Saravanan666/pypi-org huggingface dataset:
Package: clop
$ pip install clop$ pydoc clop$ python-mclop # Run the democlop provides minimal command line option specification and processing.When your command line processing requires the power of a king on a real horse,
use argparse in the standard python library.When banging two coconut halves together on a stick pony is good enough,
use clop.A single class to specify command line options,
process a command line against those options,
and make the processed results available as a dict.
Also provides a simple help string for each option and the calling program."""Your program."""

  from clop import Clop

  ...

  # Make a Clop object.
  clp = Clop()

  # Specify acceptable options and their arguments.
  clp.addOptionDef(letter = 'k',
      required = True,
      numArgs = 3,
      help = 'Help for option k')

  ...

  # optArgs is a dictionary of options and arguments
  optArgs = clp.processCmdLine(sys.argv[1:])

  ...

  # Print the help string when you need it.
  helpStr = clp.helpString('clop:\nShort demo of the clop module.')
  print(helpStr)

clop:
Short demo of the clop module.

REQUIRED OPTIONS:
k: Help for option k
  Allowed arguments: Exactly 3
m: Help for option m
  Allowed arguments: Exactly 1
n: Help for option n
  Allowed arguments: 0 or more

OPTIONAL OPTIONS:
o: Help for option o
  Allowed arguments: Exactly 0
p: Help for option p
  Allowed arguments: Exactly 1
q: Help for option q
  Allowed arguments: 0 or moreclop includes a demo in its main() function:$ python-mclop-k1 2 3-mone-two-n3 4 5-o-p1-q"one two"{'k': ['1', '2','3'],'m':['one-two'],'o': [], 'n': ['3', '4','5'],'q': ['onetwo'],'p':['1']}Options are a dash and a single letter: -oAn option can be followed by zero or more arguments.An option can only be specified with its letter (-o). No –long options.An option letter can be any “letter” where letter.isalpha() is True.Options cannot be composed. Only one character per dash, not -olmp.Options can appear in any order on the command line.Options and arguments on the command line are strings.
The calling program does any type and value checking and conversion
of arguments after clop’s command line processing.
Clop makes no judgments other than allowed options and arguments.More clop features are unlikely. If you need more, argparse is excellent,
and comes with a real horse.
Package: clops
Installationpip install clopsUsage#! /usr/bin/env python3
"""Sample module docstring.

Args:
    foo: The number of foos.
    bar: The number of bars.
    baz: The number of bazs.

