Saravanan666/pypi-org huggingface dataset:
Package: lazyseq
lazyseqis a simple library implementing a single class,LazySeq, which
provides a lazily evaluated sequence that can be used like an immutable list.
You can think of it as a Pythonic version of Clojure’sSeq. The main use ofLazySeqis for
wrapping generators orgenerator expressionsto make
them persistent, but still lazy.LazySeqimplements Python’s sequence interface, and thus has the methods__getitem__,__len__,__contains__,__iter__,__reversed__,index, andcount.Getting an item from a LazySeq is equivalent to getting an items from the
provided iterable as a list. However, only the necessary elements of the
iterable are evaluated (all those up to the maximum requested element), and all
evaluated elements are cached on theLazySeqso it can be iterated over
again. Note that some operations likelen(seq)will by necessity iterate
over (and thus cache) the entire iterable.To use LazySeq, just callLazySeqon any Python iterable:>>>fromlazyseqimportLazySeq>>>seq=LazySeq(x**2forxinrange(5))>>>seqLazySeq([...])>>>seq[:3][0,1,4]>>>seqLazySeq([0,1,4,...])>>>list(seq)[0,1,4,9,16]>>>seqLazySeq([0,1,4,9,16])
Package: lazysequence
A lazy sequence makes an iterator look like an immutable sequence:fromlazysequenceimportlazysequencedefload_records():returnrange(10)# let's pretend this is expensiverecords=lazysequence(load_records())ifnotrecords:raiseSystemExit("no records found")first,second=records[:2]print("The first record is",first)print("The second record is",second)forrecordinrecords.release():# do not cache all records in memoryprint("record",record)Why?Sometimes you need to peek ahead at items returned by an iterator. But what if later code needs to see all the items from the iterator? Then you have some options:Pass any consumed items separately. This can get messy, though.Copy the iterator into a sequence beforehand, if that does not take a lot of space or time.Duplicate the iterator usingitertools.tee, or write your own custom itertool that buffers consumed items internally. There are some good examples of this approach on SO, byAlex Martelli,Raymond Hettinger, andNed Batchelder.A lazy sequence combines advantages from option 2 and option 3. It is an immutable sequence that wraps the iterable and caches consumed items in an internal buffer. By implementingcollections.abc.Sequence, lazy sequences provide the full set of sequence operations on the iterable. Unlike a copy (option 2), but like a duplicate (option 3), items are only consumed and stored in memory as far as required for any given operation.There are some caveats:The lazy sequence will eventually store all items in memory. If this is a problem, uses.release()to obtain an iterator over the sequence items without further caching. After calling this function, the sequence should no longer be used.Explicit is better than implicit. Clients may be better off being passed an iterator and dealing with its limitations. For example, clients may not expectlen(s)to incur the cost of consuming the iterator to its end.InstallationYou can installlazysequenceviapipfromPyPI:$pipinstalllazysequenceContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,lazysequenceis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz’sHypermodern Python Cookiecuttertemplate.
Package: lazy-serve
lazy_serveA simple, easy-to-use Python package for starting HTTP servers with minimal setup. Ideal for serving static files in development environments or for lightweight file sharing.InstallationTo install lazy_serve, simply use pip:pipinstalllazy_serveUsageUsing lazy_serve is straightforward. Here's a basic example:importlazy_serveaslzif__name__=="__main__":servers=[(8080,"~/server1/out/"),(8081,"~/server2/out/")]lz.serve(servers)This code will start HTTP servers on ports 8080 and 8081, serving files from~/server1/out/and~/server2/out/respectively.FeaturesEasy to Use:Start a server in just a few lines of code.Flexible:Serve any directory by simply specifying its path.Concurrent Servers:Run multiple servers at once, each on its own port.RequirementsPython 3.6 or higherContributingContributions to lazy_serve are welcome! Please follow these steps to contribute:Fork the repository.Create a new branch for your feature.Add your feature or bug fix.Run the tests to ensure everything is working.Submit a pull request.LicenseThis project is licensed under the MIT License.
