Saravanan666/pypi-org huggingface dataset:
Package: cloudprinting
A simple interface to Google Cloud Print.Usage:>>> from cloudprinting import *
>>> auth = OAuth2(access_token="qwertyuiopasdfghjkl", token_token="Bearer")
>>> r = list_jobs(auth=auth)
>>> r['jobs']
[{"id": ...}, ...]
>>> r = submit_job(printer="0e506d12-dbe0-54d3-7392-fd69d45ff2fc", content="test.pdf", auth=auth)
>>> r['job']
{"id": "abcdefgh", ...}
>>> delete_job("abcdefgh", auth=auth)Supports both Python 2 and 3:≥ Python 2.6≥ Python 3.2InstallUse pip to install the latest version from PyPI:pip install cloudprintingCommand line interfaceThe module can be used from the command line via:python -m cloudprinting ...See--helpfor details.TestsThe test suite requires three environment variables:CP_CLIENT_ID– application “client id” (Google API)CP_CLIENT_SECRET– application “client secret” (Google API)CP_REFRESH_TOKEN– refresh token for an authorised Google AccountCP_PRINTER_ID– printer ID (optional, default:__google__docs)During the tests a PDF is printed. By default the Google Docs printer is used,
however it is more forgiving than typical printers, so I don’t recommend using
it.Example:CP_CLIENT_ID=1234567890.apps.googleusercontent.com \
CP_CLIENT_SECRET=asdfghjklzxcvbnmqwertyuiop \
CP_REFESH_TOKEN=mnbvcxzlkjhgfdspoiuytr \
CP_PRINTER_ID=0e50ed12-dbe0-54d3-a4bd-fdf9d45ff2fc \
toxChange log0.3.2Fix bug in auto-refreshing of OAuth2Remove expiration time recording from OAuth20.3.1Make OAuth2 arguments all optionalFix MANIFEST.inFix README bugs0.3.0AddOAuth2authenticationClientLoginAuthonly available on Python 2.xBy default sendcapabilities(even if it’s[{}]). This fixes some
issuesPrint jobs now default to using the filename as the title (no more automatic
inclusion of datetime)Added a crude command line interface0.2.0MakeClientLoginAuthcache authentication token0.1.0Initial version
Package: cloudprogress
cpr-pyThis is the python client for the Cloud Progress rest API.Cloud Progressis a hosted developer tool that provides progress bars with ETA for structured tasks.The swagger / openapi spec for this api is hostedas a browseable redoc pageand in our API repohttps://github.com/cloudprogress/cpr-api. The API repo has links to clients in other languages.This package targets python 3.7+ to get dataclass support -- if your app targets an older version, post a github issue and we'll make it happen.Installationpipinstall...# or if you like to live on the edge:pipinstallhttps://github.com/cloudprogress/cpr-py/archive/master.zipExampleCheck outpizza.pyin this repo 🍕 (it's even runnable).Ops concernsTimeoutsError handling
Package: cloudproof-anonymization
Data AnonymizationData anonymizationis the process of transforming data in such a way that it can no longer be used to identify individuals without the use of additional information. This is often done to protect the privacy of individuals whose data is being collected or processed.Anonymization techniques can include removing identifying information such as names and addresses, replacing identifying information with pseudonyms, and aggregating data so that individual data points cannot be distinguished. It's important to note that while anonymization can reduce the risk of re-identification, it is not foolproof and must be used in conjunction with other security measures to fully protect personal data.FeaturesCosmian anonymization provides