content
stringlengths 1
1.05M
| input_ids
sequencelengths 1
883k
| ratio_char_token
float64 1
22.9
| token_count
int64 1
883k
|
|---|---|---|---|
from rest_framework_gis import serializers
from rest_framework import serializers as s
from .models import (
Artificialisee2015to2018,
Artificielle2018,
CommunesSybarval,
CouvertureSol,
EnveloppeUrbaine2018,
Ocsge,
Renaturee2018to2015,
Sybarval,
Voirie2018,
ZonesBaties2018,
UsageSol,
)
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] | 2.422535 | 142 |
from django.contrib import admin
from .models import SearchResult
# Register your models here.
admin.site.register(SearchResult, SearchResultAdmin) | [
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import asyncio
import os
import tempfile
from contextlib import ExitStack
from typing import Text, Optional, List, Union, Dict
from rasa.importers.importer import TrainingDataImporter
from rasa import model
from rasa.model import FingerprintComparisonResult
from rasa.core.domain import Domain
from rasa.utils.common import TempDirectoryPath
from rasa.cli.utils import (
print_success,
print_warning,
print_error,
bcolors,
print_color,
)
from rasa.constants import DEFAULT_MODELS_PATH, DEFAULT_CORE_SUBDIRECTORY_NAME
def train_core(
domain: Union[Domain, Text],
config: Text,
stories: Text,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
additional_arguments: Optional[Dict] = None,
) -> Optional[Text]:
loop = asyncio.get_event_loop()
return loop.run_until_complete(
train_core_async(
domain=domain,
config=config,
stories=stories,
output=output,
train_path=train_path,
fixed_model_name=fixed_model_name,
additional_arguments=additional_arguments,
)
)
def train_nlu(
config: Text,
nlu_data: Text,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
persist_nlu_training_data: bool = False,
) -> Optional[Text]:
"""Trains an NLU model.
Args:
config: Path to the config file for NLU.
nlu_data: Path to the NLU training data.
output: Output path.
train_path: If `None` the model will be trained in a temporary
directory, otherwise in the provided directory.
fixed_model_name: Name of the model to be stored.
persist_nlu_training_data: `True` if the NLU training data should be persisted
with the model.
Returns:
If `train_path` is given it returns the path to the model archive,
otherwise the path to the directory with the trained model files.
"""
loop = asyncio.get_event_loop()
return loop.run_until_complete(
_train_nlu_async(
config,
nlu_data,
output,
train_path,
fixed_model_name,
persist_nlu_training_data,
)
)
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] | 2.385246 | 976 |
# ============================================================================
# FILE: default.py
# AUTHOR: Shougo Matsushita <Shougo.Matsu at gmail.com>
# License: MIT license
# ============================================================================
import re
import typing
from denite.util import echo, error, clearmatch, regex_convert_py_vim
from denite.util import Nvim, UserContext, Candidates, Candidate
from denite.parent import SyncParent
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# -*- coding: utf-8 -*-
"""Context managers implemented for (mostly) internal use"""
import contextlib
import functools
from io import UnsupportedOperation
import os
import sys
__all__ = ["RedirectStdout", "RedirectStderr"]
RedirectStdout = functools.partial(_stdchannel_redirected, sys.stdout)
RedirectStderr = functools.partial(_stdchannel_redirected, sys.stderr)
RedirectNoOp = functools.partial(_stdchannel_redirected, None, "")
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# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from . import __version__ as app_version
app_name = "pos_kiosk"
app_title = "Pos Kiosk"
app_publisher = "9t9it"
app_description = "Kiosk App"
app_icon = "octicon octicon-file-directory"
app_color = "grey"
app_email = "info@9t9it.com"
app_license = "MIT"
# Includes in <head>
# ------------------
# include js, css files in header of desk.html
# app_include_css = "/assets/pos_kiosk/css/pos_kiosk.css"
# app_include_js = "/assets/pos_kiosk/js/pos_kiosk.js"
# include js, css files in header of web template
# web_include_css = "/assets/pos_kiosk/css/pos_kiosk.css"
# web_include_js = "/assets/pos_kiosk/js/pos_kiosk.js"
# include js in page
# page_js = {"page" : "public/js/file.js"}
# page_js = {
# "kiosk": ["public/js/pos_page_js.js", "public/js/includes/number_to_words.js"]
# }
# include js in doctype views
# doctype_js = {"doctype" : "public/js/doctype.js"}
# doctype_list_js = {"doctype" : "public/js/doctype_list.js"}
# doctype_tree_js = {"doctype" : "public/js/doctype_tree.js"}
# doctype_calendar_js = {"doctype" : "public/js/doctype_calendar.js"}
fixtures = [
{
"doctype": "Custom Field",
"filters": [
[
"name",
"in",
[
"Sales Invoice Item-pos_kiosk",
"Mode of Payment-logo"
]
]
]
}
]
# Home Pages
# ----------
# application home page (will override Website Settings)
# home_page = "login"
# website user home page (by Role)
# role_home_page = {
# "Role": "home_page"
# }
# Website user home page (by function)
# get_website_user_home_page = "pos_kiosk.utils.get_home_page"
# Generators
# ----------
# automatically create page for each record of this doctype
# website_generators = ["Web Page"]
# Installation
# ------------
# before_install = "pos_kiosk.install.before_install"
# after_install = "pos_kiosk.install.after_install"
# Desk Notifications
# ------------------
# See frappe.core.notifications.get_notification_config
# notification_config = "pos_kiosk.notifications.get_notification_config"
# Permissions
# -----------
# Permissions evaluated in scripted ways
# permission_query_conditions = {
# "Event": "frappe.desk.doctype.event.event.get_permission_query_conditions",
# }
#
# has_permission = {
# "Event": "frappe.desk.doctype.event.event.has_permission",
# }
# Document Events
# ---------------
# Hook on document methods and events
# doc_events = {
# "*": {
# "on_update": "method",
# "on_cancel": "method",
# "on_trash": "method"
# }
# }
# Scheduled Tasks
# ---------------
# scheduler_events = {
# "all": [
# "pos_kiosk.tasks.all"
# ],
# "daily": [
# "pos_kiosk.tasks.daily"
# ],
# "hourly": [
# "pos_kiosk.tasks.hourly"
# ],
# "weekly": [
# "pos_kiosk.tasks.weekly"
# ]
# "monthly": [
# "pos_kiosk.tasks.monthly"
# ]
# }
# Testing
# -------
# before_tests = "pos_kiosk.install.before_tests"
# Overriding Whitelisted Methods
# ------------------------------
#
# override_whitelisted_methods = {
# "pos_bahrain.api.get_item_details.get_item_details": "pos_kiosk.api.item.get_item_details" # noqa
# }
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] | 2.319625 | 1,386 |
from keras import Model, Input
from keras.layers import Dense, concatenate, LSTM, Reshape, Permute, Embedding, Dropout, Convolution1D, Flatten
from keras.optimizers import Adam
from pypagai.models.base import KerasModel
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from .fully_connected import FullyConnectedLatentVariable
from .convolutional import ConvolutionalLatentVariable
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author:
''' PNASNet in PyTorch.
