Saravanan666/pypi-org huggingface dataset:
Package: paddle-parakeet
ParakeetParakeet aims to provide a flexible, efficient and state-of-the-art text-to-speech toolkit for the open-source community. It is built on PaddlePaddle Fluid dynamic graph and includes many influential TTS models proposed byBaidu Researchand other research groups.In particular, it features the latestWaveFlowmodel proposed by Baidu Research.WaveFlow can synthesize 22.05 kHz high-fidelity speech around 40x faster than real-time on a Nvidia V100 GPU without engineered inference kernels, which is faster thanWaveGlowand serveral orders of magnitude faster than WaveNet.WaveFlow is a small-footprint flow-based model for raw audio. It has only 5.9M parameters, which is 15x smalller than WaveGlow (87.9M).WaveFlow is directly trained with maximum likelihood without probability density distillation and auxiliary losses as used in Parallel WaveNet and ClariNet, which simplifies the training pipeline and reduces the cost of development.OverviewIn order to facilitate exploiting the existing TTS models directly and developing the new ones, Parakeet selects typical models and provides their reference implementations in PaddlePaddle. Further more, Parakeet abstracts the TTS pipeline and standardizes the procedure of data preprocessing, common modules sharing, model configuration, and the process of training and synthesis. The models supported here include Vocoders and end-to-end TTS models:VocodersWaveFlow: A Compact Flow-based Model for Raw AudioTTS modelsNeural Speech Synthesis with Transformer Network (Transformer TTS)Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram PredictionsUpdatesMay-07-2021, Add an example for voice cloning in Chinese. Checkexamples/tacotron2_aishell3.SetupMake sure the librarylibsndfile1is installed, e.g., on Ubuntu.sudoapt-getinstalllibsndfile1Install PaddlePaddleSeeinstallfor more details. This repo requires PaddlePaddle2.0.0rc1or above.Install Parakeetpipinstall-Upaddle-parakeetorgitclonehttps://github.com/PaddlePaddle/ParakeetcdParakeet
pipinstall-e.Seeinstallfor more details.ExamplesEntries to the introduction, and the launch of training and synthsis for different example models:>>> WaveFlow>>> Transformer TTS>>> Tacotron2>>> Tacotron2_AISHELL3>>> GE2EAudio samplesTTS models (Acoustic Model + Neural Vocoder)Check ourwebsitefor audio sampels.CheckpointsTacotron2tacotron2_ljspeech_ckpt_0.3.ziptacotron2_ljspeech_ckpt_0.3_alternative.zipTacotron2_AISHELL3tacotron2_aishell3_ckpt_0.3.zipTransformerTTStransformer_tts_ljspeech_ckpt_0.3.zipWaveFlowwaveflow_ljspeech_ckpt_0.3.zipGE2Ege2e_ckpt_0.3.zipCopyright and LicenseParakeet is provided under theApache-2.0 license.
Package: paddle-prompt
paddle-promptPrompt Toolkit for PaddlePaddle Deep Learning Framework. Inspired by :thunlp/OpenPromptAutoPromptInstallationpaddle-promptis built under Python3.7+ andPaddle. You can installpaddle-promptwith pip:pipinstallpaddle-promptTODOScomplete PLMscomplete templatecomplete verbalizercreate text classification datasets...论文复现在接下来一段时间内，使用本工具可复现以下论文，如果大家想要复现其它论文，欢迎发Issue和Contribution。PET: Exploiting Cloze Questions for Few Shot Text Classification and Natural Language InferenceP-Tuning: GPT Understands, TooP-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and TasksLM-BFF: Better Few-shot Fine-tuning of Language ModelsEntailment as Few-Shot Learner...... more contributionStargazers over timeContributorsMade withcontrib.rocksCreators@wj-Mcat- Jingjing WU (吴京京)Copyright & LicenseCode & Docs © 2022 wj-Mcathttps://github.com/wj-McatCode released under the Apache-2.0 LicenseDocs released under Creative Commons
Package: paddles
paddlesis a pedagogical algorithms and data structures library that aims tobe thoroughly tested and documentedbe easy to install, use and understandadhere to good Python coding practices.To install the library, typepip install paddlesat the command line,
preferably after activating a virtual environment.
