Saravanan666/pypi-org huggingface dataset:
Package: adal
This library, ADAL for Python, will no longer receive new feature improvements. Instead, use the new libraryMSAL for Python.If you are starting a new project, you can get started with theMSAL Python docsfor details about the scenarios, usage, and relevant concepts.If your application is using the previous ADAL Python library, you can follow thismigration guideto update to MSAL Python.Existing applications relying on ADAL Python will continue to work.Microsoft Azure Active Directory Authentication Library (ADAL) for PythonmasterbranchdevbranchReference DocsGetting StartedDocsPython SamplesSupportFeedbackThe ADAL for Python library enables python applications to authenticate with Azure AD and get tokens to access Azure AD protected web resources.You can learn in detail about ADAL Python functionality and usage documented in theWiki.Installation and UsageYou can find the steps to install and basic usage of the library underADAL Basicspage in the Wiki.Samples and DocumentationWe provide a full suite ofPython sample applications on GitHubto help you get started with learning the Azure Identity system. This will include tutorials for native clients and web applications. We also provide full walkthroughs for authentication flows such as OAuth2, OpenID Connect and for calling APIs such as the Graph API.There are also somelightweight samples existing inside this repo.You can find the relevant samples by scenarios listed in thiswiki page for acquiring tokens using ADAL Python.The documents onAuth ScenariosandAuth protocolsare recommended reading.VersionsThis library followsSemantic Versioning.You can find the changes for each version underReleases.Community Help and SupportWe leverageStack Overflowto work with the community on supporting Azure Active Directory and its SDKs, including this one! We highly recommend you ask your questions on Stack Overflow (we're all on there!) Also browser existing issues to see if someone has had your question before.We recommend you use the "adal" tag so we can see it! Here is the latest Q&A on Stack Overflow for ADAL:https://stackoverflow.com/questions/tagged/adalSubmit FeedbackWe'd like your thoughts on this library. Please completethis short survey.Security ReportingIf you find a security issue with our libraries or services please report it tosecure@microsoft.comwith as much detail as possible. Your submission may be eligible for a bounty through theMicrosoft Bountyprogram. Please do not post security issues to GitHub Issues or any other public site. We will contact you shortly upon receiving the information. We encourage you to get notifications of when security incidents occur by visitingthis pageand subscribing to Security Advisory Alerts.ContributingAll code is licensed under the MIT license and we triage actively on GitHub. We enthusiastically welcome contributions and feedback. Please read thecontributing guidebefore starting.We Value and Adhere to the Microsoft Open Source Code of ConductThis project has adopted theMicrosoft Open Source Code of Conduct. For more information see theCode of Conduct FAQor contactopencode@microsoft.comwith any additional questions or comments.
Package: adamalib
Adamalibprovides a Python library/SDK for interacting withAdama.
It is designed to be used as a standalone library in the user’s local machine to develop Adama microservices.InstallationUsepip:pip install git+git://github.com/Arabidopsis-Information-Portal/adamalib.gitIt’ll be moved to PyPI as soon as it reaches some stability.As an alternative, seeusing adamalib in Dockerbelow.Using adamalib in DockerThis repository includes aDockerfileand adocker-compose.ymlfile, which allows a zero installation version ofadamalib.The only requirement isDockeranddocker-compose, most likely
already installed in your system.Then, clone this repository and executedocker-composeas follows:$gitclonehttps://github.com/Arabidopsis-Information-Portal/adamalib.git$cdadamalib$docker-composebuild$docker-composeup(a bug indocker-composerequires doing the stepsbuildandupseparately.
In the future, onlyupwill be necessary.)Navigate tohttp://localhost:8888and access theJupyternotebook
with passwordadamalib.  The notebookExample.ipynbcontains a
full example of use.  The notebookProvenance.ipynbcontains an example of
accessing provenance information from Python.Note: If you are running on a Mac withboot2docker, substitutelocalhostby
the output of:$boot2dockeripLicenseFree software: MIT license
Package: adamic
adamic PackageOverviewThis package contains the code to build a data dictionary when passed a Pandas dataframe.InstallationThis package is available onpip. To install, run the following from your preferred shell:pipinstalladamicUseAfter installing the package to your environment, import the package to your script, Jupyter notebook, or directly to thepython3command line.fromadamicimportadamicTo create your data dictionary, pass a Pandas dataframe to thecreate_data_dictionary()function:adamic.create_data_dictionary(sample_df)The package will prompt you to supply definitions for each variable in the dataset. HitEnterafter supplying definition or if you want to define the variable later after the output file has been created.Finally, you will be prompted to name your preferred file extension..csv,.json, and.xlsxare the available options.CreditI've borrowed ideas for this package (especially the method of adding definitions) fromthis Medium article.
