code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
# coding: utf-8
import datetime
import json
import requests
import os
import re
import sys
from todoist.api import TodoistAPI
#SLACK_CHANNEL = os.environ['SLACK_CHANNEL']
#SLACK_POSTURL = os.environ['SLACK_POSTURL']
TDIAPI = TodoistAPI(os.environ['TODOISTAPITOKEN'], cache=False)
TDIAPI.sync()
name = os.environ['TODOI... | normal | {
"blob_id": "3c3d45f0844496b8d623286b36a4935a154f410a",
"index": 4133,
"step-1": "<mask token>\n\n\ndef lambda_handler(event, context):\n if event['function'] == 'tasklist':\n msg = tasklist(name)\n if event['function'] == 'activity':\n msg = activity(name)\n return\n\n\n<mask token>\n\n\n... | [
3,
5,
6,
7,
8
] |
""" Script to run pilon iteratively to correct genome assemblies """
import os
import argparse
import logging
import subprocess
def parse_arguments():
""" Parse command line arguments """
# Create parser
parser = argparse.ArgumentParser(description='Run pilon many times')
# Add arguments
pars... | normal | {
"blob_id": "fdfb71595bf86fbe1763535814ec9c3cfd312d87",
"index": 2722,
"step-1": "<mask token>\n\n\ndef run_bwa(reference_genome, forward_read, reverse_read, threads, output, i):\n \"\"\" Run bwa to align reads to reference genome \"\"\"\n print('Align reads with BWA MEM')\n bwa_index_args = ['bwa', 'in... | [
3,
4,
6,
7,
8
] |
from mcpi.minecraft import Minecraft
from time import sleep
import random
mc = Minecraft.create()
myID=mc.getPlayerEntityId("Baymax1112")
mineral = [14,15,16,56,73,129,57]
while True:
sleep(0.5)
r=random.choice(mineral)
x,y,z = mc.entity.getTilePos(myID)
mc.setBlocks(x+1,y+3,z+1,x-1,y-3,z-1,r) | normal | {
"blob_id": "b28ae19f31ae746f901dea645dfeaa211a15cd31",
"index": 1879,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n sleep(0.5)\n r = random.choice(mineral)\n x, y, z = mc.entity.getTilePos(myID)\n mc.setBlocks(x + 1, y + 3, z + 1, x - 1, y - 3, z - 1, r)\n",
"step-3": "<mask... | [
0,
1,
2,
3,
4
] |
'''
删除排序数组中的重复项：
给定一个排序数组，你需要在原地删除重复出现的元素，使得每个元素只出现一次，返回移除后数组的新长度。
不要使用额外的数组空间，你必须在原地修改输入数组并在使用 O(1) 额外空间的条件下完成。
示例 1:
给定数组 nums = [1,1,2],
函数应该返回新的长度 2, 并且原数组 nums 的前两个元素被修改为 1, 2。
你不需要考虑数组中超出新长度后面的元素。
示例 2:
给定 nums = [0,0,1,1,1,2,2,3,3,4],
函数应该返回新的长度 5, 并且原数组 nums 的前五个元素被修改为 0, 1, 2, 3, 4。
你不需要考虑数组中超出新长度后... | normal | {
"blob_id": "ac0f0fbb9bcb450ac24198069ef8bea8b049ef47",
"index": 5824,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef delete_sort_array(origin_list):\n if len(origin_list) == 0:\n return 0\n elif len(origin_list) == 1:\n return 1\n else:\n for index, item in enumerat... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
with open('sequence.protein.2.fasta', 'w') as fw:
with open('sequence.protein.fasta', 'r') as fr:
for line in fr:
fw.write(line)
<|reserved_special_token_1|>
# 내 풀이
with open("sequence.protein.2.fasta", "w") as fw:
with open("se... | flexible | {
"blob_id": "84fb0e364ee3cd846148abfc9326f404f008c510",
"index": 7908,
"step-1": "<mask token>\n",
"step-2": "with open('sequence.protein.2.fasta', 'w') as fw:\n with open('sequence.protein.fasta', 'r') as fr:\n for line in fr:\n fw.write(line)\n",
"step-3": "# 내 풀이\nwith open(\"sequence... | [
0,
1,
2
] |
class Model:
def derivativesDependsOn(self, models):
return []
def derivedVariablesDependsOn(self, models):
return []
def initializeSimplifiedModel(self, timeHistory, stateHistory, derivedVariablesHistory):
return False
def computeSimplifiedState(self, args, time):
return []
def comput... | normal | {
"blob_id": "b27e89ff799f26b87a61254e1c4a5f782fcbe605",
"index": 2540,
"step-1": "class Model:\n <mask token>\n\n def derivedVariablesDependsOn(self, models):\n return []\n <mask token>\n <mask token>\n\n def computeSimplifiedDerivedVariables(self, args, time):\n return []\n\n def... | [
4,
5,
7,
8,
10
] |
/home/openerp/production/extra-addons/productivity_analysis/report/productivity_analysis.py | normal | {
"blob_id": "6531833a4fe57c15c0668cee9015c7d43491427a",
"index": 341,
"step-1": "/home/openerp/production/extra-addons/productivity_analysis/report/productivity_analysis.py",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
class FieldDesigner:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __str__(self):
return '\n'.join(map(str, self.field))
<|reserved_special_token_1|>
class FieldDesigner:
<|reserved_special_token_0|>
def __init__(self):
self.field... | flexible | {
"blob_id": "c812419e7e024b0bb1207832b2b4a726ef61b272",
"index": 9137,
"step-1": "class FieldDesigner:\n <mask token>\n <mask token>\n <mask token>\n\n def __str__(self):\n return '\\n'.join(map(str, self.field))\n",
"step-2": "class FieldDesigner:\n <mask token>\n\n def __init__(self)... | [
2,
3,
4,
5,
6
] |
import re
import gpxpy
def extract_gpx_data(gpx_file_path, attribute='elevation'):
"""Reads in a GPX file and returns a list of values
for a specified GPX attribute.
Parameters
----------
gpx_file_path : str
File path to the GPX file (.gpx extension).
attribute: str
Name of t... | normal | {
"blob_id": "cc6d18785eff0406ff7f38f18f15476375e31b76",
"index": 9254,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef extract_gpx_data(gpx_file_path, attribute='elevation'):\n \"\"\"Reads in a GPX file and returns a list of values\n for a specified GPX attribute.\n\n Parameters\n ----... | [
0,
1,
2,
3
] |
from a10sdk.common.A10BaseClass import A10BaseClass
class MacAgeTime(A10BaseClass):
"""Class Description::
Set Aging period for all MAC Interfaces.
Class mac-age-time supports CRUD Operations and inherits from `common/A10BaseClass`.
