code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class LocationViewSet(viewsets.ModelViewSet):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class LocationViewSe... | flexible | {
"blob_id": "aef45cb8ea9fcaeffcca147da7637536bcc4b226",
"index": 6217,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass LocationViewSet(viewsets.ModelViewSet):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass LocationViewSet(viewsets.ModelViewSet):\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class SceneClass:
def __init__(self):
self._entities = {}
self.scene = None
def get_entity_id(self, identifier):
return self._entities[identifier]
def get_position_absolute(self, entity):
return tuple(entity.worldPosition)
def get_orient... | flexible | {
"blob_id": "23d4619527b5fce7fed0b0a66d834e26bb984129",
"index": 6443,
"step-1": "<mask token>\n\n\nclass SceneClass:\n\n def __init__(self):\n self._entities = {}\n self.scene = None\n\n def get_entity_id(self, identifier):\n return self._entities[identifier]\n\n def get_position_a... | [
9,
10,
11,
12,
14
] |
import pyximport
pyximport.install(build_in_temp=False,inplace=True)
import Cython.Compiler.Options
Cython.Compiler.Options.annotate = True
import numpy as np
from test1 import c_test,c_test_result_workaround
a = np.ascontiguousarray(np.array([ [1,2,3],[1,2,3],[1,2,3] ], dtype=np.long), dtype=np.long)
print '\nStar... | normal | {
"blob_id": "0276181055f2c70562c1f557a16d00ba7107d003",
"index": 1219,
"step-1": "\n\nimport pyximport\npyximport.install(build_in_temp=False,inplace=True)\nimport Cython.Compiler.Options\nCython.Compiler.Options.annotate = True\nimport numpy as np\nfrom test1 import c_test,c_test_result_workaround\n\na = np.as... | [
0
] |
import requests
import urllib.request
from utilities.read_write_utilities import read_set,write_to_csv
import time
from bs4 import BeautifulSoup
import pickledb
import json
import glob
import csv
drugs = read_set('/Users/sandeep.dey/Downloads/2020-02-06_scrape/drugs')
print(drugs)
output_records = []
# fields = ["equ... | normal | {
"blob_id": "e7f511b97f316157a768203afe9f36ea834ebb6c",
"index": 5493,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(drugs)\n<mask token>\nfor drug in drugs:\n with open('/Users/sandeep.dey/Downloads/2020-02-06_scrape/%s' % drug\n ) as json_file:\n for record in json.load(json_fil... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class TestConsoleUrlShow(TestConsole):
_server = compute_fakes.create_one_server()
def setUp(self):
super(TestConsoleUrlShow, self).setUp()
self.sdk_client.find_server.return_value = self._server
fake_console_data = {'url': 'http://localhost', 'protocol':
... | flexible | {
"blob_id": "cc9485dea0975a0974f037b129816a9359b2b622",
"index": 2875,
"step-1": "<mask token>\n\n\nclass TestConsoleUrlShow(TestConsole):\n _server = compute_fakes.create_one_server()\n\n def setUp(self):\n super(TestConsoleUrlShow, self).setUp()\n self.sdk_client.find_server.return_value = ... | [
10,
15,
18,
19,
20
] |
<|reserved_special_token_0|>
class Region:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Region:
<|reserved_special_token_0|>
def calc_feature(self, cumul_sum):
yy = self.y + self.height
xx = self.x + sel... | flexible | {
"blob_id": "03e92eae4edb4bdbe9fa73e39e7d5f7669746fe5",
"index": 3859,
"step-1": "<mask token>\n\n\nclass Region:\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Region:\n <mask token>\n\n def calc_feature(self, cumul_sum):\n yy = self.y + self.height\n xx = self.... | [
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
{'variables': {'mac_asan_dylib':
'<(PRODUCT_DIR)/libclang_rt.asan_osx_dynamic.dylib'}, 'targets': [{
'target_name': 'fletch-vm', 'type': 'none', 'dependencies': [
'src/vm/vm.gyp:fletch-vm']}, {'target_name': 'c_test_library', 'type':
'none', '... | flexible | {
"blob_id": "84b98ebf6e44d03d16f792f3586be1248c1d0221",
"index": 6957,
"step-1": "<mask token>\n",
"step-2": "{'variables': {'mac_asan_dylib':\n '<(PRODUCT_DIR)/libclang_rt.asan_osx_dynamic.dylib'}, 'targets': [{\n 'target_name': 'fletch-vm', 'type': 'none', 'dependencies': [\n 'src/vm/vm.gyp:fletch-v... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
fout.close()
<|reserved_special_token_0|>
if not drive:
drive = 'C:'
<|reserved_special_token_0|>
os.system(runString)
<|reserved_special_token_0|>
fout.close()
<|reserved_special_token_0|>
for index, line in enumerate(lines):... | flexible | {
"blob_id": "9aee715e976db632f0829a06cb9e0101c90512be",
"index": 2150,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfout.close()\n<mask token>\nif not drive:\n drive = 'C:'\n<mask token>\nos.system(runString)\n<mask token>\nfout.close()\n<mask token>\nfor index, line in enumerate(lines):\n panelD... | [
0,
1,
2,
3,
4
] |
import typing
import time
import cv2
import os
from .ABC import ABC
from .Exceptions import *
from .Constants import *
class Video(ABC):
def __init__(self, filename: str, *, scale: float = 1, w_stretch: float = 2, gradient: typing.Union[int, str] = 0, verbose: int = False):
if not os.path.isfile(filename... | normal | {
"blob_id": "24368b6c607c0524f8b52b279a6dce0fde72294b",
"index": 8936,
"step-1": "<mask token>\n\n\nclass Video(ABC):\n\n def __init__(self, filename: str, *, scale: float=1, w_stretch: float=2,\n gradient: typing.Union[int, str]=0, verbose: int=False):\n if not os.path.isfile(filename):\n ... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class UploadForm(forms.Form):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class UploadForm(forms.Form):
file = forms.FileField(label='Json с данными об отправлении')
<|re... | flexible | {
"blob_id": "0878bfa1151371ff3aaa59f8be5ea9af74ada331",
"index": 4978,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass UploadForm(forms.Form):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass UploadForm(forms.Form):\n file = forms.FileField(label='Json с данными об отправлении')\n",... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def takePicture():
global setImage
setImage = True
def addRectangles(locations):
_, axe = pt.subplots()
img = imread('hola.jpg')
cv2image = cv2.cvtColor(img, cv2.COLOR_BGR2RGBA)
axe.imshow(cv2image)
alto, ancho, _ = img.shape
for item in locations:
... | flexible | {
"blob_id": "8d8c211895fd43b1e2a38216693b0c00f6f76756",
"index": 5748,
"step-1": "<mask token>\n\n\ndef takePicture():\n global setImage\n setImage = True\n\n\ndef addRectangles(locations):\n _, axe = pt.subplots()\n img = imread('hola.jpg')\n cv2image = cv2.cvtColor(img, cv2.COLOR_BGR2RGBA)\n ... | [
4,
5,
6,
7,
8
] |
import tkinter as tk
import cnt_script as sW
import key_script as kW
import text_script as tW
class MainWindow(tk.Tk):
def __init__(self):
super().__init__()
self.title("Main Window")
self.geometry("600x400+30+30")
tk.Button(self, text = "Count Tags", command = self.new_ta... | normal | {
"blob_id": "1482c8276f9cfc912293356d04e08307edf6d367",
"index": 5133,
"step-1": "<mask token>\n\n\nclass MainWindow(tk.Tk):\n\n def __init__(self):\n super().__init__()\n self.title('Main Window')\n self.geometry('600x400+30+30')\n tk.Button(self, text='Count Tags', command=self.n... | [
4,
5,
7,
8,
11
] |
<|reserved_special_token_0|>
class plm_component(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def _insertlog(self, ids, changes={}, note={}):
ret = False
op_type,... | flexible | {
"blob_id": "06643bf4b1bded757078b0974c21ddec814f5889",
"index": 1762,
"step-1": "<mask token>\n\n\nclass plm_component(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def _insertlog(self, ids, changes={}, note={}):\n ret = False\n op_... | [
20,
34,
38,
44,
49
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from eval_lib.classification_results import analyze_one_classification_result
from eval_lib.classification_results import ClassificationBatches
from eval_lib.cloud_client import CompetitionDatastoreClient
from eval_lib.cloud_client import CompetitionStorageCl... | flexible | {
"blob_id": "64935ae910d5f330722b637dcc5794e7e07ab52d",
"index": 8375,
"step-1": "<mask token>\n",
"step-2": "from eval_lib.classification_results import analyze_one_classification_result\nfrom eval_lib.classification_results import ClassificationBatches\nfrom eval_lib.cloud_client import CompetitionDatastoreC... | [
0,
1
] |
<|reserved_special_token_0|>
def prepare_directories(options, extension, subversiondir=None):
datadir = options['datadir']
print('Creating directory {0:s}.'.format(datadir + extension))
try:
os.makedirs(datadir + extension)
except OSError as err:
if err.errno == errno.EEXIST:
... | flexible | {
"blob_id": "6aeaa2ed01e0c0dac54cd8220c5da005fccc53e9",
"index": 2609,
"step-1": "<mask token>\n\n\ndef prepare_directories(options, extension, subversiondir=None):\n datadir = options['datadir']\n print('Creating directory {0:s}.'.format(datadir + extension))\n try:\n os.makedirs(datadir + exten... | [
1,
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.4 on 2017-10-20 11:05
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('core', '0052_encounter_note'),
]
operations = [
migrations.CreateModel(
... | normal | {
"blob_id": "05851df7ae64d792e0c1faf96e2aca5b40e86d53",
"index": 2744,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('core', '005... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def get_qa_set(directory, jsonl_file):
"""Download the WMT en-fr training corpus to directory unless it's there."""
