Saravanan666/pypi-org huggingface dataset:
Package: fastMONAI
OverviewA low-code Python-based open source deep learning library built on top offastai,MONAI,TorchIO, andImagedata.fastMONAI simplifies the use of state-of-the-art deep learning
techniques in 3D medical image analysis for solving classification,
regression, and segmentation tasks. fastMONAI provides the users with
functionalities to step through data loading, preprocessing, training,
and result interpretations.Note:This documentation is also available as interactive
notebooks.InstallingFromPyPIpip install fastMONAIFromGitHubIf you want to install an editable version of fastMONAI run:git clone https://github.com/MMIV-ML/fastMONAIpip install -e 'fastMONAI[dev]'Getting startedThe best way to get started using fastMONAI is to read ourpaperand dive into our beginner-friendlyvideo tutorial. For a deeper understanding and hands-on experience, our comprehensive instructional notebooks will walk you through model training for various tasks like classification, regression, and segmentation. See the docs athttps://fastmonai.nofor more information.Notebook1-Click Notebook10a_tutorial_classification.ipynbshows how to construct a binary classification model based on MRI data.10b_tutorial_regression.ipynbshows how to construct a model to predict the age of a subject from MRI scans (“brain age”).10c_tutorial_binary_segmentation.ipynbshows how to do binary segmentation (extract the left atrium from monomodal cardiac MRI).10d_tutorial_multiclass_segmentation.ipynbshows how to perform segmentation from multimodal MRI (brain tumor segmentation).How to contributeSeeCONTRIBUTING.mdCiting fastMONAIIf you are using fastMONAI in your research, please use the following citation:@article{KALIYUGARASAN2023100583,
title = {fastMONAI: A low-code deep learning library for medical image analysis},
journal = {Software Impacts},
pages = {100583},
year = {2023},
issn = {2665-9638},
doi = {https://doi.org/10.1016/j.simpa.2023.100583},
url = {https://www.sciencedirect.com/science/article/pii/S2665963823001203},
author = {Satheshkumar Kaliyugarasan and Alexander S. Lundervold},
keywords = {Deep learning, Medical imaging, Radiology},
abstract = {We introduce fastMONAI, an open-source Python-based deep learning library for 3D medical imaging. Drawing upon the strengths of fastai, MONAI, and TorchIO, fastMONAI simplifies the use of advanced techniques for tasks like classification, regression, and segmentation. The library's design addresses domain-specific demands while promoting best practices, facilitating efficient model development. It offers newcomers an easier entry into the field while keeping the option to make advanced, lower-level customizations if needed. This paper describes the library's design, impact, limitations, and plans for future work.}
}
Package: fast-mosestokenizer
fast-mosestokenizeris a C++ implementation of the moses tokenizer
which is a favourite among the folks in NLP research.The reason for using this package over the original perl implementation is
for the purpose of portability.
With the C++ source code, you can use this library basically in every language.The C++ script was adapted from the mosesdecoder repositorycontrib/c++tokenizer.BenchmarkInstallationUsage (Command-line tool)Usage (Python)Benchmarkfast-mosestokenizeris also fast.