Your default Python version must be 3.10 or later.This is version 0.1 of the library. It is a work in progress.
It currently implements the Stack, Queue and Deque abstract data types (ADTs).The code repository is athttps://github.com/dsa-ou/paddles.
The formatted documentation is athttps://dsa-ou.github.io/paddles.Licencepaddlesis Copyright © 2024 by The Open University, UK.
The code is licensed under aBSD 3-clause licence.
The documentation is licensed under aCreative Commons Attribution 4.0 International Licence.
Package: paddle-sparse-dense
Sparse-dense operators implementation for PaddleThis module implementscoo,cscandcsrmatrix formats and their inter-ops with dense matrices.Feel free to open an issue when you feel that something is incorrect.RequirementsIt only needspaddle. It is tested onpaddle >= 2.1.0, <= 2.2.0rc1, but should work for any recent paddle versions.UsageMost functions are implemented within classes that encapsulate sparse formats:COO,CSRandCSC.Cross-format operators are implemented in dedicated sub-modules:spgemmandbatching.Supported operationsConversioncoo -> csc, csr, dense
csc -> coo
csr -> cooBatch MVP (Matrix-Vector Product) or SpMM (Sparse-Dense Matmul)Note that in this library, the batch dimensions are appended instead of prepended to the dot dimension (which makes batch MVP essentially regular matmul). Useutils.swap_axesorpaddle.transposewhen necessary.coo, dense -> densePoint-wiseSupports broadcast on the dense side.coo + coo -> coo
coo * scalar -> coo
coo * dense -> coo (equiv. coo @ diag(vec) if dense is a vector)SpGEMM (Sparse-Sparse Matmul)coo, csr -> coo (via row-wise mixed product)Batching and unbatchingMany batched operations can be efficiently represented via operation on block-diagonal sparse matrix. We also provide batching and unbatching operations for homogeneously-shaped sparse matrices.For COO matrices, this is constructing (destructing) a block-diagonal COO matrix given (into) several small COO matrices.If you know the expected shapes of matrices after unbatching you may construct it explicitly by callingBatchingInfo(shapes: [n, 2] numpy array of int). Otherwise: 1) most operations keep shapes, and there is no need to change BatchingInfo; 2)batch_info_dotis provided, for merging info between two batches of matrices that go throughSpGeMMto obtain a final batch of matrices.batch [coo] -> coo
unbatch coo -> [coo]Installationpip install paddle-sparse-denseCaveatsCurrently all stuff is implemented with pure python and no CUDA code has been written. As a result, the routines have good run-time performance in general but have a memory overhead of orderO(nnz/n).
Package: paddle-spline-conv
SplineConv implementation for PaddleThis module implements theSplineConvoperators fromMatthias Fey, Jan Eric Lenssen, Frank Weichert, Heinrich M眉ller: SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels (CVPR 2018).It is in early development, and may have problems. Feel free to open an issue if you find one.RequirementsIt only needspaddle. It is tested onpaddle >= 2.1.0, <= 2.2.0rc1, but should work for any recent paddle versions.For development -- since we run tests againsttorch-spline-conv, you will need that.Installationpip install paddle-spline-convUsageHere are some basic usage descriptions. See docstring in code for more detailed descriptions, types and shapes of parameters.Currently only degree-1 splines are supported. But the basic operators have been ready, and adding more shouldn't be very hard. You are welcome to contribute for higher degree splines!importpaddle_spline_conv# Stacked SplineConv layers implemented in SConvg=paddle_spline_conv.nn.GraphData(x,edge_index,edge_attr)# Input