Package: lazy-set
# LazySetA collection that tries to imitate a “chained” difference and union of sets and various other operations are performed
lazily:Given a base set, an iterable of sets to subtract from the base set, and an iterable of sets to now add to it
(note that the order is important!), a LazySet will act exactly like a regular set and supports all the basic
operations, but without actually performing any state-changing unions or differences to any of the participating
sets (that take O(number of items in all sets)).Instead, the LazySet goes over all participating sets according to their order to check if an item is contained
in it, thus giving a better run time in some cases for most operations. For example, if the LazySet used is after
initialization and no modifying operations were used on it, containment checking takes O(number of participating sets).Note that when using only difference and union operations, this collection is the set equivalent of ChainMap, so it can
also be called ChainSet.
Package: lazyshorts-py
LazyShortsA command-line tool to convert long-form videos into multiple short-form videos, with burned-in text and subtitles.
It also cuts out unwanted silence.PreviewOriginal videohttps://user-images.githubusercontent.com/29675001/210073372-f2a3fa63-29c2-4bc0-ab03-31188a772021.mp4Result (with manual subtitle correction)https://user-images.githubusercontent.com/29675001/210073848-09bcd7b7-6a86-4114-ab0b-72ddf674e298.mp4Result (without manual subtitle correction)As you can see, in Hungarian themediummodel works quite well: considering the bad quality of my input.
Thelargemodel could be even better:ifyou have the hardware. :)https://user-images.githubusercontent.com/29675001/210073868-f650cef7-093c-4260-9407-c6bc7de1b59e.mp4NotesArgumentsSeelazyshorts -hSubtitlesI use Whisper to transcribe audible voices to text.Obviously with non-english languages the accuracy can be lower: you can help that by......using a different (for now, only Whisper) model (be wary, themediummodel is hard to run even with 8GBs of RAM.)...editing subtitles manually from segment to segment. ({lazyshorts-py} e1 2 45 78...)Not testedI don't know if running Whisper on GPU works, you could try CUDA. See--whisper_deviceand PyTorch/Whisper documentation. Also, get the CUDA enabled PyTorch as I define the CPU one in therequirements.txt.Known issuesmoviepyis largely unmaintained at the moment, somehow it collides withmultiprocessingand the whole tool can crash-and-burn (as in, freeze) and you'll have to force exit. The renders still completed in my testing.We could userichto have nice progress bars, as currently you have to manually poll the status of the renders.Cropping is just arbitrary: I wanted to use MediaPipe. It's not easy to even get it to run, but my resources were not enough. Maybe a less demanding model or cloud is needed?Don't combine segments that are less thanend_time, you'll get an exception.
Package: lazysql
LazySql: SQL connector for lazy peopleInstallationpip3 install lazysqlTestingpython3 -m unittestUsageConnecting#Import your db connector
import sqlite3
import psycopg2
from lazysql import LazySql

read_db = LazySql(sqlite3, 'read.db', max_conn=3)

write_db = LazySql(psycopg2, "dbname='write_db' user='postgres' host='localhost' password='UnsafePassword' port=5432")Readingquery1 = f"SELECT * FROM read_table LIMIT 5;"
#Returns selection from DB
res = read_db.query(query1)Writingquery2 = f"INSERT INTO write_table(value1, value2, value3) VALUES(%s, %s, %s)"
write_db.query(query2, , data=(test, 1, 2.3), commit=True)Batch Writingquery3 = f"INSERT INTO write_table(value1, value2, value3) VALUES(%s, %s, %s)"
for i in range(0,100):
    write_db.batch(query3, data=(f"test{i}", i, i+1.5))
write_db.commit()


#Alternatives
write_db.batch(None, commit=True) #Commits directly
write_db.close() #Close without committing
write_db.batch(None, close=True) #Close without committingAsynctest1res, test2res, test3res = read_db.async_query([
    {"query":"SELECT * FROM test WHERE _str=?", "data":"test1"},
    {"query":"SELECT * FROM test WHERE _str=?", "data":"test2"},
    {"query":"SELECT * FROM test WHERE _str=?", "data":"test3"}
])

#Result will be in list of order of query sent
#i.e
[
    [
        {'id': 5, '_str': 'test1', '_int': 1, '_flt': 2.5},
        {'id': 15, '_str': 'test1', '_int': 1, '_flt': 2.5},
        {'id': 25, '_str': 'test1', '_int': 1, '_flt': 2.5}
    ],
    [
        {'id': 6, '_str': 'test2', '_int': 2, '_flt': 3.5},
        {'id': 16, '_str': 'test2', '_int': 2, '_flt': 3.5},
        {'id': 26, '_str': 'test2', '_int': 2, '_flt': 3.5}
    ],
    [
        {'id': 7, '_str': 'test3', '_int': 3, '_flt': 4.5},
        {'id': 17, '_str': 'test3', '_int': 3, '_flt': 4.5},
        {'id': 27, '_str': 'test3', '_int': 3, '_flt': 4.5}]
]
Package: lazy-stats-vx
A sample Python projectA sample project that exists as an aid to thePython Packaging User
Guide'sTutorial on Packaging and Distributing
Projects.This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.The source for this project is available here.The metadata for a Python project is defined in thepyproject.tomlfile,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.This is the README file for the project.The file should use UTF-8 encoding and can be written usingreStructuredTextormarkdownwith the appropriatekey set. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, and should be
written for that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not a
good idea, although a simple “What's New” section for the most recent version
may be appropriate.