This class is the `"PARENT"` class for this module.`
:para... | normal | {
"blob_id": "f08677430e54822abbce61d0cac5a6fea14d3872",
"index": 6078,
"step-1": "<mask token>\n\n\nclass MacAgeTime(A10BaseClass):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass MacAgeTime(A10BaseClass):\n <mask token>\n\n def __init__(self, **kwargs):\n self.ERROR_MSG... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@client.command()
async def wasitfunny():
possible_responses = [
'Per the judgement from the committee of comedy, we have decided that the joke was indeed funny'
,
'Per the judgement from the committe... | flexible | {
"blob_id": "f047afeb6462ab01a8fea1f3c8693608335eb960",
"index": 3488,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@client.command()\nasync def wasitfunny():\n possible_responses = [\n 'Per the judgement from the committee of comedy, we have decided that the joke was indeed funny'\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while len(sc_lst) < 10:
try:
sc = int(input('请第%d位评委打分：' % i))
if sc > 0 and sc < 101:
sc_lst.append(sc)
i += 1
else:
print('超出范围，输入无效')
except:
print('请输... | flexible | {
"blob_id": "a17abd3947a946daf2c453c120f2e79d2ba60778",
"index": 901,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile len(sc_lst) < 10:\n try:\n sc = int(input('请第%d位评委打分：' % i))\n if sc > 0 and sc < 101:\n sc_lst.append(sc)\n i += 1\n else:\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Tutorial(models.Model):
<|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 Tutorial(models.Model):
<|reserved_special_token_0|>
... | flexible | {
"blob_id": "32499688db51f701173ec0ea212c483bf902c109",
"index": 3048,
"step-1": "<mask token>\n\n\nclass Tutorial(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Tutorial(models.Model):\n <mask token>\n <mask token>\n <mask... | [
1,
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'meet.ui'
#
# Created by: PyQt5 UI code generator 5.8.2
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Dialog(object):
def setupUi(self, Dialog):
Dialog.setObjectName... | normal | {
"blob_id": "c076aed1bfff51f8edf5ab4ef029b7fa7ca2422c",
"index": 9479,
"step-1": "<mask token>\n\n\nclass Ui_Dialog(object):\n\n def setupUi(self, Dialog):\n Dialog.setObjectName('Dialog')\n Dialog.resize(607, 723)\n self.start = QtWidgets.QLabel(Dialog)\n self.start.setGeometry(Qt... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
default_app_config = 'child.apps.ChildConfig'
| flexible | {
"blob_id": "290f96bb210a21183fe1e0e53219ad38ba889625",
"index": 1602,
"step-1": "<mask token>\n",
"step-2": "default_app_config = 'child.apps.ChildConfig'\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
"""
This file contains the ScoreLoop which is used to show
the user thw at most 10 highest scores made by the player
"""
import pygame
from score_fetcher import fetch_scores
from entities.sprite_text import TextSprite
class ScoreLoop:
def __init__(self):
self.scores = fetch_scores()
self.sprites... | normal | {
"blob_id": "047b3398a73c9e7d75d43eeeab85f52c05ff90c3",
"index": 4534,
"step-1": "<mask token>\n\n\nclass ScoreLoop:\n\n def __init__(self):\n self.scores = fetch_scores()\n self.sprites = pygame.sprite.Group()\n self.get_score_sprites()\n self.space_cooldown = True\n <mask toke... | [
2,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class TestUrls(SimpleTestCase):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TestUrls(SimpleTestCase):
def test_profile_resolves(self):
url = reverse('profile')
self.assertEqual(... | flexible | {
"blob_id": "5dc6b54357df87077d8159192cd52697b2616db8",
"index": 9186,
"step-1": "<mask token>\n\n\nclass TestUrls(SimpleTestCase):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass TestUrls(SimpleTestCase):\n\n def test_profile_resolves(self):\n url = reverse('profile')\n ... | [
1,
2,
3,
4,
5
] |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import pytest
from sbpy.data import Phys
from sbpy import bib
@pytest.mark.remote_data
def test_from_sbdb():
""" test from_horizons method"""
# query one object
data = Phys.from_sbdb('Ceres')
assert len(data.table) == 1
# query se... | normal | {
"blob_id": "0bfb089556bfa253bf139f03cd3079ced962d858",
"index": 1021,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.mark.remote_data\ndef test_from_sbdb():\n \"\"\" test from_horizons method\"\"\"\n data = Phys.from_sbdb('Ceres')\n assert len(data.table) == 1\n data = Phys.from_... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Mas(object):
def __init__(self, module):
self.module = module
self.mas_path = self.module.get_bin_path('mas')
self._checked_signin = False
self._installed = None
self._outdated = None
self.count_install = 0
self.count_upgr... | flexible | {
"blob_id": "8b965fd91396735e0153390b4eff540d3aac3aff",
"index": 4916,
"step-1": "<mask token>\n\n\nclass Mas(object):\n\n def __init__(self, module):\n self.module = module\n self.mas_path = self.module.get_bin_path('mas')\n self._checked_signin = False\n self._installed = None\n ... | [
14,
15,
16,
17,
18
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
aws_glue_integration_tests += deployment_patterns
<|reserved_special_token_1|>
<|reserved_special_token_0|>
aws_glue_integration_tests = []
deployment_patterns = [IntegrationTestFixture(name=
'how_to_use_great_expectations_... | flexible | {
"blob_id": "e288403cb310bb7241b25e74d1b5bcc63967128c",
"index": 1031,
"step-1": "<mask token>\n",
"step-2": "<mask token>\naws_glue_integration_tests += deployment_patterns\n",
"step-3": "<mask token>\naws_glue_integration_tests = []\ndeployment_patterns = [IntegrationTestFixture(name=\n 'how_to_use_grea... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Layer:
def __init__(self, m1, m2, f=T.nnet.relu, use_bias=True, zeros=False):
if zeros:
w = np.zeros((m1, m2))
else:
w = np.random.randn(m1, m2) * np.sqrt(2 / m1)
self.w = theano.shared(w)
self.params = [self.w]
se... | flexible | {
"blob_id": "63ee25791177ead5389c14990ce6da3e2c11b683",
"index": 6356,
"step-1": "<mask token>\n\n\nclass Layer:\n\n def __init__(self, m1, m2, f=T.nnet.relu, use_bias=True, zeros=False):\n if zeros:\n w = np.zeros((m1, m2))\n else:\n w = np.random.randn(m1, m2) * np.sqrt(2... | [
9,
11,
12,
13,
17
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
graph.write_png('mydecisiontree.png')
<|reserved_special_token_0|>
plt.show()
print(X)
print(y)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
df = pandas.read_csv('show.csv')
d = {'UK': 0, 'USA': 1, 'N': 2}
df['Nati... | flexible | {
"blob_id": "c9cf65eeec49eba004312491cdd2321200fa6a61",
"index": 469,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ngraph.write_png('mydecisiontree.png')\n<mask token>\nplt.show()\nprint(X)\nprint(y)\n",
"step-3": "<mask token>\ndf = pandas.read_csv('show.csv')\nd = {'UK': 0, 'USA': 1, 'N': 2}\ndf['Na... | [
0,
1,
2,
3,
4
] |
from PIL import Image, ImageFilter
import numpy as np
import glob
from numpy import array
import matplotlib.pyplot as plt
from skimage import morphology
import scipy.ndimage
def sample_stack(stack, rows=2, cols=2, start_with=0, show_every=1, display1 = True):
if (display1):
new_list = []
new_list.a... | normal | {
"blob_id": "371c1c9e3ccf7dae35d435bdb013e0462f3add5d",
"index": 4831,
"step-1": "<mask token>\n\n\ndef sample_stack(stack, rows=2, cols=2, start_with=0, show_every=1,\n display1=True):\n if display1:\n new_list = []\n new_list.append(stack)\n new_list.append(stack)\n new_list.a... | [
1,
2,
3,
4,
5
] |
import sqlite3
import sys
import threading
from time import sleep
sq = None
def get_queue(category, parser):
if sq == None:
return liteQueue(category, parser)
return sq
"""
SqLite Job Handler class for Links
"""
class liteQueue:
_create = "CREATE TABLE IF NOT EXISTS link ( 'url' TEXT,'cat... | normal | {
"blob_id": "ed6eda4b6dbf3e94d8efb53004b19cd9c49e927e",
"index": 3979,
"step-1": "<mask token>\n\n\nclass liteQueue:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def _get_conn(self):\n id = threading.current_thread().i... | [
5,
6,
7,
8,
11
] |
import requests
from multiprocessing import Process
from atomic_counter import AtomicCounter
class Downloader:
def __init__(self, src_url, num_threads):
try:
header = requests.head(src_url).headers
self.url = src_url
self.file_size = int(header.get('content-length'))
... | normal | {
"blob_id": "3dc3bbd00f9c2d00093bf8669963d96f5019b2da",
"index": 4648,
"step-1": "<mask token>\n\n\nclass Downloader:\n <mask token>\n\n def _worker(self, download_range: tuple, counter: AtomicCounter):\n start, end = download_range\n header = {'Range': 'bytes=' + str(start) + '-' + str(end)}... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
def walk_maze(maze: list[int], width: int, height: int, start: tuple[int, int]
) ->None:
"""
Does a random walk, setting the cells as it goes, until it cant find a
path.
