Saravanan666/pypi-org huggingface dataset:
Package: fbchat-asyncio-kvgx12
A powerful and efficient library to interact withFacebook’s Messenger, using just your email and password.
This is an asyncio fork of thefbchatlibrary.This isnotan official API, Facebook has thatover herefor chat bots. This library differs by using a normal Facebook account instead.fbchatcurrently support:Sending many types of messages, with files, stickers, mentions, etc.Fetching all messages, threads and images in threads.Searching for messages and threads.Creating groups, setting the group emoji, changing nicknames, creating polls, etc.Listening for, an reacting to messages and other events in real-time.Type hints, and it has a modern codebase (e.g. only Python 3.5 and upwards).async/await(COMING).Essentially, everything you need to make an amazing Facebook bot!Caveatsfbchatworks by imitating what the browser does, and thereby tricking Facebook into thinking it’s accessing the website normally.However, there’s a catch!Using this library may not comply with Facebook’s Terms Of Service!, so be responsible Facebook citizens! We are not responsible if your account gets banned!Additionally,the APIs the library is calling is undocumented!In theory, this means that your code could break tomorrow, without the slightest warning!
If this happens to you, please report it, so that we can fix it as soon as possible!With that out of the way, you may go toRead The Docsto see the full documentation!Installation$ pip install fbchat-asyncioIf you don’t havepip,this guidecan guide you through the process.You can also install directly from source, provided you havepip>=19.0:$ pip install git+https://github.com/tulir/fbchat-asyncio.git#egg=fbchatExamplesAll examples are availablehere.Basic example:basic_usage.pyIf login using email and password doesn’t work, you can create a file in the working directory called “session.json” with the following format:{"c_user":"[15 digits]","xs":"[variable]"}These values are from the cookies stored by your browser after logging in your account. You can get these values this way:On Firefox:Openmessenger.com, login and press F12Go to “Storage”Go to “Cookies”Select “https://messenger.com” and copy the values from there.On Chrome:The process is the same, the only difference is “Storage” is called “Application” in Chrome.KEEP IN MIND: These values can be used by someone else to login to your account, so keep them private!After that you can use this code:session_handling.pyDepending on the domain you’re logging into, you have to set the “domain” argument appropriately (eitherfacebook.comormessenger.com). Here’s an example:awaitfbchat.Session.from_cookies(cookies,domain="messenger.com")MaintainerTulir Asokan /@tulirAcknowledgementsThis project was originally inspired byfacebook-chat-api.
Package: fbcli
# FogBugz Command Line Interface[<img src=”https://travis-ci.org/lbolla/fbcli.svg?branch=master”>](https://travis-ci.org/lbolla/fbcli) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/3699c35b755d41fbb540ae4e02118260)](https://www.codacy.com/app/lbolla/fbcli?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=lbolla/fbcli&amp;utm_campaign=Badge_Grade)Install with:$> python setup.py installRun with:fb
fb –logging=debug  # verbose
fb –help  # for more optionsGet help fromfb:>>> help
>>> help <command>  # for morePython2 is deprecated: please use Python3.# Configurationfbclican make use of several environmental variables:FBURL: the URL to FogBugz, e.g.https://<your_company>.fogbugz.com/FBUSER: your username, e.g.lorenzo.bolla@example.comFBTOKEN: your FogBugz API token, e.g. abcdefghilmnopqrstuvzFBPASS: your FogBugz passwordIf you have 2-factor authentication enabled on your FogBugz account,
you can’t use username/password, you must use the token.# TutorialA quick video tutorial is available [here](https://www.youtube.com/watch?v=2tunk7HD0GY).[![Tutorial](https://j.gifs.com/vJxLDD.gif)](https://www.youtube.com/watch?v=2tunk7HD0GY)# DevelopmentRun tests with:>>> python setup.py testor>>> tox# ReferencesFogBugz API Intro:https://developers.fogbugz.com/default.asp?W194FogBugz API Reference:http://help.fogcreek.com/the-fogbugz-apiFogBugzPy:https://developers.fogbugz.com/?W199# AcknowledgementsPorted to Python 3 by Jason R. Coombs.
Package: fb-cli
fbcliA command line client for facebook messengerInstallationpip install fb-cliUsageJust runfb-clifrom the command line. When in a group or direct chat, type/bto go back to the message list and/qto quit.FeaturesSend and recieve text messagesRecieve imagesSupports both groups and direct messagesTodoSupport viewing older messages and chats (Important)Support sending images
Package: fb-credentials
fb_credentialsAn extension to logging functionality for FogBugz module and for extensions to it like fborm.Function fb_credentials.FogBugz is a layer on top of the constructor fogbugz.Fogbugz or similar constructors (for example fborm.FogBugzORM) that provides a convenient parsing of credentials (token, username or password) in a configuration file (by default ~/.fogbugzrc), or prompts the user for valid credentials.Sample usageimport fb_credentials
fb = fb_credentials.FogBugz(’https://YourRepository.com/’)
fb.search(q=’53410’, cols=’ixBug’)InstallationYou can download and install using:pip install fb_credentialsThe PyPI URL for this project is:https://pypi.python.org/pypi/fb-credentials.TestsIn order to run the tests you need to install the test dependencies indicated in setup.py.Tests are stored in folder test and run withnosetests.Travisis used for Continous Integration and is connected to the github repository; every new push to the repository triggers the set of tests in Travis. At the top of this README there is a link to Travis that indicates the status of the tests.Tests need to pass for the different versions of python supported. This is setup in the .travis.yml configuration file. You can do the same locally using tox and the tox.ini file in this repository.E-mail meportu.github@gmail.comRelease Notes0.3.0New featuresAdded support for searching for token in configuration file.API changesFogBugz_cm() has option logoff (default False) to turn on log-off when exiting the context manager.Behavior changesSimplified the sequence of credential validation steps (provided token, provided username, get token, get username).Default configuration file changed from ~/.hgrc to ~/.fogbugzrc.
Package: fbdiff
fbdiffis a tool that reports a brief summary of table differences
in OpenType fonts. It reports which tables exist in only one of the
fonts, and it will report which common tables are different and which
are identical.It only looks at the binary data: tables are not parsed.Example usage:$ fbdiff fontA.ttf fontB.ttf
Tables only in font B:
  meta