On english, it is about 6x faster thantokenizer.perland 15x faster thansacremoses.see./bench/README.mdfor more information.InstallationPython users usinglinuxandosx>=10.15can install directly from PyPI.pipinstallfast-mosestokenizerSee./INSTALL.mdfor more information.Usage (Command-line tool)# Piping is the standard way to configure input and output stream.# mosestokenizer would apply tokenization to each line of the input stream.mosestokenizeren<infile>outfile# For a full list of options, refer to the help message.mosestokenizer-hUsage (Python)# Usage patterns are mostly the same as sacremoses.>>>frommosestokenizerimportMosesTokenizer>>>tokenizer=MosesTokenizer('en')>>>tokenizer.tokenize("""The English name of Singapore is an anglicisation of the native Malay name forthe country, Singapura, which was in turn derived from the Sanskrit word forlion city (romanised: Siṃhapura; Brahmi: 𑀲𑀺𑀁𑀳𑀧𑀼𑀭; literally "lion city"; siṃhameans "lion", pura means "city" or "fortress").[8]""")['The','English','name','of','Singapore','is','an','anglicisation','of','the','native','Malay','name','for','the','country',',','Singapura',',','which','was','in','turn','derived','from','the','Sanskrit','word','for','lion','city','(','romanised',':','Siṃhapura',';','Brahmi',':','𑀲𑀺𑀁𑀳𑀧𑀼𑀭',';','literally','"','lion','city','"',';','siṃha','means','"','lion','"',',','pura','means','"','city','"','or','"','fortress','"',')','.','[','8',']']
Package: fastmtl
fastmtlMulti-task learning utilities for fastaiInstallpip install fastmtlUsageLossApply a loss function per model output and get weighted sum of them. For instance, if the first model output is for classification and the second model output is for regression,fromfastmtl.lossimportCombinedLossloss_func=CombinedLoss(CrossEntropyLossFlat(),MSELossFlat(),weight=[1.0,3.0])MetricApply metrics for each model output. For instance, if we have a model making classification and regression, we can evaluate each model output with relevant metrics. Assuming that model outputs a tuple of tensors for classification and regression, respectively:fromfastai.metricsimportF1Score,R2Scorefromfastmtl.metricimportmtl_metricsclf_f1_macro=F1Score(average='macro')clf_f1_macro.name='clf_f1(macro)'clf_f1_micro=F1Score(average='micro')clf_f1_micro.name='clf_f1(micro)'reg_r2=R2Score()reg_r2.name='reg_r2'# metrics for classification in the first list# metrics for regression in the second listmetrics=mtl_metrics([clf_f1_macro,clf_f1_micro],[reg_r2])learn=Learner(...metrics=metrics,)TutorialsVideo distortion detectionTODOSupport tabular learnerSupport fastai>=2.7
Package: fastmunk
Python package for fast munkres algorithm.InstallationpipinstallfastmunkGetting startedimportfastmunkmat=np.array([[5,9,1],[10,3,2],[8,7,4],],dtype=np.float64)m=fastmunk.FastMunk()m.compute(mat)Benchmarkcdexamples
pythonbenchmark.pyOutputFastMunk:0.02310633659362793[s]Munkres:0.5914878845214844[s]
Package: fastmvc
FastMVCFastMVC is a web framework for building Web Applications
with the MVC structure (Model - View - Controller) and effortlessly deploying them to cloud platforms.Model is interchangeable depending on the cloud platform you would like to use.View uses Jinja2 to create front end pagesController is written using FastAPIFastMVC CLIfastmvc new [PROJECT_NAME]Creates a new project. Will ask which platform to build towards (GOOGLE_APP_ENGINE, or DETA) and set up the base of the project accordingly.Optionally, you can pass the flag--platformand provide one of the options above.fastmvc scaffold [MODEL_NAME] [ATTRIBUTE]:[DATA_TYPE]Scaffold out a Model, View, and Controller for your object. For example:fastmvc scaffold post title:str content:wysiwyg draft:boolfastmvc authBuilds an Authentication Framework to easily integrate user sign in for your application.fastmvc sAlias foruvicorn main:app --reloadto run your application locallySupported Cloud PlatformsBuiltGoogle App Engine (using Firestore database)Deta (using DetaBase)Coming SoonAWS Elastic Beanstalk
Package: fastmysql
fastmysql介绍简单快速的使用mysql软件架构软件架构说明安装教程pip安装pip3installfastmysqlpip安装（使用淘宝镜像加速）pip3installfastmysql-ihttps://mirrors.aliyun.com/pypi/simple使用说明demoimportfastmysqlquery_res=fastmysql.query_table_all_data(db_name='test',tb_name='test')# 获取建表语句res=fastmysql.show_create_table(db_name='test',tb_name='test')防止sql注入：
写法cursor.execute('insert into user (name,password) value (?,?)',(name,password))
　　或者cursor.execute('insert into user (name,password) value (%s,%s)',(name,password))
　　%s与?都可以作为sql语句的占位符，它们作为占位符的功能是没有区别的，mysql.connector用 %s 作为占位符；pymysql用 ? 作为占位符。但是注意不要写成cursor.execute('insert into user (name,password) value (?,?)'%(name,password))
　　这种写法是直接将参数拼接到sql语句中，这样数据库就容易被sql注入攻击，比如cursor.execute('select * from user where user=%s and password=%s'%(name,password))
　　要是name和password都等于'a or 1=1'，那么这个语句会把整个user表都查询出来默认环境默认使用的环境文件为：mysql.env
Package: fast-nbs
Fast-nbsImport jupyter notebooks fastExampleSupposing we have a module directory like:- nbpackage
--  nbs
       other.ipynb
--  mynotebook.ipynbthen we can import the ipynb codes into current Python files like:importosimportfastnbs.visasnbvis# call root notebookfromnbpackageimportmynotebookmynotebook.show_name("Chen")# call sub directory notebookfromnbpackage.nbsimportotherother.say_hi('hi')# show notebook codesnbvis.show_notebook(os.path.join("nbpackage","nbs","other.ipynb"))
Package: fastNeuron
fastNeuronwhat is it ?this is a hobby project to build a small framework to create and deploy neural networks quickly...how it all started ?this is a project that i intended to make as an attempt to understand neural network and machine learning better...i am a firm believer that building complex topics and ideas from scratch gives you a solid ground and grasp of the concepts...installationMac / Linuxpip3 install fastNeuronWindowspip install fastNeuronWhat can you do with itCurrently you can deploy any feed-forward neural network with any size.You can choose what optimizer to use ,which activations functions ,which cost function...NOTE: i am by no means an expert so if you have any improvements or found any bugs in the code please reach out!!