multiple methods:Hashing: transforms data into a fixed-length representation that is difficult to reverse and provides a high level of anonymity. Useanonymization::Hasherto apply the various hash functions.Noise Addition: adds random noise to data in order to preserve privacy. Useanonymization::NoiseGeneratorto apply various types of noise distributions tofloat,integer, anddate.Word Masking: hides sensitive words in a text. Useanonymization::WordMaskerto mask a list of words.Word Tokenization: removes sensitive words from text by replacing them with tokens. Useanonymization::WordTokenizerto replace a list of words.Word Pattern Masking: replaces a sensitive pattern in text with specific characters or strings. Useanonymization::WordPatternMaskerto replace specified pattern regex with a replacement string.Number Aggregation: rounds numbers to a desired power of ten. This method is used to reduce the granularity of data and prevent re-identification of individuals. Useanonymization::NumberAggregatorto roundfloatandintvalues.Date Aggregation: rounds dates based on the specified time unit. This helps to preserve the general time frame of the original data while removing specific details that could potentially identify individuals. Useanonymization::DateAggregatorto rounddate.Number Scaling: scales numerical data by a specified factor. This can be useful for anonymizing data while preserving its relative proportions. Useanonymization::NumberScalerto roundfloatandintvalues.Date FormatWARNING: The anonymization functions date input is in RFC3339 string format which is slightly different from ISO format.ISO formatRFC 33392023-04-07T12:34:562023-04-07T12:34:56Z2023-04-27T16:23:00+00:002023-04-27T16:23:00+00:002023-04-27T16:23:00+05:002023-04-27T16:23:00+05:002023-04-27T16:23:00-05:002023-04-27T16:23:00-05:00
Package: cloudproof-py
Cloudproof Python LibraryThe library provides a Python API to theCloudproof Encryptionproduct of theCosmian Ubiquitous Encryption platform.LicensingCryptographic primitivesGetting startedDemoBuilding and testingVersions CorrespondenceLicensingThe library is available under a dual licensing scheme Affero GPL/v3 and commercial. SeeLICENSE.mdfor details.Cryptographic primitivesThe library is based on:Covercryptalgorithm which allows
creating ciphertexts for a set of attributes and issuing user keys with access
policies over these attributes.Covercryptoffers Post-Quantum resistance.Findexwhich is a cryptographic protocol designed to securely make search queries on
an untrusted cloud server. Thanks to its encrypted indexes, large databases canGetting startedThis library requiresPython >= 3.7.To install the current release:pipinstallcloudproof_pyCode examples are available in./examplesto get you started.
Pleasecheck the online documentationfor more details on using the CloudProof APIs.DemoAn interactive CLI demo combining policy-based encryption with searchable keywords.Users data from./examples/cli_demo/data.jsonare encrypted using Covercrypt and indexed via Findex.Try the demo:examples/cli_demo/run_demo.shBuilding and testingTo build from source:scripts/build.shTo build and run the tests:scripts/build.sh-tWith PyO3 packages frompackage.cosmian.com:scripts/build.sh-itNote: editscripts/ci_install_pyo3_builds.shwith the right build name.To build the documentation:scripts/build.sh-dThe generated documentation will be in./docs/_build/html.Versions CorrespondenceThis library depends onCovercryptandFindex.This table shows the minimum version correspondence between the various components.cloudproof_pyCovercryptFindexKMS>=4.2.013.0.06.0.04.10.0>=4.1.012.0.05.0.04.5.0>=4.0.211.0.04.0.14.4.3>=4.0.111.0.04.0.14.4.2>=4.0.011.0.04.0.0,4.0.14.3.3>=3.0.011.0.03.0.04.3.3>=2.0.010.0.02.0.1,2.1.04.2.0>=1.0.08.0.12.0.0-