"""
from clops import cli


_defaults = {
    foo: 1,
    bar: 2,
    baz: 3,
}

@cli.UpdateDict(_defaults, __doc__)
def main():
    pass


if __name__ == '__main__':
    main()
Package: clopy
ClopyGeneral purpose templating program to quickly setup projects and filesUsageIn a new directory create atemplate.pyfilePrompt the user for input required for the Jinja template file
using standard outputRender the input to the template file using[template_name].renderWrite the output file to diskTemplates AliasesIf you find that a path is too long to type out or that you always find yourself typing
out the path to a certain template file, you can alias it using the~/.config/clopy/templates.jsonfile. There's an exampletemplates.jsonincluded in the root direct of the repository.
Package: closeai
🔥ChatLLM 基于知识库🔥Installpipinstall-UchatllmDocsUsagesfromchatllm.applicationsimportChatBaseqa=ChatBase()qa.load_llm4chat(model_name_or_path="THUDM/chatglm-6b")_=list(qa(query='周杰伦是谁',knowledge_base='周杰伦是傻子',role=' '))# 根据已知信息无法回答该问题，因为周杰伦是中国内地流行歌手、演员、音乐制作人、导演，# 是具有一定的知名度和专业能力的人物，没有提供足够的信息无法判断他是傻子。支持角色扮演ChatPDFClick to ChatPDF一键启动UIpipinstall"chatllm[pdf]"&&chatllm-runwebui--namechatpdfpython交互fromchatllm.applications.chatpdfimportChatPDFqa=ChatPDF(encode_model='nghuyong/ernie-3.0-nano-zh')qa.load_llm4chat(model_name_or_path="THUDM/chatglm-6b")qa.create_index('财报.pdf')# 构建知识库list(qa(query='东北证券主营业务'))# 根据已知信息，东北证券的主营业务为证券业务。公司作为证券公司，主要从事证券经纪、证券投资咨询、与证券交易、# 证券投资活动有关的财务顾问、证券承销与保荐、证券自营、融资融券、证券投资基金代销和代销金融产品待业务。支持召回结果查看DeployClick to DeployChatGLM-6B 模型硬件需求|量化等级|最低 GPU 显存（推理） |最低 GPU 显存（高效参数微调） |
| -------------- | ------------------------- | --------------------------------- |
| FP16（无量化） | 13 GB                     | 14 GB                             |
| INT8           | 8 GB                     | 9 GB                             |
| INT4           | 6 GB                      | 7 GB                              |从本地加载模型安装指南ChatGLM-6B Mac 本地部署实操记录THUDM/ChatGLM-6B#从本地加载模型TODOClick to TODOChatLLM 应用接入非结构化文档（已支持 md、pdf、docx、txt 文件格式）搜索引擎与本地网页接入结构化数据接入（如 csv、Excel、SQL 等）知识图谱/图数据库接入增加 ANN 后端，ES/RedisSearch【确保生产高可用】增加多级缓存缓存多路召回问标量匹配多种向量化，向量匹配增加相似问，换几个问法高置信度直接返回答案【匹配标准问】答高置信度篇章增加上下文信息增加夸篇章信息增加召回信息的相似信息提前生成标准问，匹配问拒绝推断增加更多 LLM 模型支持THUDM/chatglm-6bTHUDM/chatglm-6b-int8THUDM/chatglm-6b-int4THUDM/chatglm-6b-int4-qeClueAI/ChatYuan-large-v2增加更多 Embedding 模型支持nghuyong/ernie-3.0-nano-zhnghuyong/ernie-3.0-base-zhshibing624/text2vec-base-chineseGanymedeNil/text2vec-large-chinese增加一键启动 webui利用 streamlit 实现 ChatPDF，一键启动chatllm-run webui --name chatpdf利用 gradio 实现 Web UI DEMO添加输出内容及错误提示引用标注增加知识库管理选择知识库开始问答上传文件/文件夹至知识库删除知识库中文件增加 API 支持利用 Fastapi/Flask/Grpc
实现流式接口chatllm-run flask-api --model_name_or_path <MODEL_PATH> --host 127.0.0.1 --port 8000前后端分离，实现调用 API 的 Web UI Demo交流群若二维码失效加微信拉群 313303303=======
History0.0.0 (2023-04-11)First release on PyPI.
Package: close.consumer
Abstract base classes for a long running, streaming API consumer and a specific implementation that puts the data into aredisqueue and processes it in batches.Requiresgevent.  The example implementation depends onredis.Coded initially to send batches of statuses from theTwitter Streaming APIto an appengine webhook.  May (or may not) be usable for other purposes.Presumes the data coming down the pipe isChunked Transfer Encoded.
Package: closedai
Closed AI⚠️This project is in early development is a work in progress.⚠️closedaiis a drop-in replacement foropenai, but only with open models.InstallationFor now, install locally, as this project is a WIP and PyPi may be out of date.git clone https://github.com/nateraw/closedai.git
cd closedai
pip install -e .UsageThe idea is that you will run your own OpenAI-like server with whatever model you want. Once the server is running, you can hit it withopenaipython library (or any other SDK of your choosing) by overriding the api base endpoint with the URL to your running server.ServerIn your terminal, run:closedaiYou can see the available configuration flags withclosedai --help.Dockerdocker build -t closedai .