Package: fastnml
A Python library to quickly read Fortran namelists.DescriptionThefastnmlcode only works with a specific subset of the namelist format. It is not nearly as general or robust asf90nml, but it is much faster when reading very large namelists. Also, both codes are tested using multiprocessing to read many namelists in parallel.InstallingInstall fromPyPIusing pip:pip install fastnmlInstall fromconda-forgeusing conda:conda install -c conda-forge fastnmlDocumentationThe API documentation for the currentmasterbranch can be foundhere. This is generated withpdoc3by runningpdoc --html fastnml --force.Dependenciesf90nml-- the more general libraryOther linksFastnml on PyPIfastnml-feedstock for conda-forge
Package: fastnode2vec
fastnode2vecReallyfast implementation of node2vec based onnumbaandgensim. Memory usage islinearand scales with your data unlike most other implementations. The algorithm is described in thisblog post.APINode2Vecinherits from gensim'sWord2Vec, all its APi is valid.fromfastnode2vecimportGraph,Node2Vecgraph=Graph([("a","b"),("b","c"),("c","a"),("a","d")],directed=False,weighted=False)# orgraph=Graph([("a","b",1),("b","c",2),("c","a",3),("a","d",4)],directed=False,weighted=True)n2v=Node2Vec(graph,dim=10,walk_length=100,context=10,p=2.0,q=0.5,workers=2)n2v.train(epochs=100)print(n2v.wv["a"])CLIUsage: fastnode2vec [OPTIONS] FILENAME

Options:
  --directed
  --weighted
  --dim INTEGER          [required]
  --p FLOAT
  --q FLOAT
  --walk-length INTEGER  [required]
  --context INTEGER
  --epochs INTEGER       [required]
  --workers INTEGER
  --batch-walks INTEGER
  --debug PATH
  --output PATH
  --help                 Show this message and exit.Compute embeddings of theGnutella peer-to-peer network:wget https://snap.stanford.edu/data/p2p-Gnutella08.txt.gz
fastnode2vec p2p-Gnutella08.txt.gz --dim 16 --walk-length 100 --epochs 10 --workers 2Load embeddings produced by the CLIJust use theWord2VecAPI.fromgensim.modelsimportKeyedVectorswv=KeyedVectors.load("p2p-Gnutella08.txt.gz.wv",mmap='r')CitingIf you have used this software in a scientific publication, please cite it using the following BibLaTeX code:@software{fastnode2vec,
  author       = {Louis Abraham},
  title        = {fastnode2vec},
  year         = 2020,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.3902632},
  url          = {https://github.com/louisabraham/fastnode2vec}
}
Package: fast-norbert
Fast NorbertFast Norbert is an optimized fork ofhttps://github.com/sigsep/norbert.PerformanceThis is time (in seconds) that the filtering process takes on a single core:Test caseOriginal NorbertFast Norbertsong 119.37.5song 227.510.9Norbert filterWiener filter is a very popular way of filtering multichannel audio for several applications, notably speech enhancement and source separation.This filtering method assumes you have some way of estimating power or magnitude spectrograms for all the audio sources (non-negative) composing a mixture. If you only have a model for sometargetsources, and not for the rest, you may usefast_norbert.residual_modelto let Norbert create a residual model for you.Given all source spectrograms and the mixture Time-Frequency representation, this repository can build and apply the filter that is appropriate for separation, by optimally exploiting multichannel information (like in stereo signals). This is done in an iterative procedure calledExpectation Maximization, where filtering and re-estimation of the parameters are iterated.From a beginner's perspective, all you need to do is often to callfast_norbert.wienerwith the mix and your spectrogram estimates. This should handle the rest.From a more expert perspective, you will find the different ingredients from the EM algorithm as functions in the modulefast_norbert.norbert.Installationpip install fast_norbertUsageAsssuming a complex spectrogramX, and a (magnitude) estimate of a target to be extracted from the spectrogram, performing the multichannel wiener filter is as simple as this:importfast_norbertx=stft(audio)v=model(x)y=fast_norbert.wiener(v,x)estimate=istft(y)AuthorsArtyom Palvelev (this repo)Antoine Liutkus, Fabian-Robert Stöter (original repo)LicenseMIT