Different tables:
            A      B
  GPOS  29628  29574 bytes
  STAT     28    162 bytes
  fpgm   4095   4078 bytes
  glyf  36984  26626 bytes
  head     54     54 bytes
  loca    940    940 bytes
  name   3078   4557 bytes
  prep   1339   1339 bytes

Identical tables:
  GDEF, GSUB, HVAR, OS/2, TSI0, TSI1, TSI2, TSI3, TSI5, TSIC, VDMX,
  avar, cmap, cvar, cvt , fvar, gasp, gvar, hhea, hmtx, maxp, postHelp text:$ fbdiff --help
usage: fbdiff [-h] [-o] FONT_A FONT_B

Compare the binary tables of two OpenType fonts.

positional arguments:
  FONT_A             an OpenType font file
  FONT_B             an OpenType font file

optional arguments:
  -h, --help         show this help message and exit
  -o, --table-order  show the table tags in sfnt order side-by-side
Package: fbench
fBenchAboutA collection of benchmark functions:DocumentationOverview of fBench functionsExample usageAPI ReferenceInstallationfbenchis available onPyPIfor Python 3.8+:pip install fbenchExamplesTheackleyfunction:>>>importfbench>>>round(fbench.ackley([1,1]),4)3.6254Visualize function withFunctionPlotter:>>>importmatplotlib.pyplotasplt>>>plotter=fbench.viz.FunctionPlotter(func=fbench.ackley,bounds=[(-5,5)]*2)>>>plotter.plot()>>>plt.show()Contributing to fBenchYour contribution is greatly appreciated!
See the following links to help you get started:Contributing GuideDeveloper GuideContributor Code of ConductLicensefbenchwas created by fBench Developers.
It is licensed under the terms of the BSD 3-Clause license.
Package: fbenum
FBEnumLibrary provides fallback enum functionality. Fully compatible with pydantic.It can be helpful to have a fallback value for enums, so that unknown values can be
handled gracefully. This is especially useful when working with external data sources
that may contain values that are not known at the time of writing the code.InstallationpipinstallfbenumUsagefbenumprovides two possible ways to use it:As a class mixin, which can be used to enhance any enum classAs a pydantic validator, which can be used to validate enum values in pydantic modelsClass mixinClass mixin can be used to enhance any enum class with fallback functionality.fromenumimportEnumfromfbenum.enumimportFallbackEnum@enum.uniqueclassMyEnum(enum.StrInt,FallbackEnum):__unknown_name__='MISSING'A=1B=2assertMyEnum(1)==MyEnum.AassertMyEnum(44).name=='MISSING'assertMyEnum(44).value==44Pydantic validatorAs a pydantic validator,fbenumcan be used to validate enum values in pydantic models.
In this cases, the enum class no longer needs to be a subclass ofFallbackEnumor somehow
otherwise enhanced.
Two ways of using it are provided:Annotated type validatorfromenumimportEnumfromfbenum.adapterimportFallbackAdapter@enum.uniqueclassMyEnum(enum.StrInt):A=1B=2classMyModel(BaseModel):value:Annotated[MyEnum,FallbackAdapter(unknown_name='MISSING'),]model=MyModel(value=1)assertmodel.value==MyEnum.Amodel=MyModel(value=44)assertmodel.value.name=='MISSING'assertmodel.value.value==44Type wrapperType wrapper has restricted functionality, as it can only be used to wrap a single enum