Package: cloudpss
CloudPSS SDKCloudPSS SDK是基于CloudPSS-API封装的模型及软件开发套件。用户可通过编写Python、Matlab等脚本构建自定义模型，或是调用CloudPSS平台中的模型修改、仿真计算功能，实现诸如自动修改模型、批量仿真计算、自动化生成报告等复杂且繁琐的功能。用户也可在其自己的应用程序中调用CloudPSS仿真引擎，实现仿真驱动的高级分析应用。CloudPSS SDK包含模型层、算法层和应用层三种开发套件，其中：模型层开发套件帮助用户在CloudPSS SimStudio官方潮流计算、电磁暂态仿真、移频电磁暂态仿真、综合能源能量流计算等内核中开发第三方模型或用户自定义模型。目前，模型层SDK已开放基于Matlab函数的自定义控制元件接入，后续将进一步开放Python、C/C++的标准元件开发套件。算法层开发套件帮助用户在CloudPSS FuncStudio中集成自己的算法内核，从而借助CloudPSS XStudio平台快速开发并部署自己的计算应用。该部分内容将在年内发布，敬请期待。应用层开发套件帮助用户在利用脚本的形式快速调用CloudPSS官方计算内核和第三方接入的计算内核，从而方便用户开发高级计算分析应用。其中，SimStudio-SDK现已支持SimStudio中的模型修改和潮流计算和电磁暂态仿真两种计算内核。下载与安装pip install cloudpss需要升级的话，执行下面的命令:pip install --upgrade cloudpss完全卸载执行下面的命令进行完全卸载:pip uninstall cloudpss
Package: cloudpss-hydrogen
CloudPSS SDKCloudPSS SDK是基于CloudPSS-API封装的模型及软件开发套件。用户可通过编写Python、Matlab等脚本构建自定义模型，或是调用CloudPSS平台中的模型修改、仿真计算功能，实现诸如自动修改模型、批量仿真计算、自动化生成报告等复杂且繁琐的功能。用户也可在其自己的应用程序中调用CloudPSS仿真引擎，实现仿真驱动的高级分析应用。CloudPSS SDK包含模型层、算法层和应用层三种开发套件，其中：模型层开发套件帮助用户在CloudPSS SimStudio官方潮流计算、电磁暂态仿真、移频电磁暂态仿真、综合能源能量流计算等内核中开发第三方模型或用户自定义模型。目前，模型层SDK已开放基于Matlab函数的自定义控制元件接入，后续将进一步开放Python、C/C++的标准元件开发套件。算法层开发套件帮助用户在CloudPSS FuncStudio中集成自己的算法内核，从而借助CloudPSS XStudio平台快速开发并部署自己的计算应用。该部分内容将在年内发布，敬请期待。应用层开发套件帮助用户在利用脚本的形式快速调用CloudPSS官方计算内核和第三方接入的计算内核，从而方便用户开发高级计算分析应用。其中，SimStudio-SDK现已支持SimStudio中的模型修改和潮流计算和电磁暂态仿真两种计算内核。下载与安装pip install cloudpss需要升级的话，执行下面的命令:pip install --upgrade cloudpss完全卸载执行下面的命令进行完全卸载:pip uninstall cloudpss
Package: cloud-push-client
Cloud-Push-Notifications Python ClientThis repository contains a simple Python client for the Bluemix/IBM push notification serviceInstallationpipinstallcloud-push-clientGetting StartedLog into IBM/Bluemix cloud and retrieve api_key, app_id and client_secret from your push serviceimportcloudpush.Clientascppush_client=cp.Client("api_key","app_id","client_secret")Methodsdevicesregister_device(token,platform,device_id=None)delete_device(device_id)get_device(device_id=None,token=None)get_devices()get_device_stats()topicsregister_topic(name,description=None)delete_topic(name)subscriptionssubscribe(topic,device_id)unsubscribe(self,topic,device_id)get_subscriptions(topic,device_id=None)push messagesget_push_stats()get_messages(offset=0)get_message(message_id)get_message_status(message_id)get_message_delivery_status(message_id,device_id="")send_message(alert,url=None,platforms=["A","G"],payload="",device_ids=None,topics=None,badge=1,ttl=3600,type='DEFAULT',title=None,subtitle=None,validate=True)
Package: cloudquery
Cloudquery: SQL interface to your cloud resourcescloudqueryallows you to perform live queries on your cloud resources, and use the power of SQL to combine and filter across multiple types of services and resources.For example, you can find virtual machine instances and their image IDs that have a port open to the public Internet, for all regions of a cloud provider.This project is inspired byosquery. While the goal is multi-cloud support across main services for each cloud provider, it currently supports only AWS for a few services.
