n if true_prime:\n count += 1\nprint(coun... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def qInitResources():
QtCore.qRegisterResourceData(rcc_version, qt_resource_struct,
qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct,
qt_resource_name, qt_resource_data)
<|reserved_speci... | flexible | {
"blob_id": "dbf831540d11a994d5483dc97c7eab474f91f0d3",
"index": 8118,
"step-1": "<mask token>\n\n\ndef qInitResources():\n QtCore.qRegisterResourceData(rcc_version, qt_resource_struct,\n qt_resource_name, qt_resource_data)\n\n\ndef qCleanupResources():\n QtCore.qUnregisterResourceData(rcc_version, ... | [
2,
3,
4,
5,
6
] |
from django.shortcuts import render
from django.http import response, HttpResponse, Http404
from django.views.generic import TemplateView
from django.db.models import Q
# Create your views here.
class Countries(TemplateView):
template_name = 'home.html'
def get_context_data(self, **kwargs):
return Cou... | normal | {
"blob_id": "fd7fe2e4ffaa4de913931e83fd1de40f79b08d98",
"index": 6222,
"step-1": "<mask token>\n\n\nclass Countries(TemplateView):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Countries(TemplateView):\n <mask token>\n\n def get_context_data(self, **kwargs):\n return C... | [
1,
2,
3,
4,
5
] |
import sys
def tackle_mandragora(health):
health.sort()
# for all tipping points, where we change over from eating to defeating
defeating = defeating_cost_precompute(health)
opt = 0
for i in range(0, len(health) + 1):
opt = max(opt, defeating_cost(i, defeating))
return opt
def defe... | normal | {
"blob_id": "24ad62342fb9e7759be8561eaf0292736c7dcb6d",
"index": 6756,
"step-1": "<mask token>\n\n\ndef defeating_cost(i, defeating):\n return (i + 1) * defeating[i]\n\n\ndef defeating_cost_precompute(health):\n n = len(health) + 1\n defeating = [(0) for x in range(n)]\n defeating[len(health)] = 0\n ... | [
2,
4,
5,
6,
7
] |
# Python : Correct way to strip <p> and </p> from string?
s = s.replace('<p>', '').replace('</p>', '')
| normal | {
"blob_id": "7b6e73744d711188ab1a622c309b8ee55f3eb471",
"index": 7427,
"step-1": "<mask token>\n",
"step-2": "s = s.replace('<p>', '').replace('</p>', '')\n",
"step-3": "# Python : Correct way to strip <p> and </p> from string?\ns = s.replace('<p>', '').replace('</p>', '')\n",
"step... | [
0,
1,
2
] |
#!/usr/bin/env python
# -*-coding:utf-8-*-
__author__ = '李晓波'
from linux import sysinfo
#调用相应收集处理函数
def LinuxSysInfo():
#print __file__
return sysinfo.collect()
def WindowsSysInfo():
from windows import sysinfo as win_sysinfo
return win_sysinfo.collect()
| normal | {
"blob_id": "30a2e4aa88b286179e2870205e90fab4a7474e12",
"index": 2969,
"step-1": "<mask token>\n\n\ndef LinuxSysInfo():\n return sysinfo.collect()\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef LinuxSysInfo():\n return sysinfo.collect()\n\n\ndef WindowsSysInfo():\n from windows import sysinfo ... | [
1,
2,
3,
4,
5
] |
#Testing Operating System Descriptions
#OS : LMDE 4 Debbie
#Version: 4.6.7
#Kernal Version : 4.19.0-8-amd64
#Scripting Langages : Python3
#----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------... | normal | {
"blob_id": "422491852b80c2fc4a2e73c01fd01acaad4cf9c8",
"index": 7573,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nregr.fit(X_train, y_train)\nprint(regr.score(X_test, y_test))\n<mask token>\nprint('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\nprint('Mean Squared Error:', metri... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in num:
div = 0
while i > 0:
if i % 2 == 0:
i //= 2
div += 1
else:
i -= 1
plus_cnt += 1
div_max = max(div_max, div)
print(plus_cnt + div_max)
<|re... | flexible | {
"blob_id": "9247896850e5282265cd08240f6f505e675ce5f0",
"index": 5904,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in num:\n div = 0\n while i > 0:\n if i % 2 == 0:\n i //= 2\n div += 1\n else:\n i -= 1\n plus_cnt += 1\n div_max ... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while months > 0:
minimumMonPayment = monthlyPaymentRate * balance
monthlyUnpaidBalan = balance - monthlyPaymentRate
balance = monthlyUnpaidBalan + monthlyInterestRate * monthlyUnpaidBalan
months -= 1
print('Remain... | flexible | {
"blob_id": "299b437c007d78c3d9a53205de96f04d2c6118e0",
"index": 7662,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile months > 0:\n minimumMonPayment = monthlyPaymentRate * balance\n monthlyUnpaidBalan = balance - monthlyPaymentRate\n balance = monthlyUnpaidBalan + monthlyInterestRate * mo... | [
0,
1,
2,
3
] |
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