"""
maze_idx = lambda p: p[1] * width + p[0]
north = lambda p: (p[0], p[1] - 1)
east = ... | flexible | {
"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
] |
<|reserved_special_token_0|>
class Area(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class Meta:
unique_together = 'name', 'city'
ordering = 'name',
<|reserved_special_token_0|>
class ApartmentQuerySet(QuerySet):
def available(self):
return ... | flexible | {
"blob_id": "89ba805e47a9727573e1e25371a70fb887ee170d",
"index": 9141,
"step-1": "<mask token>\n\n\nclass Area(models.Model):\n <mask token>\n <mask token>\n\n\n class Meta:\n unique_together = 'name', 'city'\n ordering = 'name',\n <mask token>\n\n\nclass ApartmentQuerySet(QuerySet):\n\... | [
15,
19,
20,
21,
23
] |
class BucketSort:
def __init__(self, a):
self.a = a
def result(self, bucketCount=10):
buckets = [[] for i in range(bucketCount + 1)]
maxElement = max(self.a)
minElement = min(self.a)
bucketRange = (maxElement - minElement + 1) / bucketCount
for i in range(len(se... | normal | {
"blob_id": "3b803850418638bf65528088044918e93ecabff6",
"index": 3085,
"step-1": "<mask token>\n",
"step-2": "class BucketSort:\n <mask token>\n <mask token>\n",
"step-3": "class BucketSort:\n <mask token>\n\n def result(self, bucketCount=10):\n buckets = [[] for i in range(bucketCount + 1... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
get = requests.get('https://api.github.com/user', auth=(argv[1], argv[2])
).json().get('id')
print(get)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
from sys import argv
i... | flexible | {
"blob_id": "8280f321b102cace462761f9ece2aebf9e28a432",
"index": 3941,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n get = requests.get('https://api.github.com/user', auth=(argv[1], argv[2])\n ).json().get('id')\n print(get)\n",
"step-3": "<mask token>\nfrom s... | [
0,
1,
2,
3
] |
import json
import pandas as pd
import matplotlib.pyplot as plt
f = open('Maradona-goals.json')
jsonObject = json.load(f)
f.close()
l = []
for c, cl in jsonObject.items():
for d in cl:
d.update({'player' : c})
l.append(d)
df = pd.DataFrame(l)
labels = df["year"]
width = 0.75
fig = plt.figure(f... | normal | {
"blob_id": "33e9e45fbe0e3143d75d34c1db283c01e2693f68",
"index": 4967,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nf.close()\n<mask token>\nfor c, cl in jsonObject.items():\n for d in cl:\n d.update({'player': c})\n l.append(d)\n<mask token>\nax.set_xticks(labels)\nax.set_xticklabels(... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def reorder_sentences(output_sentences, input):
def custom_sort(s1, s2):
return input.find(s1) - input.find(s2)
output_sentences.sort(key=functools.cmp_to_key(custom_sort))
return output_sentences
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserv... | flexible | {
"blob_id": "837e84d4a58d8fd0d0ffc24973d196ae57f9a260",
"index": 1723,
"step-1": "<mask token>\n\n\ndef reorder_sentences(output_sentences, input):\n\n def custom_sort(s1, s2):\n return input.find(s1) - input.find(s2)\n output_sentences.sort(key=functools.cmp_to_key(custom_sort))\n return output_... | [
1,
4,
5,
6,
7
] |
__author__ = 'fshaw'
import gzip
import hashlib
import os
import uuid
import json
import jsonpickle
from chunked_upload.models import ChunkedUpload
from chunked_upload.views import ChunkedUploadView, ChunkedUploadCompleteView
from django.conf import settings
from django.core import serializers
from django.core.files.ba... | normal | {
"blob_id": "2b7415d86f9157ae55228efdd61c9a9e9920bc5c",
"index": 7716,
"step-1": "<mask token>\n\n\nclass CopoChunkedUploadCompleteView(ChunkedUploadCompleteView):\n do_md5_check = False\n\n def get_response_data(self, chunked_upload, request):\n \"\"\"\n Data for the response. Should return ... | [
12,
13,
14,
15,
18
] |
print("rap.sweeps.data_management level init") | normal | {
"blob_id": "7d138a0ad7e4d8f7047dd73ae503bdc7ae5aa065",
"index": 9801,
"step-1": "<mask token>\n",
"step-2": "print('rap.sweeps.data_management level init')\n",
"step-3": "print(\"rap.sweeps.data_management level init\")",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
input = """
t(Z) :- t0(Z).
t(Z) :- g(X,Y,Z), t(X), not t(Y).
t0(2).
g(5,1,3).
g(1,2,4).
g(3,4,5).
"""
output = """
t(Z) :- t0(Z).
t(Z) :- g(X,Y,Z), t(X), not t(Y).
t0(2).
g(5,1,3).
g(1,2,4).
g(3,4,5).
"""
| normal | {
"blob_id": "df5c79c79d827b6b3de7ceb4b1e3c652c8956346",
"index": 2620,
"step-1": "<mask token>\n",
"step-2": "input = \"\"\"\nt(Z) :- t0(Z).\nt(Z) :- g(X,Y,Z), t(X), not t(Y).\n\nt0(2).\ng(5,1,3).\ng(1,2,4).\ng(3,4,5).\n\n\"\"\"\noutput = \"\"\"\nt(Z) :- t0(Z).\nt(Z) :- g(X,Y,Z), t(X), not t(Y).\n\nt0(2).\ng(5... | [
0,
1
] |
import os
from PIL import Image
import urllib
import json
import math
def download_images(a,b):
image_count = 0
k = a
no_of_images = b
baseURL='https://graph.facebook.com/v2.2/'
imgURL='/picture?type=large'
sil_check='/picture?redirect=false'
while image_count<no_of_images:
obj=urllib.urlopen(baseURL+str(k)+s... | normal | {
"blob_id": "533154fe58511ac9c9c693bf07f076146b0c6136",
"index": 4445,
"step-1": "import os\nfrom PIL import Image\nimport urllib\nimport json\nimport math\n\ndef download_images(a,b):\n\timage_count = 0\n\tk = a\n\tno_of_images = b\n\tbaseURL='https://graph.facebook.com/v2.2/'\n\timgURL='/picture?type=large'\n\... | [
0
] |
# coding=utf-8
"""
PYOPENGL-TOOLBOX UTILS
General purpouse functions.
MIT License
Copyright (c) 2015-2019 Pablo Pizarro R.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, inc... | normal | {
"blob_id": "cffcfa08cd919f93dfe2ab8dc676efc76feafab3",
"index": 2123,
"step-1": "<mask token>\n\n\ndef create_axes(length, both=False, text=False, font=_glut.\n GLUT_BITMAP_HELVETICA_18):\n \"\"\"\n Create axes system.\n\n :param length: Axes length\n :param both: Both axes\n :param text: Show... | [
2,
3,
5,
6,
7
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.11 on 2018-02-24 11:30
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.Create... | normal | {
"blob_id": "56157aaf3f98abc58572b45111becb91cb93f328",
"index": 2926,
"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|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
SUCCESS = 200
NotFound = 404
url_site = 'https://petstore.swagger.io/v2'
new_username = 'Khrystyna'
new_id = 12345
invalid_new_id = 1234
error_message = 'oops we have a problem!'