Package: cloudrail-knowledge
Cloudrail KnowledgeCloudrailis a tool for doing security analysis of infrastructure-as-code before
its deployment. For example Cloudrail can inspect Terraform plans and identify configurations
that violate company policy and best practices, and stop the CI pipeline accordingly.This repository contains the rules that Cloudrail runs to conduct this analysis, as well as the context model the rules
evaluate against. You can use this repository for a few purposes:Review the rules Cloudrail has and how they work.Propose additions/changes to rules (just open a PR).Build your own custom rules using the same context model existing rules use (for examples seecloudrail-sample-custom-rules)Want to understand how Cloudrail's knowledge works? Our documentation is available athttps://knowledge.docs.cloudrail.app/.ContributingWe welcome all contributions. Simply open an issue and a PR with your additions or changes. Some requirements:Branch names should be<ticket-id>_<what_it's_trying_to_solve>. Such asissue_40_add_docdb_encryption_ruleor40_add_docdb_encryption_rule.Any rule must have tests, see thetestsdirectory on how these are built.ReleasesThis repository has frequent releases. Those with "beta" or "b" in their name are considered still in development,
not yet included in the production Cloudrail code (running in the Cloudrail SaaS). The latest non-beta release is
the one currently running within Cloudrail's production service.
Package: cloud-ranges
cloud-rangesDiscover if IP belongs to cloud providers.ExampleHere there are some usage examples:$ echo 20.236.44.162 | cloud-ranges
20.236.44.162 azure westus2 20.236.0.0/18$ echo 20.236.44.162 | cloud-ranges -j
{"cloud": "azure", "services": [], "region": "westus2", "network": "20.236.0.0/18", "target": "20.236.44.162"}Supported Cloud ProvidersAWSGCPAzureIBMDigitalOceanIBM CloudInstallationpip install cloud-ranges
Package: cloudreactor-api-client
cloudreactor-python-api-clientPython client for the CloudReactor APIOverviewThis python package allows python applications to programmatically
create, monitor, and manage Tasks and Workflows inCloudReactor. Most
notably, you can start and stop Tasks and Workflows by creating Task
Executions and Workflow Executions.See theCloudReactor landing pageto see the
benefits of monitoring and managing your tasks with CloudReactor.InstallationGet this package from PyPI:pipinstallcloudreactor_api_clientUsageFirst, create a client:fromcloudreactor_api_clientimportAuthenticatedClientclient=AuthenticatedClient(base_url="https://api.cloudreactor.io/api/v1",token="YOUR_API_KEY")To start a Task, create a Task Execution:fromcloudreactor_api_client.api.task_executionsimport(task_executions_create)fromcloudreactor_api_client.modelsimport(TaskExecution,TaskExecutionStatus)# Identify the Task by name. Alternatively, you can specifiy the "uuid".task_dict={"name":"HappyTask"}task_execution=TaskExecution.from_dict({"task":task_dict,"status":TaskExecutionStatus.MANUALLY_STARTED})response=task_executions_create.sync_detailed(client=client,json_body=task_execution)parsed_task_execution=response.parsedprint(f"Task Execution{parsed_task_execution.uuid}started!")More details on how to use API clients in general (async mode, disabling SSL)
can be found in the generatedREADME.LicenseThis software is licensed under the BSD 2-Clause License.
SeeLICENSEfor details.Contributors ✨Thanks goes to these wonderful people (emoji key):Jeff Tsay💻📖🚇🚧This project follows theall-contributorsspecification. Contributions of any kind welcome!CreditsCode generated byopenapi-python-client
Package: cloudrender
cloudrender: an OpenGL framework for pointcloud and mesh renderingA visualization framework capable of rendering large pointclouds, dynamic SMPL models and more. Used to visualize results in our Human POSEitioning System (HPS) project:http://virtualhumans.mpi-inf.mpg.de/hps/RequirementsGPU with OpenGL 4.0Optionally, if you want to run included test script:EGL support (for headless rendering)ffmpeg>=2.1 with libx264 enabled and ffprobe installed (for saving to video)InstallationStep 1. Get the codeCopy the code without installationgitclonehttps://github.com/vguzov/cloudrender
pipinstall-rrequirements.txtor install as a package withpip install git+https://github.com/vguzov/cloudrender.gitStep 2. Get the SMPL modelFollow install instructions athttps://github.com/gulvarol/smplpytorchMake sure to fix the typo for male model while unpacking SMPL .pkl files:basicmodel_m_lbs_10_207_0_v1.0.0.pkl -> basicModel_m_lbs_10_207_0_v1.0.0.pklRunning test scripttest_scene_video.pyRundownload_test_assets.sh– it will createtest_assetsfolder and download everything you need for sample to work
(3D scan pointcloud, human shape and motion files, camera trajectory file)Runtest_scene_video.pyThe following script will write a short video insidetest_assets/output.mp4which should look similar to this:More dataPlease check our HPS project page for more 3D scans and motion data:http://virtualhumans.mpi-inf.mpg.de/hps/CitationIf you find the code or data useful, please cite:@inproceedings{HPS,
    title = {Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors },
    author = {Guzov, Vladimir and Mir, Aymen and Sattler, Torsten and Pons-Moll, Gerard},
    booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {jun},
    organization = {{IEEE}},
    year = {2021},
}
Package: cloudreports
cloudreports-pythonОфициальная библиотека проектаhttps://cloudreports.ruОглавлениеЧто умеетУстановкаБыстрый стартРазработка публичной интеграцииЧто умеетПомогает быстро загрузить в Google BigQuery или ClickHouse данные из любого API в формате JSON и подготовить их для анализа в виде таблиц.Установкаpip install cloudreportsБыстрый стартПример загрузки данных  с api.nasa.govРазработка публичной интеграцииВы уже создали собственную интеграцию с API облачного сервиса с помощью нашей библиотеки?
