code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
from package.pack import *
add(2, 2)
sub(2, 3)
| normal | {
"blob_id": "9583a97ae4b1fbf5ecdf33d848b13bf0b28d2eb4",
"index": 2452,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nadd(2, 2)\nsub(2, 3)\n",
"step-3": "from package.pack import *\nadd(2, 2)\nsub(2, 3)\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
"""
Given a sentence as `txt`, return `True` if any two adjacent words have this
property: One word ends with a vowel, while the word immediately after begins
with a vowel (a e i o u).
### Examples
vowel_links("a very large appliance") ➞ True
vowel_links("go to edabit") ➞ True
vowel_links("an... | normal | {
"blob_id": "eefd94e7c04896cd6265bbacd624bf7e670be445",
"index": 4347,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef vowel_links(txt):\n import re\n lst = txt.split(' ')\n for i in range(len(lst) - 1):\n if re.search('[aeiou]', lst[i][-1]) and re.search('[aeiou]', lst[i +\n ... | [
0,
1,
2
] |
# from __future__ import annotations
from typing import List,Union,Tuple,Dict,Set
import sys
input = sys.stdin.readline
# from collections import defaultdict,deque
# from itertools import permutations,combinations
# from bisect import bisect_left,bisect_right
import heapq
# sys.setrecursionlimit(10**5)
# class UnionFi... | normal | {
"blob_id": "13b2e05f12c6d0cd91e89f01e7eef610b1e99856",
"index": 9158,
"step-1": "<mask token>\n\n\ndef main():\n N, M, K = map(int, input().split())\n G = [[] for _ in range(N)]\n for _ in range(M):\n a, b, c = map(int, input().split())\n a -= 1\n b -= 1\n G[a].append((c, b)... | [
1,
2,
3,
4,
5
] |
# 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
] |
"""Seed file to make sample data for pets db."""
from models import db, User, Feedback
from app import app
# Create all tables
db.drop_all()
db.create_all()
# If table isn't empty, empty it
User.query.delete()
Feedback.query.delete()
# Add users and posts
john = User(username="John",password="123",email="24",first... | normal | {
"blob_id": "d520f9d681125937fbd9dff316bdc5f922f25ff3",
"index": 8050,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndb.drop_all()\ndb.create_all()\nUser.query.delete()\nFeedback.query.delete()\n<mask token>\ndb.session.add(john)\ndb.session.commit()\n<mask token>\ndb.session.add(feed)\ndb.session.commi... | [
0,
1,
2,
3,
4
] |
import pytest
from components import models
pytestmark = pytest.mark.django_db
def test_app_models():
assert models.ComponentsApp.allowed_subpage_models() == [
models.ComponentsApp,
models.BannerComponent,
]
def test_app_required_translatable_fields():
assert models.ComponentsApp.get_r... | normal | {
"blob_id": "b1622aa65422fcb69a16ad48a26fd9ed05b10382",
"index": 8882,
"step-1": "<mask token>\n\n\ndef test_app_models():\n assert models.ComponentsApp.allowed_subpage_models() == [models.\n ComponentsApp, models.BannerComponent]\n\n\n<mask token>\n\n\n@pytest.mark.django_db\ndef test_set_slug(en_loca... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# Author: xurongzhong#126.com 技术支持qq群：6089740
# CreateDate: 2018-3-27
# pillow_rotate.py
import glob
import os
from PIL import Image
def rotate(files, dst, value=90):
for file_ in files:
img = Image.open(file_)
img = img.rotate(value)
name = "{... | normal | {
"blob_id": "cd104eec21be8a59e8fb3bd8ab061dd357fc126a",
"index": 667,
"step-1": "<mask token>\n\n\ndef rotate(files, dst, value=90):\n for file_ in files:\n img = Image.open(file_)\n img = img.rotate(value)\n name = '{}{}{}'.format(dst, os.sep, os.path.basename(file_))\n img.save(n... | [
1,
2,
3,
4,
5
] |
import openpyxl as opx
import pyperclip
from openpyxl import Workbook
from openpyxl.styles import PatternFill
wb = Workbook(write_only=True)
ws = wb.create_sheet()
def parseSeq(lines,seqName):
'''splits each column'''
data = []
for line in lines: data.append(line.split(' '))
'''remov... | normal | {
"blob_id": "19e387cb731dad21e5ee50b0a9812df984c13f3b",
"index": 7890,
"step-1": "<mask token>\n\n\ndef parseSeq(lines, seqName):\n \"\"\"splits each column\"\"\"\n data = []\n for line in lines:\n data.append(line.split(' '))\n \"\"\"removes any spaces\"\"\"\n for i in range(len(data)):\n ... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python2.7
# -*- coding: utf-8 -*-
import re
from blessings import Terminal
from validate_email import validate_email
import requests
import sys
_site_ = sys.argv[1]
_saida_ = sys.argv[2]
_file_ = open(_saida_, "w")
t = Terminal()
r = requests.get(_site_, headers={'User-Agent': 'Mozilla/5.0 (Windows NT 6.... | normal | {
"blob_id": "b52269237d66ea50c453395b9536f25f1310bf2e",
"index": 287,
"step-1": "#!/usr/bin/env python2.7\n# -*- coding: utf-8 -*-\nimport re\nfrom blessings import Terminal\nfrom validate_email import validate_email\nimport requests\nimport sys\n_site_ = sys.argv[1]\n_saida_ = sys.argv[2]\n_file_ = open(_saida_... | [
0
] |
# wilfred.py
# Authors
# Stuart C. Larsen (SCL)
# Daryl W. Bennet (DWB)
# Set up three main modules (command, control, reconnaissance),
# and then enter main event loop.
#
# Command:
# Gather mission priorities and objectives, such as turn left, turn right
# goto GPS 45, 65, land, take off.
#
# Control:
# Fl... | normal | {
"blob_id": "a77fb90cdc6e7f9b70f9feeefc2b7f8e93a2d8c5",
"index": 9875,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef mainLoop():\n wilfredCommunication = command.Command()\n wilfredCommunication.waitForClient()\n wilfredCommand = command.Command()\n while True:\n if not wilfre... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
from south.utils import datetime_utils as datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding field 'VideoAd.compress'
db.add_column(u'main_videoad', 'compres... | normal | {
"blob_id": "b4bcf9903f4a34c8b256c65cada29e952a436f74",
"index": 2215,
"step-1": "<mask token>\n\n\nclass Migration(SchemaMigration):\n\n def forwards(self, orm):\n db.add_column(u'main_videoad', 'compress', self.gf(\n 'django.db.models.fields.BooleanField')(default=False),\n keep... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: Swking
@File : ZDT.py
@Date : 2018/12/28
@Desc :
"""
import numpy as np
class ZDT1:
def __init__(self):
self.dimension = 30
self.objFuncNum = 2
self.isMin = True
self.min = np.zeros(self.dimension)
self.max = np.zeros(self.dimension) + 1
self.s... | normal | {
"blob_id": "8ca16947054b681a5f43d8b8029191d031d3a218",
"index": 8352,
"step-1": "<mask token>\n\n\nclass ZDT2:\n\n def __init__(self):\n self.dimension = 30\n self.objFuncNum = 2\n self.isMin = True\n self.min = np.zeros(self.dimension)\n self.max = np.zeros(self.dimension)... | [
12,
13,
16,
17,
18
] |
from .chair_model import run_chair_simulation, init_omega_t, \
JumpingModel, H_to_L
from .utils import load_hcp_peaks, Condition, average_peak_counts
| normal | {
"blob_id": "9087a7bf42070fdb8639c616fdf7f09ad3903656",
"index": 6755,
"step-1": "<mask token>\n",