set_name = os.path.splitext(os.path.basename(jsonl_file))[0]
set_path = os.path.join(directory, set_name)
src_path = set_path + '.src'
targ_path = set_pa... | flexible | {
"blob_id": "bf51da12632013c62aa543ae7f02415057138c7a",
"index": 694,
"step-1": "<mask token>\n\n\ndef get_qa_set(directory, jsonl_file):\n \"\"\"Download the WMT en-fr training corpus to directory unless it's there.\"\"\"\n set_name = os.path.splitext(os.path.basename(jsonl_file))[0]\n set_path = os.pa... | [
2,
3,
7,
8,
10
] |
<|reserved_special_token_0|>
class DLT(object):
<|reserved_special_token_0|>
def getimg(self, idx):
images = sorted(glob.glob(datadir + 'images_undistorted/*.jpg'))
return cv2.imread(images[idx])
<|reserved_special_token_0|>
def estimatePoseDLT(self, p, P, idx):
"""
D... | flexible | {
"blob_id": "50ae47c88bbc0f281ef75784377fb65192e257b0",
"index": 1206,
"step-1": "<mask token>\n\n\nclass DLT(object):\n <mask token>\n\n def getimg(self, idx):\n images = sorted(glob.glob(datadir + 'images_undistorted/*.jpg'))\n return cv2.imread(images[idx])\n <mask token>\n\n def est... | [
4,
6,
7,
8,
10
] |
from pwn import *
DEBUG = False
if DEBUG:
p = process("binary_100")
else:
p = remote("bamboofox.cs.nctu.edu.tw", 22001)
padding = 0x34 - 0xc
payload = padding * "A" + p32(0xabcd1234)
p.send(payload)
p.interactive()
p.close()
| normal | {
"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
] |
import datetime
import os
import uuid
from abc import ABC, abstractmethod
from django.conf import settings
from django.core.exceptions import ObjectDoesNotExist
from django.contrib.contenttypes.fields import (GenericForeignKey,
GenericRelation)
from django.contrib.conten... | normal | {
"blob_id": "9da995184641525cd763ecdb0bca4f28159ae740",
"index": 7617,
"step-1": "<mask token>\n\n\nclass ActExam(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n @classmethod\n def get_files_path(cls, package: 'DocumentsPackage'):\n tmp_path = package.get_s... | [
76,
93,
95,
98,
116
] |
from gurobipy import *
import math
# params.NonConvex = 2
# K = 5
# R = {0: 1000, 1: 5000, 2: 10000, 3: 20000, 4: 69354} # imbalanced
# R = {0: 50, 1: 100, 2: 150, 3: 84, 4: 400} # imbalanced
# R = {0: 100, 1: 200, 2: 484} # imbalanced
# R = {0: 10, 1: 20, 2: 30, 3: 50, 4: 100} # imbalanced
# R = {0... | normal | {
"blob_id": "2ed9eafb6e26971f642d1e33cbb3d1f3df34990a",
"index": 3401,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef solve_model(K, R, N, L_max, G):\n print(\n 'parameters==| k=%d \\t |R=%s \\t |N=%d \\t |eta=%f \\t |L_max=%f \\t |G=%f'\n % (K, R, N, eta, L_max, G))\n R_sum ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def getStatsByYear(teamID, year, data):
""" Returns the stats for a chosen team for a specific year. Choices are 2016 - 2019 """
teamStats = data[data['team_id'] == teamID]
for index, row in teamStats.iterrows():
if row['season'] == year:
teamStatsForGivenY... | flexible | {
"blob_id": "9b581df505765e895047584c5bb586faef95295f",
"index": 453,
"step-1": "<mask token>\n\n\ndef getStatsByYear(teamID, year, data):\n \"\"\" Returns the stats for a chosen team for a specific year. Choices are 2016 - 2019 \"\"\"\n teamStats = data[data['team_id'] == teamID]\n for index, row in te... | [
8,
9,
11,
12,
13
] |
def filter_long_words(word_lng, words_list):
return [word for word in words_list if len(word) > word_lng]
assert filter_long_words(5, ['piwo', 'wino', 'czasopisma', 'ubrania', 'napoje']
) == ['czasopisma', 'ubrania', 'napoje']
| normal | {
"blob_id": "e221b840239b6e9af735238760fd1157f333c1a4",
"index": 9014,
"step-1": "<mask token>\n",
"step-2": "def filter_long_words(word_lng, words_list):\n return [word for word in words_list if len(word) > word_lng]\n\n\n<mask token>\n",
"step-3": "def filter_long_words(word_lng, words_list):\n retur... | [
0,
1,
2
] |
from collections import defaultdict
class Solution:
def multiply(self, A: List[List[int]], B: List[List[int]]) -> List[List[int]]:
output = [[0 for j in range(len(B[0]))] for i in range(len(A))]
rows = defaultdict(list)
cols = defaultdict(list)
for i in ran... | normal | {
"blob_id": "8425ee79fcb41799e5edbbab822f93dd40e39d8e",
"index": 6481,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Solution:\n\n def multiply(self, A: List[List[int]], B: List[List[int]]) ->List[List[int]\n ]:\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def drawGraph(data, name1, name2):
plt.figure(figsize=(14, 8))
li = []
li2 = []
for i in critics[name1]:
if i in data[name2]:
li.append(critics[name1][i])
li2.append(critics[name2][i])
plt.text(critics[name1][i], critics[name2][i... | flexible | {
"blob_id": "b377a652eec55b03f689a5097bf741b18549cba0",
"index": 4939,
"step-1": "<mask token>\n\n\ndef drawGraph(data, name1, name2):\n plt.figure(figsize=(14, 8))\n li = []\n li2 = []\n for i in critics[name1]:\n if i in data[name2]:\n li.append(critics[name1][i])\n li2... | [
4,
5,
6,
7,
8
] |
# Copyright 2021 The JAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | normal | {
"blob_id": "79c8e87e1d247eef8dd1ca8e307bbe6d25bf48e2",
"index": 8172,
"step-1": "<mask token>\n\n\nclass PickleTest(jtu.JaxTestCase):\n\n def testPickleOfDeviceArray(self):\n x = jnp.arange(10.0)\n s = pickle.dumps(x)\n y = pickle.loads(s)\n self.assertArraysEqual(x, y)\n s... | [
6,
9,
10,
11,
13
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('Images & labels read!')