Paper: Progressive Neural Architecture Search
'''
from easyai.base_name.block_name import NormalizationType, ActivationType
from easyai.base_name.backbone_name import BackboneName
from easyai.model.backbone.utility.base_backbone import *
from easyai.model.base_block.utility.utility_block import ConvBNActivationBlock
from easyai.model.base_block.cls.pnasnet_block import CellA, CellB
__all__ = ['pnasnet_A', 'pnasnet_B']
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] | 2.891429 | 175 |
# -*- coding: utf-8 -*-
# coding=utf-8
import json
import os
import math
import logging
import requests
import time
from map_download.cmd.BaseDownloader import DownloadEngine, BaseDownloaderThread, latlng2tile_terrain, BoundBox
if __name__ == '__main__':
if 1:
logger = logging.getLogger('down')
try:
root = r'/Users/cugxy/Documents/data/downloader'
formatter = logging.Formatter('%(levelname)s-%(message)s')
hdlr = logging.StreamHandler()
log_file = os.path.join(root, 'down.log')
file_hdlr = logging.FileHandler(log_file)
file_hdlr.setFormatter(formatter)
logger.addHandler(file_hdlr)
logger.addHandler(hdlr)
logger.setLevel(logging.INFO)
min_lng = -180.0
max_lng = 180.0
min_lat = -90.0
max_lat = 90.0
start_zoom = 0
end_zoom = 5
bbox = BoundBox(max_lat, max_lng, min_lat, min_lng, start_zoom, end_zoom)
d = TerrainDownloadEngine(root, bbox, 8, logger)
d.start()
time.sleep(10000)
logger.error('main thread out')
except Exception as e:
logger.error(e)
if 0:
accessToken = get_access_token()
pass
| [
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62,
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] | 2.001536 | 651 |
# coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501
OpenAPI spec version: v1.12.4
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from kubernetes_asyncio.client.api_client import ApiClient
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 10 23:28:58 2017
@author: dhingratul
"""
import urllib.request
import os
from selenium import webdriver
from selenium.webdriver.support.ui import Select
from bs4 import BeautifulSoup
import ssl
import requests
import wget
from PyPDF2 import PdfFileReader
def is_valid_pdf(fn):
"""Check is the PDF valid """
try:
with open(fn, 'rb') as f:
pdf = PdfFileReader(f)
numpages = pdf.numPages
return (numpages > 0)
except Exception as e:
return False
| [
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] | 2.457627 | 236 |
"""
Experiment summary
------------------
Treat each province/state in a country cases over time
as a vector, do a simple K-Nearest Neighbor between
countries. What country has the most similar trajectory
to a given country?
Plots similar countries
"""
import sys
sys.path.insert(0, '..')
from utils import data
import os
import sklearn
import numpy as np
import json
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
# ------------ HYPERPARAMETERS -------------
BASE_PATH = '../COVID-19/csse_covid_19_data/'
# ------------------------------------------
confirmed = os.path.join(
BASE_PATH,
'csse_covid_19_time_series',
'time_series_covid19_confirmed_global.csv')
confirmed = data.load_csv_data(confirmed)
features = []
targets = []
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111)
cm = plt.get_cmap('jet')
NUM_COLORS = 0
LINE_STYLES = ['solid', 'dashed', 'dotted']
NUM_STYLES = len(LINE_STYLES)
dist_diff = os.path.join('../exp/results/', 'knn_raw.json')
f = open(dist_diff,)
dist_diff = json.load(f)
for region, dist in dist_diff.items():
plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111)
cm = plt.get_cmap('jet')
other_region = dist['manhattan'][0]
regions = [region, other_region]
for val in regions:
df = data.filter_by_attribute(
confirmed, "Country/Region", val)
cases, labels = data.get_cases_chronologically(df)
cases = cases.sum(axis=0)
lines = ax.plot(cases, label=val)
ax.set_ylabel('# of confirmed cases')
ax.set_xlabel("Time (days since Jan 22, 2020)")
ax.set_yscale('log')
ax.legend()
plt.tight_layout()
region = region.replace('*', '')
other_region = other_region.replace('*', '')
plt.title(f'Comparing confirmed cases in {region} and {other_region}')
plt.savefig(f'results/raw_manhattan/{region}.png')
plt.close()
print(region) | [
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] | 2.580475 | 758 |
"""
Rational Activation Functions for MXNET
=======================================
This module allows you to create Rational Neural Networks using Learnable
Rational activation functions with MXNET networks.
"""
import mxnet as mx
from mxnet import initializer
from mxnet.gluon import HybridBlock
from rational.utils.get_weights import get_parameters
from rational.mxnet.versions import _version_a, _version_b, _version_c, _version_d
from rational._base.rational_base import Rational_base
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"""Utils for criterion."""
import torch
import torch.nn.functional as F
def normalize(x, axis=-1):
"""Performs L2-Norm."""
num = x
denom = torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12
return num / denom
# Source : https://github.com/earhian/Humpback-Whale-Identification-1st-/blob/master/models/triplet_loss.py
def euclidean_dist(x, y):
"""Computes Euclidean distance."""
m, n = x.size(0), y.size(0)
xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n)
yy = torch.pow(x, 2).sum(1, keepdim=True).expand(m, m).t()
dist = xx + yy - 2 * torch.matmul(x, y.t())
dist = dist.clamp(min=1e-12).sqrt()
return dist
def cosine_dist(x, y):
"""Computes Cosine Distance."""