Package: adam-lr-decay
Adam Layer-wise LR DecayInELECTRA,
which had been published by Stanford University and Google Brain,
they had used Layerwise LR Decay technique for the Adam optimizer to prevent Catastrophic forgetting of Pre-trained model.This repo contains the implementation of Layer-wise LR Decay for Adam, with new Optimizer API that had been proposed in TensorFlow 2.11.UsageInstallations:$pipinstalladam-lr-decay# this method does not install tensorflowFor CPU:$pipinstalladam-lr-decay[cpu]# this method installs tensorflow-cpu>=2.11For GPU:$pipinstalladam-lr-decay[gpu]# this method installs tensorflow>=2.11fromtensorflow.kerasimportlayers,modelsfromadam_lr_decayimportAdamLRDecay# ... prepare training data# model definitionmodel=models.Sequential([layers.Dense(3,input_shape=(2,),name='hidden_dense'),layers.Dense(1,name='output')])# optimizer definition with layerwise lr decayadam=AdamLRDecay(learning_rate=1e-3)adam.apply_layerwise_lr_decay(var_name_dicts={'hidden_dense':0.1,'output':0.})# this config decays the key layers by the value,# which is (lr * (1. - decay_rate))# compile the modelmodel.compile(optimizer=adam)# ... training loopIn officialELECTRA repo,
they have defined the decay rate in the code. The adapted version is as follows:importcollectionsfromadam_lr_decayimportAdamLRDecaydef_get_layer_lrs(layer_decay,n_layers):key_to_depths=collections.OrderedDict({'/embeddings/':0,'/embeddings_project/':0,'task_specific/':n_layers+2,})forlayerinrange(n_layers):key_to_depths['encoder/layer_'+str(layer)+'/']=layer+1return{key:1.-(layer_decay**(n_layers+2-depth))forkey,depthinkey_to_depths.items()}# ... ELECTRA model definitionadam=AdamLRDecay(learning_rate=1e-3)adam.apply_layerwise_lr_decay(var_name_dicts=_get_layer_lrs(0.9,8))# ... custom training loopThe generated decay rate must be looked like this.0.0means there is no decay and1.0means it is zero learning rate. (non-trainable){"/embeddings/":0.6513215599,"/embeddings_project/":0.6513215599,"task_specific/":0.0,"encoder/layer_0/":0.6125795109999999,"encoder/layer_1/":0.5695327899999999,"encoder/layer_2/":0.5217030999999999,"encoder/layer_3/":0.46855899999999995,"encoder/layer_4/":0.40950999999999993,"encoder/layer_5/":0.3439,"encoder/layer_6/":0.2709999999999999,"encoder/layer_7/":0.18999999999999995}Citation@article{clark2020electra,title={Electra: Pre-training text encoders as discriminators rather than generators},author={Clark, Kevin and Luong, Minh-Thang and Le, Quoc V and Manning, Christopher D},journal={arXiv preprint arXiv:2003.10555},year={2020}}
Package: adamod
﻿# AdaModAn optimizer which exerts adaptive momental upper bounds on individual learning rates to prevent them becoming undesirably lager than what the historical statistics suggest and avoid the non-convergence issue, thus to a better performance. Strong empirical results on many deep learning applications demonstrate the effectiveness of our proposed method especially on complex networks such as DenseNet and Transformer.<p align=’center’><img src=’img/Loss.bmp’ width=”100%”/></p>## InstallationAdaMod requires Python 3.6.0 or later.### Installing via pipThe preferred way to install AdaMod is viapipwith a virtual environment.