store_inventory = {'1': 1, '4444': 2, 'teste': 1, '... | flexible | {
"blob_id": "54ed0683d0f8d907c27e2f3809f9533556593392",
"index": 5546,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nSUCCESS = 200\nNotFound = 404\nurl_site = 'https://petstore.swagger.io/v2'\nnew_username = 'Khrystyna'\nnew_id = 12345\ninvalid_new_id = 1234\nerror_message = 'oops we have a problem!'\ns... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def derf1(l, lp, kbt):
return kbt / lp * (0.5 / ((1.0 - l) * (1.0 - l) * (1.0 - l)) + 1.0)
def xdefWLC(kbt, l, p, f):
l0 = 0.9999
lnew = l0 - (f1(l0, p, kbt) - f) / derf1(l0, p, kbt)
if abs(f) < 1e-05:
return 0.0
while abs(l0 - lnew) > 1e-05:
l0 = lne... | flexible | {
"blob_id": "9817600759bc01e89f6c48bdc2d256651aedf74d",
"index": 1788,
"step-1": "<mask token>\n\n\ndef derf1(l, lp, kbt):\n return kbt / lp * (0.5 / ((1.0 - l) * (1.0 - l) * (1.0 - l)) + 1.0)\n\n\ndef xdefWLC(kbt, l, p, f):\n l0 = 0.9999\n lnew = l0 - (f1(l0, p, kbt) - f) / derf1(l0, p, kbt)\n if ab... | [
4,
6,
7,
8,
9
] |
import matplotlib.image as mpimg
import cv2
import rasterio
from ode_data_access.image_utils import view_as_blocks, is_black, align_and_crop
import os
import numpy as np
from tqdm import tqdm
class ChunkProcessor:
def write_result_blocks(self, result_blocks, window, product_name, chunk_size, save_dir=... | normal | {
"blob_id": "303e1b95c2ca60041a34b8c09e013849112a108d",
"index": 3475,
"step-1": "<mask token>\n\n\nclass ChunkProcessor:\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass ChunkProcessor:\n <mask token>\n\n def chunkify(self, img_file, product_name, chunk_size=2... | [
1,
2,
3,
5,
6
] |
import tensorflow as tf
def Float32():
return tf.float32
def Float16():
return tf.float16
| normal | {
"blob_id": "c60b8eec57d845c73ee3e00432747d23748c1706",
"index": 9537,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef Float32():\n return tf.float32\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef Float32():\n return tf.float32\n\n\ndef Float16():\n return tf.float16\n",
"step... | [
0,
1,
2,
3
] |
<|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": "e95de58828c63dc8ae24efff314665a308f6ce0c",
"index": 983,
"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 = Tr... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(
'Um funcioario que ganhava R$ {:.2f} com o aumento de 15% passa a ganhar R$ {:.2f}'
.format(salario, novo))
<|reserved_special_token_1|>
salario = float(input('Qual o valor do seu Salario atual? R$ '))
novo = sal... | flexible | {
"blob_id": "ffcd3c0086ff73eb722d867b335df23382615d20",
"index": 1657,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(\n 'Um funcioario que ganhava R$ {:.2f} com o aumento de 15% passa a ganhar R$ {:.2f}'\n .format(salario, novo))\n",
"step-3": "salario = float(input('Qual o valor do seu Sa... | [
0,
1,
2,
3
] |
"""
If you are using MultiScript Editor make sure to set PYTHONPATH to Winexs' editor.
You can use set PYTHONPATH=c:/users/username/myscripts
Set paths according to your project!
"""
CHROME_WEBDRIVER = 'c:/users/username/project/chromedriver.exe'
WEBSITE_PDF_CONVERTER = 'https://www.ilovepdf.com/merge_pdf'
PDF_FILES ... | normal | {
"blob_id": "0fdbdfe98496ebedb112c85b79836292ffa3a5a9",
"index": 9076,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nCHROME_WEBDRIVER = 'c:/users/username/project/chromedriver.exe'\nWEBSITE_PDF_CONVERTER = 'https://www.ilovepdf.com/merge_pdf'\nPDF_FILES = 'c:/users/username/project'\n",
"step-3": "\"\... | [
0,
1,
2
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Fetch screen scores with customizable search criteria
that can be tailored to match your own requirements
in tab format
"""
import requests
from core import config as cfg
screen_id = 178
request_url = cfg.BASE_URL + "/screen/" + str(screen_id)
# These parameters ca... | normal | {
"blob_id": "80c6dd1c76b3ac56f34e36f571e8db3927994311",
"index": 8162,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor row in screen:\n if row_count == 0:\n row_count = row_count + 1\n continue\n row = row.split('\\t')\n data[row[1]] = row\nprint(data['55299'])\nprint(data['5166... | [
0,
1,
2,
3,
4
] |
import pytest
from feast.pyspark.launchers.gcloud import DataprocClusterLauncher
@pytest.fixture
def dataproc_launcher(pytestconfig) -> DataprocClusterLauncher:
cluster_name = pytestconfig.getoption("--dataproc-cluster-name")
region = pytestconfig.getoption("--dataproc-region")
project_id = pytestconfig.... | normal | {
"blob_id": "ff13ac0ee401471fe5446e8149f019d9da7f3ddf",
"index": 5147,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.fixture\ndef dataproc_launcher(pytestconfig) ->DataprocClusterLauncher:\n cluster_name = pytestconfig.getoption('--dataproc-cluster-name')\n region = pytestconfig.getopt... | [
0,
1,
2,
3
] |
from django.shortcuts import render, get_object_or_404
from django.core.paginator import Paginator
from .models import Blog, BlogType
from django.conf import settings
from read_statistics.utils import read_statistics_once_read
from user.forms import LoginForm
# Create your views here.
#分页函数
def get_blogs_common_data(r... | normal | {
"blob_id": "9731f45b19d40a031216f8a430c09764fd34e984",
"index": 2594,
"step-1": "<mask token>\n\n\ndef get_blogs_common_data(request, blogs_all_list):\n page_num = request.GET.get('page', 1)\n paginator = Paginator(blogs_all_list, settings.BLOGS_PER_PAGE)\n page_of_blogs = paginator.get_page(page_num)\... | [
3,
4,
5,
6,
7
] |
from datetime import datetime
import logging
import os
import re
from bs4 import BeautifulSoup
import requests
from .utils.log import get_logger
logger = get_logger(os.path.basename(__file__))
EVENTBRITE_TOKEN = os.environ['EVENTBRITE_TOKEN']
def get_category_name(page):
if page["category_id"] is None:
... | normal | {
"blob_id": "edfc8794fab2c95e01ae254f9f13d446faafe6fd",
"index": 9213,
"step-1": "<mask token>\n\n\ndef scrape(event_id, event_cost):\n page = get(event_id, resource='events').json()\n venue = get(page['venue_id'], resource='venues').json()\n start = datetime.strptime(page['start']['local'], '%Y-%m-%dT%... | [
5,
7,
8,
9,
10
] |
# 데이터 출처: kaggle
# 데이터 개요: 511, 유리를 위한 다양한 속성(화학원소)들로부터 type 구별
# 데이터 예측 모델: 이진클래스
# 적용 머신러닝 모델: 깊은 다층 퍼셉트론 신경망
# 훈련 데이터셋: 160건
# 검증 데이터셋: 건
# 시험 데이터셋: 수집데이터로서 시험셋을 확보할 수 없으므로 고려하지 않음
# 입력 데이터: 10개 항목의 데이터
# 은닉층: 2개
# 사용한 활성화 함수
# - 제1 은닉층: Relu
# - 제2 은닉층: Relu
# - Output Layer: Softmax
# 사용한 손실함수: categorical_cros... | normal | {
"blob_id": "bfa5739949c26758e3762fcff8347d23ad70f704",
"index": 6114,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nnp.random.seed(5)\n<mask token>\nmodel.add(Dense(64, input_dim=input_data_number, activation='relu'))\nmodel.add(Dense(64, activation='relu'))\nmodel.add(Dense(7, activation='softmax'))\n... | [
0,
1,
2,
3,
4
] |
from typing import *
class Solution:
def uniquePaths(self, m: int, n: int) -> int:
map_: List[List[int]] = [[0 if (i > 0 and j > 0) else 1 for j in range(m)] for i in range(n)]
for row in range(1, n):
for col in range(1, m):
map_[row][col] = map_[row][col - 1] + map_[ro... | normal | {
"blob_id": "e2a38d38d2ab750cf775ed0fbdb56bc6fc7300c4",
"index": 8934,
"step-1": "<mask token>\n\n\nclass Solution:\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n\n def uniquePaths(self, m: int, n: int) ->int:\n map_: List[List[int]] = [[(0 if i > 0 and j > 0 e... | [
1,
2,
3,
4,
5
] |
#
# abc088 c
#
import sys
from io import StringIO
import unittest
class TestClass(unittest.TestCase):
def assertIO(self, input, output):
stdout, stdin = sys.stdout, sys.stdin
sys.stdout, sys.stdin = StringIO(), StringIO(input)
resolve()
sys.stdout.seek(0)
out = sys.stdout.r... | normal | {
"blob_id": "8b97c1e14adfcb09806e2d37e2f5c4f0b356c009",
"index": 2742,
"step-1": "<mask token>\n\n\nclass TestClass(unittest.TestCase):\n <mask token>\n\n def test_入力例_1(self):\n input = '1 0 1\\n2 1 2\\n1 0 1'\n output = 'Yes'\n self.assertIO(input, output)\n <mask token>\n <mas... | [
2,
6,
8,
9,
10
] |
from amqpstorm import management
if __name__ == '__main__':
