Saravanan666/pypi-org huggingface dataset:
Package: mmdict
What is itA set of dictionary classes that allow an aliasing of keys to other keys consistently, and a follow up implementation that does the same thing without concern for the case of string keys while preserving case for output and iteration.Neither implementation assumes string keys, so anythingHashableshould remain compatible for all keys in the API.It's still directly comparable (==,in,.get(), etc) and access compatible withdict(), and offers a mild framework for implementing key-transforming dictionaries somewhat painlessly with a decent test backing.Does it work?Yes, check this out.frommmdictimportMultiDictdata={"test":"ok"}alternatives={"test":["also","as well"]}d=MultiDict(data,aliases=alternatives)# Trued["also"]=="ok"# Trued["as well"]==d["test"]frommmdictimportCaselessMultiDictd=CaselessMultiDict({"Test":"not ok"})# A super valid write, because we're a regular dict() right?d["Test"]="ok"# Oh wow, `True`, because `"Test"` and `"test"` are caselessly the samed["test"]=="ok"# True, because, the case of the initial write is preserved for iterationlist(d.keys())==["Test"]Development Setup*nixpython-mvenv.venv
..venv/bin/activatePowerShellpython-mvenv.venv.\.venv\Scripts\Activate.ps1Then, for both platformspython-mensurepip--upgrade
python-mpipinstall--upgradepip
pipinstall-e.Running testsWith the virtual environment activated, use the multi-platform script to run the test suite.script/test.sh.ps1It's a very thin thin wrapper around bootstrapping the test run with python.
Package: mmdidpay
mmdidpay is a Django app to create pay link by IDPay and Manage transaction and following up themDetailed documentation is in the “docs” directory.Quick startAdd “mmdidpay” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'mmdidpay',
]Include the mmdidpay URLconf in your project urls.py like this:path('mmdidpay/', include('mmdidpay.urls')),Runpython manage.py migrateto create the mmdtransaction models.Visithttp://127.0.0.1:8000/mmdidpay/to participate in the mmdidpay.
Package: mmdl
mmdl - Mega Music DownloaderWhat is mmdl ❓TLDR: MMDL is a cli app which allows you to quickly and efficiently download one or multiple songs from YouTube.I wanted a tool to quickly and efficiently download songs/audios from YouTube based on the name of the song. I also wanted the songs to be tagged with metadata. This is why I created this simple to use tool.Why🕖 Fast: Thanks toasync codeandmultithreading.⚡ Simple: Typemmdl goand get started with downloading songs.✨ PowerfulFind songs based on songs query/titleSkip already existing songsAdd metadata such as artist name and artworkRequirements 👇Python 3FFmpegHere is a quick guide:https://github.com/adaptlearning/adapt_authoring/wiki/Installing-FFmpegInstall ❤️Make sure you have installed FFmpeg. Else this programm won’t work.pipinstallmmdl⚡That’s it!⚡Usage 🔥GoVery quick and easy way of running the cli. Few questions will be asked via prompts.This is the recommended way!mmdlgoDownloadFast and traditional way of running the cli. Pass arguments and have your songs downloaded. Useful for automation scripts.mmdldownload<method>#(add -h for more information)MethodsFilemmdl download file <location>: Get songs from file. Then download them.Listmmdl download list "Term1" "Term2" ...ormmdl download list -afor a prompt: Add multiple songs as cli arguments or enter them in a prompt.YTMusic(beta)mmdl download ytmusi: Download multiple songs fromYouTube Music liked songs playlist(via parsing HTML)Singlemmdl download single song name: Download a single song.Support ⚡Bugs 🐛Pleaseopen a issuewith some information to reproduce your problem.Questions ❓Pleasestart/search for a discussion.Contributing 🤜We welcome contributions very much (Even small ones). They are appreciated.StepsCheck for bugs/issues you would like to solve or think of new features you would like to implement.Fork the repo and make your changes.Send a pull request.Credits 🥂The following amazing libraries helped me make this project:https://github.com/tmbo/questionary- Questionary by Tom Bocklisch and Kian Crosshttps://github.com/alexmercerind/youtube-search-python- Youtube Search Python by Hiteh Kumar Sainihttps://github.com/willmcgugan/rich- Rich by Will McGuganhttps://github.com/ytdl-org/youtube-dl- YoutubeDlLegality ⚖️I assume no liability or responsibility for how this app is used.Please stay legal and respect the laws in your country. This tool is intended for archiving music for which you have a license.License 📜The MIT License (MIT)Copyright (c) 2021 - techboy-coder
