Python3 to Crystal Translation using Python AST Walker

Related tags

Text Data & NLPpy2cr
Overview

py2cr.py

A code translator using AST from Python to Crystal. This is basically a NodeVisitor with Crystal output. See AST documentation (https://docs.python.org/3/library/ast.html) for more information.

Status

Currently more than 80% of the relevant tests are passing. See more information below.

Installation

Execute the following:

pip install py2cr

or

git clone git://github.com/nanobowers/py2cr.git

Versions

  • Python 3.6 .. 3.9
  • Crystal 1.1+

Dependencies

Python

pip install pyyaml

# Probably not needed for much longer since py2 support is going to be removed.
pip install six 

# Probably not really needed since there is no crystal equivalent
pip install numpy

Crystal

currently there are no external dependencies

Methodology

In addition to walking and writing the AST tree and writing a Crystal syntax output, this tool either:

  • Monkey-patches some common Crystal stdlib Structs/Classes in order to emulate the Python equivalent functionality.
  • Calls equivalent Crystal methods to the Python equivalent
  • Calls wrapped Crystal methods that provide Python equivalent functionality

Usage

Generally, py2cr.py somefile.py > somefile.cr

There is a Crystal shim/wrapper library in src/py2cr (and linked into lib/py2cr) that is also referenced in the generated script. You may need to copy that as needed, though eventually it may be appropriate to convert it to a shard if that is more appropriate.

Example

TODO

Tests

$ ./run_tests.py

Will run all tests that are supposed to work. If any test fails, its a bug. (Currently there are a lot of failing tests!!)

$ ./run_tests.py -a

Will run all tests including those that are known to fail (currently). It should be understandable from the output.

$ ./run_tests.py basic

Will run all tests matching basic. Useful because running the entire test-suite can take a while.

$ ./run_tests.py -x or $ ./run_tests.py --no-error

Will run tests but ignore if an error is raised by the test. This is not affecting the error generated by the test files in the tests directory.

For additional information on flags, run:

./run_tests.py -h

Writing new tests

Adding tests for most new or existing functionality involves adding additional python files at tests/ .py .

The test-runner scripts will automatically run py2cr to produce a Crystal script, then run both the Python and Crystal scripts, then compare stdout/stderr and check return codes.

For special test-cases, it is possible to provide a configuration YAML file on a per test basis named tests/ / .config.yaml which overrides defaults for testing. The following keys/values are supported:

min_python_version: [int, int] # minimum major/minor version
max_python_version: [int, int] # maximum major/minor version
expected_exit_status: int      # exit status for py/cr test script
argument_list: [str, ... str]  # list of strings as extra args for argv

Typing

Some amount of typing support in Python is translated to Crystal. Completely untyped Python code in many cases will not be translatable to compilable Crystal. Rudimentary for python Optional and Union should convert appropriately to Crystal typing.

Some inference of bare list/dict types can now convert to [] of X and {} of X, however set and tuple may not work properly.

Status

This is incomplete and many of the tests brought forward from py2rb do not pass. Some of them may never pass as-is due to significant language / compilation differences (even moreso than Python vs. Ruby)

To some extent, it will always be incomplete. The goal is to cover common cases and reduce the additional work to minimum-viable-program.

Limitations

  • Many Python run-time exceptions are not translatable into Crystal as these issues manifest in Crystal as compile-time errors.
  • A significant portion of python code is untyped and may not translate properly in places where Crystal demands type information.
    • e.g. Crystal Lambda function parameters require typing and this is very uncommon in Python, though may be possible with Callable[] on the python side.
  • Python importing is significantly different than Crystal and thus may not ever map well.
  • Numpy and Unittest which are common in Python don't have equivalents in Crystal. With some significant additional work, converting tests into Spec format may be possible via https://github.com/jaredbeck/minitest_to_rspec as a guide

To-do

  • Remove python2/six dependencies to reduce clutter. Py2 has been end-of-lifed for a while now.
  • Remove numpy dependencies unless/until a suitable target for Crystal can be identified
  • Add additional Crystal shim methods to translate common python3 stdlib methods. Consider a mode that just maps to a close Crystal method rather than using a shim-method to reduce the python-ness.
  • Refactor the code-base. Most of it is in the __init__.py
  • Add additional unit-tests
  • Multi-thread the test-suite so it can run faster.

Contribute

Free to submit an issue. This is very much a work in progress, contributions or constructive feedback is welcome.

If you'd like to hack on py2cr, start by forking the repo on GitHub:

https://github.com/nanobowers/py2cr

Contributing

The best way to get your changes merged back into core is as follows:

  1. Fork it (https://github.com/nanobowers/py2cr/fork)
  2. Create a thoughtfully named topic branch to contain your change (git checkout -b my-new-feature)
  3. Hack away
  4. Add tests and make sure everything still passes by running crystal spec
  5. If you are adding new functionality, document it in the README
  6. If necessary, rebase your commits into logical chunks, without errors
  7. Commit your changes (git commit -am 'Add some feature')
  8. Push to the branch (git push origin my-new-feature)
  9. Create a new Pull Request

License

MIT, see the LICENSE file for exact details.