Package: lazy-string
Python library for defining strings with delayed evaluation.The package provides aLazyStringclass. Its constructor accepts a callable (say, a function) which will be called when string’s value is needed. The constructor also allows to specify positional and keyword arguments for that callable:def__init__(self,func:Callable[...,str],*args:Tuple,**kwargs:Mapping)->None:...The value is re-evaluated on every access.InstallationAvailable as aPyPIpackage:pipinstalllazy-stringUsageUsing with a function having no parameters:fromlazy_stringimportLazyStringdefmake_foo()->str:return"foo"s=LazyString(make_foo)The value is evaluated on demand:>>>s+" bar"'foo bar'>>>str(s)'foo'Representation explicitly tells it’s aLazyString:>>>sLazyString('foo')It’s safe to pass standard strings, as they will be returned as-is:>>>LazyString("foo bar")'foo bar'Supports methods of standard strings:>>>s.upper()'FOO'>>>"f"insTrue>>>dir(s)['__add__','__class__','__contains__','__delattr__','__dir__','__doc__','__eq__','__format__','__ge__','__getattribute__','__getitem__','__getnewargs__','__gt__','__hash__','__init__','__init_subclass__','__iter__','__le__','__len__','__lt__','__mod__','__mul__','__ne__','__new__','__reduce__','__reduce_ex__','__repr__','__rmod__','__rmul__','__setattr__','__sizeof__','__str__','__subclasshook__','capitalize','casefold','center','count','encode','endswith','expandtabs','find','format','format_map','index','isalnum','isalpha','isascii','isdecimal','isdigit','isidentifier','islower','isnumeric','isprintable','isspace','istitle','isupper','join','ljust','lower','lstrip','maketrans','partition','replace','rfind','rindex','rjust','rpartition','rsplit','rstrip','split','splitlines','startswith','strip','swapcase','title','translate','upper','zfill']Supplying parameters for the callable:defmake_foo(arg1,arg2):returnf"foo{arg1}{arg2}"s=LazyString(make_foo,123,arg2=456)>>>str(s)'foo 123 456'Implementation DetailsLazyStringis inherited fromcollections.UserString.>>>LazyString.__mro__(<class'lazy_string.LazyString'>, <class 'collections.UserString'>,<class'collections.abc.Sequence'>, <class 'collections.abc.Reversible'>,<class'collections.abc.Collection'>, <class 'collections.abc.Sized'>,<class'collections.abc.Iterable'>, <class 'collections.abc.Container'>,<class'object'>)SerializationPicklingSupported out of the box:>>>importpickle>>>s==pickle.loads(pickle.dumps(s))TrueTo JSONSupported with any encoder able to encodecollections.UserString:importjsonimportcollectionsclassJSONEncoder(json.JSONEncoder):defdefault(self,o):ifisinstance(o,collections.UserString):returnstr(o)returnsuper().default(o)>>>data={'s':s}>>>json.dumps(data,cls=JSONEncoder)'{"s": "foo"}'
Package: lazy-table
Apython-tabulatewrapper for producing tables from generators.Motivationlazy_table is useful when (i) each row of your table is generated by a possibly expensive computation and (ii) you want to print rows to the screen as soon as they are available.For example, the rows in the table below correspond tonumerically solving an ordinary differential equationwith progressively more steps. Seeexamples/euler_vdp.pyfor the code to generate this table.Here is the same example with a progress bar.Installation and usageInstall lazy_table via pip:$pipinstalllazy_tableIn your Python code, addimportlazy_tableasltNow, generating your own lazy table is as simple as callinglt.streamon a generator that yields one or more lists, where each list is a row of your table (see the example below).ExampleAs a toy example, consider printing the first 10Fibonacci numbersin a table.