"step-2": "from .chair_model import run_chair_simulation, init_omega_t, JumpingModel, H_to_L\nfrom .utils import load_hcp_peaks, Condition, average_peak_counts\n",
"step-3": "from .chair_model import run_chair_... | [
0,
1,
2
] |
from appJar import gui
app = gui("Calculator", "560x240")
### FUNCTIONS ###
n1, n2 = 0.0, 0.0
result = 0.0
isFirst = True
calc = ""
def doMath(btn):
global result, n1, n2, isFirst, calc
inputNumber()
if(btn == "Add"): calc = "a"
if(btn == "Substract"): calc = "s"
if(btn == "Multiply"): calc =... | normal | {
"blob_id": "084299da1c2f41de96e60d37088466c7b61de38e",
"index": 9750,
"step-1": "<mask token>\n\n\ndef doMath(btn):\n global result, n1, n2, isFirst, calc\n inputNumber()\n if btn == 'Add':\n calc = 'a'\n if btn == 'Substract':\n calc = 's'\n if btn == 'Multiply':\n calc = 'm... | [
3,
5,
6,
7,
8
] |
from marshmallow import fields, post_load
from rebase.common.schema import RebaseSchema, SecureNestedField
from rebase.views.bid_limit import BidLimitSchema
class TicketSetSchema(RebaseSchema):
id = fields.Integer()
bid_limits = SecureNestedField(BidLimitSchema, exclude=('ticket_set',),
only=('id', 'p... | normal | {
"blob_id": "5ebc4f61810f007fd345b52531f7f4318820b9c8",
"index": 6333,
"step-1": "<mask token>\n\n\nclass TicketSetSchema(RebaseSchema):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass TicketSetSchema(RebaseSchem... | [
1,
2,
4,
5
] |
rf = open('A-large.in', 'r')
wf = open('A-large.out', 'w')
cases = int(rf.readline())
for case in range(1, cases + 1):
digits = [False] * 10
n = int(rf.readline())
if n == 0:
wf.write('Case #%s: INSOMNIA\n' % case)
continue
for i in range(1, 999999):
cur = n * i
for c in ... | normal | {
"blob_id": "0074b0cd1e4317e36ef4a41f8179464c2ec6c197",
"index": 8250,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor case in range(1, cases + 1):\n digits = [False] * 10\n n = int(rf.readline())\n if n == 0:\n wf.write('Case #%s: INSOMNIA\\n' % case)\n continue\n for i in r... | [
0,
1,
2
] |
from tkinter import *
import re
class Molecule:
def __init__(self, nom, poids, adn):
self.nom = nom
self.poids = poids
self.adn = adn
def __repr__(self):
return "{} : {} g".format(self.nom, self.poids)
class Menu:
def __init__(self):
self.data = dict()
se... | normal | {
"blob_id": "4d05e65dce9f689ae533a57466bc75fa24db7b4d",
"index": 4558,
"step-1": "<mask token>\n\n\nclass Menu:\n\n def __init__(self):\n self.data = dict()\n self.main = Tk()\n self.main.title('Molécules')\n self.main.config(bg='black')\n self.main.minsize(210, 220)\n ... | [
17,
18,
23,
24,
26
] |
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 19 13:42:09 2019
@author: Administrator
"""
from config.path_config import *
import GV
def ReadTxtName(rootdir):
#读取文件中的每一行,转为list
lines = []
with open(rootdir, 'r') as file_to_read:
while True:
line = file_to_read.read... | normal | {
"blob_id": "92bbccfbfebf905965c9cb0f1a85ffaa7d0cf6b5",
"index": 3796,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef project_query_lz_main(question):\n txt_line = ReadTxtName(PROJECT_NAMES)\n for project_name in txt_line:\n if project_name in question:\n GV.SHOW = True\n ... | [
0,
1,
2,
3,
4
] |
'''
Created on Mar 27, 2019
@author: Iulia
'''
from Graph import Graph
from Controller import *
from Iterators.Vertices import *
from File import File
from Iterators.EdgesIterator import EdgesIterator
def test():
tup = File.readInput("file.txt")
graph = tup[0]
edgeData = tup[1]
ctrl = Controller(grap... | normal | {
"blob_id": "b01ff71792895bb8839e09ae8c4a449405349990",
"index": 7066,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test():\n tup = File.readInput('file.txt')\n graph = tup[0]\n edgeData = tup[1]\n ctrl = Controller(graph, edgeData)\n vertices = ctrl.nrVertices()\n itv = verti... | [
0,
1,
2,
3,
4
] |
#calss header
class _WATERWAYS():
def __init__(self,):
self.name = "WATERWAYS"
self.definitions = waterway
self.parents = []
self.childen = []
self.properties = []
self.jsondata = {}
self.basic = ['waterway']
| normal | {
"blob_id": "33daf5753b27f6b4bcb7c98e28cf2168e7f0b403",
"index": 9541,
"step-1": "<mask token>\n",
"step-2": "class _WATERWAYS:\n <mask token>\n",
"step-3": "class _WATERWAYS:\n\n def __init__(self):\n self.name = 'WATERWAYS'\n self.definitions = waterway\n self.parents = []\n ... | [
0,
1,
2,
3
] |
indelCost = 1
swapCost = 13
subCost = 12
noOp = 0
def alignStrings(x,y):
nx = len(x)
ny = len(y)
S = matrix(nx+1, ny+1) #??
for i in range (nx+1)
for j in range (ny+1)
if i == 0: #if the string is empty
S[i][j] = j #this will put all the letters from j in i
elif j == 0: #if the second string ... | normal | {
"blob_id": "65aa85675393efa1a0d8e5bab4b1dbf388018c58",
"index": 261,
"step-1": "\nindelCost = 1\nswapCost = 13\nsubCost = 12\nnoOp = 0\n\t\ndef alignStrings(x,y):\n\t\n\tnx = len(x)\n\tny = len(y)\n\tS = matrix(nx+1, ny+1) #?? \n\t\n\tfor i in range (nx+1)\n\t\tfor j in range (ny+1)\n\t\t\tif i == 0:\t#if the s... | [
0
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 13 17:34:32 2019
@author: fanlizhou
Analyze codon usage of sequence from 'SP_gene_seq.txt' and 'LP_gene_seq.txt'
Plot heatmap of amino acid usage and codon usage
Plot codon usage in each gene for each amino acid. Genes were arranged so that
the ge... | normal | {
"blob_id": "ae7a2de8742e353818d4f5a28feb9bce04d787bb",
"index": 8382,
"step-1": "<mask token>\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\n 'Analyze codon usage of SP and LP\\n')\n parser.add_argument('sp_file', help='one input SP data file\\n')\n parser.add_argument('... | [
11,
13,
16,
20,
21
] |
from flask import Blueprint
class NestableBlueprint(Blueprint):
def register_blueprint(self, blueprint, **options):
def deferred(state):
# state.url_prefix => 自己url前缀 + blueprint.url_prefix => /v3/api/cmdb/
url_prefix = (state.url_prefix or u"") + (options.get('url_prefix', bluepri... | normal | {
"blob_id": "2c505f3f1dfdefae8edbea0916873229bcda901f",
"index": 764,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass NestableBlueprint(Blueprint):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass NestableBlueprint(Blueprint):\n\n def register_blueprint(self, blueprint, **options):\... | [
0,
1,
2,
3,
4
] |
# coding: utf-8
import sys
#from operator import itemgetter
sysread = sys.stdin.readline
read = sys.stdin.read
from heapq import heappop, heappush
from collections import defaultdict
sys.setrecursionlimit(10**7)
import math
#from itertools import product#accumulate, combinations, product
#import bisect# lower_bound etc... | normal | {
"blob_id": "f73a3bd7665ac9cc90085fcac2530c93bef69d3d",
"index": 6705,
"step-1": "<mask token>\n\n\ndef run():\n mod = 1000000007\n N, *AB = map(int, read().split())\n A_B = []\n INF = float('inf')\n zerozero = 0\n for i in range(N):\n a = AB[2 * i]\n b = AB[2 * i + 1]\n if... | [
1,
2,
3,
4,
5
] |
"""
Given a list of partitioned and sentiment-analyzed tweets, run several trials