<|reserved_special_token_0|>
for i, l in zip(images, labels):
images_flat.append(Vector(Utils.normalize(Utils.flatten_2d(i), 0, 1)))
labels_oh.append(Utils.onehot_label_arr(l))
for i in test_... | flexible | {
"blob_id": "1f86fe72c90c8457715a2f400dae8d355a9a97cf",
"index": 8577,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Images & labels read!')\n<mask token>\nfor i, l in zip(images, labels):\n images_flat.append(Vector(Utils.normalize(Utils.flatten_2d(i), 0, 1)))\n labels_oh.append(Utils.oneh... | [
0,
1,
2,
3,
4
] |
final=[]
refer={2:'abc',3:'def',4:'ghi',5:'jkl',6:'mno',7:'pqrs',8:'tuv',9:'wxyz'}
##Complete this function
def possibleWords(a,N,index=0,s=''):
##Your code here
if index==N:
final.append(s)
print(s, end=' ')
return
possible_chars=refer[a[0]]
for i in possible_chars:
... | normal | {
"blob_id": "5f237a820832181395de845cc25b661878c334e4",
"index": 9965,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef possibleWords(a, N, index=0, s=''):\n if index == N:\n final.append(s)\n print(s, end=' ')\n return\n possible_chars = refer[a[0]]\n for i in possibl... | [
0,
1,
2,
3
] |
#crfで英文に固有表現認識（タグ付け）をする
#usr/bin/python3
#coding:utf-8
from itertools import chain
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import LabelBinarizer
import sklearn
import pycrfsuite
from sklearn import cross_validation
import sys
import os
import math
import random
d... | normal | {
"blob_id": "af9b83b6e213359f5e193918b6c09c22220e5457",
"index": 607,
"step-1": "<mask token>\n\n\ndef word2features(sent, i):\n word = sent[i][0]\n tag = sent[i][1]\n features = ['bias', 'word.lower=' + word.lower(), 'word[-3:]=' + word[-\n 3:], 'word[-2:]=' + word[-2:], 'word.isupper=%s' % word... | [
4,
5,
7,
8,
9
] |
<|reserved_special_token_0|>
def open_config():
if os.path.isfile('fin/config.json') != True:
return 'no config found'
else:
print('config found')
with open('fin/config.json') as conf:
conf = json.load(conf)
return conf
<|reserved_special_token_0|>
def get_local_date():... | flexible | {
"blob_id": "e690587c9b056f8d5a1be6dd062a2aa32e215f50",
"index": 2328,
"step-1": "<mask token>\n\n\ndef open_config():\n if os.path.isfile('fin/config.json') != True:\n return 'no config found'\n else:\n print('config found')\n with open('fin/config.json') as conf:\n conf = json.loa... | [
3,
6,
7,
9,
11
] |
from django.shortcuts import render,redirect
from django.http import HttpResponse
from .tasks import read_all_models, update_a_model, delete_a_model, read_a_model, create_a_model
from .forms import MyModelForm
def home(request):
content = read_all_models()
sorted_list = sorted(content, key=lambda k: k['title'].title... | normal | {
"blob_id": "dc4de382ab16f036c6174e711f5c9fe52868ccc9",
"index": 8445,
"step-1": "<mask token>\n\n\ndef delete(request, pk):\n if delete_a_model(pk):\n return redirect('myapp:home')\n else:\n return HttpResponse('Error Occured')\n\n\ndef search(request):\n if request.method == 'POST':\n ... | [
2,
4,
5,
6,
7
] |
# Procedures for automatic COBD calculation.
# The useful ones are:
# - get_heuristic4_OBD() as a heuristic one [the only heuristic one here that does not miss-out solutions]
# - getOBD2plus4() as the fastest exhaustive one [uses two filtering techniques for early detection of graphs without an OBD]
import itertool... | normal | {
"blob_id": "51711c9293f8b5d9dc4d299569da04e2d1bc0064",
"index": 1982,
"step-1": "\n\n# Procedures for automatic COBD calculation.\n# The useful ones are:\n# - get_heuristic4_OBD() as a heuristic one [the only heuristic one here that does not miss-out solutions]\n# - getOBD2plus4() as the fastest exhaustive one... | [
0
] |
from flask import Flask, request
from flask import jsonify
from preprocessing import QueryProcessor
from flask_cors import CORS
app = Flask(__name__)
CORS(app)
qp = QueryProcessor()
@app.route('/search_general', methods=['POST'])
def query():
message = None
searchQuery = request.json['searchQuery']
resul... | normal | {
"blob_id": "e582787a912f479830ed99575b2c6adb8088b4e5",
"index": 257,
"step-1": "<mask token>\n\n\n@app.route('/search_general', methods=['POST'])\ndef query():\n message = None\n searchQuery = request.json['searchQuery']\n result = qp.generateQuery(searchQuery)\n response = jsonify(result)\n resp... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [path('blog/', PostListView.as_view()), path('blog/<pk>/',
PostDetailView.as_view())]
<|reserved_special_token_1|>
from django.urls import path
from .views import PostListView, PostDetailView
urlpatterns = [pa... | flexible | {
"blob_id": "be7fb94c3c423b67aa917a34328acda5926cf78a",
"index": 3133,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('blog/', PostListView.as_view()), path('blog/<pk>/',\n PostDetailView.as_view())]\n",
"step-3": "from django.urls import path\nfrom .views import PostListView, Po... | [
0,
1,
2,
3
] |
"""Generic utilities module"""
from . import average
from . import extract_ocean_scalar
from . import git
from . import gmeantools
from . import merge
from . import netcdf
from . import xrtools
__all__ = [
"average",
"extract_ocean_scalar",
"git",
"gmeantools",
"merge",
"netcdf",
"xrtools"... | normal | {
"blob_id": "ab6450ee9038e0c58ca8becf6d2518d5e00b9c90",
"index": 9393,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n__all__ = ['average', 'extract_ocean_scalar', 'git', 'gmeantools', 'merge',\n 'netcdf', 'xrtools']\n",
"step-3": "<mask token>\nfrom . import average\nfrom . import extract_ocean_sca... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function, with_statement
"""
cosi299a- Cinderella
alexluu@brandeis.edu
"""
def truecase_is(string):
""" -> lower/title/upper/other """
if string.islower():
return 'l'
if string.istitle():
return 't'
if string... | normal | {
"blob_id": "75ddcdd4e80b962198ff9de1d996837927c3ac1a",
"index": 824,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef truecase_is(string):\n \"\"\" -> lower/title/upper/other \"\"\"\n if string.islower():\n return 'l'\n if string.istitle():\n return 't'\n if string.isuppe... | [
0,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('Train Benign: ' + str(np.count_nonzero(Y_Train == 0)))
print('Train Malignant: ' + str(np.count_nonzero(Y_Train == 1)))
print('Test Benign: ' + str(np.count_nonzero(Y_Test == 0)))
print('Test Malignant: ' + str(np.count_non... | flexible | {
"blob_id": "42ae3804c2d8f6a0d440e2bb6231186a868630b1",
"index": 2772,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Train Benign: ' + str(np.count_nonzero(Y_Train == 0)))\nprint('Train Malignant: ' + str(np.count_nonzero(Y_Train == 1)))\nprint('Test Benign: ' + str(np.count_nonzero(Y_Test == 0))... | [
0,
1,
2,
3,
4
] |
import unittest.mock
import assist
import pytest
def test_simple_query():
q = assist.build_query(select='time, value', from_='system_load',
where='L2=\'cpuload\' and time > \'2021-06-16 00:00:00\' and time < \'2021-06-17 00:00:00\' and "name" != \'Idle\'',
gr... | normal | {
"blob_id": "8aa9ba145b6c7347a7a926d50dca35383ddd52a3",
"index": 9217,
"step-1": "<mask token>\n\n\ndef test_nested_query_with_datetime():\n inner_q = assist.build_query(select='time, value', from_='system_load',\n where='L2=\\'cpuload\\' and \"name\" != \\'Idle\\'', groupby=('host', 'L3'))\n outer_... | [