Давайте предоставим ее остальным пользователямCloudReports.
Если интеграция понравиться пользователям, вы сможете зарабатывать на каждой ее установке, а также на разработке отчетов и консультациях.
Напишите нам вtelegramи будем развивать CloudReports вместе.
Package: cloudreveimport
cloudreveimportImport your existing files intoCloudreve!一键将外部文件导入Cloudreve中。InstallBuild executable filegitclonehttps://github.com/yindaheng98/cloudreveimportcdcloudreveimport
gobuildThen you shall get an executable filecloudreveimport, you can try it:./cloudreveimport-h
UsageofD:\Documents\MyPrograms\cloudreveimport\cloudreveimport.exe:-cstringPathtotheconfigfile.(default"D:\\Documents\\MyPrograms\\cloudreveimport\\conf.ini")-mstringAfoldertobeimported.-tstringImportthefoldertowhichfolderincloudreve.-ustringEmailofthetargetuser.(default"admin@cloudreve.org")UsageCommand lineJust run it in the device where the Cloudreve is installed:Assume your Cloudreve config file is/root/conf.ini, and you want to import a folder/mnt/galleryinto the userme@cloudreve.org'smy/galleryfolder in Cloudreve.
Just run:./cloudreveimport-c"/root/conf.ini"-u"me@cloudreve.org"-m"/mnt/gallery"-t"my/gallery"Then openme@cloudreve.org'smy/galleryfolder in Cloudreve, and you will see those files in/mnt/galleryhere.Note: 上述文件操作是直接对数据库进行修改，会导致用户已用空间与实际不符，所以在完成文件的导入或文件大小修改后需要执行数据库脚本校准用户容量。Python使用Python，你的导入过程可以更加灵活。
比如，在有大量文件需要定期导入而你的存储设备列出文件列表的速度较慢时，一次导入可能会花费很长时间；
或者，在自动定期导入时不希望目录中的某些文件被导入。
这时，你可以编写自己的Python程序选择要导入哪些文件、删除哪些文件，还能修改文件或文件夹的创建和修改时间。This project also provides a Python package for advanced usage:pipinstallcloudreveimportYou can write your scripts in Python:importtimefromcloudreveimportimportInvokerinvoker=Invoker(execuable="./cloudreveimport",config="/root/conf.ini",email="me@cloudreve.org")invoker.start()invoker.import_file(source_name="path to the file on your disk",dst_path=["path","you","want","to","import","to","in","cloudreve"],size=100,# filesize in Bytes)invoker.update_file_stat(dst_path=["path","to","file","in","cloudreve"],ctime=int(time.time()),# created timemtime=int(time.time()),# updated timesize=100,# filesize in Bytes)invoker.delete_file(dst_path=["path","to","file","in","cloudreve"],)invoker.update_folder_time(dst_path=["path","to","folder","in","cloudreve"],ctime=int(time.time()),# created timemtime=int(time.time()),# updated time)
Package: cloud-run-compose
cloud-run-composeDeploy a stack of services to Cloud Rus using the docker-compose syntax.Creates a terraform plan based on the docker-compose configuration.Missing docker-compose featuresservice discoveryvolumesnon https trafficInstallpip3 install cloud-run-composeRequirementsterraformin PATHdocker-composein PATHa google cloud dervice account .json fileUsageDownload a service account json from the console, with the cloud run persmissions, place it in./account.jsonUse the followingdocker-compose.ymlfileversion: '3'

services:
    example-cloudrun-compose:
        image: gcr.io/cloudrun/hello
        expose:
            - 8080
        environment:
            URL_0: 'http://mongoke/'
            URL_1: 'http://server'
    example-cloudrun-compose2:
        image: gcr.io/cloudrun/hello
        expose:
            - 8080Run the following command to deploy the servicescompose --project {your-project} --credentials ./account.jsonThe command will generate amain.tffile in the working directory, to deploy the plan runterraform applyThe services will be deployed in google cloud run and will be available at url likehttps://{service-name}-zakzcx4zxq-uc.a.run.appBuild and pushIf you want to also build and push the services you can use the--buildflag, it willbuild the service using the docker-compose cachepush the imagecompose --build --project {your-project} --credentials ./account.jsonTODOadd private option inlabelsonly support remote bucket usageadd constraints of concurrency, memory, cpu, maxScale via labels
Package: cloudRunStayAwake
Cloud Run Stay AwakePython implementation of the cold start mitigation proposed by@guillaumeblaquiere(already implemented inGo&NodeJS)PurposeA Cloud Run GCP service stop after an idle period, this time cannot be forseen.
As a result, the next request after the stop, will have acold start, a delay of one or several seconds, Cloud Run takes to start a new container.
One of the cheap solutions proposed by Guillaume (https://medium.com/google-cloud/3-solutions-to-mitigate-the-cold-starts-on-cloud-run-8c60f0ae7894) is to schedule an http call every minute to keep alive the service. However this solution is not accurate as the stop can occurs in less than a minute after the last request. And Cloud Scheduling can't go under a minute.Another solutions is presented to trigger the stop signal (a SIGTERM signal) to self call the service to try to keep it alive. This is what this code is about.How to UseEasy steps:pip install cloudRunStayAwakeIn the main file of your code (Flask, Django, other...), addimport cloudRunStayAwakeTestsIn case you want to perform some tests before deploying to your code, there is a Dockerfile that can be used to test it on Cloud RunLicenseThis library is licensed under Apache 2.0. Full license text is available inLICENSE.
Package: clouds2env
A tool to convert from an OpenStackclouds.yamlfile to environment
variables. This can be useful when all you have is aclouds.yamlfile but
you need to work with a legacy tool likenovaclient.Likeopenstacksdk,clouds2envwill look in the following location forclouds.yamlfiles:.(the current directory)$HOME/.config/openstack/etc/openstackThe first file found wins.You can also set the environment variableOS_CLIENT_CONFIG_FILEto an
absolute path of a file to look for and that location will be inserted at the
front of the file search list.You can specify the cloud to generate environment variables for by setting theOS_CLOUDenvironment variable. Alternatively, you may provide the cloud
name as an argument to tool.Usage$clouds2env--helpusage:clouds2env[-h][--set|--unset][cloud]AtooltoconvertfromanOpenStack'clouds.yaml'fileto'OS_'-prefixedenvironmentvariablessuitableforusewithlegacytoolinglikenovaclient.positionalarguments:cloudthecloudtouseoptions:-h,--helpshowthishelpmessageandexit--setsetenvironmentvariables(default)--unsetunsetenvironmentvariablesExample$exportOS_CLOUD=devstack-admin$clouds2envexportOS_AUTH_URL=http://10.0.108.84/identityexportOS_PASSWORD=passwordexportOS_PROJECT_DOMAIN_ID=defaultexportOS_PROJECT_NAME=adminexportOS_USER_DOMAIN_ID=defaultexportOS_USERNAME=adminexportOS_IDENTITY_API_VERSION=3exportOS_REGION_NAME=RegionOneexportOS_VOLUME_API_VERSION=3$eval$(clouds2env)MIT LicenseCopyright (c) 2022 Stephen Finucane <stephen@that.guru>Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.