x = F.normalize(x, dim=1)
y = F.normalize(y, dim=1)
dist = 2 - 2 * torch.mm(x, y.t())
return dist
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] | 2.197917 | 384 |
"""Tests for the sbahn_munich integration"""
line_dict = {
"name": "S3",
"color": "#333333",
"text_color": "#444444",
}
| [
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#! /usr/bin/env python2.7
# -*- coding: latin-1 -*-
from flask import Blueprint
from flask import current_app
from flask import render_template
from flask_login import login_required
homestack = Blueprint("homestack", __name__, url_prefix="/homestack")
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"""Forms for RTD donations"""
import logging
from django import forms
from django.conf import settings
from django.utils.translation import ugettext_lazy as _
from readthedocs.payments.forms import StripeModelForm, StripeResourceMixin
from readthedocs.payments.utils import stripe
from .models import Supporter
log = logging.getLogger(__name__)
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from .base import DataReaderBase
from ..tools import COL, _get_dates, to_float, to_int
import pandas as pd
#from pandas.tseries.frequencies import to_offset
from six.moves import cStringIO as StringIO
import logging
import traceback
import datetime
import json
import token, tokenize
def ymd_to_date(y, m, d):
"""
Returns date
>>> expiration = {u'd': 1, u'm': 12, u'y': 2014}
>>> ymd_to_date(**expiration)
datetime.date(2014, 12, 1)
>>> ymd_to_date(2014, 3, 1)
datetime.date(2014, 3, 1)
"""
return(datetime.date(year=y, month=m, day=d))
def date_to_ymd(date):
"""
Returns dict like {'y': ..., 'm': ..., 'd': ...}
>>> date_to_ymd(datetime.date(year=2010, month=1, day=3))
{'y': 2010, 'm': 1, 'd': 3}
"""
d = {
'y': date.year,
'm': date.month,
'd': date.day
}
return(d)
def fix_lazy_json(in_text):
"""
Handle lazy JSON - to fix expecting property name
this function fixes the json output from google
http://stackoverflow.com/questions/4033633/handling-lazy-json-in-python-expecting-property-name
"""
tokengen = tokenize.generate_tokens(StringIO(in_text).readline)
result = []
for tokid, tokval, _, _, _ in tokengen:
# fix unquoted strings
if (tokid == token.NAME):
if tokval not in ['true', 'false', 'null', '-Infinity', 'Infinity', 'NaN']:
tokid = token.STRING
tokval = u'"%s"' % tokval
# fix single-quoted strings
elif (tokid == token.STRING):
if tokval.startswith ("'"):
tokval = u'"%s"' % tokval[1:-1].replace ('"', '\\"')
# remove invalid commas
elif (tokid == token.OP) and ((tokval == '}') or (tokval == ']')):
if (len(result) > 0) and (result[-1][1] == ','):
result.pop()
# fix single-quoted strings
elif (tokid == token.STRING):
if tokval.startswith ("'"):
tokval = u'"%s"' % tokval[1:-1].replace ('"', '\\"')
result.append((tokid, tokval))
return tokenize.untokenize(result)
if __name__ == "__main__":
import doctest
doctest.testmod()
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] | 2.140267 | 1,048 |
# !/usr/bin/python
# -*- coding: utf-8 -*-
# @time : 2019/11/2 21:08
# @author : Mo
# @function:
from keras_textclassification.data_preprocess.text_preprocess import load_json, save_json
from keras_textclassification.conf.path_config import path_model_dir
path_fast_text_model_vocab2index = path_model_dir + 'vocab2index.json'
path_fast_text_model_l2i_i2l = path_model_dir + 'l2i_i2l.json'
import numpy as np
import os
| [
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355,
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198,
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628,
628,
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] | 2.505814 | 172 |
# Copyright 2014 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
from gpu_tests.gpu_test_expectations import GpuTestExpectations
# See the GpuTestExpectations class for documentation.
| [
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# -*- coding: utf-8 -*-
# Generated by Django 1.11.13 on 2018-06-08 22:54
from __future__ import unicode_literals
from django.conf import settings
import django.contrib.postgres.fields.jsonb
from django.db import migrations, models
import django.db.models.deletion
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] | 2.902174 | 92 |
import setuptools #enables develop
setuptools.setup(
name='pysvm',
version='0.1',
description='PySVM : A NumPy implementation of SVM based on SMO algorithm',
author_email="191300064@smail.nju.edu.cn",
packages=['pysvm'],
license='MIT License',
long_description=open('README.md', encoding='utf-8').read(),
install_requires=[ #
'numpy', 'sklearn'
],
url='https://github.com/Kaslanarian/PySVM',
)
| [
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] | 2.445055 | 182 |
######## Image Object Detection Using Tensorflow-trained Classifier #########
#
# Author: Evan Juras
# Date: 1/15/18
# Description:
# This program uses a TensorFlow-trained classifier to perform object detection.
# It loads the classifier uses it to perform object detection on an image.
# It draws boxes and scores around the objects of interest in the image.
## Some of the code is copied from Google's example at
## https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
## and some is copied from Dat Tran's example at
## https://github.com/datitran/object_detector_app/blob/master/object_detection_app.py
## but I changed it to make it more understandable to me.
# Import packages
import os
import cv2
import numpy as np
import tensorflow as tf
import sys
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
# Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util
# Name of the directory containing the object detection module we're using
MODEL_NAME = 'inference_graph'
IMAGE_NAME = 'test1.jpg'
# Grab path to current working directory
CWD_PATH = os.getcwd()
# Path to frozen detection graph .pb file, which contains the model that is used
# for object detection.
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')
# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt')
# Path to image
PATH_TO_IMAGE = os.path.join(CWD_PATH,IMAGE_NAME)
# Number of classes the object detector can identify
NUM_CLASSES = 6
# Load the label map.
# Label maps map indices to category names, so that when our convolution
# network predicts `5`, we know that this corresponds to `king`.
# Here we use internal utility functions, but anything that returns a
# dictionary mapping integers to appropriate string labels would be fine
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
# Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph)
# Define input and output tensors (i.e. data) for the object detection classifier
# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Output tensors are the detection boxes, scores, and classes
# Each box represents a part of the image where a particular object was detected
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represents level of confidence for each of the objects.
# The score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Load image using OpenCV and
# expand image dimensions to have shape: [1, None, None, 3]
# i.e. a single-column array, where each item in the column has the pixel RGB value
image = cv2.imread(PATH_TO_IMAGE)
image_expanded = np.expand_dims(image, axis=0)
# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_expanded})
# Draw the results of the detection (aka 'visulaize the results')
vis_util.visualize_boxes_and_labels_on_image_array(
image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8,
min_score_thresh=0.60)
# All the results have been drawn on image. Now display the image.
cv2.imshow('Object detector', image)
# Press any key to close the image
cv2.waitKey(0)
# Clean up
cv2.destroyAllWindows()
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5985,
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33967,
17,
13,
41659,
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] | 3.122769 | 1,401 |
from data_collection.management.commands import BaseXpressDemocracyClubCsvImporter
| [
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] | 3.952381 | 21 |
from django.db.models import Q
from django.shortcuts import render
from django.http import Http404
# Create your views here.
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework.decorators import api_view
from .models import Product, Category
from .serializers import ProductSerializer, CategorySerializer | [
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] | 3.923913 | 92 |
from sys import maxsize
| [
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628,
198
] | 3.714286 | 7 |
##########################################################################
#
# Copyright (c) 2010-2012, Image Engine Design Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# * Neither the name of Image Engine Design nor the names of any
# other contributors to this software may be used to endorse or
# promote products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
##########################################################################
from __future__ import with_statement
import os
import sys
import shutil
import unittest
import IECore
if __name__ == "__main__":
unittest.main()
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1298,
198,
197,
403,
715,
395,
13,
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3419,
198
] | 3.498195 | 554 |
"""Pacman game domain."""