# If using a self-signed certificate, change verify=True to point at your CA bundle.
# You can disable certificate verification for testing by passing in verify=False.
API = management.ManagementApi('https://rmq.amqpstorm.io:15671', 'guest',
... | normal | {
"blob_id": "0279057b3962e4b9839a86fc2e2683ac1da11b1a",
"index": 8665,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n API = management.ManagementApi('https://rmq.amqpstorm.io:15671',\n 'guest', 'guest', verify=True)\n try:\n result = API.aliveness_test('/'... | [
0,
1,
2,
3
] |
import random
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import fetch_mldata
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelBinarizer
from ann.act import relu, softmax_with_xentropy
from ann.loss import xentropy_with_softmax
fr... | normal | {
"blob_id": "2f6e5ed4e2d52190551dec2ac18441b8355699b5",
"index": 7096,
"step-1": "<mask token>\n\n\ndef plot(ax, ls_batch, ls_dev, its, title):\n ax.plot(range(len(ls_batch)), ls_batch, label='Batch')\n ax.plot(range(len(ls_dev)), ls_dev, label='Dev')\n ax.text(0.3, 0.93, 'Batch: {:.3f}'.format(ls_batch... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
router.register('', views.RoomViewSet)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
app_name = 'rooms'
router = DefaultRouter()
router.register('', views.RoomViewSet)
urlpatterns = rout... | flexible | {
"blob_id": "96708216c5ffa56a60475b295c21b18225e6eed9",
"index": 6056,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nrouter.register('', views.RoomViewSet)\n<mask token>\n",
"step-3": "<mask token>\napp_name = 'rooms'\nrouter = DefaultRouter()\nrouter.register('', views.RoomViewSet)\nurlpatterns = rou... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class YahooHelper:
<|reserved_special_token_0|>
def __init__(self):
"""
Default constructor which initiates object
"""
pass
<|reserved_special_token_0|>
def get_stock_data(symbol):
"""
Function to get stock data for current... | flexible | {
"blob_id": "b4b4dad5cf630dc1a627e323ea63577583d1e1c3",
"index": 1551,
"step-1": "<mask token>\n\n\nclass YahooHelper:\n <mask token>\n\n def __init__(self):\n \"\"\"\n Default constructor which initiates object\n \"\"\"\n pass\n <mask token>\n\n def get_stock_data(symbol)... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
def save_location(ip_addr):
try:
existing_location = Location.query.filter_by(ip=ip_addr).first()
if existing_location:
location_data = existing_location.location
else:
location_data = get_location(ip_addr=ip_addr)
location =... | flexible | {
"blob_id": "eb8aec947cc1eeeb56b3884286b46ec7468dcc23",
"index": 9035,
"step-1": "<mask token>\n\n\ndef save_location(ip_addr):\n try:\n existing_location = Location.query.filter_by(ip=ip_addr).first()\n if existing_location:\n location_data = existing_location.location\n else:... | [
1,
2,
3,
4
] |
from pythongame.core.buff_effects import get_buff_effect, register_buff_effect, StatModifyingBuffEffect
from pythongame.core.common import ItemType, Sprite, BuffType, Millis, HeroStat
from pythongame.core.game_data import UiIconSprite, register_buff_text
from pythongame.core.game_state import Event, PlayerDamagedEnemy,... | normal | {
"blob_id": "61454a3d6b5b17bff871ededc6ddfe8384043884",
"index": 59,
"step-1": "<mask token>\n\n\nclass ItemEffect(AbstractItemEffect):\n <mask token>\n\n\nclass BuffedByHealingWand(StatModifyingBuffEffect):\n\n def __init__(self):\n super().__init__(BUFF_TYPE, {HeroStat.HEALTH_REGEN: HEALTH_REGEN_B... | [
3,
4,
6,
7,
8
] |
from django.shortcuts import render
from rest_framework import status
from rest_framework.views import APIView
from rest_framework.response import Response
from django.conf import settings
import subprocess
import os
import json
class HookView(APIView):
def post(self, request, *args, **kwargs):
SCRIPT_PAT... | normal | {
"blob_id": "6f5bca8c1afcd9d9971a64300a576ca2b2f6ef70",
"index": 1694,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass HookView(APIView):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass HookView(APIView):\n\n def post(self, request, *args, **kwargs):\n SCRIPT_PATH = os.path.... | [
0,
1,
2,
3,
4
] |
cardlist = []
card = []
for j in range(1,5):
for k in range(1,14):
if j == 1:
cardlist.append(["S", "{}".format(k)])
elif j == 2:
cardlist.append(["H", "{}".format(k)])
elif j == 3:
cardlist.append(["C", "{}".format(k)])
elif j == 4:
c... | normal | {
"blob_id": "937a101cf5c7e943fc62d18b77357eea151fdfaf",
"index": 7789,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor j in range(1, 5):\n for k in range(1, 14):\n if j == 1:\n cardlist.append(['S', '{}'.format(k)])\n elif j == 2:\n cardlist.append(['H', '{}'.for... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Cell(Enum):
BasicRNN = 1
BasicLSTM = 2
LSTMCellPeephole = 3
GRU = 4
<|reserved_special_token_0|>
def normalize_data(df):
min_max_scaler = sklearn.preprocessing.MinMaxScaler()
df['Open'] = min_max_scaler.fit_transform(df['Open'].values.reshape(-1, 1))
... | flexible | {
"blob_id": "4379d89c2ada89822acbf523d2e364599f996f8c",
"index": 5456,
"step-1": "<mask token>\n\n\nclass Cell(Enum):\n BasicRNN = 1\n BasicLSTM = 2\n LSTMCellPeephole = 3\n GRU = 4\n\n\n<mask token>\n\n\ndef normalize_data(df):\n min_max_scaler = sklearn.preprocessing.MinMaxScaler()\n df['Open... | [
7,
8,
9,
10,
12
] |
# coding=utf-8
import pyautogui
from xpinyin import Pinyin
rubbish_dic=1
if rubbish_dic==0:
chinese_rubbish=(
u"草泥马",
u"你妈死了",
u"你是不是",
u"低能",
u"人话都听不懂",
u"没家教的狗东西",
)
elif rubbish_dic==1:
rubbish_file=open("rubbish_dic.txt")
chinese_rubbish=rubbish_file.read().splitlines()
... | normal | {
"blob_id": "23e673909b2f1eb9a265ce84ad63464e20e99c6a",
"index": 3449,
"step-1": "<mask token>\n\n\ndef trans_screen():\n pyautogui.doubleClick(492, 974)\n pyautogui.typewrite(['enter'], 0.01)\n\n\ndef trans_chinese():\n for c_rubbish in chinese_rubbish:\n pin = p.get_pinyin(c_rubbish, '')\n ... | [
4,
5,
6,
7,
8
] |
from django import forms
from . import models
class PhotoForm(forms.Form):
image = forms.ImageField()
| normal | {
"blob_id": "3983f8dfb9c7b7e664af05857a0f6fe380154424",
"index": 3684,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass PhotoForm(forms.Form):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass PhotoForm(forms.Form):\n image = forms.ImageField()\n",
"step-4": "from django import form... | [
0,
1,
2,
3
] |
IS_ZERO = lambda x: x == 0
ONE = 1
SUB1 = lambda x: x - 1
MULT = lambda x: lambda y: x * y
IF = lambda cond: lambda t_func: lambda f_func: t_func(None) if cond else f_func(None)
print(
(
lambda myself: (
lambda n: (
IF(
IS_ZERO(n)
)(
... | normal | {
"blob_id": "f8601ed7ba7c2b8d2dd8d5f74f7b5ae8e99dad78",
"index": 186,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint((lambda myself: lambda n: IF(IS_ZERO(n))(lambda _: ONE)(lambda _:\n MULT(n)(myself(myself)(SUB1(n)))))(lambda myself: lambda n: IF(IS_ZERO(\n n))(lambda _: ONE)(lambda _: MULT(... | [
0,
1,
2,
3
] |
import sys
n= int(sys.stdin.readline())
dp = {1:'SK', 2: 'CY', 3:'SK', 4:'SK', 5:'SK',6:'SK'}
def sol(k):
if k in dp:
return dp[k]
else:
for i in range(7, k+1):
if dp[i-3]=='SK' and dp[i-1]=='SK' and dp[i-4]=='SK':
dp[i] = 'CY'
else:
dp[... | normal | {
"blob_id": "4b85479af7d65d208fab08c10afbf66086877329",
"index": 8981,
"step-1": "<mask token>\n\n\ndef sol(k):\n if k in dp:\n return dp[k]\n else:\n for i in range(7, k + 1):\n if dp[i - 3] == 'SK' and dp[i - 1] == 'SK' and dp[i - 4] == 'SK':\n dp[i] = 'CY'\n ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class FinalLevel(BaseLevel):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class FinalLevel(BaseLevel):
def __init__(self):
lvl_map = DefinedMap('levels/demon_lair.xp')
super().__init__... | flexible | {
"blob_id": "7ba8f0bd962413f6ff825df27330447b11360f10",
"index": 6089,
"step-1": "<mask token>\n\n\nclass FinalLevel(BaseLevel):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass FinalLevel(BaseLevel):\n\n def __init__(self):\n lvl_map = DefinedMap('levels/demon_lair.xp')\n ... | [
1,
2,
3,
4
] |
# My Godzilla Hat Code - @alt_bier
from adafruit_circuitplayground.express import cpx
import random
#cpx.pixels.brightness = 0.5 # 50 pct
cpx.pixels.fill((0, 0, 0)) # Turn off the NeoPixels if they're on!