n_features and output n_featuressconv=paddle_spline_conv.nn.SConv(10,40)sconv(g)# Standalone SplineConv layerpaddle_spline_conv.nn.SplineConv(in_channels:int,out_channels:int,dim:int,kernel_size:int,is_open_spline:bool=True,degree:int=1,aggr:str='mean',root_weight:bool=True,bias:bool=True)# Standalone SplineConv functional APIpaddle_spline_conv.functional.spline_conv(x:paddle.Tensor,edge_index:paddle.Tensor,pseudo:paddle.Tensor,weight:paddle.Tensor,kernel_size:paddle.Tensor,is_open_spline:paddle.Tensor,degree:int=1,aggr:str='mean',root_weight:Optional[paddle.Tensor]=None,bias:Optional[paddle.Tensor]=None)# SplineConv-specific operatorspaddle_spline_conv.ops.spline_basispaddle_spline_conv.ops.spline_weightingpaddle_spline_conv.ops.basis_kernel_1d
Package: paddle-template
paddle-models-templatethe template for paddle models which can reduce your lots of workFeaturesFull test suit for modelingFull used Trainer which can control train/dev/test stageBase Data structure for different tasksData Processo interface for different corpusMakefile for small shell scripts...Quick Start1. clone the repo or create a repo from this templategitclonehttps://github.com/wj-Mcat/paddle-models-template2. write your own modelsthis will happen insrc/model.pyfile3. write your own data processorthis will happen insrc/data_processors.py4. train your modelpythontrainer.pyCreators@wj-Mcat- Jingjing WU (吴京京)Copyright & LicenseCode & Docs © 2022 wj-Mcathttps://github.com/wj-McatCode released under the Apache-2.0 LicenseDocs released under Creative Commons
Package: paddlets
简体中文|EnglishPaddleTS 是一个易用的深度时序建模的Python库，它基于飞桨深度学习框架PaddlePaddle，专注业界领先的深度模型，旨在为领域专家和行业用户提供可扩展的时序建模能力和便捷易用的用户体验。PaddleTS 的主要特性包括：设计统一数据结构，实现对多样化时序数据的表达，支持单目标与多目标变量，支持多类型协变量封装基础模型功能，如数据加载、回调设置、损失函数、训练过程控制等公共方法，帮助开发者在新模型开发过程中专注网络结构本身内置业界领先的深度学习模型，包括NBEATS、NHiTS、LSTNet、TCN、Transformer、DeepAR、Informer等时序预测模型，
TS2Vec、CoST等时序表征模型，以及
Autoencoder、VAE、AnomalyTransformer等时序异常检测模型内置多样化的数据转换算子，支持数据处理与转换，包括缺失值填充、异常值处理、归一化、时间相关的协变量提取等内置经典的数据分析算子，帮助开发者便捷实现数据探索，包括数据统计量信息及数据摘要等功能自动模型调优AutoTS，支持多类型HPO(Hyper Parameter Optimization)算法，在多个模型和数据集上展现显著调优效果第三方机器学习模型及数据转换模块自动集成，支持包括sklearn、pyod等第三方库的时序应用支持在GPU设备上运行基于PaddlePaddle的时序模型时序模型集成学习能力最新更新：新增时序分类能力全新发布6个深度时序模型。
USAD(UnSupervised Anomaly Detection)与MTAD_GAT(Multivariate Time-series Anomaly Detection via Graph Attention Network)异常检测模型，
CNN与Inception Time时序分类模型，
SCINet(Sample Convolution and Interaction Network)与TFT(Temporal Fusion Transformer)时序预测模型新发布Paddle Inference支持，已适配时序预测与时序异常检测新增模型可解释性能力。包括模型无关的可解释性与模型相关的可解释性新增支持基于表征的聚类与分类您也可以参考发布说明获取更详尽的更新列表。未来，更多的高级特性会进一步发布，包括但不限于：更多时序模型场景化Pipeline，支持端到端真实场景解决方案关于 PaddleTS具体来说，PaddleTS 时序库包含以下子模块：模块简述paddlets.datasets时序数据模块，统一的时序数据结构和预定义的数据处理方法paddlets.autots自动超参寻优paddlets.transform数据转换模块，提供数据预处理和特征工程相关能力paddlets.models.forecasting时序模型模块，基于飞桨深度学习框架PaddlePaddle的时序预测模型paddlets.models.representation时序模型模块，基于飞桨深度学习框架PaddlePaddle的时序表征模型paddlets.models.anomaly时序模型模块，基于飞桨深度学习框架PaddlePaddle的时序异常检测模型paddlets.models.classify时序模型模块，基于飞桨深度学习框架PaddlePaddle的时序分类模型paddlets.pipeline建模任务流模块，支持特征工程、模型训练、模型评估的任务流实现paddlets.metrics效果评估模块，提供多维度模型评估能力paddlets.analysis数据分析模块，提供高效的时序特色数据分析能力paddlets.ensemble时序集成学习模块，基于模型集成提供时序预测能力paddlets.xai时序模型可解释性模块paddlets.utils工具集模块，提供回测等基础功能安装前置条件python >= 3.7paddlepaddle >= 2.3pip 安装 paddlets 命令如下：pipinstallpaddlets更多安装方式请参考：环境安装文档开始使用API文档社区欢迎通过扫描下面的微信二维码加入PaddleTS开源社区，与PaddleTS维护者及社区成员随时进行技术讨论：代码发布与贡献我们非常感谢每一位代码贡献者。如果您发现任何Bug，请随时通过提交issue的方式告知我们。如果您计划贡献涉及新功能、工具类函数、或者扩展PaddleTS的核心组件相关的代码，请您在提交代码之前先提交issue，并针对此次提交的功能与我们进行讨论。如果在没有讨论的情况下直接发起的PR请求，可能会导致此次PR请求被拒绝。原因是对于您提交的PR涉及的模块，我们也许希望该模块朝着另一个不同的方向发展。许可证PaddleTS 使用Apache风格的许可证, 可参考LICENSE文件.