from rlpy.Tools import __rlpy_location__
from .Domain import Domain
from .PacmanPackage import layout, pacman, game, ghostAgents
from .PacmanPackage import graphicsDisplay
import numpy as np
from copy import deepcopy
import os
import time
__copyright__ = "Copyright 2013, RLPy http://acl.mit.edu/RLPy"
__credits__ = ["Alborz Geramifard", "Robert H. Klein", "Christoph Dann",
"William Dabney", "Jonathan P. How"]
__license__ = "BSD 3-Clause"
__author__ = "Austin Hays"
| [
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9800,
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40245,
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592,
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628,
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] | 2.942529 | 174 |
from zeit.cms.i18n import MessageFactory as _
import zope.interface
import zope.schema
| [
6738,
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72,
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3008,
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] | 3.142857 | 28 |
N, M = map(int, input().split())
for i in range(1, M + 1):
if i % 2 == 1:
j = (i - 1) // 2
print(1 + j, M + 1 - j)
else:
j = (i - 2) // 2
print(M + 2 + j, 2 * M + 1 - j)
| [
45,
11,
337,
796,
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] | 1.743802 | 121 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import re
import sys
from setuptools import setup
from setuptools.command.test import test as TestCommand
__version__ = ''
with open('facebookbot/__about__.py', 'r') as fd:
reg = re.compile(r'__version__ = [\'"]([^\'"]*)[\'"]')
for line in fd:
m = reg.match(line)
if m:
__version__ = m.group(1)
break
with open('README.rst', 'r') as fd:
long_description = fd.read()
setup(
name="fbsdk",
version=__version__,
author="Sam Chang",
author_email="t0915290092@gmail.com",
maintainer="Sam Chang",
maintainer_email="t0915290092@gmail.com",
url="https://github.com/boompieman/fbsdk",
description="Facebook Messaging API SDK for Python",
long_description=long_description,
license='Apache License 2.0',
packages=[
"facebookbot", "facebookbot.models"
],
install_requires=_requirements(),
classifiers=[
"Development Status :: 5 - Production/Stable",
"License :: OSI Approved :: Apache Software License",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Topic :: Software Development"
]
)
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513,
1600,
198,
220,
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366,
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7904,
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7712,
1,
198,
220,
220,
220,
2361,
198,
8,
198
] | 2.744186 | 645 |
# coding=utf-8
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch MMBT model. """
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
from ...modeling_utils import ModuleUtilsMixin
from ...utils import logging
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "MMBTConfig"
MMBT_START_DOCSTRING = r"""
MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and
obtain state-of-the-art performance on various multimodal classification benchmark tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic
methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
pruning heads etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.
Parameters:
config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration.
transformer (:class: *~nn.Module*): A text transformer that is used by MMBT.
It should have embeddings, encoder, and pooler attributes.
encoder (:class: *~nn.Module*): Encoder for the second modality.
It should take in a batch of modal inputs and return k, n dimension embeddings.
"""
MMBT_INPUTS_DOCSTRING = r"""
Args:
input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):
The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image
Encoder, the shape would be (batch_size, channels, height, width)
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's
appended to the end of other modality embeddings. Indices can be obtained using
[`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification
tasks.
modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.
attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:
Segment token indices to indicate different portions of the non-text modality. The embeddings from these
tokens will be summed with the respective token embeddings for the non-text modality.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.
Selected in the range `[0, config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
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] | 2.849831 | 2,657 |
from abc import (
ABC,
abstractmethod,
)
import logging
from typing import (
TYPE_CHECKING,
Tuple,
Type,
)
from eth._utils.datatypes import (
Configurable,
)
from eth.db.backends.base import (
BaseAtomicDB,
)
from eth.exceptions import (
BlockNotFound,
)
from eth.validation import (
validate_word,
)
from eth_typing import (
Hash32,
)
from eth_utils import (
ValidationError,
encode_hex,
)
from eth2._utils.ssz import (
validate_imported_block_unchanged,
)
from eth2.beacon.db.chain import (
BaseBeaconChainDB,
BeaconChainDB,
)
from eth2.beacon.exceptions import (
BlockClassError,
StateMachineNotFound,
)
from eth2.beacon.types.blocks import (
BaseBeaconBlock,
)
from eth2.beacon.types.states import (
BeaconState,
)
from eth2.beacon.typing import (
FromBlockParams,
Slot,
)
from eth2.beacon.validation import (
validate_slot,
)
if TYPE_CHECKING:
from eth2.beacon.state_machines.base import ( # noqa: F401
BaseBeaconStateMachine,
)
#
# Block API
#
def get_block_class(self, block_root: Hash32) -> Type[BaseBeaconBlock]:
slot = self.chaindb.get_slot_by_root(block_root)
sm_class = self.get_state_machine_class_for_block_slot(slot)
block_class = sm_class.block_class
return block_class
def create_block_from_parent(self,
parent_block: BaseBeaconBlock,
block_params: FromBlockParams) -> BaseBeaconBlock:
"""
Passthrough helper to the ``StateMachine`` class of the block descending from the
given block.
"""
return self.get_state_machine_class_for_block_slot(
slot=parent_block.slot + 1 if block_params.slot is None else block_params.slot,
).create_block_from_parent(parent_block, block_params)
def get_block_by_root(self, block_root: Hash32) -> BaseBeaconBlock:
"""
Return the requested block as specified by block hash.
Raise ``BlockNotFound`` if there's no block with the given hash in the db.
"""
validate_word(block_root, title="Block Hash")
block_class = self.get_block_class(block_root)
return self.chaindb.get_block_by_root(block_root, block_class)
def get_canonical_head(self) -> BaseBeaconBlock:
"""
Return the block at the canonical chain head.
Raise ``CanonicalHeadNotFound`` if there's no head defined for the canonical chain.
"""
block_root = self.chaindb.get_canonical_head_root()
block_class = self.get_block_class(block_root)
return self.chaindb.get_block_by_root(block_root, block_class)
def get_score(self, block_root: Hash32) -> int:
"""
Return the score of the block with the given hash.
Raise ``BlockNotFound`` if there is no matching black hash.
"""
return self.chaindb.get_score(block_root)
def ensure_block(self, block: BaseBeaconBlock=None) -> BaseBeaconBlock:
"""
Return ``block`` if it is not ``None``, otherwise return the block
of the canonical head.