docker run -p 7860:7860ClientIf using localhost, you canfrom closedai import openai. If running remotely, for now you can justimport openaiand overrideopenai.api_basewith your endpoint and openai.api_key with a dummy value.Then, use it as you normally would...Completionsfromclosedaiimportopenaicompletion=openai.Completion.create(model='asdf',prompt='hi there, my name is',stream=False)print(completion)Completions streamingfromclosedaiimportopenaicompletion=openai.Completion.create(model='asdf',prompt='hi there, my name is',stream=True)fornew_textincompletion:print(new_text)Chat Completionsfromclosedaiimportopenaicompletion=openai.ChatCompletion.create(model="gpt-3.5-turbo",messages=[{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Who won the world series in 2020?"},{"role":"assistant","content":"The Los Angeles Dodgers won the World Series in 2020."},{"role":"user","content":"Where was it played?"},],stream=False,)print(completion)Chat Completions streamingfromclosedaiimportopenaicompletion=openai.ChatCompletion.create(model="gpt-3.5-turbo",messages=[{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Who won the world series in 2020?"},{"role":"assistant","content":"The Los Angeles Dodgers won the World Series in 2020."},{"role":"user","content":"Where was it played?"},],stream=True,)forxincompletion:print(x)
Package: closedexpressions
fpp-closed-expressionsClosed expressions for the most common functions related to shot noise processes.installationThe package is published to PyPI and can be installed withpipinstallclosedexpressionsIf you want the development version you must first clone the repo to your local machine,
then install the project and its dependencies with [poetry]:gitclonehttps://github.com/uit-cosmo/fpp-closed-expressionscdfpp-closed-expresions
poetryinstallUseImport functions directly, i.e.:importclosedexpressionsascepsd=ce.psd(omega,td,l)
Package: closek
Close-k ClassifierThis repository contains code accompanyingMinimizing Close-k Aggregate Loss Improves ClassificationBryan He, James Zou.We provide a Python 3 implementation using thescikit-learn API,
and provide code to reproduce the figures and tables from the paper.InstallationOur package is available onPyPy, and can be installed usingpip install -ihttps://pypi.org/project/closekYou can also install this package by cloning theGithub repository, and runningpip install closekIf you want directly use the implementation in your package, you can also copyclosek/closek.pyinto your code.UsageAn example of how to use our package is shown intest.py.Generating Results from PaperThe code used for the paper is inexperiments. See the README
there for more details.
Package: closely
Closely :triangular_ruler:Find the closest pairs in an array.Closely uses principal component analysis (PCA) to reduce the dimensions to 2 and a divide and conquer algorithm to find the closest pair of points.Getting Startedpipinstallcloselyor install from source:gitclonehttps://github.com/justinshenk/closelycdclosely
pipinstall.How to useimportclosely# X is an n x m numpy arraypairs,distances=closely.solve(X,n=1)You can specify how many pairs you want to identify withn.Exampleimportcloselyimportnumpyasnpimportmatplotlib.pyplotasplt# Create datasetX=np.random.random((100,2))pairs,distance=closely.solve(X,n=1)# Plot pointsz,y=np.split(X,2,axis=1)fig,ax=plt.subplots()ax.scatter(z,y)fori,txtinenumerate(X):ifiinpairs:ax.annotate(i,(z[i],y[i]),color='red')else:ax.annotate(i,(z[i],y[i]))plt.show()Check pairs:In [10]: pairs                                                                                                                                
Out[10]: 
array([[ 7, 16],
       [96, 50]])Output:CaveatsIf your data has more than 3 features,closelywill reduce the dimensionality by projecting it onto two directions that explain most of the variance. This speeds up processing, but is not 100% precise. In other words, if your data has four columns (eg, x, y, z, a), it will apply divide-and-conquer on the new projection bases P1 and P2.It also removes the first point in a pair ifn>1. In rare cases this leads to false negatives if the data is highly overlapping.Credit and ExplanationPython code modified fromAndriy Lazorenko, packaged and made useful for >2 features by Justin Shenk.
Package: closest-pairs
Closest Pairs :triangular_ruler:Find the closest pairs in an array.Getting Startedpipinstallclosest_pairsor install from source:gitclonehttps://github.com/justinshenk/closest-pairscdclosest_pairs
pipinstall.How to useimportclosest_pairs# X is an n x m numpy arraypairs,distances=closest_pairs.solve(X,n=1)You can specify how many pairs you want to identify withn.Exampleimportclosest_pairsimportnumpyasnpimportmatplotlib.pyplotasplt# Create datasetX=np.random.random((100,2))pairs,distance=closest_pairs.solve(X,n=1)# Plot pointsz,y=np.split(X,2,axis=1)fig,ax=plt.subplots()ax.scatter(z,y)fori,txtinenumerate(X):ifiinpairs:ax.annotate(i,(z[i],y[i]),color='red')else:ax.annotate(i,(z[i],y[i]))Check pairs:In [10]: pairs                                                                                                                                
Out[10]: 
array([[[ 7],
        [16]],