class, and it does not support any additional arguments. However, it is more concise.fromenumimportEnumfromfbenum.adapterimportFallbackAdapter@enum.uniqueclassMyEnum(enum.StrInt):A=1B=2classMyModel(BaseModel):value:FallbackAdapter[MyEnum]model=MyModel(value=1)assertmodel.value==MyEnum.Amodel=MyModel(value=44)assertmodel.value.name=='MISSING'assertmodel.value.value==44Or a more realistic and complex example:fromenumimportEnumfromfbenum.adapterimportFallbackAdapter@enum.uniqueclassUserStatus(enum.StrInt):"""User status."""ACTIVE=1INACTIVE=2classUserRequest(BaseModel):name:stremail:strstatus:FallbackAdapter[UserStatus]classUserResponse(BaseModel):name:stremail:strstatus:FallbackAdapter[UserStatus]defcreate_user(request:UserRequest)->UserResponse:"""Create a user."""# Do some stuffresp=...returnUserResponse.validate_model(resp)defget_user(user_id:int)->UserResponse:"""Get a user."""# Do some stuffresp=...returnUserResponse.validate_model(resp)defget_users()->list[UserResponse]:"""Get all users."""# Do some stuffresp=...returnUserResponse.validate_model(resp)if__name__=='__main__':user=create_user(UserRequest(name='John Doe',email='test@emaik.com',status=1,),)# on user cretion status always will be knownusers=get_users()# user == [#     UserResponse(#         name='John Doe',#         email='test@emaik',#         status=1,#     ),#     UserResponse(#         name='Jane Doe',#         email='test2@emaik',#         status=3, # unknown status# ]# on users_getting status can be unknown, so using fallback wrapper for response# model will be a good idea to handle unknown values gracefully.
Package: fbetl
OverviewExtracts, transforms, and loads your Facebook data from theDownload Your Informationtool into an SQLite DB.Installationfbetlis available on PyPIhttp://pypi.python.org/pypi/fbetlInstall viapip$ pip install fbetlOr viaeasy_install$ easy_install fbetlOr directly fromfbetl’sgit repo <https://github.com/gregology/fbetl>$ git clone git://github.com/gregology/fbetl.git
$ cd fbetl
$ python setup.py installBasic usageUse Facebook’sDownload Your Informationtool using the format JSON and unzip the file.>>> from fbetl import Fbetl
>>> fbetl = Fbetl('path/to/unzipped/facebook-user/data')
>>> fbetl.load_all()
>>> fbetl.sql('SELECT COUNT(*) FROM comments')[0][0]
5000
>>> fbetl.sql('SELECT timestamp FROM posts LIMIT 5;')
[('2018-07-27 14:04:24',), ('2018-07-23 11:34:12',), ('2018-07-17 09:47:19',), ('2018-07-13 23:56:44',), ('2018-07-12 09:54:13',)]
>>> fbetl.save('foo.db') # Saves SQLite db to diskRunning Test$ python tests/tests.pyPython compatibilityDeveloped for Python 3. May work but not tested in Python 2.
Package: fb-friend-list-scraper
Facebook Friend List ScraperOSINT tool to scrape names and usernames from large friend lists on Facebook, without being rate limited.Getting started:Install using pip:python -m pip install fb-friend-list-scraperScript is now installed asfbfriendlistscraperRun with-hor--helpto show usage information.Usage:usage: fbfriendlistscraper [-h] -e EMAIL [-p PASSWORD] -u USERNAME [-o OUTFILE] [-w] [-q] [-x] [-s SLEEPMULTIPLIER] [-i PROXY] [-c CMD]