Since this is a relatively cheap operation, we will pretend it is expensive by adding an artificialsleep(1)call beteween Fibonacci numbers.importtimeimportlazy_tableasltdeffib_table(n):x0,x1=0,1yield[0,x0]yield[1,x1]foriinrange(2,n+1):time.sleep(1)# Simulate workx0,x1=x1,x0+x1yield[i,x1]lt.stream(fib_table(10),headers=['N','F_N'])Your final table should look like this:N    F_N
---  -----
  0      0
  1      1
  2      1
  3      2
  4      3
  5      5
  6      8
  7     13
  8     21
  9     34
 10     55You can also specify an "artist" that determines how the table is rendered.
For example, theConsoleWithProgressartist displays a progress bar alongside the table indicating the percentage of rows completed:n=10artist=lt.artists.ConsoleWithProgress()lt.stream(fib_table(n),headers=['N','F_N'],n_rows=n+1,artist=artist)
Package: lazy-testdata
lazy_testdatatestdata include chinese personal four element and offen use datetime 测试数据包含随机生成的中国公民四要素，及常用的日期时间
shell命令执行方法封装，以及常用的文件处理方法
swagger2case，通过读取swagger文档，生成规则的测试用例，如-全参数，不传，传空。仅必填项和必填项逐个校验占未实现，敬请期待。
Package: lazy-text-classifiers
lazy-text-classifiersBuild and test a variety of text multi-class classification models.InstallationStable Release:pip install lazy-text-classifiersDevelopment Head:pip install git+https://github.com/evamaxfield/lazy-text-classifiers.gitQuickstartfromlazy_text_classifiersimportLazyTextClassifiersfromsklearn.datasetsimportfetch_20newsgroupsfromsklearn.model_selectionimporttrain_test_split# Example data from sklearn# `x` should be an iterable of strings# `y` should be an iterable of string labelsdata=fetch_20newsgroups(subset="all",remove=("header","footers","quotes"))x=data.data[:1000]y=data.target[:1000]y=[data.target_names[id_]forid_iny]# Split the data into train and testx_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.4,random_state=12,)# Init and fit all modelsltc=LazyTextClassifiers(random_state=12)results=ltc.fit(x_train,x_test,y_train,y_test)# Results is a dataframe# | model                  |   accuracy |   balanced_accuracy |   precision |   recall |       f1 |    time |# |:-----------------------|-----------:|--------------------:|------------:|---------:|---------:|--------:|# | semantic-logit         |    0.73    |            0.725162 |    0.734887 |  0.73    | 0.728247 |  13.742 |# | tfidf-logit            |    0.70625 |            0.700126 |    0.709781 |  0.70625 | 0.702073 | 187.217 |# | fine-tuned-transformer |    0.11125 |            0.1118   |    0.10998  |  0.11125 | 0.109288 | 220.105 |# Get a specific modelsemantic_logit=ltc.fit_models["semantic-logit"]# either an scikit-learn Pipeline or a custom Transformer wrapper class# All models have a `save` function which will store into the normal format# * pickle for scikit-learn pipelines# * torch model directory for TransformersDocumentationFor full package documentation please visitevamaxfield.github.io/lazy-text-classifiers.AcknowledgementsThis package was heavily inspired bylazypredict.DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.MIT License
Package: lazytime
lazytime介绍基于python3的时间模块的懒人封包软件架构软件架构说明安装教程pip安装pip3installlazytimepip安装（使用淘宝镜像加速）pip3installlazytime-ihttps://mirrors.aliyun.com/pypi/simple使用说明demoimportlazytimet=lazytime.get_datetime()
Package: lazytoolz
Lazy programming for Pythonpip install lazytoolsThis package adds lazily evaluated immutable objects.fromlazytoolzimportLazyListmyList=LazyList([1,2,3])myNumbers=LazyList(4,5,6)myRange=LazyList(range(7,1000000))allNumbers=myList+myNumbers+myRangeprint(allNumbers.drop(2).map(lambdax:x**2).filter(lambdax:x%3==0).take(10))LazyList(9, 36, 81, 144, 225, 324, 441, 576, 729, 900)
Package: lazytui
A terminal UI for the la(z)ypersonimportlazytuifromlazytui.fmtimportfmtclassHello(lazytui.TUI):defsetup(self):self.enable_title_bar('hello world',style="underline")self.enable_status_bar('press q to quit',fg="magenta")defdraw(self):self.write(2,0,'hello world '+fmt('from lazytui',fg="blue",bg="white",style=["underline","bold"]))defkey_pressed(self,key):ifkey=='q':self.exit()Hello().run()Lazytui is a python UI framework built around the built-incursesmodule.