to guess who won the election
"""
import json
import math
import sys
import pprint
import feature_vector
def positive_volume(f):
return f['relative_volume'] * f['positive_percent']
def inv_negative_volume(f):
return 1.0 - f['r... | normal | {
"blob_id": "d508cb0a8d4291f1c8e76d9d720be352c05ef146",
"index": 8651,
"step-1": "<mask token>\n\n\ndef positive_volume(f):\n return f['relative_volume'] * f['positive_percent']\n\n\n<mask token>\n\n\ndef normalized_sentiment(f):\n return (f['average_sentiment'] + 1) / 2\n\n\ndef normalized_square_sentimen... | [
6,
7,
10,
12,
13
] |
from odoo import models, fields, api
from datetime import datetime, timedelta
from odoo import exceptions
import logging
import math
_logger = logging.getLogger(__name__)
class BillOfLading(models.Model):
_name = 'freight.bol'
_description = 'Bill Of Lading'
_order = 'date_of_issue desc, writ... | normal | {
"blob_id": "f8e287abc7e1a2af005aa93c25d95ce770e29bf9",
"index": 7378,
"step-1": "<mask token>\n\n\nclass BillOfLading(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>... | [
30,
32,
38,
47,
49
] |
import pandas as pd
import numpy as np
import geopandas as gp
from sys import argv
import os
import subprocess
n, e, s, w = map(int, argv[1:5])
output_dir = argv[5]
print(f'{(n, e, s, w)=}')
for lat in range(s, n + 1):
for lon in range(w, e + 1):
latdir = 'n' if lat >= 0 else 's'
londir = 'e' if ... | normal | {
"blob_id": "9f36b846619ca242426041f577ab7d9e4dad6a43",
"index": 3797,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(f'(n, e, s, w)={n, e, s, w!r}')\nfor lat in range(s, n + 1):\n for lon in range(w, e + 1):\n latdir = 'n' if lat >= 0 else 's'\n londir = 'e' if lon >= 0 else 'w'\n... | [
0,
1,
2,
3,
4
] |
s=int(input())
print(s+2-(s%2)) | normal | {
"blob_id": "0412369f89842e2f55aa115e63f46a1b71a0f322",
"index": 2685,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(s + 2 - s % 2)\n",
"step-3": "s = int(input())\nprint(s + 2 - s % 2)\n",
"step-4": "s=int(input())\nprint(s+2-(s%2))",
"step-5": null,
"step-ids": [
0,
1,
2,
... | [
0,
1,
2,
3
] |
# Generated by Django 3.1.1 on 2020-10-07 04:04
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('articals', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='artical',
name='thumb',
fi... | normal | {
"blob_id": "d69bffb85d81ab3969bfe7dfe2759fa809890208",
"index": 503,
"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 = [('articals', '... | [
0,
1,
2,
3,
4
] |
# flush in poker
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 = number_of_zero
big = small + 1
whi... | normal | {
"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
] |
import typ
@typ.typ(items=[int])
def gnome_sort(items):
"""
>>> gnome_sort([])
[]
>>> gnome_sort([1])
[1]
>>> gnome_sort([2,1])
[1, 2]
>>> gnome_sort([1,2])
[1, 2]
>>> gnome_sort([1,2,2])
[1, 2, 2]
"""
i = 0
n = len(items)
while i < n:
if i and items[i] < items[i - 1]:
... | normal | {
"blob_id": "70aba6c94b7050113adf7ae48bd4e13aa9a34587",
"index": 1023,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@typ.typ(items=[int])\ndef gnome_sort(items):\n \"\"\"\n >>> gnome_sort([])\n []\n >>> gnome_sort([1])\n [1]\n >>> gnome_sort([2,1])\n [1, 2]\n >>> gnome_sort([1,2])\n [1, ... | [
0,
1,
2
] |
import json
from constants import *
from coattention_layer import *
from prepare_generator import *
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import LearningRateScheduler, ModelCheckpoint, Early... | normal | {
"blob_id": "a8d52d81ef6538e9cb8a0a9cab7cd0a778454c8e",
"index": 6424,
"step-1": "<mask token>\n\n\ndef coattention(num_embeddings):\n image_input = Input(shape=(196, 512))\n question_input = Input(shape=(SEQ_LENGTH,))\n output = CoattentionModel(num_embeddings)(question_input, image_input)\n model =... | [
3,
4,
5,
6,
7
] |
##
# hunt_and_kill.py
# 05 Oct 2021
# Generates a maze using the hunt and kill algorithm
# S
from sys import argv
from enum import Enum
import random
# Cardinal directions, can be OR'd and AND'd
DIRS = {
'N': 1 << 0,
'E': 1 << 1,
'S': 1 << 2,
'W': 1 << 3
}
O_DIRS = {
'N': 'S',
... | normal | {
"blob_id": "54002bc7e2a1991d2405acbe1d399e8803ac5582",
"index": 7210,
"step-1": "<mask token>\n\n\ndef walk_maze(maze: list[int], width: int, height: int, start: tuple[int, int]\n ) ->None:\n \"\"\"\n Does a random walk, setting the cells as it goes, until it cant find a\n path.\n \"\"\"\n maz... | [
4,
6,
7,
8,
9
] |
import numpy
import numpy.fft
import numpy.linalg
import copy
from astropy.io import fits
from scipy.interpolate import RectBivariateSpline
from scipy.signal import convolve
import offset_index
# some basic definitions
psSize = 9 # psSize x psSize postage stamps of stars
# zero padded RectBivariateSpline, if on
def R... | normal | {
"blob_id": "2ab6488276c74da8c3d9097d298fc53d1caf74b1",
"index": 6243,
"step-1": "<mask token>\n\n\ndef RectBivariateSplineZero(y1, x1, map1, kx=1, ky=1):\n return RectBivariateSpline(y1, x1, map1, kx=kx, ky=ky)\n y2 = numpy.zeros(numpy.size(y1) + 2)\n y2[1:-1] = y1\n y2[0] = 2 * y2[1] - y2[2]\n y... | [
13,
14,
15,
18,
23
] |
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(torch.nn.Module):
def __init__(self, layer_sizes=[256, 128, 2], dropout_prob=None, device=None):
super(Net, self).__init__()
self.device = device
if dropout_prob is not None and dropout_prob > 0.5:
pr... | normal | {
"blob_id": "4711adcc7c95993ec13b9d06fa674aa064f79bfd",
"index": 314,
"step-1": "<mask token>\n\n\nclass Net(torch.nn.Module):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Net(torch.nn.Module):\n\n def __init__(self, layer_sizes=[256, 128, 2], dropout_prob=None, device\n ... | [
1,
2,
3,
4,
5
] |
from ShazamAPI import Shazam
import json
import sys
print("oi")
| normal | {
"blob_id": "c248d653556ecdf27e56b57930832eb293dfd579",
"index": 5413,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('oi')\n",
"step-3": "from ShazamAPI import Shazam\nimport json\nimport sys\nprint('oi')\n",
"step-4": "from ShazamAPI import Shazam\nimport json\nimport sys\n\nprint(\"oi\")\n",... | [
0,
1,
2,
3
] |
# -*- coding: UTF-8 -*-
# File name: ukWorkingDays
# Created by JKChang
# 29/07/2020, 11:20
# Tag:
# Description:
from datetime import date,timedelta,datetime
from workalendar.europe import UnitedKingdom
cal = UnitedKingdom()
print(cal.holidays(2020))
def workingDate(start,end):
cal = UnitedKingdom()
res = [... | normal | {
"blob_id": "feed412278d9e711e49ef209ece0876c1de4a873",
"index": 886,
"step-1": "<mask token>\n\n\ndef workingDate(start, end):\n cal = UnitedKingdom()\n res = []\n delta = end - start\n for i in range(delta.days + 1):\n day = start + timedelta(days=i)\n if cal.is_working_day(day) or da... | [
1,
2,
3,
4,
5
] |
"""Google Scraper
Usage:
web_scraper.py <search> <pages> <processes>
web_scraper.py (-h | --help)
Arguments:
<search> String to be Searched
<pages> Number of pages
<processes> Number of parallel processes
Options:
-h, --help Show this screen.