"""
if block is None:
head = self.get_canonical_head()
return self.create_block_from_parent(head, FromBlockParams())
else:
return block
def get_block(self) -> BaseBeaconBlock:
"""
Return the current TIP block.
"""
return self.get_state_machine().block
def get_canonical_block_by_slot(self, slot: Slot) -> BaseBeaconBlock:
"""
Return the block with the given number in the canonical chain.
Raise ``BlockNotFound`` if there's no block with the given number in the
canonical chain.
"""
validate_slot(slot)
return self.get_block_by_root(self.chaindb.get_canonical_block_root(slot))
def get_canonical_block_root(self, slot: Slot) -> Hash32:
"""
Return the block hash with the given number in the canonical chain.
Raise ``BlockNotFound`` if there's no block with the given number in the
canonical chain.
"""
return self.chaindb.get_canonical_block_root(slot)
def import_block(
self,
block: BaseBeaconBlock,
perform_validation: bool=True
) -> Tuple[BaseBeaconBlock, Tuple[BaseBeaconBlock, ...], Tuple[BaseBeaconBlock, ...]]:
"""
Import a complete block and returns a 3-tuple
- the imported block
- a tuple of blocks which are now part of the canonical chain.
- a tuple of blocks which were canonical and now are no longer canonical.
"""
try:
parent_block = self.get_block_by_root(block.previous_block_root)
except BlockNotFound:
raise ValidationError(
"Attempt to import block #{}. Cannot import block {} before importing "
"its parent block at {}".format(
block.slot,
block.signed_root,
block.previous_block_root,
)
)
base_block_for_import = self.create_block_from_parent(
parent_block,
FromBlockParams(),
)
state, imported_block = self.get_state_machine(base_block_for_import).import_block(block)
# Validate the imported block.
if perform_validation:
validate_imported_block_unchanged(imported_block, block)
# TODO: Now it just persists all state. Should design how to clean up the old state.
self.chaindb.persist_state(state)
(
new_canonical_blocks,
old_canonical_blocks,
) = self.chaindb.persist_block(imported_block, imported_block.__class__)
self.logger.debug(
'IMPORTED_BLOCK: slot %s | signed root %s',
imported_block.slot,
encode_hex(imported_block.signed_root),
)
return imported_block, new_canonical_blocks, old_canonical_blocks
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] | 2.345961 | 2,587 |
#!/usr/local/bin/python3
import paramiko,time
#using as SSH Client
client = paramiko.SSHClient()
# check dir(client) to find available options.
# auto adjust host key verification with yes or no
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
# time for connecting to remote Cisco IOS
"""
Manually taking input
addr = input('Provide IP address to connect to: ')
user = input('Username: ')
pwd = getpass.getpass('Password: ')"""
# Taking input from files
f1 = open("devices.txt","r")
f2 = open("commands.txt","r")
for line in f1:
client = paramiko.SSHClient()
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
data = line.split(" ")
# print(data)
addr = data[0]
user = data[1]
pwd = data[2]
f3 = open(addr+".txt","w+")
# print(addr +" "+ user +" " +pwd)
client.connect(addr,username=user,password=pwd,allow_agent=False,look_for_keys=False)
# we have to ask for Shell
device_access = client.invoke_shell()
for line in f2:
device_access.send(line)
time.sleep(1)
output = device_access.recv(55000).decode('ascii')
f3.write(output)
"""
THIS CODE IS FOR SINGLE COMMAND, FOR MULTIPLE COMMANDS CODE BELOW
# send command to the device
device_access.send("ter len 0\nshow run \n")
time.sleep(2)
# receive output from the device, convert it to byte-like format and print it
print(device_access.recv(550000).decode('ascii'))
# We can print the same to a file too
with open("csr1000v.txt","w") as f:
f.write(device_access.recv(550000).decode('ascii'))""" | [
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4480,
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2816,
2388,
737,
12501,
1098,
10786,
292,
979,
72,
6,
4008,
37811
] | 2.658744 | 589 |
# for n in range(400,500):
# i = n // 100
# j = n // 10 % 10
# k = n % 10
# if n == i ** 3 + j ** 3 + k ** 3:
# print(n)
# (16)
# input("():")
# s1 = input("():")
# l1 = s1.split(' ')
# l2 = []
# for i in l1:
# if i.isdigit():
# l2.append(int(i))
# for i in l2:
# if not (i % 6):
# print(i, end=" ")
# (17)
out_l1 = []
while 1:
in_1 = input(":")
nums_l1 = in_1.split(' ')
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2,
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8,
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16,
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17635,
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4514,
352,
25,
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220,
220,
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287,
62,
16,
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7,
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8,
198,
220,
220,
220,
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16,
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62,
16,
13,
35312,
10786,
705,
8,
628,
628,
628,
628,
628,
628,
198
] | 1.74902 | 255 |
"""
A query transformer is a function that accepts a program and returns a program, plus a priority level.
Higher priority transformers are placed closer to the front of the list. Were ensuring is a function,
because were going to evaluate it later 31 .
Well assume there wont be an enormous number of transformer additions,
and walk the list linearly to add a new one.
Well leave a note in case this assumption turns out to be false
a binary search is much more time-optimal for long lists,
but adds a little complexity and doesnt really speed up short lists.
"""
"""
Dagoba.T = [] # transformers (more than meets the eye)
"""
"""
Dagoba.addTransformer = function(fun, priority) {
if(typeof fun != 'function')
return Dagoba.error('Invalid transformer function')
for(var i = 0; i < Dagoba.T.length; i++) # OPT: binary search
if(priority > Dagoba.T[i].priority) break
Dagoba.T.splice(i, 0, {priority: priority, fun: fun})
}
"""
"""
Dagoba.transform = function(program) {
return Dagoba.T.reduce(function(acc, transformer) {
return transformer.fun(acc)
}, program)
}
"""
"""
Dagoba.addAlias = function(newname, oldname, defaults) {
defaults = defaults || [] # default arguments for the alias
Dagoba.addPipetype(newname, function() {}) # because there's no method catchall in js
Dagoba.addTransformer(function(program) {
return program.map(function(step) {
if(step[0] != newname) return step
return [oldname, Dagoba.extend(step[1], defaults)]
})
}, 100) # these need to run early, so they get a high priority
}
"""
"""
Dagoba.extend = function(list, defaults) {
return Object.keys(defaults).reduce(function(acc, key) {
if(typeof list[key] != 'undefined') return acc
acc[key] = defaults[key]
return acc
}, list)
}
"""
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1441,
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198,
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8964,
1351,
8,
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92,
198,
37811,
198
] | 2.632979 | 752 |
#!/usr/bin/python3.6.8+
# -*- coding:utf-8 -*-
"""
@auth: cml
@date: 2020-12-2
@desc: ...