Package: mmds
MMDS: A general-purpose multimodal dataset wrapperThis project is under construction, API may change from time to time.InstallationStable (not stable yet though)pip install mmdsLatestpip install mmds --preExample Usage# example.pyimporttimeitfrompathlibimportPathfrommultiprocessingimportManagerfrommmdsimportMultimodalDataset,MultimodalSamplefrommmds.exceptionsimportPackageNotFoundErrorfrommmds.modalities.rgbsimportRgbsModalityfrommmds.modalities.wavimportWavModalityfrommmds.modalities.melimportMelModalityfrommmds.modalities.f0importF0Modalityfrommmds.modalities.ge2eimportGe2eModalityfrommmds.utils.spectrogramimportLogMelSpectrogramtry:importyoutube_dlimportffmpegimporttorchfromtorchvisionimporttransformsexceptImportError:raisePackageNotFoundError("youtube_dl","ffmpeg-python","torch","torchvision",by="example.py",)defdownload():Path("data").mkdir(exist_ok=True)ydl_opts={"postprocessors":[{"key":"FFmpegExtractAudio","preferredcodec":"mp3","preferredquality":"192",}],"postprocessor_args":["-ar","16000"],"outtmpl":"data/%(id)s.%(ext)s","keepvideo":True,}withyoutube_dl.YoutubeDL(ydl_opts)asydl:ydl.download(["https://www.youtube.com/watch?v=BaW_jenozKc"])path=Path("data/BaW_jenozKc")ifnotpath.exists():path.mkdir(exist_ok=True)(ffmpeg.input("data/BaW_jenozKc.mp4").filter("fps",fps="25").output("data/BaW_jenozKc/%06d.png",start_number=0).overwrite_output().run(quiet=True))classMyMultimodalSample(MultimodalSample):defgenerate_info(self):wav_modality=self.get_modality_by_name("wav")rgbs_modality=self.get_modality_by_name("rgbs")returndict(t0=0,t1=wav_modality.duration/10,original_wav_seconds=wav_modality.duration,original_rgbs_seconds=rgbs_modality.duration,)classMyMultimodalDataset(MultimodalDataset):Sample=MyMultimodalSampledefmain():download()# optional multiprocessing cache managermanager=Manager()dataset=MyMultimodalDataset(["BaW_jenozKc"],modality_factories=[RgbsModality.create_factory(name="rgbs",root="data",suffix="*.png",sample_rate=25,transform=transforms.Compose([transforms.Resize((28,28)),transforms.ToTensor(),transforms.Normalize(0.5,1),],),aggragate=torch.stack,cache=manager.dict(),),WavModality.create_factory(name="wav",root="data",suffix=".mp3",sample_rate=16_000,cache=manager.dict(),),MelModality.create_factory(name="mel",root="data",suffix=".mel.npz",mel_fn=LogMelSpectrogram(sample_rate=16_000),base_modality_name="wav",cache=manager.dict(),),F0Modality.create_factory(name="f0",root="data",suffix=".f0.npz",mel_fn=LogMelSpectrogram(sample_rate=16_000),base_modality_name="wav",cache=manager.dict(),),Ge2eModality.create_factory(name="ge2e",root="data",suffix=".ge2e.npz",sample_rate=16_000,base_modality_name="wav",cache=manager.dict(),fetching=False,),],)# first loadprint(timeit.timeit(lambda:dataset[0],number=1))# second loadprint(timeit.timeit(lambda:dataset[0],number=1))print(dataset[0]["info"])forkey,valueindataset[0].items():try:print(key,value.shape,type(value))except:passif__name__=="__main__":main()
Package: mmdzanata