6,
8,
9,
11,
12
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for x in range(1, 100000):
b = a * x
print(x, '*', a, '=', b)
if b > 100:
break
<|reserved_special_token_1|>
a = int(input('Choose a number: '))
for x in range(1, 100000):
b = a * x
print(x, '*', a, ... | flexible | {
"blob_id": "043dd97d4d4ade29536a83c3557a34db3a4cb0f9",
"index": 2002,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor x in range(1, 100000):\n b = a * x\n print(x, '*', a, '=', b)\n if b > 100:\n break\n",
"step-3": "a = int(input('Choose a number: '))\nfor x in range(1, 100000):\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
@router.get('/salary/{user_id}', status_code=200)
def read_base_salary(user_id: int, db: Session=Depends(get_db),
current_user: User=Depends(get_current_user)):
return crud.get_base_salarys(db, user_id=user_id, current=current_user)
<|reserved_special_token_0|>
<|reserved_spec... | flexible | {
"blob_id": "f10e20d5c409930d697c36d1897ebcb648511e27",
"index": 3694,
"step-1": "<mask token>\n\n\n@router.get('/salary/{user_id}', status_code=200)\ndef read_base_salary(user_id: int, db: Session=Depends(get_db),\n current_user: User=Depends(get_current_user)):\n return crud.get_base_salarys(db, user_id=... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def rule(type):
if booktabs:
if type == 'top':
return '\\toprule'
if type == 'mid':
return '\\midrule'
if type == 'bottom':
return '\\bottomrule'
else:
return '\\hline'
def make_header(alignment, border, custom,... | flexible | {
"blob_id": "591ac07e735e08bcafa8274eb1a1547a01261f55",
"index": 8430,
"step-1": "<mask token>\n\n\ndef rule(type):\n if booktabs:\n if type == 'top':\n return '\\\\toprule'\n if type == 'mid':\n return '\\\\midrule'\n if type == 'bottom':\n return '\\\\bo... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def RetrieveMemory():
ram_info = psutil.virtual_memory()
typePresented = 'Total : ', 'Used : ', 'Free : ', 'Usage : '
counter = 0
print()
for info in ram_info:
try:
if info > 100:
print(typePresented[counter], convert_size(info))
... | flexible | {
"blob_id": "d960d3d1680f825f0f68fc6d66f491bbbba805ce",
"index": 5004,
"step-1": "<mask token>\n\n\ndef RetrieveMemory():\n ram_info = psutil.virtual_memory()\n typePresented = 'Total : ', 'Used : ', 'Free : ', 'Usage : '\n counter = 0\n print()\n for info in ram_info:\n try:\n ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class ChrDuplicates(ElementwiseDuplicateElimination):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ChrDuplicates(ElementwiseDuplicateElimination):
<|reserved_special_token_0|>
def is_equal(s... | flexible | {
"blob_id": "9276c4106cbe52cf0e2939b5434d63109910a45c",
"index": 8801,
"step-1": "<mask token>\n\n\nclass ChrDuplicates(ElementwiseDuplicateElimination):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass ChrDuplicates(ElementwiseDuplicateElimination):\n <mask token>\n\n def is_eq... | [
1,
2,
3,
4
] |
#! /usr/bin/python
# encode:utf-8
import subprocess
import sys
import pdb
argvs = sys.argv
if len(argvs) != 2:
print "Please input 1 argument"
quit()
searchWord = argvs[1]
cmd1 = "ls -a /etc/"
p1 = subprocess.Popen(cmd1.strip().split(" "), stdout=subprocess.PIPE)
stdout_data, stderr_data = p1.communicate()
p1.st... | normal | {
"blob_id": "c12d45644098aef5c042a62095eeae5829d70f45",
"index": 7641,
"step-1": "#! /usr/bin/python\n# encode:utf-8\nimport subprocess\nimport sys\nimport pdb\n\nargvs = sys.argv\nif len(argvs) != 2:\n print \"Please input 1 argument\"\n quit()\n\nsearchWord = argvs[1]\n\ncmd1 = \"ls -a /etc/\"\np1 = subproce... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def twenty():
pass
| flexible | {
"blob_id": "3727c4413cd69305c8ee8d02f4532629da7d25de",
"index": 7135,
"step-1": "<mask token>\n",
"step-2": "def twenty():\n pass\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
a = list(range(1, 501))
b = list(range(1, 501))
c = list(range(1, 501))
for i in a:
for j in b:
for k in c:
if i + k + j == 1000 and i < j < k and j ** 2 + i ** 2 == k ** 2:
print(i)
print(j)
print(k)
break
| normal | {
"blob_id": "34947b7ed300f2cbcbf9042fee3902458921d603",
"index": 2912,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in a:\n for j in b:\n for k in c:\n if i + k + j == 1000 and i < j < k and j ** 2 + i ** 2 == k ** 2:\n print(i)\n print(j)\n ... | [
0,
1,
2
] |
import numpy as np
import random
import sys
import canton as ct
from canton import *
import tensorflow as tf
time_steps = 16
def get_text_data(filename):
import codecs
with open(filename,'rb') as f:
text = f.read()
length = len(text)
print('got corpus length:', length)
return text
def mod... | normal | {
"blob_id": "0016e38d39ed2a4c7a75bed103bc47a5b6fd0e8c",
"index": 2538,
"step-1": "<mask token>\n\n\ndef r(ep=100):\n length = len(corpus)\n batch_size = 256\n mbl = time_steps * batch_size\n sr = length - mbl - time_steps - 2\n for i in range(ep):\n print('---------------------iter', i, '/'... | [
6,
9,
12,
13,
14
] |
import math #h=g^x
h=input("h: ")
g=input("g: ")
p=input("p: ")
m=math.ceil(math.sqrt(p))
m=int(m)
aj=[0]*m
for i in range(m):
aj[i]=pow(g,i*m)
ainvm=pow(g,p-2,p)
gamma = h
for i in range(m):
if gamma in aj:
j = aj.index(gamma)
print (j*m)+i
break
gamma=(gamma*ainvm)%p
| normal | {
"blob_id": "94439ffe3303f5efe15562f26d693e1e7a8115df",
"index": 2009,
"step-1": "import math #h=g^x\nh=input(\"h: \")\ng=input(\"g: \")\np=input(\"p: \")\nm=math.ceil(math.sqrt(p))\nm=int(m)\naj=[0]*m\nfor i in range(m):\n aj[i]=pow(g,i*m)\nainvm=pow(g,p-2,p)\ngamma = h\nfor i in range(m):\n if gamma in ... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(i)
<|reserved_special_token_0|>
print(i)
<|reserved_special_token_0|>
print(i)
<|reserved_special_token_0|>
print(i)
<|reserved_special_token_0|>
print(s)
<|reserved_special_token_0|>
print(s)
<|reserved_special_token_0|>
pr... | flexible | {
"blob_id": "bea7853d1f3eac50825bc6eb10438f3f656d6d04",
"index": 1947,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(i)\n<mask token>\nprint(i)\n<mask token>\nprint(i)\n<mask token>\nprint(i)\n<mask token>\nprint(s)\n<mask token>\nprint(s)\n<mask token>\nprint(s)\n<mask token>\nprint(s)\n<mask tok... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def best_rank_selection(generation):
max_selected = len(generation) // 10
sorted_by_fitness = sorted(generation, key=lambda x: x.fitness, reverse
=True)
return sorted_by_fitness[:max_selected]
| flexible | {
"blob_id": "05a80a904548e90bea635469b94264f219062560",
"index": 7968,
"step-1": "<mask token>\n",
"step-2": "def best_rank_selection(generation):\n max_selected = len(generation) // 10\n sorted_by_fitness = sorted(generation, key=lambda x: x.fitness, reverse\n =True)\n return sorted_by_fitness... | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
os.chdir(masterdir)
exec(open('01_newqs_directory.py', 'r', encoding='utf8').read())
exec(open('02_new_register.py', 'r', encoding='utf8').read())
exec(open('03_moved_in.py', 'r', encoding='utf8').read())
exec(open('04_false_death... | flexible | {
"blob_id": "5a2716fc7b4c0a56fbd0de5d45d71fb33320adf0",