"""
import re
from functools impo... | normal | {
"blob_id": "68dcac07bbdb4dde983939be98ece127d963c254",
"index": 3610,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_urls(search_string, start):\n temp = []\n url = 'http://www.google.com/search'\n payload = {'q': search_string, 'start': start}\n my_headers = {'User-agent': 'Mozi... | [
0,
2,
3,
4,
5
] |
import time
import argparse
import utils
from data_loader import DataLoader
from generate_model_predictions import sacrebleu_metric, compute_bleu
import tensorflow as tf
import os
import json
from transformer import create_masks
# Since the target sequences are padded, it is important
# to apply a padding mask when c... | normal | {
"blob_id": "7613dde4f49044fbca13acad2dd75587ef68f477",
"index": 2903,
"step-1": "<mask token>\n\n\ndef loss_function(real, pred, loss_object, pad_token_id):\n \"\"\"Calculates total loss containing cross entropy with padding ignored.\n Args:\n real: Tensor of size [batch_size, length_logits, voca... | [
5,
6,
7,
8,
9
] |
def check(root, a, b):
if root:
if (root.left == a and root.right == b) or (root.left ==b and root.right==a):
return False
return check(root.left, a, b) and check(root.right, a, b)
return True
def isCousin(root, a, b):
# Your code here
if check(root, a, b)==False:
ret... | normal | {
"blob_id": "96cfee85194c9c30b3d74bbddc2a31b6933eb032",
"index": 2226,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef isCousin(root, a, b):\n if check(root, a, b) == False:\n return False\n q = []\n q.insert(0, root)\n tmp = set()\n while len(q):\n l = len(q)\n ... | [
0,
1,
2,
3
] |
import MySQLdb
import settings
import redis
import socket
import fcntl
import struct
import datetime
db = MySQLdb.connect(settings.host, settings.user, settings.pwd, settings.db)
cursor = db.cursor()
def connect_mysql():
try:
db.ping()
except:
db = MySQLdb.connect... | normal | {
"blob_id": "b46b9b086fc089e24cb39a0c2c4ac252591b2190",
"index": 1540,
"step-1": "import MySQLdb\nimport settings\nimport redis\nimport socket\nimport fcntl\nimport struct\nimport datetime\n\n\ndb = MySQLdb.connect(settings.host, settings.user, settings.pwd, settings.db)\ncursor = db.cursor()\ndef connect_mysql(... | [
0
] |
from numpy import empty
import pickle
from dataset import Dataset
from image import Image
f = open("./digitdata/trainingimages", "r")
reader = f.readlines()
labels = open("./digitdata/traininglabels", "r")
lreader = labels.readlines()
trainImageList = []
j = 0
i = 0
while(j < len(reader)):
image_array = empty([... | normal | {
"blob_id": "aff439361716c35e5f492680a55e7470b4ee0c42",
"index": 5905,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile j < len(reader):\n image_array = empty([28, 28])\n for r in range(0, 28):\n row = reader[j]\n j += 1\n for c in range(0, 28):\n if row[c] == '#... | [
0,
1,
2,
3,
4
] |
from disaggregation import DisaggregationManager
import numpy as np
from more_itertools import windowed
x = np.random.random_sample(10 * 32 * 1024)
w = windowed(x, n=1024, step=128)
z = DisaggregationManager._overlap_average(np.array(list(w)), stride=128)
print(z.shape)
print(x.shape)
assert z.shape == x.shape
| normal | {
"blob_id": "6d4950ca61cd1e2ee7ef8b409577e9df2d65addd",
"index": 4462,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(z.shape)\nprint(x.shape)\nassert z.shape == x.shape\n",
"step-3": "<mask token>\nx = np.random.random_sample(10 * 32 * 1024)\nw = windowed(x, n=1024, step=128)\nz = Disaggregation... | [
0,
1,
2,
3
] |
from context import vicemergencyapi
from vicemergencyapi.vicemergency import VicEmergency
from geographiclib.geodesic import Geodesic
from shapely.geometry import Point
def geoDistance(p1, p2):
return Geodesic.WGS84.Inverse(p1.y, p1.x, p2.y, p2.x)['s12']
melbourne = Point(144.962272, -37.812274)
def compare(f... | normal | {
"blob_id": "920f00632599945397364dd0f52f21234e17f9ef",
"index": 9445,
"step-1": "<mask token>\n\n\ndef geoDistance(p1, p2):\n return Geodesic.WGS84.Inverse(p1.y, p1.x, p2.y, p2.x)['s12']\n\n\n<mask token>\n\n\ndef compare(f):\n return geoDistance(f.getLocation(), melbourne)\n\n\n<mask token>\n",
"step-2... | [
2,
3,
4,
5,
6
] |
__title__ = 'pyaddepar'
__version__ = '0.6.0'
__author__ = 'Thomas Schmelzer'
__license__ = 'MIT'
__copyright__ = 'Copyright 2019 by Lobnek Wealth Management'
| normal | {
"blob_id": "cc985ae061c04696dbf5114273befd62321756ae",
"index": 9569,
"step-1": "<mask token>\n",
"step-2": "__title__ = 'pyaddepar'\n__version__ = '0.6.0'\n__author__ = 'Thomas Schmelzer'\n__license__ = 'MIT'\n__copyright__ = 'Copyright 2019 by Lobnek Wealth Management'\n",
"step-3": null,
"step-4": null... | [
0,
1
] |
import unittest
import TicTacToe
class pVpTestCase(unittest.TestCase):
# def test_something(self):
# self.assertEqual(True, False)
def twoplayer_setup(self):
game1 = TicTacToe.Game()
player1 = TicTacToe.Player('X', game1)
player2 = TicTacToe.Player('O', game1)
return (g... | normal | {
"blob_id": "de0521db3909054c333ac3877ff0adf15ab180fb",
"index": 1732,
"step-1": "<mask token>\n\n\nclass CvPTestCase(unittest.TestCase):\n\n def onecompplayer_setup(self):\n game1 = TicTacToe.Game()\n computer1 = TicTacToe.Computer('X', game1)\n player2 = TicTacToe.Player('O', game1)\n ... | [
13,
15,
17,
20,
22
] |
from joecceasy import Easy
def main():
paths = ['..','.']
absOfEntries = [ i.abs for i in Easy.WalkAnIter(paths) ]
for i in absOfEntries:
print( i )
if __name__=='__main__':
main()
"""
def main(maxEntries = 99):
i = -1
print( "Walker test, Walking cu... | normal | {
"blob_id": "b720a52f1c2e6e6be7c0887cd94441d248382242",
"index": 1836,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n paths = ['..', '.']\n absOfEntries = [i.abs for i in Easy.WalkAnIter(paths)]\n for i in absOfEntries:\n print(i)\n\n\n<mask token>\n",
"step-3": "<mask... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
from sklearn.feature_extraction.text import TfidfVectorizer
import sentimentAnalysis as sA
import sys
import os
import numpy as np
from sklearn import decomposition
from gensim import corpora, models
if len(sys.argv) > 1:
keyword = sys.argv[1]
else:
keyword = 'data'
... | normal | {
"blob_id": "ee47b60274ed2eb53a05203e0086d7815bcaaa6e",
"index": 7759,
"step-1": "# -*- coding: utf-8 -*-\r\n\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nimport sentimentAnalysis as sA\r\nimport sys\r\nimport os\r\nimport numpy as np\r\nfrom sklearn import decomposition\r\nfrom gensim impor... | [
0
] |
# Write a function that receives a string as a parameter and returns a dictionary in which the keys are the characters in the character string and the values are the number of occurrences of that character in the given text.
# Example: For string "Ana has apples." given as a parameter the function will return the dicti... | normal | {
"blob_id": "14807568af046594644095a2682e0eba4f445b26",
"index": 8053,
"step-1": "<mask token>\n\n\ndef funct(string):\n dict = {}\n for i in string:\n if i in dict:\n dict[i] += 1\n else:\n dict[i] = 1\n return dict\n\n\n<mask token>\n\n\ndef counter():\n string =... | [
2,
3,
4,
5,
6
] |
from http import HTTPStatus
#from pytest_chalice.handlers import RequestHandler
import app
from chalice.test import Client
def test_index_with_url():
with Client(app.app) as client:
response = client.http.get('/?url=https://google.com')
assert response.status_code == HTTPStatus.MOVED_PERMANENTLY
... | normal | {
"blob_id": "e7e9a53d4c41448521b324d51641a46827faa692",
"index": 2607,