# mmdzanata## Prerequisites* [libmodulemd](https://github.com/fedora-modularity/libmodulemd)* [koji](https://pagure.io/koji)* [zanata-client](https://github.com/zanata/zanata-client)To install on Fedora 28+, run:```dnf install libmodulemd koji zanata-client```## InstallationWith the prerequisites installed, the latest version can be installed with`pip install mmdzanata` or on Fedora 28+ with `dnf install mmdzanata`(recommended).## CLI Usage### Zanata Client SetupUse of the upload feature for string extraction requires setting up theZanata client appropriately. Follow the[instructions](http://docs.zanata.org/en/release/client/configuration/) fromZanata to set up your `~/.config/zanata.ini` file appropriately. Note thatyou will want to do this againsthttps://fedora.zanata.organd nothttps://translate.zanata.org### Extract Translatable StringsTo extract translatable strings from modules for a particular Fedorarelease (e.g. f29):```mmdzanata --branch f29 extract [--upload]```This will read all of the module metadata from the Koji build-system andconvert the translatable strings to a Zanata-compatible gettext document. If`--upload` is passed, it will also attempt to use the `zanata-cli` tool toupload the strings to the Zanata server. See the "Zanata Client Setup"section above for information on how to configure this.### Produce modulemd-translations YAMLTo read the translated strings from Zanata and convert them intomodulemd-translations YAML documents that can be included in repodata:```mmdzanata --branch f29 generate_modulemd```This will produce a YAML file in the current directory with all knowntranslated strings.## API### mmdzanataThe mmdzanata class has two primary methods:* get_module_catalog_from_tags()* generate_metadata()#### mmdzanata.get_module_catalog_from_tags()This returns a babel.message.Catalog object containing all of thetranslatable strings from any module tagged with one of the passed tags.#### mmmdzanata.generate_metadata()This returns an iterable of modulemd-translation objects from the suppliedZanata project and branch.### mmdzanata.fedoraThis class provides helper routines for dealing with translations in FedoraModules.
Package: mmemoji
mmemojiCustom Emoji manager command-line forMattermost😎Features:Create custom EmojisDelete custom EmojisList custom EmojisSearch custom EmojisExport custom EmojisInstallationpipinstallmmemoji
mmemoji--help(Requires Python >=3.8)Usage exampleLet's take theParty ParrotEmojis as an example.First, clone the Git repository or retrieve an archive of it:gitclonehttps://github.com/jmhobbs/cultofthepartyparrot.com.gitcdcultofthepartyparrot.comThen you'll need your Mattermost credentials. You can either pass them tommemojiwith the arguments--url/--login-id/--passwordor via environment variables, for example:exportMM_URL='http://127.0.0.1:8065/api/v4'exportMM_LOGIN_ID='user-1@sample.mattermost.com'exportMM_PASSWORD='user-1'Finally, runmmemojito import all the parrots:mmemojicreate--no-clobber{parrots,guests}/hd/*.gif{parrots,guests}/*.gifNoteHere we rely onshell globbingto select all emojis from the directories.Specifying thehddirectories first with--no-clobberensures these emojis are created first and not overwritten by their lower quality counterpart.If you ever want to remove them all, simply run the following:mmemojidelete--force{parrots,guests}/hd/*.gif{parrots,guests}/*.gifNoteThe emoji names are extracted from the filenames the same way they have been during creation.--forceis used to ignore the absent low quality duplicates.DevelopmentYou can clone this repository and install the project withPoetry:poetryinstallYou'll find a script to create a localDockertest instance undertests/:./tests/scripts/setup-mattermost.shYou can run the test suite with:pytestAnd last thing, you can install thepre-commithooks to help with the formatting of your code.pre-commitinstall
Package: mmeson
mmesonTUI to configure Meson projects similar toccmakefor CMake projectsInstallationVia pippipinstallmmesonRunningpython3-mmmesonpath/to/builddirIt is not need to run it in the project's source folder, however you need to setup the project beforehand.Changed items will start with a star, pressenterto edit a filed and enter to exit editing (except fields of type
boolean or combo). Presscto configure and exit andqto exit without configuring.Note: you can always set options viameson configure path/to/builddir -Doption=valuein addition tommeson. This
does not reconfigure the project though, this needs to be done viameson setup --reconfigure path/to/builddir. When
exitingmmesonwithc, it combines both of these steps, while exiting withgdoes not reconfigure the project.