"index": 2889,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nos.chdir(masterdir)\nexec(open('01_newqs_directory.py', 'r', encoding='utf8').read())\nexec(open('02_new_register.py', 'r', encoding='utf8').read())\nexec(open('03_moved_in.py', 'r', enco... | [
0,
1,
2,
3,
4
] |
from unittest import TestCase
from tests import AuthHelperTestCase
class TestTestHelper(TestCase):
"""
Test our helper functions
"""
def test_assertAnyIn_fails(self):
"""
Make sure assertInAny fails correctly
:return:
"""
test_case = AuthHelperTestCase('asser... | normal | {
"blob_id": "ae1aab7563443db3a31fe98b5b26b32944d57c9d",
"index": 1473,
"step-1": "<mask token>\n\n\nclass TestTestHelper(TestCase):\n <mask token>\n <mask token>\n\n def test_assertAnyIn_suceeds(self):\n \"\"\"\n Make sure assertInAny succeeds\n \n :return: \n \"\"\"\n... | [
2,
3,
4,
5
] |
import tensorflow as tf
def data_rescale(x):
return tf.subtract(tf.divide(x, 127.5), 1)
def inverse_rescale(y):
return tf.round(tf.multiply(tf.add(y, 1), 127.5))
| normal | {
"blob_id": "1a09b38838f40c4c6049da8e6a72ba3d56806c07",
"index": 3703,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef inverse_rescale(y):\n return tf.round(tf.multiply(tf.add(y, 1), 127.5))\n",
"step-3": "<mask token>\n\n\ndef data_rescale(x):\n return tf.subtract(tf.divide(x, 127.5), 1)\... | [
0,
1,
2,
3
] |
from os import listdir
from os.path import isfile, join
import sys
cat_list = dict();
def onImport():
mypath = "../../data/roget_processed";
onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))];
for f_name in onlyfiles:
f_temp = open(mypath + "/" + f_name);
f_lines = f_temp.readlines();
for l... | normal | {
"blob_id": "b1b9840fabc96c901e5ed45e22ee63af2f3550cb",
"index": 8643,
"step-1": "<mask token>\n\n\ndef onImport():\n mypath = '../../data/roget_processed'\n onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n for f_name in onlyfiles:\n f_temp = open(mypath + '/' + f_name)\n ... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class LoginPage(BasePage):
<|reserved_special_token_0|>
@teststeps
def __init__(self):
self.home = HomePage()
self.toast = Toast()
<|reserved_special_token_0|>
@teststeps
def wait_check_test1(self):
"""以微信主界面“tab:微信”的text为依据"""
try... | flexible | {
"blob_id": "600b49c7884f8b6e3960549702a52deb20089f5a",
"index": 3503,
"step-1": "<mask token>\n\n\nclass LoginPage(BasePage):\n <mask token>\n\n @teststeps\n def __init__(self):\n self.home = HomePage()\n self.toast = Toast()\n <mask token>\n\n @teststeps\n def wait_check_test1(s... | [
17,
18,
20,
21,
22
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def subset2_helper(num, mid_result, result, position):
result.append(mid_result[:])
for i in range(position, len(num)):
mid_result.append(num[i])
subset2_helper(num, mid_result, result, i + 1)
mid... | flexible | {
"blob_id": "829910af55ca84838537a2e1fa697713c7a6c6ca",
"index": 8400,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef subset2_helper(num, mid_result, result, position):\n result.append(mid_result[:])\n for i in range(position, len(num)):\n mid_result.append(num[i])\n subset2_h... | [
0,
1,
2,
3,
4
] |
import tensorflow as tf
import csv
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
import math
def plot_loss(history):
plt.plot(history.history['loss'], label='loss')
plt.plot(his... | normal | {
"blob_id": "196147d7b2b0cf7176b5baa50d7e7618f88df493",
"index": 7911,
"step-1": "<mask token>\n\n\ndef plot_loss(history):\n plt.plot(history.history['loss'], label='loss')\n plt.plot(history.history['val_loss'], label='val_loss')\n plt.ylim([0, 10])\n plt.xlabel('Epoch')\n plt.ylabel('Error')\n ... | [
1,
2,
3,
4,
5
] |
#
# PySNMP MIB module Nortel-MsCarrier-MscPassport-AtmEbrMIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/Nortel-MsCarrier-MscPassport-AtmEbrMIB
# Produced by pysmi-0.3.4 at Mon Apr 29 20:19:41 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4... | normal | {
"blob_id": "202670314ad28685aaa296dce4b5094daab3f47a",
"index": 4889,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif mibBuilder.loadTexts:\n mscAtmIfVpcSrcEbrOvRowStatusTable.setStatus('mandatory')\n<mask token>\nif mibBuilder.loadTexts:\n mscAtmIfVpcSrcEbrOvRowStatusEntry.setStatus('mandatory'... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Menu:
def __init__(self):
self.data = dict()
self.main = Tk()
self.main.title('Molécules')
self.main.config(bg='black')
self.main.minsize(210, 220)
self.mean = float
Button(self.main, width=14, bg='black', fg='white', text... | flexible | {
"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
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(calculation)
print(calculation2)
print(calculation3)
<|reserved_special_token_0|>
print('Hi there, You are ' + myage + ' years old')
<|reserved_special_token_0|>
print('The result is ' + result)
print('average: %.2f' % ((3 +... | flexible | {
"blob_id": "03f73a55e0a0773bbdbb0d5e29a2db598ba2e080",
"index": 149,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(calculation)\nprint(calculation2)\nprint(calculation3)\n<mask token>\nprint('Hi there, You are ' + myage + ' years old')\n<mask token>\nprint('The result is ' + result)\nprint('avera... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
import Ploneboard
import PloneboardForum
import PloneboardConversation
import PloneboardComment
| flexible | {
"blob_id": "abdf5aee77ee879c50d0e605d5fd95e28a7ef7aa",
"index": 5631,
"step-1": "<mask token>\n",
"step-2": "import Ploneboard\nimport PloneboardForum\nimport PloneboardConversation\nimport PloneboardComment\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
# Bradley N. Miller, David L. Ranum
# Introduction to Data Structures and Algorithms in Python
# Copyright 2005
#
__all__=['BinaryTree', 'Stack']
class Stack:
def __init__(self):
self.items = []
def isEmpty(self):
return self.items == []
def push(self, item):
self.items.append... | normal | {
"blob_id": "5f48c7a68cb9734d84dee2cf8ff4d7be490cf328",
"index": 2888,
"step-1": "<mask token>\n\n\nclass BinaryTree:\n <mask token>\n\n def __init__(self, rootObj):\n self.key = rootObj\n self.leftChild = None\n self.rightChild = None\n self.parent = None\n\n def insertLeft(... | [
12,
19,
30,
31,
37
] |
<|reserved_special_token_0|>
def test_uri_manager_mock_write():
mock_file = mock.Mock()
opener = mock.Mock(spec=open, return_value=mock_file)
manager = URIManager(opener, 'filename')
f = manager.acquire()
f.write('contents')
manager.close()
opener.assert_called_once_with('filename', mode='... | flexible | {
"blob_id": "8fe71e87512dfd2ccfcd21c9c175cb50274d9661",
"index": 1867,
"step-1": "<mask token>\n\n\ndef test_uri_manager_mock_write():\n mock_file = mock.Mock()\n opener = mock.Mock(spec=open, return_value=mock_file)\n manager = URIManager(opener, 'filename')\n f = manager.acquire()\n f.write('con... | [
5,
6,
7,
8,
9
] |
#!/usr/bin/env python
from pathlib import Path
import os
from setuptools import setup, find_packages
install_requires = [
"numpy",
"tensorflow-hub==0.4.0",
"bert-tensorflow==1.0.1",
"click"
]
# Hacky check for whether CUDA is installed
has_cuda = any("CUDA" in name.split("_") for name in os.environ.... | normal | {
"blob_id": "a1141e6aae6992a5037d53093378f0d346f2ca29",