"step-1": "<mask token>\n\n\ndef test_index_with_url():\n with Client(app.app) as client:\n response = client.http.get('/?url=https://google.com')\n assert response.status_code == HTTPStatus.MOVED_PERMANENTLY\n assert ... | [
1,
2,
3,
4,
5
] |
import csv
from functools import reduce
class Csvread:
def __init__(self, fpath):
self._path=fpath
with open (fpath) as file:
read_f=csv.reader(file)
print(read_f) #<_csv.reader object at 0x000002A53144DF40>
self._sheet = list(read_f)[1:] #utworzenie listy
... | normal | {
"blob_id": "67793c8851e7107c6566da4e0ca5d5ffcf6341ad",
"index": 8867,
"step-1": "<mask token>\n\n\nclass Csvcalc:\n\n def __init__(self, cont):\n self._cont = cont\n\n def row_count(self):\n return len(self._cont)\n\n def get_row(self, row_no):\n return self._cont[row_no]\n\n de... | [
7,
10,
11,
13,
15
] |
# coding:utf-8
from flask_sqlalchemy import SQLAlchemy
from config.manager import app
from config.db import db
class Category(db.Model):
__tablename__ = 'category'
id = db.Column(db.Integer, primary_key=True) # 编号
name = db.Column(db.String(20), nullable=False) # 账号
addtime = db.Column(db.DateTime, ... | normal | {
"blob_id": "743aa4ccbb9a131b5ef3d04475789d3d1da1a2fa",
"index": 2407,
"step-1": "<mask token>\n\n\nclass Category(db.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Category(db.Model):\n __tablename__... | [
1,
3,
4,
5,
6
] |
from django.views.generic import (ListView, DetailView, CreateView,
DeleteView, UpdateView, TemplateView)
from django.views.generic.edit import ModelFormMixin
from django.urls import reverse_lazy
from django.utils.decorators import method_decorator
from django.contrib.auth.decorators i... | normal | {
"blob_id": "a63e5186c0eb8b5ae8510b473168db3461166513",
"index": 7784,
"step-1": "<mask token>\n\n\nclass BaseModelApi(TemplateView, ModelFormMixin):\n\n def get_template_names(self):\n prefix = self.request.method\n if prefix in ['PUT', 'PATCH', 'POST']:\n prefix = 'form'\n na... | [
14,
15,
21,
25,
26
] |
import matplotlib.pyplot as plt
import numpy as np
from tti_explorer.contacts import he_infection_profile
plt.style.use('default')
loc = 0
# taken from He et al
gamma_params = {
'a': 2.11,
'loc': loc,
'scale': 1/0.69
}
t = 10
days = np.arange(t)
mass = he_infection_profile(t, gamma_params)
fig, ax = pl... | normal | {
"blob_id": "fa5cbbd03641d2937e4502ce459d64d20b5ee227",
"index": 8630,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.style.use('default')\n<mask token>\nax.bar(np.arange(5) + 0.1, [1 / 5, 1 / 5, 1 / 5, 1 / 5, 1 / 5], label=\n 'Kucharski profile', align='edge', color='C1', zorder=1, alpha=0.6)\nax... | [
0,
1,
2,
3,
4
] |
import random
from datetime import timedelta
from typing import Union, Type, Tuple, List, Dict
from django import http
from django.test import TestCase, Client
from django.utils import timezone
from exam_web import errors
from exam_web.models import Student, AcademyGroup, uuid_str, ExamSession, \
UserSession, Que... | normal | {
"blob_id": "44e4151279884ce7c5d5a9e5c82916ce2d3ccbc2",
"index": 9789,
"step-1": "<mask token>\n\n\nclass TestGetExamTickets(ApiTestCase):\n get_exams: ApiClient\n session: ExamSession\n student_session: UserSession\n questions: List[Question]\n tickets: List[ExamTicket]\n ticket_map: Dict[str,... | [
15,
34,
42,
44,
45
] |
# -*- coding: utf-8 -*-
import io
import urllib.request
from pymarc import MARCReader
class Item:
"""
Represents an item from our
Library catalogue (https://www-lib.soton.ac.uk)
Usage:
#>>> import findbooks
#>>> item = findbooks.Item('12345678')
#>>> item.getMarcFields()
... | normal | {
"blob_id": "abfff0901e5f825a473119c93f53cba206609428",
"index": 7482,
"step-1": "<mask token>\n\n\nclass Item:\n <mask token>\n <mask token>\n\n def __init__(self, barcode):\n self.barcode = barcode\n self.marc = None\n self.record = None\n self.title = None\n self.au... | [
6,
7,
8,
11,
12
] |
"""
Task. Given two integers a and b, find their greatest common divisor.
Input Format. The two integers a, b are given in the same line separated by space.
Constraints. 1<=a,b<=2·109.
Output Format. Output GCD(a, b).
"""
def EuclidGCD(a, b):
if b == 0:
return a
else:
a = a%b
return Euc... | normal | {
"blob_id": "39d82267f966ca106ee384e540c31a3e5e433318",
"index": 2248,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef EuclidGCD(a, b):\n if b == 0:\n return a\n else:\n a = a % b\n return EuclidGCD(b, a)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef EuclidGCD... | [
0,
1,
2,
3,
4
] |
# CSE 415 Winter 2019
# Assignment 1
# Jichun Li 1531264
# Part A
# 1
def five_x_cubed_plus_1(x):
return 5 * (x ** 3) + 1
#2
def pair_off(ary):
result = []
for i in range(0, int(len(ary) / 2 * 2), 2):
result.append([ary[i], ary[i + 1]])
if (int (len(ary) % 2) == 1):
result.append([ar... | normal | {
"blob_id": "681788ffe7672458e8d334316aa87936746352b1",
"index": 4054,
"step-1": "def five_x_cubed_plus_1(x):\n return 5 * x ** 3 + 1\n\n\n<mask token>\n",
"step-2": "def five_x_cubed_plus_1(x):\n return 5 * x ** 3 + 1\n\n\ndef pair_off(ary):\n result = []\n for i in range(0, int(len(ary) / 2 * 2),... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python3
# ---------------------------------------------------
# SSHSploit Framework
# ---------------------------------------------------
# Copyright (C) <2020> <Entynetproject>
#
# This program is free soft... | normal | {
"blob_id": "caf83d35ce6e0bd4e92f3de3a32221705a529ec1",
"index": 9467,
"step-1": "<mask token>\n\n\ndef banner():\n os.system('clear')\n os.system('cat banner/banner.txt')\n print('')\n print('SSHSploit Framework v1.0')\n print('------------------------')\n print('')\n\n\n<mask token>\n",
"st... | [
1,
2,
4,
5,
6
] |
# $Header: //depot/cs/s/ajax_support.wsgi#10 $
from werkzeug.wrappers import Response
from p.DRequest import DRequest
from db.Support import SupportSession
from db.Exceptions import DbError, SupportSessionExpired
import db.Db as Db
import db.Support
import cgi
import simplejson as json
def application(environ, start_... | normal | {
"blob_id": "be58862b66708c9de8cf7642c9de52ec744b079e",
"index": 805,
"step-1": "<mask token>\n\n\ndef application(environ, start_response):\n \"\"\"AJAX scripts for email templates.\"\"\"\n request = DRequest(environ)\n resp = None\n try:\n Db.start_transaction()\n form = cgi.FieldStor... | [
4,
5,
6,
7,
8
] |
import datetime
import time
import rfc822
from django.conf import settings
from urllib2 import Request, urlopen, URLError, HTTPError
from urllib import urlencode
import re
import string
try:
import django.utils.simplejson as json
except:
import json
from django.core.cache import cache
from tagging.models import T... | normal | {
"blob_id": "f720eaf1ea96ccc70730e8ba1513e1a2bb95d29d",
"index": 4842,
"step-1": "import datetime\nimport time\nimport rfc822\nfrom django.conf import settings\nfrom urllib2 import Request, urlopen, URLError, HTTPError\nfrom urllib import urlencode\nimport re \nimport string\ntry:\n import django.utils.simplejs... | [
0
] |
# Generate some object patterns as save as JSON format
import json
import math
import random
from obstacle import *
def main(map):
obs = []
for x in range(1,35):
obs.append(Obstacle(random.randint(0,map.getHeight()), y=random.randint(0,map.getWidth()), radius=20).toJsonObject())
jsonOb={'map': {'obstacle': obs}}... | normal | {
"blob_id": "b849a2902c8596daa2c6da4de7b9d1c07b34d136",
"index": 7883,