Package: fast-nosql-manager
IntroduçãoEssa biblioteca foi criada como uma forma de abstração das operações mais simples
de um banco noSQL.InícioApós fazer a instalação com o pip install fast_nosql_manager
é necessário que você importe a classe correspondente ao banco
que deseja manipular.fromfast_nosql_managerimportMongoRepositoryAo importar a classe você pode instância-la ou
usa-la diretamente preenchendo os parâmetros necessários.db=MongoRepository(db_str_connection='mongodb://localhost:27017/',db_name='local')Métodoscreate_collectionmongo.create_collection(collection_name='teste')delete_collectionmongo.delete_collection(collection_name='teste')create_documentmongo.create_document(connection_name='teste',documents=[{'name':'Oscar'},{'name':'Oscar'}])delete_documentmongo.delete_document(collection_name='teste',where={'name':'Oscar'})select_allmongo.select_all(collection_name='teste')
Package: fastnotifier
fastnotifier介绍快速使用第三方机器人软件架构软件架构说明支持企业微信（fastnotifier.wechat）安装教程pip安装pipinstallfastnotifierpip安装（使用阿里镜像加速）pipinstallfastnotifier-ihttps://mirrors.aliyun.com/pypi/simple使用说明demoimportfastnotifierquery_res=fastnotifier.wechat.send_text(msg='test')envwebhook=https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=
at_ids=aaa,bbb
at_mobiles=aaa,bbb
Package: fast-oad
Future Aircraft Sizing Tool - Overall Aircraft DesignFAST-OAD is a framework for performing rapid Overall Aircraft Design.It proposes multi-disciplinary analysis and optimisation by relying on
theOpenMDAO framework.FAST-OAD allows easy switching between models for a same discipline, and
also adding/removing/developing models to match the need of your study.More details can be found in theofficial documentation.Important notice:Since version 1.3.0, FAST-OAD models for commercial transport aircraft have moved in
packageFAST-OAD-CS25. This package is installed along with
FAST-OAD, to keep backward compatibility.Keep in mind that any update of these models will now come through new releases of FAST-OAD-CS25.To get FAST-OAD without these models, you may installFAST-OAD-core.:warning: Upgrading from an earlier version than 1.3 may break thefastoadcommand line (no
impact on PythonAPI). Seethis issuefor details and fix.Want to try quickly?You can run FAST-OAD tutorialswithout installationusing ourBinder-hosted Jupyter notebooks.InstallPrerequisite:FAST-OAD needs at leastPython 3.8.0.It is recommended (but not required) to install FAST-OAD in a virtual
environment (conda,venv, ...)Once Python is installed, FAST-OAD can be installed using pip.Note: If your network uses a proxy, you may have to dosome
settingsfor pip to work correctlyYou can install the latest version with this command:$ pip install --upgrade fast-oador, if you want the minimum installation without the CS25-related models:$ pip install --upgrade fast-oad-core
Package: fast-oad-core
Future Aircraft Sizing Tool - Overall Aircraft DesignFAST-OAD is a framework for performing rapid Overall Aircraft Design.It proposes multi-disciplinary analysis and optimisation by relying on
theOpenMDAO framework.FAST-OAD allows easy switching between models for a same discipline, and
also adding/removing/developing models to match the need of your study.More details can be found in theofficial documentation.Important notice:Since version 1.3.0, FAST-OAD models for commercial transport aircraft have moved in