It is simple to set up, easy to use and comes with all the built in power of the curses framework.Installationpip install lazytuiDesignLazytui design is based off of the design philosophy ofProcessing/P5js.
The TUI triggers certain methods that the user can override to control the flow of the program.Event MethodDescriptionsetup(self)After__init__()is called but before we itereate over thedraw()loopdraw(self)Continuously executes the lines of code contained inside its block until the program is stopped.key_pressed(self, key)Function called when user presses a key. Key is given as a string as an argument.window_resized(self)Function called when the window is resized.cleanup(self)Called at the very end of the programs execution, right before it exits.Helper methodsMethodDescriptionself.get_width()Returns the number of columns of the terminal.self.get_height()Returns the number of rows of the terminal.self.write()Write formatted text to the terminal.ColorsSeecurses constants documentationFG/BG colorsColorCurses equivalent"black"curses.COLOR_BLACK"blue"curses.COLOR_BLUE"cyan"curses.COLOR_CYAN"green"curses.COLOR_GREEN"magenta"curses.COLOR_MAGENTA"red"curses.COLOR_RED"white"curses.COLOR_WHITE"yellow"curses.COLOR_YELLOWStylesStyleCurses equivalent"altcharset"curses.A_ALTCHARSET"blink"curses.A_BLINK"bold"curses.A_BOLD"dim"curses.A_DIM"invis"curses.A_INVIS"italic"curses.A_ITALIC"normal"curses.A_NORMAL"protect"curses.A_PROTECT"reverse"curses.A_REVERSE"standout"curses.A_STANDOUT"underline"curses.A_UNDERLINE"horizontal"curses.A_HORIZONTAL"left"curses.A_LEFT"low"curses.A_LOW"right"curses.A_RIGHT"top"curses.A_TOP"vertical"curses.A_VERTICAL"chartext"curses.A_CHARTEXT
Package: lazyuptimerobot.py
lazyuptimerobot.pyUptimeRobot API Python integration with docs.fromlazyuptimerobotimportUptimeRobotmonitor=UptimeRobot(api_key="YOUR_API_KEY")help(monitor)...print(monitor.getMonitors())print(monitor.newAlertContact(type=2,value="email@email",friendly_name="John"))...
Package: lazyutils
Lazyutils provides a few simple utilities for lazy evaluation of code.Lazy attribute==============The lazy decorator defines an attribute with deferred initialization::.. code::pythonimport mathfrom lazyutils import lazyclass Vec:def __init__(self, x, y):self.x, self.y = x, y@lazydef magnitude(self):print('computing...')return math.sqrt(self.x**2 + self.y**2)Now the ``magnitude`` attribute is initialized and cached upon first use:>>> v = Vec(3, 4)>>> v.magnitudecomputing...5.0The attribute is writable and apart from the deferred initialization, it behavesjust like any regular Python attribute.>>> v.magnitude = 42>>> v.magnitude42Lazy attributes can be useful either to simplify the implementation of the__init__ method of objects that initialize a great number or variables or as anoptimization that delays potentially expensive computations that may not benecessary in the object's lifecycle.Delegation==========The delegate_to() function delegates some attribute to an attribute during theclass definition::.. code::pythonfrom lazyutils import delegate_toclass Arrow:magnitude = delegate_to('vector')def __init__(self, vector, start=Vec(0, 0)):self.vector = vectorself.start = startNow, the ``.magnitude`` attribute of ``Arrow`` instances is delegated to``.vector.magnitude``. Delegate fields are useful in class composition when onewants to expose a few selected attributes from the inner objects. delegate_to()handles attributes and methods with no distinction.>>> a = Arrow(Vec(6, 8))>>> a.magnitudecomputing...10.0Aliasing========Aliasing is a very simple form of delegation. We can create simple aliases forattributes using the alias() and readonly() functions::class MyArrow(Arrow):abs_value = readonly('magnitude')origin = alias('start')This exposes two additional properties: "abs_value" and "origin". The first isjust a read-only view on the "magnitude" property. The second exposes read andwrite access to the "start" attribute.