Package: paddypy
paddypyUnder construction! Not ready for use yet! Currently experimenting and planning!Developed by Patrik (c) 2022Examples of How To Use (Alpha Version)Print and get azure app-configuration keys and flagsfrompaddypyimportaccess# does a logging print out of the azure app-configuration keys and flags.access.listConfig()key="username"# gets the lastest value for a given key# deactivate_kv_access choose if kv-ref should be accessed (access policies need to be correct)value=access.getValue(key=key,deactivate_kv_access=True)logging.info("Lastest value: "+value)# sets a key value# deactivate_kv_access choose if kv-ref should be accessed (access policies need to be correct)value=access.setValue(key=key,value="julia",content_type=None,tags:dict={},label=None,deactivate_kv_access=False):logging.info("Lastest value: "+value)
Package: pademelon
pademelonUsed to retrospectively add static type checking on legacy project by just
running the checks on only files modified in a pull request. Can be used as a
soft touch approach to bringing a new checking tool into a large project
without one big bang.Supported checks include:isortbanditflake8pytestminimum coverage percentage.pyspellingMore details can be found in theOnline Documentation.InstallationYou can install pademelon forPythonviapipfromPyPI.$ pip install pademelonPrerequisites:clickGitPythonDownload from PyPI.orghttps://pypi.org/project/pademelon/ContributingContributions are very welcome, consider using thefile an issueto discuss the work before beginning, but if you already have a Pull Request
ready then this is no problem, please submit it and it will be very gratefully
considered. TheContribution Guidelinesoutlines the pademelon commitment to ensuring all
contributions receive appropriate recognition.LicenseDistributed under the terms of theGPLv3license, "pademelon" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.Additional Documentation:Online DocumentationNews.Template Updates.Code of Conduct.Contribution Guidelines.
Package: paderbox
Paderbox: A collection of utilities for audio / speech processingThis repository started in late 2014 as an internal development repository for theCommunications Engineering Groupat Paderborn University, Germany.
Over the years it emerged to a collection ofIO helper,feature extractionmodules and numerous smaller tools adding functionality to Numpy, Pandas, and others.The main purpose here is to limit code duplication across ourother public repositories.We ensured that most functions/ classes contain Python Docstrings such that automatic tooltips for most functions are supported.
It was deliberately decided against a lengthy documentation: most emphasis is put on the Python Docstrings and code readability itself.ExamplesWithout going through all functions, we here select two examples which demonstrate why we rely on this very implementation.Short-time Fourier transformThe Short-time Fourier transform (STFT) is a widely used feature extraction method when dealing with time series such as audio/ speech.
Most repositories, including Deep Learning frameworks such as TensorFlow, provide an STFT implementation.
However, it is rarely seen, that these implementations allow an exact reconstruction when applying the STFT followed by an inverse STFT.Two important issues often overseen are:How do I need to calculate the biorthogonal reconstruction window when usinganySTFT window function?How much padding depeding on STFT window length, DFT length, and shift is needed to compensate for fade-in, fade-out, and uneven signal length?OurSTFT implementationaddresses aforementioned issues, can operate on any number of independent dimensions and is already battle tested in our publications on audio/ speech since 2015.