packageFAST-OAD-CS25. This package is installed along with
FAST-OAD, to keep backward compatibility.Keep in mind that any update of these models will now come through new releases of FAST-OAD-CS25.To get FAST-OAD without these models, you may installFAST-OAD-core.:warning: Upgrading from an earlier version than 1.3 may break thefastoadcommand line (no
impact on PythonAPI). Seethis issuefor details and fix.Want to try quickly?You can run FAST-OAD tutorialswithout installationusing ourBinder-hosted Jupyter notebooks.InstallPrerequisite:FAST-OAD needs at leastPython 3.8.0.It is recommended (but not required) to install FAST-OAD in a virtual
environment (conda,venv, ...)Once Python is installed, FAST-OAD can be installed using pip.Note: If your network uses a proxy, you may have to dosome
settingsfor pip to work correctlyYou can install the latest version with this command:$ pip install --upgrade fast-oador, if you want the minimum installation without the CS25-related models:$ pip install --upgrade fast-oad-core
Package: fast-oad-cs23
FAST-(OAD)-GA: Future Aircraft Sizing Tool - Overall Aircraft Design (General Aviation extension)FAST-(OAD)-GA is derived from FAST-OAD framework performing rapid Overall Aircraft Design.It proposes multi-disciplinary analysis and optimisation by relying on
theOpenMDAO framework.FAST-(OAD)-GA allows easy switching between models for a same discipline, and
also adding/removing disciplines to match the need of your study.Currently, FAST-(OAD)-GA is bundled with models for general aviation and conventional
propulsion (ICE propeller based). Other models will come soon, and you may create
your own models and use them instead of bundled ones.More details can be found in theofficial
documentation.Want to try quickly ?You can run FAST-OAD-GA notebooks number 1, 2 and 3 using ourBinder-hosted Jupyter notebooks. Just go to src/fastga/notebooks/tutorial after you click the link.As these Binder-hosted notebooks are not setup to work in a Windows environment, external applications such as OpenVSP and Xfoil can't be run. Therefore, notebook number 4 will not fully work.InstallPrerequisite:FAST-(OAD)-GA needs at leastPython 3.7.0.It is recommended (but not required) to install FAST-(OAD)-GA in a virtual
environment (conda,venv, ...).Once Python is installed, FAST-(OAD)-GA can be installed using pip, by doing the following:$ pip install fast-oad-cs23
Package: fast-oad-cs25
FAST-OAD CS-25/FAR-25-related modelsThis package is a plugin for FAST-OAD (version 1.3 or above). It contains models related to classic
commercial transport aircraft.Want to try quickly?You can run FAST-OAD-CS25 notebookswithout installationusing ourBinder-hosted Jupyter notebooks.InstallPrerequisite:FAST-OAD needs at leastPython 3.7.0.It is recommended (but not required) to do the install in a virtual
environment (conda,venv, ...)Once Python is installed, installation can be done using pip. FAST-OAD, the core software, will be
installed at the same time.Note: If your network uses a proxy, you may have to dosome
settingsfor pip to work correctlyYou can install the latest version (including the main software FAST-OAD) with this command:$ pip install --upgrade fast-oad-cs25
Package: fastobo
fastobo-pyFaultless AST for Open Biomedical Ontologies in Python.Overviewfastobois a Rust library implementing a
reliable parser for the OBO file format 1.4. This extension module exports
idiomatic Python bindings that can be used to load, edit and serialize ontologies
in the OBO format.InstallationIf your platform has no pre-built binaries available, you will need to have the Rust