Note that this will happen automatically though when you runmeson compile.DependenciesPython3 >=3.9urwidContributingContributions are welcome, the code is well documented, especially the TUI part. You can find documentation of the
internal function and classeshere.ToDo-ListCustom Widget for array editing (instead of writing string only edit array entries)Search function for build optionPath completion viaTABwhen editing strings / arraysUnit tests and coverage reportMore elaborate error handling (e.g. array with choices)Fix workaround for colored outputLicenseCode:EUPL-1.2Documentation:CC-BY-SA-4.0Notable mentionscmeson
Package: mmf
MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research. See full list of project inside or built on MMFhere.MMF is powered by PyTorch, allows distributed training and is un-opinionated, scalable and fast. Use MMF tobootstrapfor your next vision and language multimodal research project by following theinstallation instructions. Take a look at list of MMF featureshere.MMF also acts asstarter codebasefor challenges around vision and
language datasets (The Hateful Memes, TextVQA, TextCaps and VQA challenges). MMF was formerly known as Pythia. The next video shows an overview of how datasets and models work inside MMF. Checkout MMF'svideo overview.InstallationFollow installation instructions in thedocumentation.DocumentationLearn more about MMFhere.CitationIf you use MMF in your work or use any models published in MMF, please cite:@misc{singh2020mmf,
  author =       {Singh, Amanpreet and Goswami, Vedanuj and Natarajan, Vivek and Jiang, Yu and Chen, Xinlei and Shah, Meet and
                 Rohrbach, Marcus and Batra, Dhruv and Parikh, Devi},
  title =        {MMF: A multimodal framework for vision and language research},
  howpublished = {\url{https://github.com/facebookresearch/mmf}},
  year =         {2020}
}LicenseMMF is licensed under BSD license available inLICENSEfile
Package: mmfewshot
IntroductionEnglish |简体中文mmfewshot is an open source few shot learning toolbox based on PyTorch. It is a part of theOpenMMLabproject.The master branch works withPyTorch 1.5+.