Just run`bash pip install adamod `in your Python environment and you are ready to go!### Using source codeAs AdaMod is a Python class with only 100+ lines, an alternative way is directly downloading
[adamod.py](./adamod/adamod.py) and copying it to your project.## UsageYou can use AdaMod just like any other PyTorch optimizers.`python3 optimizer =adamod.AdaMod(model.parameters(),lr=1e-3,beta3=0.999) `As described in the paper, AdaMod can smooths out unexpected large learning rates throughout the training process. Thebeta3parameter is the smoothing coefficient for actual learning rate, which controls the average range. In common cases, abeta3in{0.999,0.9999}can achieve relatively good and stable results. See the paper for more details.## DemosFor the full list of demos, please refer to [this page](./demos).## Contributors[@luoruixuan](https://github.com/luoruixuan)
Package: adamr
AdamRAdam with weight Recovery optimizerTL;DRAdamW tends to decay parameters towards zero, which makes the model "forget" the pretrained parameters during finetuning. Instead, AdamWR tries to recover parameters towards pretrained values during finetuning.Have a tryJust like other PyTorch optimizers,from adamr import AdamR
from xxx import SomeModel, SomeData, SomeDevice, SomeLoss

model = SomeModel()
dataloader = SomeData()
model.to(SomeDevice)

adamr = AdamR(
   model.parameters(),
   lr=1e-5,
   betas=(0.9, 0.998), # Adam's beta parameters
   eps=1e-8,
   weight_recovery=0.1
   )

loss_fn = SomeLoss()

for x, y in dataloader:
   adamwr.zero_grad()
   y_bar = model(x)
   loss = loss_fn(y_bar, y)
   loss.backward()
   adamr.step()AlgorithmTODO: improve the readabilityHere is a paper snippet:
Package: adam-sdk
Название проекта: AdamSDK - Разработка программного обеспечения для управления роботом АдамОписание проекта:AdamSDK - это инновационный проект, направленный на разработку мощного и гибкого программного обеспечения для управления роботом Адам. Робот Адам представляет собой передовую платформу, способную выполнять широкий спектр задач в различных областях, включая промышленность, здравоохранение, образование и многое другое.AdamSDK разрабатывается с целью обеспечить максимальную гибкость и простоту в использовании для разработчиков и конечных пользователей. С помощью AdamSDK разработчики получают доступ к широкому набору инструментов, библиотек и API, которые позволяют им создавать и настраивать функциональность робота Адам в соответствии с требованиями конкретного применения.Основные возможности и функции AdamSDK включают:Управление движением: AdamSDK предоставляет программные интерфейсы для управления движениями робота Адам. Разработчики могут программировать перемещение робота, его скорость, повороты и другие аспекты, необходимые для выполнения задач.Восприятие окружающей среды: AdamSDK позволяет роботу Адам взаимодействовать с окружающей средой. Он обладает различными датчиками, такими как камеры, микрофоны, глаза и так далее, что позволяет роботу распознавать объекты, лица, звуки и принимать решения на основе полученных данных.Интеграция с другими системами: AdamSDK поддерживает интеграцию с другими программными и аппаратными системами. Разработчики могут использовать SDK для интеграции робота Адам с существующими системами управления, базами данных или другими устройствами, что обеспечивает ещё больше возможностей для реализации различных задач.Расширяемость и настраиваемость: AdamSDK разработан с упором на расширяемость и настраиваемость функциональности. Разработчики могут создавать собственные модули и плагины, чтобы добавлять новые возможности и функции робота Адам в соответствии с уникальными потребностями и требованиями проекта.Поддержка разработчиков: AdamSDK предоставляет обширную документацию, примеры кода и руководства по использованию. Разработчики получают поддержку от команды разработчиков AdamSDK, которая готова помочь в решении проблем, предоставить консультации и помощь в интеграции SDK с проектами.AdamSDK создан с целью сделать робота Адам доступным и удобным для разработчиков и пользователей, чтобы они могли воплотить свои идеи и реализовать сложные задачи с помощью передовой робототехники. Этот проект открывает двери для инноваций и прогресса в различных отраслях и способствует развитию современных решений, основанных на робототехнике.
Package: adam-sim
ADAM SimulatorADAM (Autonomous Domestic Ambidextrous Manipulator) is a mobile robot manipulator consisting of a base with two Degrees of Freedom (DoF) and two Universal Robots UR3 of 6 DoF each.The simulation was built usingMuJoCo, a free and open source physics engine designed from the ground up for the purpose of model-based optimization, and in particular optimization through contacts.InstallationFollow the next steps for installing the simulation on your device.Requierements:Python 3.10.0 or higherNote: The Adam Simulator works on Linux, Windows and Mac.Install miniconda (highly-recommended)It is highly recommended to install all the dependencies on a new virtual environment. For more information check the conda documentation forinstallationandenvironment management. For creating the environment use the following commands on the terminal.condacreate-nadampython==3.10.9
condaactivateadamInstall from pipThe ADAM simulator is available as a pip package. For installing it just use:pip install adam-simInstall from sourceFirstly, clone the repository in your system.gitclonehttps://github.com/vistormu/adam_simulator.gitThen, enter the directory and install the required dependenciescdadam_simulator
pipinstall-rrequirements.txtInstallation for the communicationThe communication uses mosquitto as a broker. For installing it on your system, follow the instructions on themosquitto website.It is also necessary to install docker. For more information check thedocker documentation.DocumentationThe official documentation of the package is available onRead the Docs. Here you will find theinstallation instructions, theAPI referenceand someminimal working examples.