"index": 7666,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ninstall_requires.append('tensorflow-gpu==1.13.1' if has_cuda else\n 'tensorflow==1.13.1')\n<mask token>\nsetup(name='easybert', version=version, url=\n 'https://github.com/robrua/ea... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def mig_loss_function(output1, output2, p):
new_output = output1 / p
m = new_output @ output2.transpose(1, 0)
noise = torch.rand(1) * 0.0001
m1 = torch.log(m * I + I * noise + E - I)
m2 = m * (E - I)
return -(sum(sum(m1)) + Config.batch_size) / normalize_1 + sum(su... | flexible | {
"blob_id": "be9179b33991ba743e6e6b7d5dd4dc85ffc09fc3",
"index": 6331,
"step-1": "<mask token>\n\n\ndef mig_loss_function(output1, output2, p):\n new_output = output1 / p\n m = new_output @ output2.transpose(1, 0)\n noise = torch.rand(1) * 0.0001\n m1 = torch.log(m * I + I * noise + E - I)\n m2 = ... | [
10,
11,
12,
14,
15
] |
# -*- coding: utf-8 -*-
# Define here the models for your scraped items
#
# See documentation in:
# https://doc.scrapy.org/en/latest/topics/items.html
import scrapy
class JiayuanItem(scrapy.Item):
# define the fields for your item here like:
# name = scrapy.Field()
person_id = scrapy.Field(... | normal | {
"blob_id": "9dbadb2421b04961e8e813831d06abc1ff301566",
"index": 3283,
"step-1": "<mask token>\n\n\nclass PersonInfo(scrapy.Item):\n person_id = scrapy.Field()\n buy_car = scrapy.Field()\n address = scrapy.Field()\n\n\nclass OtherItem(scrapy.Item):\n \"\"\"\n 可以定义另外一个item\n \"\"\"\n ... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def k_most_frequent(arr: list, k: int):
""" """
counts = defaultdict(int)
for n in nums:
counts[n] += 1
counts = [(k, v) for k, v in counts.items()]
ordered = list(reversed(sorted(counts, key=lambda d... | flexible | {
"blob_id": "1298c2abae519a5365cc0d9d406196db987eb219",
"index": 5923,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef k_most_frequent(arr: list, k: int):\n \"\"\" \"\"\"\n counts = defaultdict(int)\n for n in nums:\n counts[n] += 1\n counts = [(k, v) for k, v in counts.items()]... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for _ in range(times):
removed = elements.pop()
elements.insert(0, removed)
print(elements)
<|reserved_special_token_1|>
elements = str(input('Type the elements of list: ')).split()
elements = list(map(float, elements))... | flexible | {
"blob_id": "307bb7461a729ba979f6a862fe7c292c42f96ce6",
"index": 1164,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor _ in range(times):\n removed = elements.pop()\n elements.insert(0, removed)\nprint(elements)\n",
"step-3": "elements = str(input('Type the elements of list: ')).split()\neleme... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class App(npyscreen.StandardApp):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class App(npyscreen.StandardApp):
def onStart(self):
self.MainForm = self.addForm('MA... | flexible | {
"blob_id": "dc2c429bae10ee14737583a3726eff8fde8306c7",
"index": 6940,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass App(npyscreen.StandardApp):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass App(npyscreen.StandardApp):\n\n def onStart(self):\n self.MainForm = self.addFor... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
pickled = True
create_sets = True
normed = False
if len(sys.argv) > 2:
filename = sys.argv[1]
else:
filename = os.path.join(os.path.pardir, os.path.pardir, 'data',
... | flexible | {
"blob_id": "18a17c7326a6ae96f74c843d1a902074b377a6d2",
"index": 2701,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n pickled = True\n create_sets = True\n normed = False\n if len(sys.argv) > 2:\n filename = sys.argv[1]\n else:\n filename = os.pat... | [
0,
1,
2
] |
<|reserved_special_token_0|>
def menu(x):
""" Takes a list as argument and displays as a menu """
for i in range(len(x)):
print('{0:>4s} {1:<3s}{2:^5s}{3:<15}'.format(str(i + 1) + ')', x[i]
[1], '-->', x[i][0]))
<|reserved_special_token_0|>
def secondary_message(t, unit):
""" A mes... | flexible | {
"blob_id": "235bb1b9d4c41c12d7667a6bac48737464c685c7",
"index": 568,
"step-1": "<mask token>\n\n\ndef menu(x):\n \"\"\" Takes a list as argument and displays as a menu \"\"\"\n for i in range(len(x)):\n print('{0:>4s} {1:<3s}{2:^5s}{3:<15}'.format(str(i + 1) + ')', x[i]\n [1], '-->', x[i... | [
3,
5,
7,
10,
11
] |
class Person:
def __init__(self, fname, lname):
self.fname = fname
self.lname = lname
def GetName(self):
return (self.fname + ' ' + self.lname)
| normal | {
"blob_id": "ff358136bc96fa7f3eb41d019ddfd10fc4db8f0d",
"index": 5558,
"step-1": "<mask token>\n",
"step-2": "class Person:\n <mask token>\n <mask token>\n",
"step-3": "class Person:\n <mask token>\n\n def GetName(self):\n return self.fname + ' ' + self.lname\n",
"step-4": "class Person:... | [
0,
1,
2,
3,
4
] |
Python 3.6.8 (tags/v3.6.8:3c6b436a57, Dec 24 2018, 00:16:47) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license()" for more information.
>>> import turtle
turtle.setup(650,350,200,200)
turtle.penup()
turtle.fd(-250)
turtle.pendown() ... | normal | {
"blob_id": "e069ad88b5173e5859f1b01b9fb45951d1e82593",
"index": 4280,
"step-1": "Python 3.6.8 (tags/v3.6.8:3c6b436a57, Dec 24 2018, 00:16:47) [MSC v.1916 64 bit (AMD64)] on win32\nType \"help\", \"copyright\", \"credits\" or \"license()\" for more information.\n>>> import turtle \nturtle.setup(65... | [
0
] |
# Copyright Amazon.com Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may
# not use this file except in compliance with the License. A copy of the
# License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanyin... | normal | {
"blob_id": "6f107d0d0328c2445c0e1d0dd10e51227da58129",
"index": 3900,
"step-1": "<mask token>\n\n\n@service_marker\nclass TestTrainingDebuggerJob:\n\n def _wait_sagemaker_training_rule_eval_status(self, training_job_name,\n rule_type: str, expected_status: str, wait_periods: int=30,\n period_le... | [
4,
7,
8,
9,
11
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# menuScrnTxt.py
# Created on Mon Mar 8 16:17:50 2021
# @author: jcj52436999
# menuScrnTxt.py-2021-03-08-1641-just noting a general restart in efforts here
import sys
def printTest2():
if 0 == 0 :
print(" ")
print("# jcj-jcj-jcj- TOP START OF... | normal | {
"blob_id": "e4f7e0c40edde4aac6ba0a7529a2e028a09689ae",
"index": 7260,
"step-1": "<mask token>\n\n\ndef printTest2():\n if 0 == 0:\n print(' ')\n print(\n '# jcj-jcj-jcj- TOP START OF PROGRAM - jcj-jcj-jcj-jcj-jcj-jcj-jcj-jcj-jcj'\n )\n thisProgramIs = 'menuScrnTxt.p... | [
3,
4,
5,
6,
7
] |
from utils import *
from wordEmbedding import *
print("bat dau")
def predict(text, phobert, tokenizer):
model = load_model('model.h5')
X_test = word2vec(text, phobert, tokenizer)
x_test_tensor = tf.convert_to_tensor(X_test)
X_tests = []
X_tests.append(x_test_tensor)
X_tests = tf.convert_to_t... | normal | {
"blob_id": "d2c9ee64472c74767812d842d2c49eec962e28c6",
"index": 4451,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef predict(text, phobert, tokenizer):\n model = load_model('model.h5')\n X_test = word2vec(text, phobert, tokenizer)\n x_test_tensor = tf.convert_to_tensor(X_test)\n X_te... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