compiler installed. See thedocumentation onrust-lang.orgto learn how to install Rust on your machine.Installation is then supported throughpip:$pipinstallfastobo--userUsageAnOboDocinstance can be instantiated from a path or from a binary file handle
using thefastobo.loadfunction, or from a string using thefastobo.loadsfunction.importfastoboobodoc=fastobo.load("../data/ms.obo")Loading from agzipfile is supported:importfastoboimportgzipgzdoc=fastobo.load(gzip.open("../data/cl.obo.gz"))Comments can be parsed but neither edited nor serialized, because of a limitation
withpyo3(the library used to generate the Python bindings). They are supported
in the Rust version offastobo.FeedbackFound a bug ? Have an enhancement request ? Head over to theGitHub issue trackerof the project if
you need to report or ask something. If you are filling in on a bug, please include as much
information as you can about the issue, and try to recreate the same bug in a simple, easily
reproducible situation.The following people have contributed to this project:Alex Henrie (@alexhenrie)Patrick Kalita (@pkalita-lbl)AboutThis project was developed byMartin Larraldeas part of a Master's Degree internship in theBBOP teamof theLawrence Berkeley National Laboratory, under the supervision ofChris Mungall. Cite this project as:Larralde M.Developing Python and Rust libraries to improve the ontology ecosystem[version 1; not peer reviewed].F1000Research 2019, 8(ISCB Comm J):1500 (poster)
(https://doi.org/10.7490/f1000research.1117405.1)
Package: fastocr
FastOCRFastOCR 是一款开源免费的提供在线 OCR 文字识别的桌面工具支持的 OCR API 后端百度 AI 文字识别服务https://ai.baidu.com/tech/ocr有道文字识别服务https://ai.youdao.com/product-ocr-print.s旷视 Face++ 文字识别服务 (Experimental)https://www.faceplusplus.com.cn/general-text-recognition/本地 OCR特性通用文字识别 / 多个在线 OCR API 支持多语言支持 / 百度高精度接口支持 / 百度识别文字搜索本地 OCR 后端自定义快捷动作支持操作系统Linux X11WindowsmacOS (Experimental)Linux Wayland (Experimental)支持的 Wayland 环境 (Linux)Gnome WaylandKDE WaylandSway安装Windows64 位预编译版，解压即可使用点击下载 ->Download解压到安装目录，执行 FastOCR.exemacOS理论上支持但无构建环境，打包过程类似 Windows 如下，生成的文件位于项目的 dist 目录下pipinstallpoetry
poetryupdate
poetryrunpyinstallerbuild.specArch Linux稳定版本https://aur.archlinux.org/packages/fastocr开发版本https://aur.archlinux.org/packages/fastocr-git从 AUR 下载脚本编译或使用你喜欢的 AUR 辅助工具yay-Sfastocr# 使用 yaypikaur-Sfastocr# 使用 pikaurNix/NixOS使用NixOS CN flakes或berberman flakes安装nixrungithub:berberman/flakes#fastocr通用 (PyPI)fastocr @ PyPIpipinstall--userfastocr# Linux 复制 desktop 文件到 ~/.local/share/applications通用 (手动安装)pythonsetup.pyinstall# Linux 复制 desktop 文件到 ~/.local/share/applications通用 (仅本地开发)自豪地使用Poetry进行开发poetryinstall使用方法fastocr# 运行fastocr--help# 查看帮助DBus (Linux only)Service name:io.github.brucezhang1993.FastOCRObject:/io/github/brucezhang1993/FastOCR方法ActionsArgumentsDescriptioncaptureToClipboard{seconds} {no_copy}执行 OCR 捕获到系统剪贴板或触发 dbus 信号seconds: 延迟执行单位为秒no_copy: 如果为 true 则仅触发 dbus 信号而不复制到系统剪贴板quitApp--退出应用信号SignalsArgumentsDescriptioncaptured{text}OCR 识别结果文本会触发此信号text: 文本识别结果贡献Pull requests are welcome.For major changes, please open an issue first to discuss what you would
like to change.LicenseLGPL3
Package: fastode
FastodeA toolset to help make python coding fasterINSTALLpipinstallfastode1. FastLogfromfastodeimportFastLogflog=FastLog(log_fp='log.txt',log_level='INFO')# These values can be selected for log_level:# 'CRITICAL','FATAL','ERROR','WARN','WARNING','INFO','DEBUG','NOTSET'flog.logger.debug("this is a debug")flog.logger.info("this is a info")flog.logger.warning("this is a warning")flog.logger.error("this is a error")flog.logger.fatal("this is a fatal")flog.logger.critical("this is a critical")2. FastXMLfromfastodeimportFastXMLdom=FastXML.parse_string("<root><A><B>b</B><C>c</C></A><A1 upper='A1'>a1</A1></root>")dic=FastXML.xml_to_json(dom)FastXML.get_token_tag_pairs_and_attrs(dom,['A'],['upper'])