"""
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] | 1.846154 | 52 |
# -*- coding: utf-8 -*-
"""
pyboleto.html
~~~~~~~~~~~~~
Classe Responsvel por fazer o output do boleto em html.
:copyright: 2012 by Artur Felipe de Sousa
:license: BSD, see LICENSE for more details.
"""
import os
import string
import sys
import codecs
import base64
from itertools import chain
if sys.version_info < (3,):
from itertools import izip_longest as zip_longest
zip_longest # chamando para evitar erro de nao uso do zip_longest
else:
from itertools import zip_longest
DIGITS = [
['n', 'n', 'w', 'w', 'n'],
['w', 'n', 'n', 'n', 'w'],
['n', 'w', 'n', 'n', 'w'],
['w', 'w', 'n', 'n', 'n'],
['n', 'n', 'w', 'n', 'w'],
['w', 'n', 'w', 'n', 'n'],
['n', 'w', 'w', 'n', 'n'],
['n', 'n', 'n', 'w', 'w'],
['w', 'n', 'n', 'w', 'n'],
['n', 'w', 'n', 'w', 'n'],
]
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13,
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11,
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220,
220,
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220,
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62,
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395,
355,
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62,
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395,
198,
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220,
220,
19974,
62,
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395,
220,
1303,
442,
321,
25440,
31215,
819,
7940,
1931,
305,
390,
299,
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514,
78,
466,
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62,
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395,
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25,
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220,
422,
340,
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35,
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86,
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6,
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705,
77,
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77,
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705,
86,
6,
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220,
220,
37250,
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220,
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77,
3256,
705,
86,
3256,
705,
77,
6,
4357,
198,
60,
628
] | 2.09204 | 402 |
summary = 0
i = 0
while i < 5:
summary = summary + i
print(summary)
i = i + 1
| [
49736,
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657,
198,
198,
72,
796,
657,
198,
4514,
1312,
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220,
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49736,
8,
198,
220,
220,
220,
1312,
796,
1312,
1343,
352,
198
] | 2.219512 | 41 |
import imtreat
img = imtreat.imageManagerClass.openImageFunction("../images/soleil.png", 0)
img = imtreat.definedModesClass.detailEnhanceFunction(img)
imtreat.imageManagerClass.saveImageFunction("/Tlchargements/", "image_1", ".png", img)
| [
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1600,
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9060,
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1600,
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8,
198
] | 2.939024 | 82 |
# Copyright (c) 2016 TUBITAK BILGEM
# 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. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from oslo_config import cfg
hyperv_opt_group = cfg.OptGroup("hyperv",
title='The Hyper-V feature',
help="""
The hyperv feature allows you to configure the Hyper-V hypervisor
driver to be used within an OpenStack deployment.
""")
hyperv_opts = [
cfg.FloatOpt('dynamic_memory_ratio',
default=1.0,
help="""
Dynamic memory ratio
Enables dynamic memory allocation (ballooning) when set to a value
greater than 1. The value expresses the ratio between the total RAM
assigned to an instance and its startup RAM amount. For example a
ratio of 2.0 for an instance with 1024MB of RAM implies 512MB of
RAM allocated at startup.
Possible values:
* 1.0: Disables dynamic memory allocation (Default).
* Float values greater than 1.0: Enables allocation of total implied
RAM divided by this value for startup.
"""),
cfg.BoolOpt('enable_instance_metrics_collection',
default=False,
help="""
Enable instance metrics collection
Enables metrics collections for an instance by using Hyper-V's
metric APIs. Collected data can be retrieved by other apps and
services, e.g.: Ceilometer.
"""),
cfg.StrOpt('instances_path_share',
default="",
help="""
Instances path share
The name of a Windows share mapped to the "instances_path" dir
and used by the resize feature to copy files to the target host.
If left blank, an administrative share (hidden network share) will
be used, looking for the same "instances_path" used locally.
Possible values:
* "": An administrative share will be used (Default).
* Name of a Windows share.
Related options:
* "instances_path": The directory which will be used if this option
here is left blank.
"""),
cfg.BoolOpt('limit_cpu_features',
default=False,
help="""
Limit CPU features
This flag is needed to support live migration to hosts with
different CPU features and checked during instance creation
in order to limit the CPU features used by the instance.
"""),
cfg.IntOpt('mounted_disk_query_retry_count',
default=10,
min=0,
help="""
Mounted disk query retry count
The number of times to retry checking for a mounted disk.
The query runs until the device can be found or the retry
count is reached.
Possible values:
* Positive integer values. Values greater than 1 is recommended
(Default: 10).
Related options:
* Time interval between disk mount retries is declared with
"mounted_disk_query_retry_interval" option.
"""),
cfg.IntOpt('mounted_disk_query_retry_interval',
default=5,
min=0,
help="""
Mounted disk query retry interval
Interval between checks for a mounted disk, in seconds.
Possible values:
* Time in seconds (Default: 5).
Related options:
* This option is meaningful when the mounted_disk_query_retry_count
is greater than 1.
* The retry loop runs with mounted_disk_query_retry_count and
mounted_disk_query_retry_interval configuration options.
"""),
cfg.IntOpt('power_state_check_timeframe',
default=60,
min=0,
help="""
Power state check timeframe
The timeframe to be checked for instance power state changes.
This option is used to fetch the state of the instance from Hyper-V
through the WMI interface, within the specified timeframe.
Possible values:
* Timeframe in seconds (Default: 60).
"""),
cfg.IntOpt('power_state_event_polling_interval',
default=2,
min=0,
help="""
Power state event polling interval
Instance power state change event polling frequency. Sets the
listener interval for power state events to the given value.
This option enhances the internal lifecycle notifications of
instances that reboot themselves. It is unlikely that an operator
has to change this value.
Possible values:
* Time in seconds (Default: 2).
"""),
cfg.StrOpt('qemu_img_cmd',
default="qemu-img.exe",
help="""
qemu-img command
qemu-img is required for some of the image related operations
like converting between different image types. You can get it
from here: (http://qemu.weilnetz.de/) or you can install the
Cloudbase OpenStack Hyper-V Compute Driver
(https://cloudbase.it/openstack-hyperv-driver/) which automatically
sets the proper path for this config option. You can either give the
full path of qemu-img.exe or set its path in the PATH environment
variable and leave this option to the default value.
Possible values:
* Name of the qemu-img executable, in case it is in the same
directory as the nova-compute service or its path is in the
PATH environment variable (Default).