Tool to scrape names and usernames from large friend lists on Facebook, without being rate limited

options:
  -h, --help            show this help message and exit
  -e EMAIL, --email EMAIL
                        Email address or phone number to login with.
  -p PASSWORD, --password PASSWORD
                        Password to login with. If not supplied you will be prompted. You really shouldn't use this for security reasons.
  -u USERNAME, --username USERNAME
                        Username of the user to scrape.
  -o OUTFILE, --outfile OUTFILE
                        Path of the output file. (Default: ./scraped_friends.txt)
  -w, --headless        Run webdriver in headless mode.
  -q, --quiet           Do not print scraped users to screen.
  -x, --onlyusernames   Only the usernames/IDs will be written to the output file.
  -s SLEEPMULTIPLIER, --sleepmultiplier SLEEPMULTIPLIER
                        Multiply sleep time between each page scrape by n. Useful when being easily rate-limited.
  -i PROXY, --proxy PROXY
                        Proxy server to use for connecting. Username/password can be supplied like: socks5://user:pass@host:port
  -c CMD, --cmd CMD     Shell command to run after each page scrape. Useful for changing proxy/VPN exit.

examples:
        fbfriendlistscraper -e your@email.com -p YourPassword123 -u someusername.123 -o my_file.txt
        fbfriendlistscraper --email your@email.com --username another.user --headless -s 2 -x
        fbfriendlistscraper -e your@email.com -u username.johnson -w --proxy socks5://127.0.0.1:9050
        fbfriendlistscraper -e your@email.com -u xxuserxx --headless --cmd "mullvad relay set provider Quadranet"
        fbfriendlistscraper -e your@email.com -u markzuckerburger -w -o ./test.txt --cmd "killall -HUP tor"NOTE:Facebook changes the markup of it's pages regularly, so the script might break from time to time. Please open an issue if something doesn't work and I'll take a look at it. Pull requests are welcome as well.TODO:Make script check for followers if friend list isn't public.Add more error handling.Add proxy rotation.
Package: fbgemm-gpu
FBGEMM_GPUFBGEMM_GPU (FBGEMM GPU Kernels Library) is a collection of high-performance PyTorch
GPU operator libraries for training and inference.  The library provides efficient
table batched embedding bag, data layout transformation, and quantization supports.FBGEMM_GPU is currently tested with cuda 12.1.0 and 11.8 in CI, and with PyTorch
packages (2.1+) that are built against those CUDA versions.Only Intel/AMD CPUs with AVX2 extensions are currently supported.See ourDocumentationfor more information.InstallationThe full installation instructions
for the CUDA, ROCm, and CPU-only variants of FBGEMM_GPU can be foundhere.  In addition, instructions for running
example tests and benchmarks can be foundhere.Build InstructionsThis section is intended for FBGEMM_GPU developers only.  The full build
instructions for the CUDA, ROCm, and CPU-only variants of FBGEMM_GPU can be
foundhere.Join the FBGEMM_GPU CommunityFor questions, support, news updates, or feature requests, please feel free to:File a ticket inGitHub IssuesPost a discussion inGitHub DiscussionsReach out to us on the#fbgemmchannel inPyTorch SlackFor contributions, please see theCONTRIBUTINGfile for
ways to help out.LicenseFBGEMM_GPU is BSD licensed, as found in theLICENSEfile.
Package: fbgemm-gpu-cpu
FBGEMM_GPUFBGEMM_GPU (FBGEMM GPU Kernels Library) is a collection of high-performance PyTorch
GPU operator libraries for training and inference.  The library provides efficient
table batched embedding bag, data layout transformation, and quantization supports.FBGEMM_GPU is currently tested with cuda 12.1.0 and 11.8 in CI, and with PyTorch