       [[96],
        [50]]])Output:Caveatsclosest_pairswill reduce the dimensionality with PCA of your data to two-dimensions for faster processing.It also removes the first point in a pair ifn>1. In rare cases this leads to false negatives if the data is highly overlapping.Credit and ExplanationPython code modified fromAndriy Lazorenko, packaged and made useful for >2 features by Justin Shenk.
Package: closure
TheClosure Compileris
a tool for reducing the size of Javascript files to make them download
and run faster.It’s a Java-based tool. This package, in the spirit of theyuicompressor <http://pypi.python.org/pypi/yuicompressor/>package,
provides a simple way to install and use the the Closure compiler from
Python, bundling theclosure.jarwith the Python package.I recommend looking atwebassetsfor your CSS/JS compression needs.VersionsVersionYYYYMMDDof this package will bundle the corresponding
version of the compiler (Closure uses the build date as the version).If you want a new version to be uploaded, open a ticket at theGithub page.
Package: closure-problem
Closure problems on graphsBare-bones Python package for solving closure problems on directed graphs. Package also include a class for solving Hochbaum's normalized cut (HNC) on an undirected graph.InstallationUsepipto install the package:pipinstallclosure-problemExampleSeetestsfolder for examples.Further ReadingUC Berkeley IEOR 266 lecture notesDS Hochbaum (2013). A polynomial time algorithm for rayleigh ratio on discrete variables: Replacing spectral techniques for expander ratio, normalized cut, and cheeger constant. Operations Research, 61(1), 184-198.
Package: closure-soy
Usagepip installclosure-soyclosure-soy[<flag1> <flag2>...]--outputPathFormat<formatString> <soyFile1> <soyFile2> ...Note: Consider integrating this package into webassets or another such package for automatic soy template compilation.AboutClosure Templatesis
a client and server side templating system for building reusable HTML and UI elements.
Closure’s templating system is also commonly known as Soy templates.This is a Java-based tool. This package, based on theClosure Compilerpackage,
provides a simple way to install and use the the Closure Template compiler from
Python, bundling thesoy.jarwith the Python package.This package was built for integrating withwebassetsfor automatic template compiliation.VersionsVersionYYYYMMDDof this package will bundle the corresponding
version of the compiler (Closure uses the build date as the version).If you want a new version to be uploaded, open a ticket at theGithub page.
Package: closure-tree
Django closure tree model.Abstract base model for creating a Closure Tree using a recursive Postgres view.http://schinckel.net/2016/01/27/django-trees-via-closure-view/UsageInherit from the Node model:fromclosure_tree.modelsimportNodeclassMyNode(Node):name=models.CharField(max_length=30)Create migrations:$./manage.pymakemigrationsAdd the CreateTreeClosure migration step:$./manage.pymakenigrations--emptymyappfromclosure_tree.migrationsimportCreateTreeClosureclassMigration(migrations.Migration):dependencies=[('dummy','0001_initial'),]operations=[CreateTreeClosure('MyNode'),]
Package: clo-tetris-game
Tetris - Une version simple du jeu TetrisCe packagage implÃ©mente une version simple du jeu TetrisVous pouvez l'installer avec pip:pip install clo-tetris-gameExemple d'usage:>>> from tetris_game import TetrisGame
>>> TetrisGame()Ou directement dans un terminal :playTetrisCe code est sous licence MIT.
Package: clothesline
clotheslineA library to handle sets, made of intervals over a continuous
infinite axis (a "domain") such as the real numbers or date/times.importclotheslinebld=clothesline.RealIntervalSet.builder()set1=bld[-10](10)-bld(0)[1]set1# [-10, 0] U (1, 10)set1.extension()# 19set2=set1+bld[100](...)set2.complement()# (-inf, -10) U (0, 1] U [10, 100)set2+set2.complement()# (-inf, +inf)QuickstartNote: for the full documentation, visitclothesline.readthedocs.io.Work with setsThe library provides ready-to-useRealIntervalSetto deal with sets over a number line
andDatetimeIntervalSetfor date intervals.Start by importing the library and getting abuilderfor interval sets on the real line:importclotheslinebld=clothesline.RealIntervalSet.builder()Create a couple of intervals:set1=bld(0)(10)set2=bld[0][1]+bld(2)(3)+bld[20](...)print(set2)Here, round brackets mean that the boundary is excluded; square brackets mean the point is included.
Three dots stand for the point at infinity.You can also get anutilsobject, to create standard sets such as open or closed intervals, point-like sets and so on:uti=clothesline.RealIntervalSet.utils()set3=uti.open(0,5)set4=uti.high_slice(10,included=True)Most set operations are supported: from complement to difference,
from inclusion tests to XOR. Interval Sets should be treated as immutable objects.
Moreover, since the internal representation of a set is always
normalized to a canonical form, equality tests work as you would
expect:set5=set4.union(set2)set4-set2set3+bld[3](10)==set1# TrueFor more ways to create and manipulate sets, have a look at theUser Guidein the full documentation.Store and retrieveYou can convert any interval set into a string (e.g. to store it into