Package: adani
ADANIADANI (Approximate DIS At N3LO Implementation) is a C++ code that computes an approximation for the DIS coefficient functions at N3LO in heavy quark production, that are not fully known yet.DependenciesThe code depends on the public librarygsl.Optional dependencies are the librarypybind11and the Python modulescikit-build(both public), that are required for building the Pyhton bindings.InstallationIn order to install the C++ library runmkdirbuild&&cdbuild
cmake-DCMAKE_INSTALL_PREFIX=/your/installation/path/..
make&&makeinstallIn order to install the Python package runpipinstalladaniCompile a programIn order to compile a simple program rung++-Wall-otest.exetest.cpp-ladani`adani-config--cppflags--ldflags--cxxflags`org++-Wall-I/your/installation/path/include-L/your/installation/path/lib/-otest.exetest.cpp-ladaniIn the first case remember to runexportPATH=$PATH:/your/installation/path/binexportLD_LIBRARY_PATH=$LD_LIBRARY_PATH:/your/installation/path/libFor MacOS users: add the flags-std=c++17 -stdlib=libc++.Python moduleIn order to use the Python module doimportadaniremembering to runexportPYTHONPATH=$PYTHONPATH:/your/installation/path/lib/python3.X/site-packages/whereXis your Python version.ContactsContact me atniccolo.laurenti@mi.infn.it.Words of our prophetLa garra charrúa! L'ultima parola agli uruguagi, sempre loro! L'ultima parola nel calcio è la loro: hanno un cuore differente, lo capisci o no? L'artiglio che graffia,
che lascia il segno nella storia dell'Inter: questa è la storia che si ripete! [...] Il graffio che aveva portato l'Inter in Champions serve per rimarcare il territorio:
questo è l'Uruguay quando va in campo con tutto se stesso...ecco chi è Vecino: stanco, si, ma lascia in campo Vecino che parla alla fine, lascialo in campo, che la dice
lui l'ultima cosa nel calcio! Stanno a insegnare cos'è il calcio agli uruguagi, ma vedi un po' te.18/09/2018Messi! Messi! Leo Messi! Il sinistro migliore del mondo. Da Di Maria a Messi, dalla Bajada alla Perdriel sempre Rosario, la città del calcio. Uno per l'altro. Si sblocca
la partita. Football! [...] Tutti in piedi per il miglior giocatore del mondo. Rispetto per il numero uno. [...] Rosario, città del calcio, per questo calciatore che troppe
volte è stato criticato. Anche quando era a Barcellona, quando è arrivato, sofferente, da giovane, non ha mai dimenticato l'argentino. Parla rosarino, sente l'argentino nel
sangue, ha pianto per la Seleccion ed è lui che la tiene in vita. [...] La mistica che entra in campo. Abbiamo nominato Diego [Armando Maradona] 10 minuti fa. Con Diego dentro
tutto è possibile ed è col pianto dell'Argentina e gli occhi spiritati del miglior giocatore del mondo. [...] VAMO!26/11/2022
Package: adapta
AdaptaThis project aim at providing tools needed for everyday activities of data scientists and engineers:Connectors for various cloud APIsSecure secret handlers for various remote storagesLogging frameworkMetrics reporting frameworkStorage drivers for various clouds and storage typesDelta LakeThis module provides basic Delta Lake operations without Spark session, based ondelta-rsproject.Please refer to themoduledocumentation for examples.Secret StoragesPlease refer to themoduledocumentation for examples.NoSql (Astra DB)Please refer to themoduledocumentation for examples.