# Function to give us a nice color swirl on the built in NeoPixel (R,G,B)
def wheeln(pos, sft):
if (pos + sf... | normal | {
"blob_id": "1dd223854c10e69a397098511eab50b9ebd347c8",
"index": 6027,
"step-1": "<mask token>\n\n\ndef wheeln(pos, sft):\n if pos + sft > 255:\n pos = pos + sft - 256\n else:\n pos = pos + sft\n if pos < 0 or pos > 255:\n return 0, 0, 0\n if pos < 85:\n return int(255 - p... | [
4,
5,
6,
7,
8
] |
import discord
from discord.ext import commands
class TestCommands(commands.Cog, description="Unstable test commands", command_attrs=dict(hidden=True, description="Can only be used by an Owner")):
def __init__(self, bot):
self.bot = bot
self.hidden = True
print("Loaded", __name__)
as... | normal | {
"blob_id": "d5a5c6f9d483b2998cd0d9e47b37ab4499fa1c2a",
"index": 6279,
"step-1": "<mask token>\n\n\nclass TestCommands(commands.Cog, description='Unstable test commands',\n command_attrs=dict(hidden=True, description='Can only be used by an Owner')\n ):\n <mask token>\n\n async def cog_check(self, ct... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def str2int(strtime: str):
hh, mm, ss = strtime.split(':')
return 3600 * int(hh) + 60 * int(mm) + int(ss)
def int2str(inttime: int):
hh = inttime // 3600
mm = inttime % 3600 // 60
ss = inttime % 60
return str(hh).zfill(2) + ':' + str(mm).zfill(2) + ':' + str(ss).... | flexible | {
"blob_id": "cb50a5352b0ad7b04dee9393c50da54fdf507376",
"index": 2018,
"step-1": "<mask token>\n\n\ndef str2int(strtime: str):\n hh, mm, ss = strtime.split(':')\n return 3600 * int(hh) + 60 * int(mm) + int(ss)\n\n\ndef int2str(inttime: int):\n hh = inttime // 3600\n mm = inttime % 3600 // 60\n ss ... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
print('hello')
print('===================================================')
print('Nama Lengkap : Agung Dharmawan')
print('Kelas : Teknik Informatika 2018 A')
print('Kampus : Universitas Nahdlatul Ulama Sidoarjo')
print('=========================... | flexible | {
"blob_id": "4e10bc876797d0939c91cff5eff497b36af35dcb",
"index": 1932,
"step-1": "<mask token>\n",
"step-2": "print('hello')\nprint('===================================================')\nprint('Nama Lengkap : Agung Dharmawan')\nprint('Kelas : Teknik Informatika 2018 A')\nprint('Kampus : Universit... | [
0,
1,
2
] |
rom diseas import Disease
from parse import analyzing
from config import FILE_NAME
from random import randint
if __name__ == '__main__':
"""
Main module that runs the program.
"""
def working_with_user(disea):
print('Choose what you want to know about that disease:\naverage_value(will return th... | normal | {
"blob_id": "b33af7aff0f3fde6499d5e24fc036d5bd74b6e47",
"index": 3550,
"step-1": "rom diseas import Disease\nfrom parse import analyzing\nfrom config import FILE_NAME\nfrom random import randint\n\nif __name__ == '__main__':\n \"\"\"\n Main module that runs the program.\n \"\"\"\n def working_with_us... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class State:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class State:
def __init__(self, id):
self.id = id
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class State:
def __... | flexible | {
"blob_id": "200deda300e39b07e0e558277a340b7ad01c7dee",
"index": 2216,
"step-1": "<mask token>\n",
"step-2": "class State:\n <mask token>\n\n\n<mask token>\n",
"step-3": "class State:\n\n def __init__(self, id):\n self.id = id\n\n\n<mask token>\n",
"step-4": "class State:\n\n def __init__(s... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class ChatService:
@staticmethod
def is_room_exists(room_id: int) ->bool:
return RoomGroup.objects.filter(id=room_id).exists()
@staticmethod
def create_users_room(**data) ->RoomGroup:
room = RoomGroup.objects.create(room_id=data.get('room_id'))
ro... | flexible | {
"blob_id": "d71ffd022d87aa547b2a379f4c92d767b91212fd",
"index": 3827,
"step-1": "<mask token>\n\n\nclass ChatService:\n\n @staticmethod\n def is_room_exists(room_id: int) ->bool:\n return RoomGroup.objects.filter(id=room_id).exists()\n\n @staticmethod\n def create_users_room(**data) ->RoomGro... | [
6,
9,
11,
12,
13
] |
import torch,cv2,os,time
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# GPU kullanımı
device=torch.device(0)
class NET(nn.Module):
def __init__(self):
super(). __init__()
... | normal | {
"blob_id": "ad63beedc460b3d64a51d0b1f81f8e44cb559749",
"index": 1655,
"step-1": "<mask token>\n\n\nclass NET(nn.Module):\n <mask token>\n\n def uzunluk(self, x):\n x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))\n x = F.max_pool2d(F.re... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for myfile in files:
if myfile[-4:] != 'xlsx':
continue
tg_xlsx = load_workbook(os.path.join(path, myfile), read_only=True)
tg_sheet = tg_xlsx.active
for row in tg_sheet.iter_rows():
row_data = []
... | flexible | {
"blob_id": "d23700f03e8498a5ff3d1d03d8808048ba79a56b",
"index": 9381,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor myfile in files:\n if myfile[-4:] != 'xlsx':\n continue\n tg_xlsx = load_workbook(os.path.join(path, myfile), read_only=True)\n tg_sheet = tg_xlsx.active\n for row ... | [
0,
1,
2,
3,
4
] |
from django import forms
from django.contrib.auth.models import User
from ServicePad.apps.account.models import UserProfile
import hashlib, random, datetime
from ServicePad.apps.registration.models import ActivationKey
MIN_PASSWORD_LENGTH=8
MAX_PASSWORD_LENGTH=30
class UserRegistrationForm(forms.Form):
first_name... | normal | {
"blob_id": "5f680fb21fe1090dfb58f5b9260739b91ae04d99",
"index": 9922,
"step-1": "<mask token>\n\n\nclass UserRegistrationForm(forms.Form):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def save(self):\n new_user = User... | [
6,
8,
9,
10,
11
] |
<|reserved_special_token_0|>
class StepUtilTest(wf_testcase.WaterfallTestCase):
def testGetLowerBoundBuildNumber(self):
self.assertEqual(5, step_util._GetLowerBoundBuildNumber(5, 100))
self.assertEqual(50, step_util._GetLowerBoundBuildNumber(None, 100,
200))
self.assertEqual(1... | flexible | {
"blob_id": "325efe65030ad3488a7fc45c0d4a289eb0b17196",
"index": 1311,
"step-1": "<mask token>\n\n\nclass StepUtilTest(wf_testcase.WaterfallTestCase):\n\n def testGetLowerBoundBuildNumber(self):\n self.assertEqual(5, step_util._GetLowerBoundBuildNumber(5, 100))\n self.assertEqual(50, step_util._... | [
26,
32,
43,
49,
55
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = [(... | flexible | {
"blob_id": "7f63097265b1058785e90441f85b7f0088946717",
"index": 7785,
"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 = [('guac_auth',... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class ContentKind(models.Model):
<|reserved_special_token_0|>
def __str__(self):
return self.kind
class FileFormat(models.Model):
extension = models.CharField(primary_key=True, max_length=40, choices=
file_formats.choices)
mimetype = models.CharField(max... | flexible | {
"blob_id": "32e904a39d03d3166369420b49db0b9b118110a3",
"index": 4179,
"step-1": "<mask token>\n\n\nclass ContentKind(models.Model):\n <mask token>\n\n def __str__(self):\n return self.kind\n\n\nclass FileFormat(models.Model):\n extension = models.CharField(primary_key=True, max_length=40, choice... | [
65,
102,
158,
169,
216
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def lis(n1, n2):
"""
Generate and print last 5 element in list.