"step-1": "# Generate some object patterns as save as JSON format\nimport json\nimport math\nimport random\nfrom obstacle import *\n\ndef main(map):\n\tobs = []\n\tfor x in range(1,35):\n\t\tobs.append(Obstacle(random.randint(0,map.getHeight(... | [
0
] |
# MÁSTER EN BIG DATA Y BUSINESS ANALYTICS
# MOD 1 - FINAL EVALUATION - EX. 2: dado un archivo que contiene en cada línea
# una palabra o conjunto de palabras seguido de un valor numérico denominado
# “sentimiento” y un conjunto de tweets, se pide calcular el sentimiento de
# aquellas palabras o conjunto de palabras que... | normal | {
"blob_id": "acd2d84529e197d6f9d134e8d7e25a51a442f3ae",
"index": 8615,
"step-1": "<mask token>\n\n\ndef get_tweets(filename):\n \"\"\" Process a json formatted file with tweets using pandas read_json \"\"\"\n try:\n tweets = []\n pd_tweets = pd.read_json(filename, lines=True)\n pd_twee... | [
2,
3,
4,
5,
6
] |
import os
from typing import List, Optional, Sequence
import boto3
from google.cloud import storage
from ..globals import GLOBALS, LOGGER
def set_gcs_credentials():
if os.path.exists(GLOBALS.google_application_credentials):
return
secrets_client = boto3.client(
"secretsmanager",
reg... | normal | {
"blob_id": "a5eeafef694db04770833a4063358e8f32f467b0",
"index": 8310,
"step-1": "<mask token>\n\n\ndef set_gcs_credentials():\n if os.path.exists(GLOBALS.google_application_credentials):\n return\n secrets_client = boto3.client('secretsmanager', region_name=GLOBALS.\n aws_region, endpoint_ur... | [
2,
3,
4,
5,
6
] |
import math
def normal(data,mean,variance):
# print data-mean
return -1*(((data-mean)**2)/(2*variance)) - (0.5 * math.log(2*3.1415*variance))
a = math.log(0.33333) + normal(67.7854,6.0998,13.5408)
b = math.log(0.33333) + normal(67.7854,119.3287,9.4803)
c = math.log(0.33333) + normal(67.7854,65.7801,12.6203)
d =... | normal | {
"blob_id": "0edca9893d62eea6513543a1d3dd960e9e95d573",
"index": 7505,
"step-1": "import math\n\ndef normal(data,mean,variance):\n\t# print data-mean\n\treturn -1*(((data-mean)**2)/(2*variance)) - (0.5 * math.log(2*3.1415*variance))\n\na = math.log(0.33333) + normal(67.7854,6.0998,13.5408)\nb = math.log(0.3333... | [
0
] |
from django.db import models
ch=[
('Garment','Garment'),
('Hardgoods','Hardgoods'),
('Home Furnishing','Home Furnishing'),
]
class Factory(models.Model):
name = models.CharField(max_length=30,choices=ch)
def __str__(self):
return self.name
class Fabric(models.Model):
n... | normal | {
"blob_id": "a0dcfb738451c11ed4ff1428629c3f7bbf5c52c9",
"index": 5649,
"step-1": "<mask token>\n\n\nclass Category(models.Model):\n <mask token>\n <mask token>\n <mask token>\n\n def __str__(self):\n return self.category\n\n\nclass Subcategory(models.Model):\n name = models.ForeignKey(Categ... | [
17,
20,
22,
24,
30
] |
#from getData import getRatings
import numpy as np
num_factors = 10
num_iter = 75
regularization = 0.05
lr = 0.005
folds=5
#to make sure you are able to repeat results, set the random seed to something:
np.random.seed(17)
def split_matrix(ratings, num_users, num_movies):
#Convert data into (IxJ... | normal | {
"blob_id": "b4267612e7939b635542099e1ba31e661720607a",
"index": 3129,
"step-1": "<mask token>\n\n\ndef split_matrix(ratings, num_users, num_movies):\n X = np.zeros((num_users, num_movies))\n for r in np.arange(len(ratings)):\n X[ratings[r, 0] - 1, ratings[r, 1] - 1] = ratings[r, 2]\n return X\n\... | [
2,
3,
4,
5,
7
] |
# coding: utf-8
"""
MailSlurp API
MailSlurp is an API for sending and receiving emails from dynamically allocated email addresses. It's designed for developers and QA teams to test applications, process inbound emails, send templated notifications, attachments, and more. ## Resources - [Homepage](https://ww... | normal | {
"blob_id": "a4ccf373695b7df60039bc8f6440a6ad43d265c1",
"index": 3750,
"step-1": "<mask token>\n\n\nclass FormControllerApi(object):\n <mask token>\n <mask token>\n <mask token>\n\n def submit_form_with_http_info(self, **kwargs):\n \"\"\"Submit a form to be parsed and sent as an email to an ad... | [
2,
3,
4,
5,
7
] |
#!/usr/bin/python
import sys
BLACK = '\033[30;0m'
RED = '\033[31;0m'
GREEN = '\033[32;0m'
YELLOW = '\033[33;0m'
BLUE = '\033[34;0m'
PINK = '\033[35;0m'
CBLUE = '\033[36;0m'
WHITE = '\033[37;0m'
def colorPrint(color, str):
print(color + str + '\033[0m');
def main():
if sys.argv.__len__() < ... | normal | {
"blob_id": "a49c00dab8d445ce0b08fd31a4a41d6c8976d662",
"index": 2263,
"step-1": "<mask token>\n\n\ndef colorPrint(color, str):\n print(color + str + '\\x1b[0m')\n\n\ndef main():\n if sys.argv.__len__() < 2:\n print('Wrong usage, exit')\n return\n colorPrint(YELLOW, sys.argv[1])\n\n\n<mask... | [
2,
3,
4,
5,
6
] |
from urllib import request
from urllib import error
from urllib.request import urlretrieve
import os, re
from bs4 import BeautifulSoup
import configparser
from apng2gif import apng2gif
config = configparser.ConfigParser()
config.read('crawler.config')
# 下載儲存位置
directoryLocation = os.getcwd() + '\\img'
# 設置要爬的頁面
urlLis... | normal | {
"blob_id": "7bcdd6c5c6e41b076e476e1db35b663e34d74a67",
"index": 1885,
"step-1": "<mask token>\n\n\ndef saveImg(imgurl, downLoadType):\n fileLocation = directoryLocation + '\\\\' + downLoadType + '\\\\' + title\n if not os.path.exists(fileLocation):\n os.makedirs(fileLocation)\n file = fileLocati... | [
3,
4,
5,
6,
7
] |
from pythonforandroid.recipe import CompiledComponentsPythonRecipe
from multiprocessing import cpu_count
from os.path import join
class NumpyRecipe(CompiledComponentsPythonRecipe):
version = '1.18.1'
url = 'https://pypi.python.org/packages/source/n/numpy/numpy-{version}.zip'
site_packages_name = 'numpy'
... | normal | {
"blob_id": "610610e7e49fc98927a4894efe62686e26e0cb83",
"index": 3502,
"step-1": "<mask token>\n\n\nclass NumpyRecipe(CompiledComponentsPythonRecipe):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def build_compiled_components(self, arch):\n ... | [
3,
4,
5,
6,
7
] |
import os.path as path
from googleapiclient.discovery import build
from google.oauth2 import service_account
# If modifying these scopes, delete the file token.pickle.
SCOPES = ['https://www.googleapis.com/auth/spreadsheets.readonly']
# The ID and range of a sample spreadsheet.
SAMPLE_SPREADSHEET_ID = '1FSMATLJUNCbV8... | normal | {
"blob_id": "f9261c1844cc629c91043d1221d0b76f6e22fef6",
"index": 6157,
"step-1": "<mask token>\n\n\ndef main():\n service_account_json = path.join(path.dirname(path.abspath(__file__)),\n 'service_account.json')\n credentials = service_account.Credentials.from_service_account_file(\n service_a... | [
3,
5,
6,
7,
8
] |
#################################################
### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
#################################################
# file to edit: dev_nb/10_DogcatcherFlatten.ipynb
import pandas as pd
import argparse
import csv
import os
import numpy as np
import string
def FivePrimeArea(df):
... | normal | {
"blob_id": "5c5922fd3a7a5eec121d94e69bc972089e435175",
"index": 9406,
"step-1": "<mask token>\n\n\ndef FivePrimeArea(df):\n df = df.sort_values(by=['chr', 'end'], ascending=True)\n df['FA_start'] = df['gene_start']\n df_exon = df[df['type'] == 'exon'].copy()\n df_exon = df_exon.drop_duplicates(subse... | [
4,
6,
8,
9,
11
] |
from collections import deque
s = list(input().upper())
new = list(set(s)) # 중복 제거 한 알파벳 리스트로 카운트 해줘야 시간초과 안남
n = {}
for i in new:
n[i] = s.count(i)
cnt = deque()
for k, v in n.items():
cnt.append(v)
if cnt.count(max(cnt)) >1:
print('?')