Package: adaptationism
### Last Updated: 7/1/2018
# Adaptationism is defined as…_”The belief or assumption, now generally held, that each feature of an organism is the result of evolutionary adaptation for a particular function.”_Our use of language is no different. We are constantly evolving our own group-specific languages (as well as learning the languages of others - both groups and individuals),the catalyst of development rooted in the functions that they serve.Like any scholarly endeavor, the wikipedia article that I pulled this information from defines the traits of anadaptationinclude:The trait is a variation of an earlier form.The trait is heritable through the transmission of genes.The trait enhances reproductive success.The last metaphorical comparison that I’ll make to this idea is that like all of the above, language (and features of language):Can be derived from variations of a broader parent language,Are heritable through the groups, communities, and experiences we partake in, and…The feature enhances the success of achieving a descriptive and/or actionable outcomethroughlanguage.## So what is this package _actually_ for…?*“Adaptationism” is meant to help answer the three following questions:Uses language to describe features of a journey or experience,How the use of language differs against another group, a broader (parent) group, or a subset of the same group, and finally,Changes its use of language following some event.The gap that I hope to fill through the development of this package is notjusta set oftools, but alsoa framework for interpretation and action.## What is your roadmap?My roadmap for this package is structured as a pyramid (3 levels), flowing from:(level 1)descriptive features of words and phrases, to…(level 2)the analysis and description of meta-language related features (i.e. POS, polarity, patterns of POS, named entities… etc.), and finally…(level 3)the descriptive statistics of text (word length, statement length, corpus length, average length… etc.).While I primarily spend my time analyzing comments…the types of [corpora](https://wiki.apache.org/spamassassin/PluralOfCorpus) this package can analyze includes:CommentsChat / Text ConversationsBooksSpeechesFor a full list of different kinds of corpora, [check this out](https://weblearn.ox.ac.uk/access/content/group/3a217dfd-a8cd-4034-8564-c27a58f89b9b/Handouts/CorpusTypes.pdf).## How did I come up with this idea?This package has a few origins… mostly stemming from unanswered Stack Overflow posts on NLTK. As I continue to add to my own roadmap, I will add the sets of SO posts that I draw inspiration from.Current list of TA questions:[Generating N-Gram Markov Chain Transition Table](https://stackoverflow.com/questions/23374694/n-gram-markov-chain-transition-table)
Package: adaptavist
adaptavistThis python package provides functionality for Jira Test Management (tm4j).Table of ContentsInstallationGetting StartedExamples and FeaturesGeneral WorkflowInstallationTo install adaptavist, you can use the following command(s):python-mpipinstalladaptavistTo uninstall adaptavist, you can use the following command:python-mpipuninstalladaptavistGetting Startedadaptavist is using the REST API of Adaptavist Test Management for Jira Server (seehttps://docs.adaptavist.io/tm4j/server/api/) and Jira's internal REST API, both with HTTP Basic authentication.In order to access Adaptavist/Jira, valid credentials are necessary. In addition,getpass.getuser().lower()must be a known Jira user as well.Examples and FeaturesGeneral WorkflowfromadaptavistimportAdaptavist# create a new instanceatm=Adaptavist(jira_server,jira_username,jira_password)# create a test plantest_plan_key=atm.create_test_plan(project_key="TEST",test_plan_name="my test plan")# create a test cycle (formerly test run) with a set of test cases and add it to test plantest_run_key=atm.create_test_run(project_key="TEST",test_run_name="my test cycle",test_cases=["TEST-T1"],test_plan_key=test_plan_key)# as test cycle creation also creates/initializes test results, we can just edit theseatm.edit_test_script_status(test_run_key=test_run_key,test_case_key="TEST-T1",step=1,status="Pass")# (optional) edit/overwrite the overall execution status of the test case (by default this is done automatically when editing status of a single step)atm.edit_test_result_status(test_run_key=test_run_key,test_case_key="TEST-T1",status="Pass")There's much more inside (like adding attachments, creating folders and environments, cloning test cycles). Additional code examples will follow.
Package: adaptavist-fixed
adaptavistThis python package provides functionality for Jira Test Management (tm4j).Table of ContentsInstallationGetting StartedExamples and FeaturesGeneral WorkflowInstallationTo install adaptavist, you can use the following command(s):python-mpipinstalladaptavistTo uninstall adaptavist, you can use the following command:python-mpipuninstalladaptavistGetting Startedadaptavist is using the REST API of Adaptavist Test Management for Jira Server (seehttps://docs.adaptavist.io/tm4j/server/api/) and Jira's internal REST API, both with HTTP Basic authentication.In order to access Adaptavist/Jira, valid credentials are necessary. In addition,getpass.getuser().lower()must be a known Jira user as well.Examples and FeaturesGeneral WorkflowfromadaptavistimportAdaptavist# create a new instanceatm=Adaptavist(jira_server,jira_username,jira_password)# create a test plantest_plan_key=atm.create_test_plan(project_key="TEST",test_plan_name="my test plan")# create a test cycle (formerly test run) with a set of test cases and add it to test plantest_run_key=atm.create_test_run(project_key="TEST",test_run_name="my test cycle",test_cases=["TEST-T1"],test_plan_key=test_plan_key)# as test cycle creation also creates/initializes test results, we can just edit theseatm.edit_test_script_status(test_run_key=test_run_key,test_case_key="TEST-T1",step=1,status="Pass")# (optional) edit/overwrite the overall execution status of the test case (by default this is done automatically when editing status of a single step)atm.edit_test_result_status(test_run_key=test_run_key,test_case_key="TEST-T1",status="Pass")There's much more inside (like adding attachments, creating folders and environments, cloning test cycles). Additional code examples will follow.