* Path of qemu-img command (DRIVELETTER:\PATH\TO\QEMU-IMG\COMMAND).
Related options:
* If the config_drive_cdrom option is False, qemu-img will be used to
convert the ISO to a VHD, otherwise the config drive will
remain an ISO. To use config drive with Hyper-V, you must
set the ``mkisofs_cmd`` value to the full path to an ``mkisofs.exe``
installation.
"""),
cfg.StrOpt('vswitch_name',
help="""
External virtual switch name
The Hyper-V Virtual Switch is a software-based layer-2 Ethernet
network switch that is available with the installation of the
Hyper-V server role. The switch includes programmatically managed
and extensible capabilities to connect virtual machines to both
virtual networks and the physical network. In addition, Hyper-V
Virtual Switch provides policy enforcement for security, isolation,
and service levels. The vSwitch represented by this config option
must be an external one (not internal or private).
Possible values:
* If not provided, the first of a list of available vswitches
is used. This list is queried using WQL.
* Virtual switch name.
"""),
cfg.IntOpt('wait_soft_reboot_seconds',
default=60,
min=0,
help="""
Wait soft reboot seconds
Number of seconds to wait for instance to shut down after soft
reboot request is made. We fall back to hard reboot if instance
does not shutdown within this window.
Possible values:
* Time in seconds (Default: 60).
"""),
cfg.BoolOpt('config_drive_cdrom',
default=False,
help="""
Mount config drive as a CD drive.
OpenStack can be configured to write instance metadata to a config drive, which
is then attached to the instance before it boots. The config drive can be
attached as a disk drive (default) or as a CD drive.
Related options:
* This option is meaningful with ``force_config_drive`` option set to ``True``
or when the REST API call to create an instance will have
``--config-drive=True`` flag.
* ``config_drive_format`` option must be set to ``iso9660`` in order to use
CD drive as the config drive image.
* To use config drive with Hyper-V, you must set the
``mkisofs_cmd`` value to the full path to an ``mkisofs.exe`` installation.
Additionally, you must set the ``qemu_img_cmd`` value to the full path
to an ``qemu-img`` command installation.
* You can configure the Compute service to always create a configuration
drive by setting the ``force_config_drive`` option to ``True``.
"""),
cfg.BoolOpt('config_drive_inject_password',
default=False,
help="""
Inject password to config drive.
When enabled, the admin password will be available from the config drive image.
Related options:
* This option is meaningful when used with other options that enable
config drive usage with Hyper-V, such as ``force_config_drive``.
"""),
cfg.IntOpt('volume_attach_retry_count',
default=10,
min=0,
help="""
Volume attach retry count
The number of times to retry attaching a volume. Volume attachment
is retried until success or the given retry count is reached.
Possible values:
* Positive integer values (Default: 10).
Related options:
* Time interval between attachment attempts is declared with
volume_attach_retry_interval option.
"""),
cfg.IntOpt('volume_attach_retry_interval',
default=5,
min=0,
help="""
Volume attach retry interval
Interval between volume attachment attempts, in seconds.
Possible values:
* Time in seconds (Default: 5).
Related options:
* This options is meaningful when volume_attach_retry_count
is greater than 1.
* The retry loop runs with volume_attach_retry_count and
volume_attach_retry_interval configuration options.
"""),
cfg.BoolOpt('enable_remotefx',
default=False,
help="""
Enable RemoteFX feature
This requires at least one DirectX 11 capable graphics adapter for
Windows / Hyper-V Server 2012 R2 or newer and RDS-Virtualization
feature has to be enabled.
Instances with RemoteFX can be requested with the following flavor
extra specs:
**os:resolution**. Guest VM screen resolution size. Acceptable values::
1024x768, 1280x1024, 1600x1200, 1920x1200, 2560x1600, 3840x2160
``3840x2160`` is only available on Windows / Hyper-V Server 2016.
**os:monitors**. Guest VM number of monitors. Acceptable values::
[1, 4] - Windows / Hyper-V Server 2012 R2
[1, 8] - Windows / Hyper-V Server 2016
**os:vram**. Guest VM VRAM amount. Only available on
Windows / Hyper-V Server 2016. Acceptable values::
64, 128, 256, 512, 1024
"""),
cfg.BoolOpt('use_multipath_io',
default=False,
help="""
Use multipath connections when attaching iSCSI or FC disks.
This requires the Multipath IO Windows feature to be enabled. MPIO must be
configured to claim such devices.
"""),
cfg.ListOpt('iscsi_initiator_list',
default=[],
help="""
List of iSCSI initiators that will be used for estabilishing iSCSI sessions.
If none are specified, the Microsoft iSCSI initiator service will choose the
initiator.
""")
]
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1037,
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11,
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262,
198,
259,
8846,
1352,
13,
198,
15931,
4943,
198,
60,
628,
198
] | 3.21266 | 3,207 |
import requests
words_list = requests.get("https://raw.githubusercontent.com/atebits/Words/master/Words/fr.txt").text
words_list = filter(lambda x: len(x) > 4, words_list.split('\n'))
path = input("Chemin d'criture ? (words.txt) ")
if path == "":
path = "./words.txt"
with open(path, "w", encoding="utf-8") as file:
file.write('\n'.join(words_list)) | [
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] | 2.623188 | 138 |
import unittest
from unittest import mock
import os
import subprocess
from testfixtures import TempDirectory
from simplegallery.upload.uploader_factory import get_uploader
if __name__ == '__main__':
unittest.main()
| [
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] | 3.313433 | 67 |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'add_subject.ui'
#
# Created by: PyQt4 UI code generator 4.11.4
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore, QtGui
try:
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
try:
_encoding = QtGui.QApplication.UnicodeUTF8
except AttributeError:
if __name__ == "__main__":
import sys
app = QtGui.QApplication(sys.argv)
Dialog_add_subject = QtGui.QDialog()
ui = Ui_Dialog_add_subject()
ui.setupUi(Dialog_add_subject)
Dialog_add_subject.show()
sys.exit(app.exec_())
| [
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] | 2.302817 | 284 |
from django.db.models import signals
from django.test import TestCase
from django.core import management
from django.utils import six
from shared_models import models
PRE_SYNCDB_ARGS = ['app', 'create_models', 'verbosity', 'interactive', 'db']
SYNCDB_DATABASE = 'default'
SYNCDB_VERBOSITY = 1
SYNCDB_INTERACTIVE = False
# We connect receiver here and not in unit test code because we need to
# connect receiver before test runner creates database. That is, sequence of
# actions would be:
#
# 1. Test runner imports this module.
# 2. We connect receiver.
# 3. Test runner calls syncdb for create default database.
# 4. Test runner execute our unit test code.
pre_syncdb_receiver = OneTimeReceiver()
signals.pre_syncdb.connect(pre_syncdb_receiver, sender=models)
| [
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62,
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198,
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874,
13,
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13,
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7,
3866,
62,
27261,
9945,
62,
260,
39729,
11,
29788,
28,
27530,
8,
628
] | 3.273109 | 238 |
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
import re
from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
log = logging.getLogger(__name__)
| [
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7,
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8,
628
] | 3.782828 | 198 |
#!/usr/bin/env python
import time
import os
import math
from trackball import TrackBall
print("""Trackball: Mouse
Use the trackball as a mouse in Raspbian, with right-click
when the switch is pressed.