NumerousSTFT testsensure that the code remains stable and in particular test for the aforementioned problems.Fast access to the IPython audio playerThe functionpaderbox.play.play()is a somewhat elaborated wrapper aroundIPython.display.Audio.
A single function allows to play audio from the waveform, from the STFT signal, and from file.
It therefore serves as a great tool within Jupyter Notebooks and helps for quick inspection of simulation results.InstallationInstall it from PyPI with pippipinstallpaderbox[all]The[all]flag is optional and indicates to install all dependencies.
Remove it, when you want to have the minimal dependencies.Alternatively, you can clone this repository and install it as followsgitclonehttps://github.com/fgnt/paderbox.gitcdpaderbox
pipinstall--editable.[all]How to cite?There is no clear way how to cite this repository for research.
However, we would be grateful for direct imports from this repository if you use, e.g., the STFT.
We are also fine when you copy the code as long as it remains visible where you copied the code from.If you use one of our other repositories relying on this work we would be thankful if you respect citation hints for that repository.
Package: padic
padicThis is a package implementing p-adic numbers.
Includes Padic class for p-adic numbers representation as
well as method of finding roots of nupy.polynomial.Polynomial roots
in Z_p via Hensel Lemma and some common functions: log, exp, sin, cos, binomial.Each p-adic number is represented as an p-adic interval, namely a*p^v + O(p^N).
This allows for quick computations with certainty of corectness of computed digits
(which is not the case for standard floating point implementation of real numbers
as computation errors may produce incorrect results of arithmetic operations).Please note that optional argument N of log, exp, sin, cos, binomial refers to
the number of terms of series defining respective functions NOT the number of
calculated correct digits of the final result. This may change in the (near?)
future.Also note that series function gives easy way of defining one's own p-adic valued
functions via power series.Will add more info here one day.
Package: padio
padioZero pad numeric filenamesTurn a bunch of files like this:file1.txt
file10.txt
file5.txtand want them to be sorted like this:file01.txt
file05.txt
file10.txtyou can run:padio *.txtInstallationpip install padioUsageusage: padio [-h] [-l LENGTH] [-f] [-v] [-d] [-i REGEX] [--ignore-files IGNOREFILE [IGNOREFILE ...]] file [file ...]

Pads numbers in file names so they consistently align and sort

positional arguments:
  file                  Files to be renamed

optional arguments:
  -h, --help            show this help message and exit
  -l LENGTH, --length LENGTH
                        Length of numbers after padding (default: auto)
  -f, --force           Force rename, even if file at destination exists
  -v, --verbose         Print all actions
  -d, --dry-run         Print actions only without modifying any file. Implies --verbose
  -i REGEX, --ignore REGEX
                        Regular expression used to ignore files matching the name
  --ignore-files IGNOREFILE [IGNOREFILE ...]
                        Files to ignore for renaming. Must add -- before positional argumentsOriginal repoOriginally hosted athttps://git.iamthefij.com/iamthefij/padio.git
Package: padlang
Yet another language written in Python.ContainsBasic parser for converting tokens into AST (Abstract syntax tree).Interpreter. Handles the flow of instructions in AST.Contains basic types: Integer, Real, String.Class support, inheritance.List and dictionary support.Scope module imports, global program imports.Class member call support, which supports local and Python object
members. (ie. you can call append on your list, but also call member
of class declared locally.)Scoping, ie. scope can have its own local variables and still access
those declared in outer scope.Methods with their own scope. They clone program’s main scope rules,
therefore they support nested methods.Conditional statementsCase switchBasic variable existence checks when using variables.Variable references (ex. you can pass variable reference to a
procedure, which can then modify it.)How to useEither clone the repo orpip install padlang, seesamplesfor
an example of how to use the package.CopyrightPADLang is licensed under theApache 2 License, see LICENSE for
more information.