param:n1,n2
"""
i = 0
if n1 and n2 <= 20:
for x in range(n1, n2 + 1):
lis1.append(x * x)
lis1.reverse()
for y in ... | flexible | {
"blob_id": "24c1f5195bad17f995fb97a03218fc9bbe5ce4cd",
"index": 2476,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef lis(n1, n2):\n \"\"\"\n\tGenerate and print last 5 element in list.\n\tparam:n1,n2\n\t\"\"\"\n i = 0\n if n1 and n2 <= 20:\n for x in range(n1, n2 + 1):\n ... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python
import pyglet
from pyglet.gl import *
win = pyglet.window.Window()
@win.event
def on_draw():
# Clear buffers
glClear(GL_COLOR_BUFFER_BIT)
# Draw outlines only
glPolygonMode(GL_FRONT_AND_BACK, GL_LINE)
# Draw some stuff
glBegin(GL_TRIANGLES)
glVertex3i(0, 0, 0)
glVertex3i(300, 0, 0)
glVe... | normal | {
"blob_id": "86c4193ec0fee8a0c06858913ec8153fcf0df6d9",
"index": 4114,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@win.event\ndef on_draw():\n glClear(GL_COLOR_BUFFER_BIT)\n glPolygonMode(GL_FRONT_AND_BACK, GL_LINE)\n glBegin(GL_TRIANGLES)\n glVertex3i(0, 0, 0)\n glVertex3i(300, 0,... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def is_prime(x):
if x < 2:
return False
for i in range(2, int(sqrt(x)) + 1):
if x % i == 0:
return False
return True
def primes(x):
return islice((p for p in count() if is_prime(p)), x)
<|reserved_special_token_0|>
<|reserved_special_token... | flexible | {
"blob_id": "0f1bad350faaff6aab339944b4d24c4801fa8c64",
"index": 4965,
"step-1": "<mask token>\n\n\ndef is_prime(x):\n if x < 2:\n return False\n for i in range(2, int(sqrt(x)) + 1):\n if x % i == 0:\n return False\n return True\n\n\ndef primes(x):\n return islice((p for p in... | [
2,
3,
4,
5,
6
] |
import os.path
from flask import url_for
from sqlalchemy import Column, Integer, String, Sequence, ForeignKey
from sqlalchemy.orm import relationship
from tuneful import app
from .database import Base, engine, session
class Song(Base):
__tablename__ = 'songs'
id = Column(Integer, primary_key=True)
file_i... | normal | {
"blob_id": "d5c2b73c202c9944cd64798ef5ddc08ce68a4a9a",
"index": 3446,
"step-1": "<mask token>\n\n\nclass Song(Base):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass File(Base):\n __tablename__ = 'files'\n id = Column(Integer, primary_key=True)\n filename = Column(Stri... | [
4,
5,
6,
7,
8
] |
#####
# Created on Oct 15 13:13:11 2019
#
# @author: inesverissimo
#
# Do pRF fit on median run, make iterative fit and save outputs
####
import os
# issue with tensorflow, try this suggestion
#NUM_PARALLEL_EXEC_UNITS = 16
#os.environ['OMP_NUM_THREADS'] = str(NUM_PARALLEL_EXEC_UNITS)
#os.environ["KMP_AFFINI... | normal | {
"blob_id": "d9156e240d49e0a6570a5bc2315f95a7a670fd4f",
"index": 6327,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif len(sys.argv) < 2:\n raise NameError(\n 'Please add subject number (ex:1) as 1st argument in the command line!'\n )\nelif len(sys.argv) < 3:\n raise NameError(\n ... | [
0,
1,
2,
3,
4
] |
import cv2
cam = cv2.VideoCapture("./bebop.sdp")
while True:
ret, frame = cam.read()
cv2.imshow("frame", frame)
cv2.waitKey(1)
| normal | {
"blob_id": "d13b402b90bb948e5722f45096a8c0a33e4cac67",
"index": 6968,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n ret, frame = cam.read()\n cv2.imshow('frame', frame)\n cv2.waitKey(1)\n",
"step-3": "<mask token>\ncam = cv2.VideoCapture('./bebop.sdp')\nwhile True:\n ret, fr... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = [(... | flexible | {
"blob_id": "8dff22249abbae9e30ba1ad423457270e0cd9b20",
"index": 7027,
"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 = [('backend', '... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def ping_calculate_pong(expression, operator_index):
"""The function takes two arguments.
Argument 1: an expression from which we will extract one subexpression.
Argument 2: the index of the mathematical operator around which function takes the subexpression to extract.
Th... | flexible | {
"blob_id": "c336bb6cdadfb836ab68ebd5bbb210f63af3d084",
"index": 2287,
"step-1": "<mask token>\n\n\ndef ping_calculate_pong(expression, operator_index):\n \"\"\"The function takes two arguments.\n Argument 1: an expression from which we will extract one subexpression.\n Argument 2: the index of the math... | [
2,
3,
4,
5,
6
] |
import numpy as np
from numpy import random
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split
from numpy.random import shuffle
import matplotlib.pyplot as plt
import numpy.linalg as la
import sklearn.preprocessing as proc
import csv
def get_accuracy(a, b, X_test, y_... | normal | {
"blob_id": "f5c4057babc873099ae2a4d8c1aca960ab9fa30a",
"index": 9692,
"step-1": "<mask token>\n\n\ndef get_accuracy(a, b, X_test, y_test):\n size = len(y_test)\n count = 0\n for i in range(size):\n x = X_test[i]\n real = y_test[i]\n x = np.array(x)\n x = x.reshape(1, 6)\n ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def get_first_timestamp(log_file):
with open(log_file) as f:
for line in f:
line_json = json.loads(line)
return line_json['timestamp']
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
send_socket.connect(connec... | flexible | {
"blob_id": "49679782ac696b3dc4f5038565f88304a44098e1",
"index": 6188,
"step-1": "<mask token>\n\n\ndef get_first_timestamp(log_file):\n with open(log_file) as f:\n for line in f:\n line_json = json.loads(line)\n return line_json['timestamp']\n\n\n<mask token>\n",
"step-2": "<ma... | [
1,
2,
3,
4,
5
] |
Desafios:
1: Crie um script python que leia o nome de uma pessoa e mostre uma mensagem de boas-vindas de acordo com o valor digitado.