else:
print(max(n, key=n.get))
| normal | {
"blob_id": "5dcb20f52b5041d5f9ea028b383e0f2f10104af9",
"index": 9486,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in new:\n n[i] = s.count(i)\n<mask token>\nfor k, v in n.items():\n cnt.append(v)\nif cnt.count(max(cnt)) > 1:\n print('?')\nelse:\n print(max(n, key=n.get))\n",
"step... | [
0,
1,
2,
3,
4
] |
from datetime import datetime
import pytz
from pytz import timezone
##PDXtime = datetime.now()
##print(PDXtime.hour)
##
##NYCtime = PDXtime.hour + 3
##print(NYCtime)
##
##Londontime = PDXtime.hour + 8
##print(Londontime)
Londontz = timezone('Europe/London')
Londonlocaltime = datetime.now(Londontz)
print(Londo... | normal | {
"blob_id": "d8cfd9de95e1f47fc41a5389f5137b4af90dc0f1",
"index": 3949,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(Londonlocaltime)\nprint(Londonlocaltime.strftime('%H'))\n<mask token>\nprint(PDXlocaltime)\nprint(PDXlocaltime.strftime('%H'))\n<mask token>\nprint(NYClocaltime)\nprint(NYClocaltime... | [
0,
1,
2,
3,
4
] |
from collections import OrderedDict
import copy
import numpy as np
from scipy.optimize import curve_fit
from ... import Operation as opmod
from ...Operation import Operation
from ....tools import saxstools
class SpectrumFit(Operation):
"""
Use a measured SAXS spectrum (I(q) vs. q),
to optimize the param... | normal | {
"blob_id": "7b5713c9a5afa911df1c2939751de30412162f15",
"index": 446,
"step-1": "<mask token>\n\n\nclass SpectrumFit(Operation):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass SpectrumFit(Operation):\n <mask token>\n\n def __init__(self):\n input_names... | [
1,
3,
4,
5,
6
] |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
month = ['Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec', 'Jan', 'Feb',
'Mar', 'Apr', 'May']
df = pd.DataFrame([[53, 0, 5, 3, 3], [51, 0, 1, 3, 2], [70, 4, 7, 5, 1], [
66, 4, 1, 4, 2], [64, 4, 4, 3, 2], [69, 4, 7, 8, 2], [45, 2, 8, 4, 2],
... | normal | {
"blob_id": "f5c277da2b22debe26327464ae736892360059b4",
"index": 781,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.pcolor(df)\nplt.colorbar()\nplt.yticks(np.arange(0.5, len(df.index), 1), df.index)\nplt.xticks(np.arange(0.5, len(df.columns), 1), df.columns)\nplt.show()\n",
"step-3": "<mask token>... | [
0,
1,
2,
3
] |
# -*- encoding:utf-8 -*-
import os
import unittest
from HTMLTestRunner_cn import HTMLTestRunner
from time import sleep
from framework.SunFlower import SunFlower
from testcase.TestCRM import TestCRM
class TestCRMcreateCustomer(TestCRM):
# 创建客户
def createCustomer(self):
# 点击客户图标
self.driver.... | normal | {
"blob_id": "74bc530d53cd86c52c44ba8e98d4d8f502032340",
"index": 2423,
"step-1": "<mask token>\n\n\nclass TestCRMcreateCustomer(TestCRM):\n <mask token>\n\n def test_weiChat(self):\n self.login()\n self.createCustomer()\n self.logout()\n\n\n<mask token>\n",
"step-2": "<mask token>\n\... | [
2,
3,
4,
5,
6
] |
import os
from unittest import TestCase
class TestMixin(TestCase):
@classmethod
def setUpClass(cls):
cls.base_dir = os.path.dirname(os.path.abspath(__file__))
cls.fixtures_dir = os.path.join(cls.base_dir, 'fixtures')
cls.bam_10xv2_path = os.path.join(cls.fixtures_dir, '10xv2.bam')
... | normal | {
"blob_id": "268a8252f74a2bdafdadae488f98997c91f5607c",
"index": 2686,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TestMixin(TestCase):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass TestMixin(TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.base_dir = os.pat... | [
0,
1,
2,
3
] |
import sys
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from uraeus.nmbd.python import simulation
from uraeus.nmbd.python.engine.numerics.math_funcs import A, B
database_directory = os.path.abspath('../../')
sys.path.append(database_directory)
from uraeus_fsae.simenv.assemblies i... | normal | {
"blob_id": "e0541c377eb6631e4ef5eb79b1204612ce8af48c",
"index": 6107,
"step-1": "<mask token>\n\n\ndef generate_circular_path(radius, offset):\n theta = np.deg2rad(np.linspace(0, 360, 360))\n x_data = radius * np.sin(theta) + offset[0]\n y_data = radius * np.cos(theta) + offset[1]\n radii = radius *... | [
3,
8,
9,
10,
11
] |
# Imports
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils import data
from torch.utils.data import DataLoader
import torchvision.datasets as datasets
import torchvision.transforms as transforms
# Create Fully Connected Network
class NN(nn.Module):
def... | normal | {
"blob_id": "1edb92a4905048f3961e3067c67ef892d7b8a034",
"index": 9154,
"step-1": "<mask token>\n\n\nclass NN(nn.Module):\n\n def __init__(self, input_size, num_classes):\n super(NN, self).__init__()\n self.fc1 = nn.Linear(input_size, 50)\n self.fc2 = nn.Linear(50, num_classes)\n\n def ... | [
4,
5,
6,
7,
8
] |
import logging
from typing import Dict
import numpy as np
from meshkit import Mesh
from rendkit.materials import DepthMaterial
from vispy import gloo, app
from vispy.gloo import gl
logger = logging.getLogger(__name__)
class Renderable:
def __init__(self,
material_name: str,
at... | normal | {
"blob_id": "061c287d5f0a5feeeaedc80eea6b3fc4ff02286e",
"index": 7191,
"step-1": "<mask token>\n\n\nclass Renderable:\n\n def __init__(self, material_name: str, attributes: Dict[str, np.ndarray\n ], model_mat=np.eye(4), uv_scale=1.0):\n self.model_mat = model_mat\n self.material_name = ma... | [
10,
11,
13,
16,
17
] |
import numpy as np
import cv2 as cv
import methods as meth
from numpy.fft import fft2, fftshift, ifft2, ifftshift
import pandas
import os
import noGPU as h
import matplotlib.pyplot as plt
class fullSys():
def __init__(self, dir, file, size, line):
csv_reader = pandas.read_csv(file, index_col='Objective')
... | normal | {
"blob_id": "e3c9487f3221ca89b9014b2e6470ca9d4dbc925a",
"index": 2239,
"step-1": "<mask token>\n\n\nclass section:\n\n def __init__(self, i0, j0, subImg, Params):\n self.Params = Params\n self.subParams = {}\n self.subParams['wLen'] = [6.3e-07, 5.3e-07, 4.3e-07]\n self.subParams['s... | [
9,
11,
13,
15,
19
] |
import time
import random
import math
people = [('Seymour', 'BOS'),
('Franny', 'DAL'),
('Zooey', 'CAK'),
('Walt', 'MIA'),
('Buddy', 'ORD'),
('Les', 'OMA')]
destination = 'LGA'
flights = dict()
for line in file('schedule.txt'):
origin, dest, depart, arrive, price... | normal | {
"blob_id": "bd5f298027f82edf5451f5297d577005674de4c3",
"index": 3577,
"step-1": "import time\nimport random\nimport math\n\npeople = [('Seymour', 'BOS'),\n ('Franny', 'DAL'),\n ('Zooey', 'CAK'),\n ('Walt', 'MIA'),\n ('Buddy', 'ORD'),\n ('Les', 'OMA')]\n\ndestination ... | [
0
] |
from math import *
import math
import re
import numpy as np
class atom:
aid=0
atype=''
x=0.0
y=0.0
z=0.0
rid=0
rtype=''
model=[]
chainid=''
def getlen(atm1,atm2):
dist=sqrt(pow(atm1.x-atm2.x,2)+pow(atm1.y-atm2.y,2)+pow(atm1.z-atm2.z,2))
return dist
def ... | normal | {
"blob_id": "78123c806e5a8c0cc7511a5024769f8c61621efa",
"index": 9877,
"step-1": "<mask token>\n\n\nclass atom:\n aid = 0\n atype = ''\n x = 0.0\n y = 0.0\n z = 0.0\n rid = 0\n rtype = ''\n model = []\n chainid = ''\n\n\ndef getlen(atm1, atm2):\n dist = sqrt(pow(atm1.x - atm2.x, 2) ... | [
5,
6,
8,
9,
10
] |
"""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
] |
# -*- coding: utf-8 -*-
"""
Created on Tue Mai 15 11:34:22 2018
@author: Diogo Leite
"""
from SQL_obj_new.Dataset_config_dataset_new_sql import _DS_config_DS_SQL
class Dataset_conf_ds(object):
"""
This class treat the datasets configuration connection tables object has it exists in DATASET_CONF_DS table data... | normal | {
"blob_id": "76d2c3f74e8fae160396b4015ccec478dba97b87",
"index": 7422,
"step-1": "<mask token>\n\n\nclass Dataset_conf_ds(object):\n <mask token>\n\n def __init__(self, id_ds_conf_ds=-1, value_configuration=-1,\n FK_id_configuration_DCT_DCD=-1, FK_id_dataset_DS_DCD=-1):\n \"\"\"\n Cons... | [
2,
3,
5,
6,
7
] |
from flask import Flask
from raven.contrib.flask import Sentry
from flask.signals import got_request_exception
app = Flask(__name__)
sentry = Sentry(dsn=app.config['SENTRY_DSN'])
@got_request_exception.connect
def log_exception_to_sentry(app, exception=None, **kwargs):
"""
Logs an exception to sentry.