Package: padl-extensions
PADL-EXTENSIONSPADL-ExtensionsTrainers, monitors, connectors for PADLPADL-Extensionswas developed atLF1, an AI innovation lab based in Berlin, Germany.Installationpip install padl-extensionsExtras Installationpip install padl-extensions[pytorch_lightning]
pip install padl-extensions[torchserve]
pip install padl-extensions[trainer]If you are installing locally usepip install -e ".[pytorch_lightning]"
pip install -e ".[torchserve]"
pip install -e ".[trainer]"Notebooks on colabTrainerTorchServePyTorch LightningHugging Face
Package: padlock
Padlock is a Python library that provides a lock through a single, simple interface and offers several backends
(actually, for now, only one, until someonecontributes another) so
you can choose the backend that best fits your needs.It’s really easy to use. Here, for example, is how to create a cassandra row lock:>>> import padlock, pycassa
>>> pool = pycassa.ConnectionPool('my_keyspace')
>>> with padlock.get('cassandra, pool=pool, column_family='my_column_family'):
...    do_important_shit()
"success!"Huzzah!
Package: padlock-env
Padlock Gymnasium EnvPadlock Gymnasium Env is a simple gymnasium environment that models a padlock with 4 discs, each with 4 symbols. The agent's goal is to find the secret combination of symbols to unlock the padlock.The action space is a MultiDiscrete space with 2 dimensions, representing the selected disc and symbol. The observation space is a Box space of shape (4, 2) representing the current combination and a valid mask, where the valid mask is a binary array of shape (4,), with 1 indicating that the corresponding symbol in the current combination is correct and 0 indicating otherwise.Install it from PyPIpipinstallpadlock_envInstall it locallypipinstall-e.UsageHere's an example of how to use the environment:importgymnasiumimportpadlock_envenv=gymnasium.make("PadlockEnv-v0")observations,_=env.reset()done=Falsewhilenotdone:action=env.action_space.sample()observations,reward,done,_,_=env.step(action)print(f"Observations:{observations}, Reward:{reward}, Done:{done}")DevelopmentRead theCONTRIBUTING.mdfile.
Package: padme
===================================================Padme - a mostly transparent proxy class for Python===================================================.. image:: https://badge.fury.io/py/padme.png:target: http://badge.fury.io/py/padme.. image:: https://travis-ci.org/zyga/padme.png?branch=master:target: https://travis-ci.org/zyga/padme.. image:: https://pypip.in/d/padme/badge.png:target: https://pypi.python.org/pypi/padmeFeatures========* Free software: LGPLv3 license* Support for Python 2.7 and Python 3.2+* Documentation: https://padme.readthedocs.org.* Create proxy classes for any object with :class:`padme.proxy`.* Redirect particular methods in subclasses using :class:`padme.unproxied`.History=======1.1.1 (2015-03-04)------------------* Add general support for **Python 2.7**.* All numeric methods are now supported with some methodsexclusive to Python 2.x (``__div__()``, ``__coerce__()``,``__oct__()``, ``__hex__()``).* Add support for the new matrix multiplication operator ``@``.* Make ``__nonzero__()`` and ``__unicode__()`` exlusive to Python 2.x.* Make ``__bool__()`` and ``__bytes__()`` exclusive to Python 3.x.* Make ``__length_hint()`` exclusive to Python 3.4.* Add support for the ``__cmp__()`` method, exclusive to Python 2.x.* Add support for accessing the proxied object with the new:meth:`~padme.proxy.original()` function.* Add support for accessing proxy state with the new:meth:`~padme.proxy.state()` function.* De-couple proxy classes from proxied objects, much more lightweight proxydesign is possible this way (less objects, lower cost to create each new proxy).1.0 (2014-02-11)----------------* First release on PyPI.* Add a short introduction.* Enable travis-ci.org integration.* Remove numbering of generated meta-classes2015----* Released on PyPI as a part of plainbox as ``plainbox.impl.proxy``
Package: padmet-utils
author: meziane aite (meziane.aite@inria.fr)## DescriptionThe main concept underlying padmet-utils is to provide solutions that ensure the consistency, the internal standardization and the reconciliation of the information used within any workflow that combines several tools involving metabolic networks reconstruction or analysis. The PADMet package is at the core of the AuReMe workflow, dedicated to the primary reconstruction of genome-scale metabolic networks from raw data. It allows the study of organisms for which few experimental data are available. Its main feature is to undergo the reconstruction of the metabolic network by combining several heterogeneous knowledge and data sources, including the information reported by several scaffold metabolic networks for cousin species.## Documentationhttps://padmet-utils.readthedocs.io/en/latest/