The compatibility to earlier versions of PyTorch is not fully tested.Documentation:https://mmfewshot.readthedocs.io/en/latest/.Major featuresSupport multiple tasks in Few Shot LearningMMFewShot provides unified implementation and evaluation of few shot classification and detection.Modular DesignWe decompose the few shot learning framework into different components,
which makes it much easy and flexible to build a new model by combining different modules.Strong baseline and State of the artThe toolbox provides strong baselines and state-of-the-art methods in few shot classification and detection.LicenseThis project is released under theApache 2.0 license.Model ZooSupported algorithms:classificationBaseline(ICLR'2019)Baseline++(ICLR'2019)NegMargin(ECCV'2020)MatchingNet(NeurIPS'2016)ProtoNet(NeurIPS'2017)RelationNet(CVPR'2018)MetaBaseline(ICCV'2021)MAML(ICML'2017)DetectionTFA(ICML'2020)FSCE(CVPR'2021)AttentionRPN(CVPR'2020)MetaRCNN(ICCV'2019)FSDetView(ECCV'2020)MPSR(ECCV'2020)ChangelogInstallationPlease refer toinstall.mdfor installation of mmfewshot.Getting StartedPlease seegetting_started.mdfor the basic usage of mmfewshot.CitationIf you find this project useful in your research, please consider cite:@misc{mmfewshot2021,title={OpenMMLab Few Shot Learning Toolbox and Benchmark},author={mmfewshot Contributors},howpublished={\url{https://github.com/open-mmlab/mmfewshot}},year={2021}}ContributingWe appreciate all contributions to improve mmfewshot. Please refer toCONTRIBUTING.mdin MMFewShot for the contributing guideline.Acknowledgementmmfewshot is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new methods.Projects in OpenMMLabMMCV: OpenMMLab foundational library for computer vision.MIM: MIM Installs OpenMMLab Packages.MMClassification: OpenMMLab image classification toolbox and benchmark.MMDetection: OpenMMLab detection toolbox and benchmark.MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.MMTracking: OpenMMLab video perception toolbox and benchmark.MMPose: OpenMMLab pose estimation toolbox and benchmark.MMEditing: OpenMMLab image and video editing toolbox.MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding.MMGeneration: OpenMMLab image and video generative models toolbox.MMFlow: OpenMMLab optical flow toolbox and benchmark.MMFewShot: OpenMMLab FewShot Learning Toolbox and Benchmark.
Package: mm-fitting
Michaelis-Menten equation fitting to dataA Python module that implements five methods to fit the Michaelis-menten equation to a set of points of rate vs substrate concentration.The methods are:Non-linear regression (Levemberg-Marquard algorithm applied to Michaelis-Menten equation)The Direct Linear PlotThe Lineweaver-Burk linearizationThe Hanes linearizationThe Eddie-Hofstee linearizationShort usage:Given twonumpy1D-arrays,aandv0containing substrate concentrations and initial rates, respectively,results=mm_fitting.compute_methods(a,v0)will apply all five methods and generate adictwith keysaandv0andresults. The value ofresultswill be a list of namedtuples containg the results for each method.numpy-only dependencyAll methods are implemented in numpy and do not require eitherscipyor data analysis module likepandas:linearizations are computed by a thin wrapper ofnumpy.polyfit()with degree one.non-linear regression is computed using a numpy-only version of the Levemberg-Marquard algorithm. Code was adapted from Abner Bogan's Github repo for a numpy-only version of the algorithm (abnerbog/
levenberg-marquardt-method).
Package: mmflyer
Author:Roie R. BlackEmail:roie.black@gmail.comDocs:https://rblack42.github.io/math-magik-flight-timetestspackageThis project is part of theMath Magik- project. You can launch an
interactive version of this project by clicking on this link:This site hosts a revisit of Doug McLean’s 1976 NFFS Symposium article “A
Method For Predicting Indoor Model Duration”. In the current paper, Python code
is presented that will provide an estimate of model flight times. The code uses
theSymPypackage to validate the math presented, andPintto make sure
engineering units of measure are consistent.This code is part of theMath Magicproject which  provides
tools to assist in the designing, building, and flying of indoor model
airplanes. .Comments and suggestions are welcome. Please contact the author if you find any
of this useful.