:p... | normal | {
"blob_id": "f739fb56eae1ada2409ef7d75958bad2018f5134",
"index": 2743,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@got_request_exception.connect\ndef log_exception_to_sentry(app, exception=None, **kwargs):\n \"\"\"\n Logs an exception to sentry.\n\n :param app: The current application\n ... | [
0,
1,
2,
3
] |
import time
import itertools
import re
from pyspark import SparkContext, SparkConf
from pyspark.rdd import portable_hash
from datetime import datetime
APP_NAME = 'in-shuffle-secondary-sort-compute'
INPUT_FILE = '/data/Taxi_Trips.csv.xsmall'
OUTPUT_DIR = '/data/output-in-shuffle-sort-compute-{timestamp}.txt'
COMMA_DE... | normal | {
"blob_id": "05d6f15102be41937febeb63ed66a77d3b0a678e",
"index": 8517,
"step-1": "<mask token>\n\n\ndef key_func(entry):\n return entry[0], entry[1]\n\n\ndef make_pair(entry):\n key = entry[FIRST_KEY], entry[SECOND_KEY]\n return key, entry\n\n\ndef unpair(entry):\n return entry[0][0], entry[1][0], en... | [
5,
6,
8,
10,
11
] |
def solution(A):
if not A:
return 1
elif len(A) == 1:
if A[0] == 1:
return 2
else:
return 1
A.sort()
prev = 0
for i in A:
if i != (prev + 1):
return i - 1
else:
prev = i
return prev + 1
| normal | {
"blob_id": "8c3c066ed37fe0f67acfd2d5dc9d57ec2b996275",
"index": 5640,
"step-1": "<mask token>\n",
"step-2": "def solution(A):\n if not A:\n return 1\n elif len(A) == 1:\n if A[0] == 1:\n return 2\n else:\n return 1\n A.sort()\n prev = 0\n for i in A:\n... | [
0,
1,
2
] |
import visgraph.dbcore as vg_dbcore
dbinfo = {
'user':'visgraph',
'password':'ohhai!',
'database':'vg_test',
}
def vgtest_basic_database():
#vg_dbcore.initGraphDb(dbinfo)
gstore = vg_dbcore.DbGraphStore(dbinfo)
n1 = gstore.addNode(ninfo={'name':'foo', 'size':20})
n2 = gstore.addNode(ninfo={'name':'bar',... | normal | {
"blob_id": "ffee0b0e00b4cebecefc3671332af3e2ffe7491b",
"index": 8155,
"step-1": "import visgraph.dbcore as vg_dbcore\n\ndbinfo = {\n'user':'visgraph',\n'password':'ohhai!',\n'database':'vg_test',\n}\n\ndef vgtest_basic_database():\n#vg_dbcore.initGraphDb(dbinfo)\n\n gstore = vg_dbcore.DbGraphStore(dbinfo)\n\... | [
0
] |
import os
from celery import Celery
import django
from django.conf import settings
from django.apps import apps
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'nightcrawler.settings')
#celery_app = Celery('nightcrawler.tasks.keep_it', broker=settings.CELERY_BROKER_URL)
celery_app = Celery('nightcrawler', broker=sett... | normal | {
"blob_id": "d4bc6bfe6bef730273db38f3c99352bbc3f48a5f",
"index": 7604,
"step-1": "<mask token>\n\n\n@celery_app.task(bind=True)\ndef debug_task(self):\n print('Request: {0!r}'.format(self.request))\n",
"step-2": "<mask token>\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'nightcrawler.settings')\n<mask t... | [
1,
2,
3,
4,
5
] |
from turtle import *
def drawSquare():
for i in range(4):
forward(100)
left(90)
if __name__ == '__main__':
drawSquare()
up()
forward(200)
down()
drawSquare()
mainloop()
| normal | {
"blob_id": "1ce5b97148885950983e39b7e99d0cdfafe4bc16",
"index": 5382,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef drawSquare():\n for i in range(4):\n forward(100)\n left(90)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef drawSquare():\n for i in range(4):\n ... | [
0,
1,
2,
3
] |
import os
import pandas as pd
import numpy as np
from dataloader import *
from keras.optimizers import Adam, SGD
from mylib.models.misc import set_gpu_usage
set_gpu_usage()
from mylib.models import densesharp, metrics, losses
from keras.callbacks import ModelCheckpoint, CSVLogger, TensorBoard, EarlyStopping, ReduceL... | normal | {
"blob_id": "94b3fa700d7da0ca913adeb0ad5324d1fec0be50",
"index": 7104,
"step-1": "<mask token>\n\n\ndef main(batch_size, crop_size, learning_rate, segmentation_task_ratio,\n weight_decay, save_folder, epochs, alpha):\n print(learning_rate)\n print(alpha)\n print(weight_decay)\n train_dataset = Clf... | [
1,
2,
3,
4,
5
] |
from flask import Flask, render_template
app = Flask(__name__)
@app.route('/',methods=["GET","POST"])
def inicio():
nombre = "jose"
return render_template("inicio.html",nombre=nombre)
app.run(debug=True) | normal | {
"blob_id": "caa28bd64141c8d2f3212b5e4e77129d81d24c71",
"index": 2290,
"step-1": "<mask token>\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef inicio():\n nombre = 'jose'\n return render_template('inicio.html', nombre=nombre)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\n@app.route('/', methods=[... | [
1,
2,
3,
4,
5
] |
#coding=utf-8
'初始化Package，加载url，生成app对象'
import web
from myapp.urls import urls
app = web.application(urls, globals())
| normal | {
"blob_id": "4480b305a6f71ff64022f2b890998326bf402bf0",
"index": 1669,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp = web.application(urls, globals())\n",
"step-3": "<mask token>\nimport web\nfrom myapp.urls import urls\napp = web.application(urls, globals())\n",
"step-4": "#coding=utf-8\r\n'初始... | [
0,
1,
2,
3
] |
import cv2
import numpy as np
import os
from tqdm import tqdm
DIR = '/home/nghiatruong/Desktop'
INPUT_1 = os.path.join(DIR, 'GOPR1806.MP4')
INPUT_2 = os.path.join(DIR, '20190715_180940.mp4')
INPUT_3 = os.path.join(DIR, '20190715_181200.mp4')
RIGHT_SYNC_1 = 1965
LEFT_SYNC_1 = 1700
RIGHT_SYNC_2 = 5765
LEFT_SYNC_2 = 128... | normal | {
"blob_id": "f8f538773693b9d9530775094d9948626247a3bb",
"index": 6950,
"step-1": "<mask token>\n\n\ndef add_frame_id(video, output_dir):\n reader = cv2.VideoCapture(video)\n if not reader.isOpened():\n return -1\n os.makedirs(output_dir, exist_ok=True)\n frame_count = int(reader.get(cv2.CAP_PR... | [
2,
3,
4,
5,
6
] |
from models.bearing_registry import BearingRegistry
from models.faction import Faction
from models.maneuver import Maneuver
import time
class Activation:
"""
This class represents the Activation phase of a turn
"""
def __init__(self, game):
"""
Constructor
game: Th... | normal | {
"blob_id": "0774bad4082e0eb04ae3f7aa898c0376147e9779",
"index": 2645,
"step-1": "<mask token>\n\n\nclass Activation:\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Activation:\n <mask token>\n\n def __init__(self, game):\n \"\"\"\n Constructor\... | [
1,
3,
4,
5,
6
] |
from django.contrib.auth import get_user_model
from django.test import TestCase
from .models import Order
from markets.models import Market
from tickers.models import Ticker
from trades.models import Trade
USER_MODEL = get_user_model()
class Matching:
@staticmethod
def get_bid_ask( market : Market):
... | normal | {
"blob_id": "866ee2c4fa52bf9bda4730c7a9d46bb4798adcd4",
"index": 1775,
"step-1": "<mask token>\n\n\nclass Matching:\n <mask token>\n <mask token>\n\n @staticmethod\n def process_order(self, order: Order):\n if order.status == Order.STATUS_WAITING_NEW:\n order.status = Order.STATUS_N... | [
7,
8,
9,
11,
12
] |
from rest_framework import serializers
from .models import Good, Favorite, Comment
class GoodSerializer(serializers.ModelSerializer):
class Meta:
model = Good
fields = ('user', 'article', 'created_at')
class FavoriteSerializer(serializers.ModelSerializer):
class Meta:
model = Favori... | normal | {
"blob_id": "fc8b9029955de6b11cbfe8e24107c687f49685c1",
"index": 9179,
"step-1": "<mask token>\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Comment\n fields = 'text', 'image', 'user', 'article', 'created_at'\n",
"step-2": "<mask token>\n\n\nclass Favor... | [
1,
2,
3,
4,
5
] |
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