packages (2.1+) that are built against those CUDA versions.Only Intel/AMD CPUs with AVX2 extensions are currently supported.See ourDocumentationfor more information.InstallationThe full installation instructions
for the CUDA, ROCm, and CPU-only variants of FBGEMM_GPU can be foundhere.  In addition, instructions for running
example tests and benchmarks can be foundhere.Build InstructionsThis section is intended for FBGEMM_GPU developers only.  The full build
instructions for the CUDA, ROCm, and CPU-only variants of FBGEMM_GPU can be
foundhere.Join the FBGEMM_GPU CommunityFor questions, support, news updates, or feature requests, please feel free to:File a ticket inGitHub IssuesPost a discussion inGitHub DiscussionsReach out to us on the#fbgemmchannel inPyTorch SlackFor contributions, please see theCONTRIBUTINGfile for
ways to help out.LicenseFBGEMM_GPU is BSD licensed, as found in theLICENSEfile.
Package: fbgemm-gpu-nightly
FBGEMM_GPUFBGEMM_GPU (FBGEMM GPU Kernels Library) is a collection of high-performance
PyTorch GPU operator libraries for training and inference.  The library provides
efficient table batched embedding bag, data layout transformation, and
quantization supports.FBGEMM_GPU is currently tested with CUDA 12.1 and 11.8 in CI, and with PyTorch
packages (2.1+) that are built against those CUDA versions.See the fullDocumentationfor more information
on building, installing, and developing with FBGEMM_GPU, as well as the most
up-to-date support matrix for this library.Join the FBGEMM_GPU CommunityFor questions, support, news updates, or feature requests, please feel free to:File a ticket inGitHub IssuesPost a discussion inGitHub DiscussionsReach out to us on the#fbgemmchannel inPyTorch SlackFor contributions, please see theCONTRIBUTINGfile for
ways to help out.LicenseFBGEMM_GPU is BSD licensed, as found in theLICENSEfile.
Package: fbgemm-gpu-nightly-cpu
FBGEMM_GPUFBGEMM_GPU (FBGEMM GPU Kernels Library) is a collection of high-performance
PyTorch GPU operator libraries for training and inference.  The library provides
efficient table batched embedding bag, data layout transformation, and
quantization supports.FBGEMM_GPU is currently tested with CUDA 12.1 and 11.8 in CI, and with PyTorch
packages (2.1+) that are built against those CUDA versions.See the fullDocumentationfor more information
on building, installing, and developing with FBGEMM_GPU, as well as the most
up-to-date support matrix for this library.Join the FBGEMM_GPU CommunityFor questions, support, news updates, or feature requests, please feel free to:File a ticket inGitHub IssuesPost a discussion inGitHub DiscussionsReach out to us on the#fbgemmchannel inPyTorch SlackFor contributions, please see theCONTRIBUTINGfile for
ways to help out.LicenseFBGEMM_GPU is BSD licensed, as found in theLICENSEfile.
Package: fbgs
Facebook group scraperA simple selenium-dependent python scraper for Facebook groups.Requirements:Python 3.6 or newerSelenium (automatically installed)Installing# pypipip3installfbgs# localgitclonehttps://github.com/lesander/facebook-group-scraper.gitcdfacebook-group-scraper/
pip3install-rrequirements.txtUsageCli usage:# local & pypipython3-mfbgs\--usernameuser@example.com\--password"example-password"\--urlhttps://m.facebook.com/groups/group-name\--output./path/for/output.json\--no-shell# toggle to disable default selenium interactive promptProgrammatic usage:fromfbgsimportFacebookScraperfromfbgs.exceptionsimportScraperExceptiontry:scraper=FacebookScraper(username="user@example.com",password="example-password")scraper.login()g=scraper.scrape_group(url="https://m.facebook.com/groups/group-name",out="output-example.json")scraper.interactive()exceptScraperExceptionase:print(f'ScraperException:{e}')
Package: fbi