Press Ctrl+C to exit!
""")
trackball = TrackBall(interrupt_pin=4)
trackball.set_rgbw(0, 0, 0, 0)
# Check for xte (used to control mouse)
use_xte = os.system('which xte') == 0
if use_xte == 0:
raise RuntimeError("xte not found. Did you sudo apt install xautomation?")
while True:
up, down, left, right, switch, state = trackball.read()
# Send movements and clicks to xte
if switch:
cmd = 'xte "mouseclick 1"'
os.system(cmd)
elif right or up or left or down:
x = right - left
x = math.copysign(x**2, x)
y = down - up
y = math.copysign(y**2, y)
cmd = 'xte "mousermove {} {}"'.format(int(x), int(y))
os.system(cmd)
time.sleep(0.0001)
| [
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] | 2.404145 | 386 |
"""GaussianMLPRegressorModel."""
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from garage.experiment import deterministic
from garage.tf.models import GaussianMLPModel
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import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import os
import argparse
from torch.autograd import Variable
from extensions.utils import progress_bar
from extensions.model_refinery_wrapper import ModelRefineryWrapper
from extensions.refinery_loss import RefineryLoss
from models import ShuffleNetv2_wrapper
from models import DiracDeltaNet_wrapper
parser = argparse.ArgumentParser(description='PyTorch imagenet inference')
parser.add_argument('--datadir', help='path to dataset')
parser.add_argument('--inputdir', help='path to input model')
args = parser.parse_args()
# Data
print('==> Preparing data..')
# Data loading code
valdir = os.path.join(args.datadir, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform_test = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])
#imagenet
testset = datasets.ImageFolder(valdir, transform_test)
num_classes=1000
testloader = torch.utils.data.DataLoader(testset, batch_size=1000, shuffle=False, pin_memory=True, num_workers=30)
use_cuda = torch.cuda.is_available()
print('Using input path: %s' % args.inputdir)
checkpoint = torch.load(args.inputdir)
init_net = checkpoint['net']
net=init_net.to('cpu')
label_refinery=torch.load('./resnet50.t7')
net = ModelRefineryWrapper(net, label_refinery)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
net = nn.DataParallel(net)
net=net.to(device)
criterion = RefineryLoss()
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k)
return res
acc1,acc5,loss=test()
print('top-1 accuracy: {0:.3f}%, top-5 accuracy: {1:.3f}%'.format(acc1,acc5))
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201,
198
] | 2.402585 | 1,083 |
"""Model Parameters Module."""
import torch.optim as optim
from .search import SamplingSearch, GreedySearch, BeamSearch
SEARCH_FACTORY = {
'sampling': SamplingSearch,
'greedy': GreedySearch,
'beam': BeamSearch,
}
OPTIMIZER_FACTORY = {
'adadelta': optim.Adadelta,
'adagrad': optim.Adagrad,
'adam': optim.Adam,
'adamax': optim.Adamax,
'rmsprop': optim.RMSprop,
'sgd': optim.SGD
}
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] | 2.404624 | 173 |
#
# Copyright (c) 2021 The GPflux Contributors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import abc
import numpy as np
import pytest
import tensorflow as tf
import tensorflow_probability as tfp
from gpflow.kullback_leiblers import gauss_kl
from gpflux.encoders import DirectlyParameterizedNormalDiag
from gpflux.layers import LatentVariableLayer, LayerWithObservations, TrackableLayer
tf.keras.backend.set_floatx("float64")
############
# Utilities
############
def _zero_one_normal_prior(w_dim):
""" N(0, I) prior """
return tfp.distributions.MultivariateNormalDiag(loc=np.zeros(w_dim), scale_diag=np.ones(w_dim))
############
# Tests
############
class ArrayMatcher:
def __init__(self, expected):
self.expected = expected
def test_no_tensorflow_metaclass_overwritten():
"""
LayerWithObservations is a subclass of tf.keras.layers.Layer (via TrackableLayer);
this test ensures that TrackableLayer does not have a metaclass, and hence by adding
the ABCMeta to LayerWithObservations we are not accidentally removing some required
TensorFlow magic metaclass.
"""
assert LayerWithObservations.__bases__ == (TrackableLayer,)
assert type(TrackableLayer) is type
assert type(LayerWithObservations) is abc.ABCMeta
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] | 3.170213 | 564 |
#!/usr/bin/env python
#
from actingweb import actor
from actingweb import config
from actingweb import trust
from actingweb import auth
import webapp2
import os
from google.appengine.ext.webapp import template
import json
import logging
import datetime
import time
# /trust handlers
#
# GET /trust with query parameters (relationship, type, and peerid) to retrieve trust relationships (auth: only creator and admins allowed)
# POST /trust with json body to initiate a trust relationship between this
# actor and another (reciprocal relationship) (auth: only creator and admins allowed)
# POST /trust/{relationship} with json body to create new trust
# relationship (see config.py for default relationship and auto-accept, no
# auth required)
# GET /trust/{relationship}}/{actorid} to get details on a specific relationship (auth: creator, admin, or peer secret)
# POST /trust/{relationship}}/{actorid} to send information to a peer about changes in the relationship
# PUT /trust/{relationship}}/{actorid} with a json body to change details on a relationship (baseuri, secret, desc) (auth: creator,
# admin, or peer secret)
# DELETE /trust/{relationship}}/{actorid} to delete a relationship (with
# ?peer=true if the delete is from the peer) (auth: creator, admin, or
# peer secret)
# Handling requests to trust/
# Handling requests to /trust/*, e.g. /trust/friend
# Handling requests to specific relationships, e.g. /trust/friend/12f2ae53bd
application = webapp2.WSGIApplication([
webapp2.Route(r'/<id>/trust<:/?>', rootHandler, name='rootHandler'),
webapp2.Route(r'/<id>/trust/<relationship><:/?>',
relationshipHandler, name='relationshipHandler'),
webapp2.Route(r'/<id>/trust/<relationship>/<peerid><:/?>', trustHandler, name='trustHandler'),
], debug=True)
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] | 3.332721 | 544 |
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