sg.ChangeLookAndFeel('Black')
<|reserved_special_token_0|>
for parole in words:
parola = lmtzr.lemmatize(parole, 'v')
appendFile = open('filteredtext.txt', 'a')
appendFile.write(' ' + str(parola))
appendFile.close(... | flexible | {
"blob_id": "46bf5866d5353c58e130b20ffa4d95df8abf986b",
"index": 6609,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsg.ChangeLookAndFeel('Black')\n<mask token>\nfor parole in words:\n parola = lmtzr.lemmatize(parole, 'v')\n appendFile = open('filteredtext.txt', 'a')\n appendFile.write(' ' + st... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def search_word(qst):
global txt
for h in qst:
temp = []
for n, l in enumerate(txt):
if [n for i, j in enumerate(l) if h in j] != []:
temp.append(n)
if temp != []:
fnd.append(temp)
<|reserved_special_token_0|>
def... | flexible | {
"blob_id": "d30129248f5245560ee0d3ee786e118427e169d7",
"index": 4616,
"step-1": "<mask token>\n\n\ndef search_word(qst):\n global txt\n for h in qst:\n temp = []\n for n, l in enumerate(txt):\n if [n for i, j in enumerate(l) if h in j] != []:\n temp.append(n)\n ... | [
3,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
def but(a):
global op
op = op + str(a)
displayStr.set(op)
def eq():
global op
result = str(eval(op))
displayStr.set(result)
op = ''
def clrbut():
displayStr.set('')
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_... | flexible | {
"blob_id": "106cca8af164fa4ae946f77b40c76e03accf171c",
"index": 9645,
"step-1": "<mask token>\n\n\ndef but(a):\n global op\n op = op + str(a)\n displayStr.set(op)\n\n\ndef eq():\n global op\n result = str(eval(op))\n displayStr.set(result)\n op = ''\n\n\ndef clrbut():\n displayStr.set(''... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
app.run(debug=True, host='0.0.0.0')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
os.environ['CITY_CONF'] = '/opt/ris-web/city/duisburg.py'
<|reserved_special_token_0|>
app.run(debug=True, host='0.0.0.0')
<|reserv... | flexible | {
"blob_id": "4276fd61ad48b325961cd45be68eea6eab51f916",
"index": 6085,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp.run(debug=True, host='0.0.0.0')\n",
"step-3": "<mask token>\nos.environ['CITY_CONF'] = '/opt/ris-web/city/duisburg.py'\n<mask token>\napp.run(debug=True, host='0.0.0.0')\n",
"step... | [
0,
1,
2,
3
] |
from django.db import models
from skills.models import skill
from offres.models import Offer
# Create your models here.
class OfferRequirement(models.Model):
skill = models.ForeignKey(skill, on_delete=models.DO_NOTHING ,default="")
offer = models.ForeignKey(Offer , on_delete=models.CASCADE, default="") | normal | {
"blob_id": "3640f1df412b43b42fb4e856604508f698a208ad",
"index": 6385,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass OfferRequirement(models.Model):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass OfferRequirement(models.Model):\n skill = models.ForeignKey(skill... | [
0,
1,
2,
3,
4
] |
import tensorflow as tf
from models.base_model import BaseModel
from utils.im_utils import batch_convert_2_int
from datasets.single_dataset import SingleDataset
from datasets.unpaired_dataset import UnpairedDataset
from models.generators.maskshadowgan_generators import Generator
from models.discriminators.maskshadowgan... | normal | {
"blob_id": "cbbe273a19a4e60b760e35aeb8d43972a46760f5",
"index": 3436,
"step-1": "<mask token>\n\n\nclass MaskShadowGANModel(BaseModel):\n <mask token>\n <mask token>\n\n def generate_dataset(self):\n \"\"\"\n Add ops for dataset loaders to graph\n \"\"\"\n if self.training:\... | [
8,
9,
11,
12,
13
] |
two_digit_number=input("Type a two digit number: ")
first_digit=two_digit_number[0]
second_digit=two_digit_number[1]
print(int(first_digit)+int(second_digit)) | normal | {
"blob_id": "7d65e4e925e90d6b013ae2c059cde58538884d22",
"index": 7239,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(int(first_digit) + int(second_digit))\n",
"step-3": "two_digit_number = input('Type a two digit number: ')\nfirst_digit = two_digit_number[0]\nsecond_digit = two_digit_number[1]\n... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def build_default_args_for_node_classification(dataset):
cpu = not torch.cuda.is_available()
args = {'lr': 0.01, 'weight_decay': 0.0005, 'max_epoch': 1000,
'max_epochs': 1000, 'patience': 100, 'cpu': cpu, 'device_id': [0],
'seed': [42], 'dropout': 0.5, 'hidden_size... | flexible | {
"blob_id": "2396f7acab95260253c367c62002392760157705",
"index": 1236,
"step-1": "<mask token>\n\n\ndef build_default_args_for_node_classification(dataset):\n cpu = not torch.cuda.is_available()\n args = {'lr': 0.01, 'weight_decay': 0.0005, 'max_epoch': 1000,\n 'max_epochs': 1000, 'patience': 100, '... | [
5,
6,
7,
8,
10
] |
<|reserved_special_token_0|>
class Fuzzer:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
@classmethod
def from_list(cls, params):
raise NotImplementedError('ABSTRACT METHOD')
@property
def prng_state(self):
raise NotImplementedError('ABSTRACT METHOD')
def fuz... | flexible | {
"blob_id": "aa2a268143856d8f33b1aaf24f4e28ffd95cab01",
"index": 4658,
"step-1": "<mask token>\n\n\nclass Fuzzer:\n <mask token>\n <mask token>\n\n @classmethod\n def from_list(cls, params):\n raise NotImplementedError('ABSTRACT METHOD')\n\n @property\n def prng_state(self):\n rai... | [
7,
8,
9,
10,
11
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
__author__ = 'raek'
web = '910d59f0-30bd-495b-a54c-bf5addc81a8a'
app = '21ec74fb-e941-43be-8772-a2f8dc6ccc4f'
<|reserved_special_token_1|>
#! /usr/bin/python
# -*- coding: utf-8 -*-
__author__ = 'raek'
web = '910d59f0-30bd-495b-a54c-bf5addc81a8a'
app = '2... | flexible | {
"blob_id": "cce645073ba117b9e297dfccf5a39710b0c6cd14",
"index": 8479,
"step-1": "<mask token>\n",
"step-2": "__author__ = 'raek'\nweb = '910d59f0-30bd-495b-a54c-bf5addc81a8a'\napp = '21ec74fb-e941-43be-8772-a2f8dc6ccc4f'\n",
"step-3": "#! /usr/bin/python\n# -*- coding: utf-8 -*-\n__author__ = 'raek'\n\nweb ... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [path('user/<int:id>/', views.getUser), path('user/addImage/',
views.addImage), path('user/getImage/<int:id>/', views.getImage), path(
'user/signup/', views.signUp), path('user/login/', views.logIn), path(
... | flexible | {
"blob_id": "2458b8169029b3af501b650d548925770b0da74e",
"index": 6656,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('user/<int:id>/', views.getUser), path('user/addImage/',\n views.addImage), path('user/getImage/<int:id>/', views.getImage), path(\n 'user/signup/', views.signUp... | [
0,
1,
2,
3
] |
import os
import string
filenames = os.listdir('data/SENTIMENT_test')
filenames.sort()
outfile = open('sentiment_test.txt', 'w')
remove_punctuation_map = dict((ord(char), None) for char in string.punctuation)
for filename in filenames:
infile = open('data/SENTIMENT_test/' + filename, errors='ignore')
infiletext... | normal | {
"blob_id": "6434e427c9015544985a38104cffeaa10866b9ea",
"index": 4585,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfilenames.sort()\n<mask token>\nfor filename in filenames:\n infile = open('data/SENTIMENT_test/' + filename, errors='ignore')\n infiletext = infile.read()\n infiletext = infilet... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Person:
def __call__(self, name):
print('__call__' + ' Hello ' + name)
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Person:
def __call__(self, name):
print('__call__' + ... | flexible | {
"blob_id": "7b1c7228c1fc9501ab857cba62a7e073691e75c9",
"index": 755,
"step-1": "<mask token>\n\n\nclass Person:\n\n def __call__(self, name):\n print('__call__' + ' Hello ' + name)\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Person:\n\n def __call__(self, name):\n ... | [
2,
3,
4,
5,
6
] |
from unittest.mock import MagicMock
import pytest
from charpe.mediums.email_handler import EmailHandler
from charpe.errors import InsuficientInformation
def test_send_requirements(config):
handler = EmailHandler(config)
with pytest.raises(InsuficientInformation):
handler.publish({})
with pytest... | normal | {
"blob_id": "e2d8a1e13a4162cd606eec12530451ab230c95b6",
"index": 3103,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_send_requirements(config):\n handler = EmailHandler(config)\n with pytest.raises(InsuficientInformation):\n handler.publish({})\n with pytest.raises(Insuficie... | [
0,
1,
2,
3,
4
] |
import os
import config
############################
# NMJ_RNAI LOF/GOF GENE LIST
def nmj_rnai_set_path():
return os.path.join(config.datadir, 'NMJ RNAi Search File.txt')
def nmj_rnai_gain_of_function_set_path():
return os.path.join(config.datadir, 'NMJ_RNAi_gain_of_function_flybase_ids.txt')
def get_n... | normal | {
"blob_id": "6a9d64b1ef5ae8e9d617c8b0534e96c9ce7ea629",
"index": 4951,
"step-1": "\nimport os\n\nimport config\n\n\n############################\n# NMJ_RNAI LOF/GOF GENE LIST\n\ndef nmj_rnai_set_path():\n return os.path.join(config.datadir, 'NMJ RNAi Search File.txt')\n\n\ndef nmj_rnai_gain_of_function_set_pa... | [
0
] |
import tensorflow as tf
from rnn_cells import gru_cell, lstm_cell
from tensorflow.python.ops import rnn
def shape_list(x):
ps = x.get_shape().as_list()
ts = tf.shape(x)
return [ts[i] if ps[i] is None else ps[i] for i in range(len(ps))]
def bi_dir_lstm(X, c_fw, h_fw, c_bw, h_bw, units, scope='bi_dir_lstm')... | normal | {
"blob_id": "e550a2d46e46f0e07d960e7a214fbaa776bab0d5",
"index": 4697,
"step-1": "<mask token>\n\n\ndef shape_list(x):\n ps = x.get_shape().as_list()\n ts = tf.shape(x)\n return [(ts[i] if ps[i] is None else ps[i]) for i in range(len(ps))]\n\n\ndef bi_dir_lstm(X, c_fw, h_fw, c_bw, h_bw, units, scope='bi... | [
6,
7,
8,
9,
12
] |
from __future__ import unicode_literals
from django.db import models
# Create your models here.
class Group(models.Model):
name = models.CharField(max_length=200, db_index=True)
loan_eligibility = models.CharField(max_length=200, db_index=True)
account_number = models.CharField(max_length=200, db_index=Tr... | normal | {
"blob_id": "0c8b58acf33bdfa95984d29a75ae01e49d0da149",
"index": 9202,
"step-1": "<mask token>\n\n\nclass Member(models.Model):\n name = models.CharField(max_length=200, db_index=True)\n age = models.CharField(max_length=200)\n phone = models.CharField(max_length=200)\n address1 = models.CharField(ma... | [
2,
3,
4,
5,
6
] |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file '/home/cypher/.eric6/eric6plugins/vcsGit/ConfigurationPage/GitPage.ui'
#
# Created by: PyQt5 UI code generator 5.8
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_GitPage(object):... | normal | {
"blob_id": "80891a4c9703f91509d2c1b22304f33426dfb962",
"index": 4419,
"step-1": "<mask token>\n\n\nclass Ui_GitPage(object):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Ui_GitPage(object):\n\n def setupUi(self, GitPage):\n GitPage.setObjectName('GitPage')\n GitP... | [
1,
2,
3,
4,
5
] |
# Generated by Django 2.1.7 on 2019-03-18 02:25
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('training_area', '0006_remove_event_day'),
]
operations = [
migrations.Crea... | normal | {
"blob_id": "9905559909f10831373e659cde0f275dc5d71e0d",
"index": 7041,
"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 = [('training_ar... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class TestH2FrameEnabledDisabledTsoGroGso(
TestH2FrameEnabledDisabledTsoGroGsoBase, NetWorker):
def test_headers_frame_with_continuation(self):
client, server = self.setup_tests()
self.run_test_tso_gro_gso_disabled(client, server, self.
_test_headers_f... | flexible | {
"blob_id": "e474cb3db74b5344bd861aacf779cb9f77830ef6",
"index": 5661,
"step-1": "<mask token>\n\n\nclass TestH2FrameEnabledDisabledTsoGroGso(\n TestH2FrameEnabledDisabledTsoGroGsoBase, NetWorker):\n\n def test_headers_frame_with_continuation(self):\n client, server = self.setup_tests()\n sel... | [
27,
33,
37,
46,
48
] |
<|reserved_special_token_0|>
@view_config(route_name='auto_api', request_method='GET', renderer='json')
def single_auto(request: Request):
car_id = request.matchdict.get('car_id')
car = Repository.car_by_id(car_id)
if not car:
msg = "The car with id '{}' was not found.".format(car_id)
retu... | flexible | {
"blob_id": "cb903f3f7fd3c4f3ba5f8ff2ce12aac9c680aa15",
"index": 6116,
"step-1": "<mask token>\n\n\n@view_config(route_name='auto_api', request_method='GET', renderer='json')\ndef single_auto(request: Request):\n car_id = request.matchdict.get('car_id')\n car = Repository.car_by_id(car_id)\n if not car:... | [
1,
2,
3,
4,
5
] |
import numpy as np
import tensorflow as tf
import math
from .. import util
def debug_inference(inference, dummy, entropy, cross_entropy, expected_log_likelhood):
dummy = tf.Print(dummy, [entropy], 'entropy: ')
dummy = tf.Print(dummy, [cross_entropy], 'cross_entropy: ')
dummy = tf.Print(dummy, [expected_log... | normal | {
"blob_id": "4758d6efde21e3b5d91f107188f24b6ddf7cbbe4",
"index": 7935,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef debug_inference(inference, dummy, entropy, cross_entropy,\n expected_log_likelhood):\n dummy = tf.Print(dummy, [entropy], 'entropy: ')\n dummy = tf.Print(dummy, [cross_en... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def update(stdin=None):
"""Update the current address book installation."""
curr_path = Path.cwd() / CURRENT_NAME
if not curr_path.exists():
print('ERROR: There is no symlink named {!r} in the current directo... | flexible | {
"blob_id": "f5274f5d838d484ca0c1cc5a5192a2fd698cf827",
"index": 9432,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef update(stdin=None):\n \"\"\"Update the current address book installation.\"\"\"\n curr_path = Path.cwd() / CURRENT_NAME\n if not curr_path.exists():\n print('ERROR... | [
0,
1,
2,
3,
4
] |
import sys
from PySide2.QtWidgets import QApplication, QDialog, QLineEdit, QPushButton,QVBoxLayout, QLabel, QWidget
from docx import Document
from docx.shared import Inches
class Form(QDialog):
def __init__(self, parent=None):
super(Form, self).__init__(parent)
#set the size
#Creat widgets... | normal | {
"blob_id": "bad13218a7a9e687fbd29099ca80771296789d36",
"index": 1321,
"step-1": "<mask token>\n\n\nclass Form(QDialog):\n\n def __init__(self, parent=None):\n super(Form, self).__init__(parent)\n self.setWindowTitle('Cover Letter Developer')\n self.label1 = QLabel('Input Company Name')\n... | [
2,
3,
4,
5,
6
] |
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