Package: mmf-meta
MMF-metaЭта библиотека - часть проекта Model Management Framework.Отвечает за оформление пользовательских функцийПример использованияimportmmf_meta.formatsimportpickleimporttimeimportpandasimportmmf_metaasmmf@mmf.target(description="Супер-функция",returns=mmf.DataFrame(out_format=mmf_meta.formats.DataFrameFormat.CSV),)defscore(df:pandas.DataFrame=mmf.DataFrame(description="Описание df"),# other: dict = mmf.JsonFile(description="Описание other"),):returndf@mmf.target(description="Другая супер-функция",returns=mmf.String(),)defother(inp=mmf.String(description="важный параметр"),another=mmf.Integer(description="еще один важный параметр"),):time.sleep(30)returninpmmf.artifact("some_file")@mmf.artifact("other")defmodel():withopen("other","br")asf:returnpickle.load(f)Подробная документация
Package: mmf-serve
MMF-metaЭта библиотека - часть проекта Model Management Framework.Отвечает за servingПример использованияmmfserveserve-rabbitКонфигурацияRABBIT__USER=core
RABBIT__PASSWORD=somesecret
RABBIT__HOST=localhost
EXCHANGE_NAME=test_exchange
QUEUE_NAME=test_queue
MAIN_SCRIPT=main.pyПодробная документация
Package: mmg
Multilingual Markdown GeneratorThis package provides a command-line interface to manage multilingual contents and generate i18n markdown from a single base file.🌏
English |Français|한국어|日本語Available in Bash, Zsh, and Windows PowerShell.Table of Contents⚡OverviewHow It WorksFeaturesInstallationLinuxmacOSWindowsHow to UseTroubleshootingChangelogContributorsOverview 🔎How It WorksBy managing only one Base file, we can reduce the number of errors caused by missing or mismatched translations.
Additionally, thanks to editing in a single file, we can expect convenient translation with the auto-completion function of AI tools such asCopilot.Markdown:Jupyter Notebook:FeaturesSupports the following features:Markdown, Jupyter Notebook(.ipynb) as input formatsAs-is, HTML, PDF as ouput formatsCommand-line interface for Bash, Zsh, Windows PowerShellPython APIRecursive traversal mode with-roption (As-is, HTML, PDF are all supported)Batch processing mode with YAML file (OnlyAs-isis supported)IETF language tagsUTF-8 encodingAutomatic generation of table of contents with level and emoji options (Markdown and Jupyter Notebook are both supported)Base file validation (Check the number of tags of each language)Validation only mode for CI/CD (Disable file generation)Installation 📦Linuxpip3installmmgmacOSpip3installmmgIf you have any issues withWeasyPrint, install it with the following command. WeasyPrint is only used to create PDFs.brewinstallweasyprintWindowsMMG usesWeasyPrintto create PDFs. WeasyPrint requires the GTK library, so download and run the latestGTK3 installer.If you are not interested in creating PDFs, you can skip this step.Other features of MMG are available without GTK.Install MMG using Pip.pip3installmmgHow to Use 💡Please refer to thedocumentationfor detailed usage and examples.$mmg--help
Usage:mmg[OPTIONS][FILE_NAMES]...FILE_NAMES:Basefilenamestoconvert.`*.base.md`or`*.base.ipynb`areavailable.Herearesomeexamples:mmg*.base.mdmmg*.base.ipynbmmg*.base.md*.base.ipynb-opdf--cssgithub-darkmmg--recursivemmg--recursive--validation-onlymmg--batchmmg.yml

Options:-r,--recursiveThiswillsearchallsubfoldersbasedoncurrentdirectory.-b,--batchFILEYAMLfilepathforbatchconversion.(Default:None)-o,--output-format[as-is|html|pdf]Outputformat.(Default:as-is)--cssTEXTCSSfilepathorpreset('github-light'/'github-dark').OnlyfortheHTML/PDFoutput.(Default:github-light)-y,--yesThiswillconfirmtheconversionwithoutasking.(Default:False)-s,--skip-validationSkipthehealthcheck.(Default:False)--validation-onlyOnlycheckthehealth.(Default:False)-v,--verboseVerbositylevelfrom0to2.--verbose:1,-v:1,-vv:2(Default:0)--versionShowthecurrentversion.--helpShowthismessageandexit.Troubleshooting 💊Please refer to thetroubleshootingpage on the website.Changelog 📝CHANGELOG.mdContributors 🤝Made withcontrib.rocks.If you are interested in how to contribute, please refer to thecontribution guide.