Table of contentsIntroductionInstallationInstall the fbi utility from PyPIInstall the fbi utility from GitHubUpgrade the fbi utility from PyPIUninstall the fbi utilityDevelopment installationQuickstartCLI clientHelpVersionInit the fbi utilityEncode password in a fileDecode password from a fileClient libraryGet password from an encoded fileIntroductionThe passwords encryption utility. Do not save your passwords as plaintext!InstallationInstall the fbi utility from PyPI$ sudo pip install fbiInstall the fbi utility from GitHub$ sudo pip install git+git://github.com/korniichuk/fbi#egg=fbiUpgrade the fbi utility from PyPI$ sudo pip install -U fbior:$ sudo pip install --upgrade fbiUninstall the fbi utility$ sudo pip uninstall fbiDevelopment installation$ git clone git://github.com/korniichuk/fbi.git
$ cd fbi
$ sudo pip install .QuickstartFirst, init the fbi utility:$ fbi initSecond, encode password in a file:$ fbi encode PATHExample:$ fbi encode ~/.key/netezza.encThird, decode password from a file:>>> from fbi import getpassword
>>> path = "~/.key/netezza.enc"
>>> passwd = getpassword(path)CLI clientA command line interface for managing an encoded password files.HelpThe standard output for –help:$ fbi -hor:$ fbi --helpFor information on using subcommand “SUBCOMMAND”, do:$ fbi SUBCOMMAND -hor:$ fbi SUBCOMMAND --helpExample:$ fbi init -hVersionThe standard output for –version:$ fbi -vor:$ fbi --versionInit the fbi utility$ fbi initEncode password in a file$ fbi encode PATHWhere:PATH– destination path.Example:$ fbi encode /home/titan/.key/netezza.encor:$ fbi encode ~/.key/netezza.encDecode password from a file$ fbi decode PATHWhere:PATH– source path.Example:$ fbi decode /home/titan/.key/netezza.encor:$ fbi decode ~/.key/netezza.encNoteDo not use$ fbi decode PATHfor your automation scripting.Client libraryA Python client for managing an encoded password files.Get password from an encoded file>>> from fbi import getpassword
>>> getpassword(path)Where:path– source path.Example:>>> from fbi import getpassword
>>> path = "/home/titan/.key/netezza.enc"
>>> passwd = getpassword(path)or:>>> from fbi import getpassword
>>> path = "~/.key/netezza.enc"
>>> passwd = getpassword(path)
Package: fbi-api
A Python wrapper around the FBI API (unofficial)Assists in retrieving information from the FBI Wanted program, this includes lists of wanted criminals, accomplices of a crime and or victims.DocumentationRefer to the Wikifor installation instructions, code examples, in addition to all other documentation.NoteThis project is in no way affiliated with the FBI lol. I only use their API.LicenseSupportIf you like this project and would like to show support, you can Buy Me A Coffee using the button belowYour support will be much appreciated!😊
Package: fbiradio
FBi Radio currently playingFind out which track is currently playing onFBi Radio,
Australia's best radio station.InstallationInstall from PyPI:pip install fbiradioOr clone this repository and install locally:git clone https://github.com/cchristiansen/fbiradio/
cd fbiradio
pip install .Usage in consoleList the lastNtracks by replacingNfor 5 or any other positive integer.
IfNis 0, all tracks that have been played in the current radio show are
displayed.$ fbiplaying -n NIf no number is given, the latest track is displayed.$ fbiplaying
01:37 pm: SOPHIE - UNISILImport as libraryimport fbiradio

# Get the last three tracks
tracks = fbiradio.currently_playing(n=3)

# Find the artist name of the first track
print(tracks[0].artist)

# Find when the second track was played
print(tracks[1].played_at)

# What was the track name of the latest track?
print(tracks[-1].track)

# Show all information on the latest three tracks
for track in tracks:
    print(track)DisclaimerAside from supporting the radio station and loving the music FBi plays, I am
not affiliated with the station in any way, shape or form.CopyrightCopyright 2021, Christian Christiansen.Code is distributed underGNU Affero Public License 3.Listen to 94.5 FM. Peace out.