Script:
Desafio 01:
1: Crie um script python que leia o nome de uma pessoa
e mostre uma mensagem de boas-vindas de acordo com o valor digitado."""
nome=input('Qual é o seu nome?')
print... | normal | {
"blob_id": "80454a3935f0d42b5535440fc316af1b5598d8a1",
"index": 7090,
"step-1": "Desafios:\n1: Crie um script python que leia o nome de uma pessoa e mostre uma mensagem de boas-vindas de acordo com o valor digitado.\n\nScript:\nDesafio 01:\n1: Crie um script python que leia o nome de uma pessoa\ne mostre uma me... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if DEBUG:
p = process('binary_100')
else:
p = remote('bamboofox.cs.nctu.edu.tw', 22001)
<|reserved_special_token_0|>
p.send(payload)
p.interactive()
p.close()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
DE... | flexible | {
"blob_id": "fab75c5b55d85cef245fa6d7e04f4bf3a35e492c",
"index": 7068,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif DEBUG:\n p = process('binary_100')\nelse:\n p = remote('bamboofox.cs.nctu.edu.tw', 22001)\n<mask token>\np.send(payload)\np.interactive()\np.close()\n",
"step-3": "<mask token>... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class TestFrame:
<|reserved_special_token_0|>
def teardown(self):
self.driver.quit()
def test_frame(self):
self.driver.switch_to.frame('iframeResult')
action = ActionChains(self.driver)
drag = self.driver.find_element_by_id('draggable')
... | flexible | {
"blob_id": "74843dea00a88513c3a9237eb024e1e14e8b1ff8",
"index": 3088,
"step-1": "<mask token>\n\n\nclass TestFrame:\n <mask token>\n\n def teardown(self):\n self.driver.quit()\n\n def test_frame(self):\n self.driver.switch_to.frame('iframeResult')\n action = ActionChains(self.drive... | [
3,
4,
5,
6,
7
] |
from .models import RecommendedArtifact
from .serializers import RecommendedArtifactSerialize
from rest_framework.decorators import api_view
from rest_framework.response import Response
from datetime import datetime
import requests, bs4
# constant value
service_key = "{jo's museum key}"
@api_view(['GET'])
def artifa... | normal | {
"blob_id": "707e3e60d6d9a3db5b9bc733e912b34e2cec5974",
"index": 8585,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@api_view(['GET'])\ndef artifact_save_recommend(request, pageNo):\n artifact_url = (\n f'http://www.emuseum.go.kr/openapi/relic/list?serviceKey={service_key}&numOfRows=100&p... | [
0,
2,
3,
4,
5
] |
import tensorflow as tf
from util.helper import focal_loss
from util.helper import conv_elu_bn
from util.helper import deconv_elu_bn
from util.helper import residual_block_elu
from util.helper import conv_elu
from util.helper import conv
from util.helper import reg_l1_loss
from util.helper import conv_bn
from util.hel... | normal | {
"blob_id": "e24a62f2a3ff0122922f472a7b37f1773dfe9c11",
"index": 7605,
"step-1": "<mask token>\n\n\nclass model_objectdetection_ppm_centernet_v1:\n <mask token>\n\n def _build_net(self):\n self.learning_rate_tensor = tf.compat.v1.placeholder(tf.float32,\n shape=[], name='learning_rate')\n... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class TestMaxInteger(unittest.TestCase):
<|reserved_special_token_0|>
def test_max(self):
"""Tests max_integer"""
self.assertEqual(max_integer([1, 2, 3]), 3)
self.assertEqual(max_integer([6, 2, 6]), 6)
self.assertEqual(max_integer([0, 0, 0]), 0)
... | flexible | {
"blob_id": "f799fdfde537bbe8f6c49a5e1a15cf6f910a0d45",
"index": 889,
"step-1": "<mask token>\n\n\nclass TestMaxInteger(unittest.TestCase):\n <mask token>\n\n def test_max(self):\n \"\"\"Tests max_integer\"\"\"\n self.assertEqual(max_integer([1, 2, 3]), 3)\n self.assertEqual(max_intege... | [
2,
3,
5,
6,
7
] |
<|reserved_special_token_0|>
class Vts:
def __init__(self):
self.last_comment_id = 0
self.vk = None
def update_vk(self):
if self.vk is not None:
return
vk_session = vk_api.VkApi(VK_LOGIN, VK_PASSWORD)
try:
vk_session.authorization()
exc... | flexible | {
"blob_id": "885e02cbf78412d77bd17eba64a8a1a52aaed0df",
"index": 5837,
"step-1": "<mask token>\n\n\nclass Vts:\n\n def __init__(self):\n self.last_comment_id = 0\n self.vk = None\n\n def update_vk(self):\n if self.vk is not None:\n return\n vk_session = vk_api.VkApi(V... | [
7,
8,
9,
10,
11
] |
from django.shortcuts import render
from django.http import HttpResponse
# from appTwo.models import User
from appTwo.forms import NewUserForm
# Create your views here.
# def index(request):
# return HttpResponse("<em>My Second Project</em>")
def welcome(request):
# welcomedict={'welcome_insert':'Go to /user... | normal | {
"blob_id": "d5f66d92371838c703abbf80e2b78717cdd4a4fb",
"index": 7140,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef welcome(request):\n return render(request, 'welcome.html')\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef welcome(request):\n return render(request, 'welcome.html'... | [
0,
1,
2,
3,
4
] |
from newspaper import Article
import random
import string
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import nltk
import numpy as np
import warnings
import speech_recognition as sr
warnings.filterwarnings('ignore')
nltk.download('pun... | normal | {
"blob_id": "53b56cf9265a658d999388f0a1e03d7ceb186213",
"index": 2836,
"step-1": "<mask token>\n\n\ndef LemNormalize(text):\n return nltk.word_tokenize(text.lower().translate(remove_punct_dict))\n\n\n<mask token>\n\n\ndef greeting(sentence):\n for word in sentence.split():\n if word.lower() in GREET... | [
3,
4,
5,
6,
7
] |
__all__ = '''
calc_common_prefix_length
'''.split()
import operator
import itertools
def calc_common_prefix_length(lhs_iterable, rhs_iterable, /, *, __eq__=None):
if __eq__ is None:
__eq__ = operator.__eq__
idx = -1
for a, b, idx in zip(lhs_iterable, rhs_iterable, itertools.count(0)):
... | normal | {
"blob_id": "2b73c4e07bba7ed5c89a31ebd45655eaa85dcdcc",
"index": 2689,
"step-1": "<mask token>\n\n\ndef calc_common_prefix_length(lhs_iterable, rhs_iterable, /, *, __eq__=None):\n if __eq__ is None:\n __eq__ = operator.__eq__\n idx = -1\n for a, b, idx in zip(lhs_iterable, rhs_iterable, itertools... | [
1,
2,
3,
4,
5
] |
from fastapi import APIRouter, Depends
from fastapi.responses import RedirectResponse
import app.setting as setting
from app.dependencies import get_project_by_prefix
from app.entities.project import Project
router = APIRouter(
prefix="/go",
)
@router.get("/{prefix_id}")
def redirect_to_board(project: Project ... | normal | {
"blob_id": "49b295c3e323695779eb32181193ef88b678b34d",
"index": 6340,
"step-1": "<mask token>\n\n\n@router.get('/{prefix_id}')\ndef redirect_to_board(project: Project=Depends(get_project_by_prefix)):\n return RedirectResponse(url=project.notion_board_url)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\n... | [
1,
2,
3,
4,
5
] |
'''
vetor = ["pares de pregos ligados por uma linha"]
indice do vetor representa os pregos na vertical, e o
inteiro em cada pos, os pregos na horizontal.
i(vertical) e j(horizontal) entao:
vetor[i] = j
pregos a(vertical) e pregos b(horizontal)
se a>i and b<j or a<i and b>j
a e i(são indices) b e j(são os elemnt... | normal | {
"blob_id": "fe081a422db6b7f10c89179beab852c6b74ec687",
"index": 2795,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef merge(p, n):\n global vet\n global aux\n if n <= 1:\n return 0\n c = merge(p, n // 2) + merge(p + n // 2, n - n // 2)\n d, a, b = 0, 0, n // 2\n while d <... | [
0,
1,
2,
3,
4
] |
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