a database) and re-hydrate it later:importjsonserialized=json.dumps(set3.to_dict())# ...new_set3=uti.from_dict(json.loads(serialized))new_set3==set3# TrueDatetimesSupport for datetimes is ready out-of-the-box:fromdatetimeimportdatetimedbld=clothesline.DatetimeIntervalSet.builder()timespan1=dbld[datetime(2010,10,10)](datetime(2011,2,20))print(timespan1.complement())print(timespan1.extension())That's it! For more, check thefull documentation.ContributingComing soon.
Package: cloths-segmentation
cloths_segmentationCode for binary segmentation of clothsData PreparationDownload the dataset fromhttps://www.kaggle.com/c/imaterialist-fashion-2019-FGVC6Process the data using scripthttps://github.com/ternaus/iglovikov_helper_functions/tree/master/iglovikov_helper_functions/data_processing/prepare_cloths_segmentationThe script will create process the data and store images to folderimagesand binary masks to folderlabels.TrainingDefine the config.Example atcloths_segmentation/configsYou can enable / disable datasets that are used for training and validation.Define the environmental variableIMAGE_PATHthat points to the folder with images.Example:exportIMAGE_PATH=<pathtothethefolderwithimages>Define the environmental variableLABEL_PATHthat points to the folder with masks.Example:exportMASK_PATH=<pathtothefolderwithmasks>Trainingpython -m cloths_segmentation.train -c <path to config>Inferencepython-mtorch.distributed.launch--nproc_per_node=<num_gpu>cloths_segmentation/inference.py\-i<pathtoimages>\-c<pathtoconfig>\-w<pathtoweights>\-o<output-path>\--fp16
Package: clot-terminal
Clot v.0.0.2AboutA terminal which have some random cool features.Present Features:HistoryColorfull ForegroundMade for Windows and Linux usersCommand Book [A place to notedown commands and stuff]Installationpip install clot-terminalOr run with out installinggit clone https://github.com/merwin-asm/Clot.gitcd Clotpython3 main.pyHow to use ?Just open your terminal and type in clot :)UpdatingType in update to any clot sessionupdateExitingType in exitexitPreviewNote :This terminal may be lacking some features which others offer like , going back to previous commands. [ We be fixin it ]Some tools like vim doesnt work on it... [ We will fix it]The terminal is slow at printing when you run somethin which takes hella time. [ Will be def fixed]Collabing and DonatingDm on ma discord - cactochan#2004 , If you wanna give some suggestions or you wanna collab with me..Duino coin - cactiBNB / Eth - 0x9CE5E2179A95Ba94f1B723FbfAd4710a9891c9e3Patreon - cactochan
Package: cloud
PiCloudis a cloud-computing platform that integrates into the Python Programming Language. It enables you to leverage the computing power of Amazon Web Services without having to manage, maintain, or configure virtual servers.When using this Python library known ascloud, PiCloud will integrate seamlessly into your existing code base. To offload the execution of a function to our servers, all you must do is pass your desired function into thecloudlibrary. PiCloud will run the function on its high-performance cluster. As you run more functions, our cluster auto-scales to meet your computational needs.Before using this package, you will need to sign up aPiCloudaccount.Thecloudlibrary also features a simulator, which can be used without a PiCloud account.  The simulator uses themultiprocessinglibrary to create a stripped down version of the PiCloud service.  This simulated service can then run jobs locally across all CPU cores.Quick command-line example:>>> import cloud
>>> def square(x):
...     return x*x
...
>>> jid = cloud.call(square,3)  #square(3) evaluated on PiCloud
>>> cloud.result(jid)
9Full package documentation is available athttp://docs.picloud.com.  Some dependencies may be required depending on your platform and Python version; see INSTALL for more information.
Package: cloud2
CLOUD AUDIT TOOLCloud Audit tool helps you to check whether the application built on AWS is following the AWS Well-Architected framework by validating it against
various AWS services security requirements and the best practices. Generates report to validate the security requirements and security best practices.Cloud Audit tool setupThe project basically uses boto client to connect to the aws services.Update the session credentials in the terminal or give them through the arguments in the command line by specifying --access-key, --secret-key, and --session-token.While running the program, You can specify the region name, config file path, report file path, and tags through --region, --config-path, -report-path arguments.If no region is specified us-east-1 is taken as the default region, Similarly if no config file path and report path are given, the current working directory's path is taken as the default path and the audit checks are performed for the default values.Tags are checked by passing as an argument --tags or updating account tags in the config.json file. Moreover, the tags passed through the argument in the command line take priority.The format of the tags that are passed through the argument in the command line should be separated using commas (,).Cloud Audit Tool ExecutionCommand to Run the audit tool in the command line after installing the package.cloud-audit-toolArguments that can be passedcloud-audit-tool --region {region-name} --config-path {config-path} --report-path {report-path} --access-key {access-key} --secret-key {secret-key} --session-token {session-token} --tags {tag1,tag2,..}Final ReportThe overall report is provided as a HTML document in the path specified in the report-path argument. If no path is specified report is generated in the current working directory.