Natural Language Processing

NLP Natural Language Processing apps Multilingual_NLP.py start #This script is demonstartion of Mul

Ritesh Sharma 1 Oct 31, 2021
Easy Language Model Pretraining leveraging Huggingface's Transformers and Datasets

Easy Language Model Pretraining leveraging Huggingface's Transformers and Datasets What is LASSL • How to Use What is LASSL LASSL은 LAnguage Semi-Super

LASSL: LAnguage Self-Supervised Learning 116 Dec 27, 2022
A library for finding knowledge neurons in pretrained transformer models.

knowledge-neurons An open source repository replicating the 2021 paper Knowledge Neurons in Pretrained Transformers by Dai et al., and extending the t

EleutherAI 96 Dec 21, 2022
AI and Machine Learning workflows on Anthos Bare Metal.

Hybrid and Sovereign AI on Anthos Bare Metal Table of Contents Overview Terraform as IaC Substrate ABM Cluster on GCE using Terraform TensorFlow ResNe

Google Cloud Platform 8 Nov 26, 2022
Community and sentiment analysis based on tweets

The project has set itself the goal of analyzing the thoughts and interaction of Italian users through the social posts expressed through the Twitter platform on the day of the entry into force of th

3 Nov 17, 2022
Application for shadowing Chinese.

chinese-shadowing Simple APP for shadowing chinese. With this application, it is very easy to record yourself, play the sound recorded and listen to s

Thomas Hirtz 5 Sep 06, 2022
Kurumi ChatBot

KurumiChatBot Just another Telegram AI chat bot written in Python using Pyrogram. A public running instance can be found on telegram as @TokisakiChatB

Yoga Pranata 3 Jun 28, 2022
FireFlyer Record file format, writer and reader for DL training samples.

FFRecord The FFRecord format is a simple format for storing a sequence of binary records developed by HFAiLab, which supports random access and Linux

77 Jan 04, 2023
Code for the paper in Findings of EMNLP 2021: "EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation".

This repository contains the code for the paper in Findings of EMNLP 2021: "EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation".

Chenhe Dong 28 Nov 10, 2022
华为商城抢购手机的Python脚本 Python script of Huawei Store snapping up mobile phones

HUAWEI STORE GO 2021 说明 基于Python3+Selenium的华为商城抢购爬虫脚本,修改自近两年没更新的项目BUY-HW,为女神抢Nova 8(什么时候华为开始学小米玩饥饿营销了?) 原项目的登陆以及抢购部分已经不可用,本项目对原项目进行了改正以适应新华为商城,并增加一些功能

ZhangLiang 111 Dec 22, 2022
Random Directed Acyclic Graph Generator

DAG_Generator Random Directed Acyclic Graph Generator verison1.0 简介 工作流通常由DAG(有向无环图)来定义,其中每个计算任务$T_i$由一个顶点(node,task,vertex)表示。同时,任务之间的每个数据或控制依赖性由一条加权

Livion 17 Dec 27, 2022
Conversational-AI-ChatBot - Intelligent ChatBot built with Microsoft's DialoGPT transformer to make conversations with human users!

Conversational AI ChatBot Intelligent ChatBot built with Microsoft's DialoGPT transformer to make conversations with human users! In this project? Thi

Rajkumar Lakshmanamoorthy 6 Nov 30, 2022
Takes a string and puts it through different languages in Google Translate a requested amount of times, returning nonsense.

PythonTextObfuscator Takes a string and puts it through different languages in Google Translate a requested amount of times, returning nonsense. Requi

2 Aug 29, 2022
📝An easy-to-use package to restore punctuation of the text.

✏️ rpunct - Restore Punctuation This repo contains code for Punctuation restoration. This package is intended for direct use as a punctuation restorat

Daulet Nurmanbetov 72 Dec 30, 2022
:id: A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.

Dedupe Python Library dedupe is a python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on

Dedupe.io 3.6k Jan 02, 2023
Fast, DB Backed pretrained word embeddings for natural language processing.

Embeddings Embeddings is a python package that provides pretrained word embeddings for natural language processing and machine learning. Instead of lo

Victor Zhong 212 Nov 21, 2022
Code associated with the "Data Augmentation using Pre-trained Transformer Models" paper

Data Augmentation using Pre-trained Transformer Models Code associated with the Data Augmentation using Pre-trained Transformer Models paper Code cont

44 Dec 31, 2022
keras implement of transformers for humans

keras implement of transformers for humans

苏剑林(Jianlin Su) 4.8k Jan 03, 2023
FewCLUE: 为中文NLP定制的小样本学习测评基准

FewCLUE: 为中文NLP定制的小样本学习测评基准

CLUE benchmark 387 Jan 04, 2023
Train and use generative text models in a few lines of code.

blather Train and use generative text models in a few lines of code. To see blather in action check out the colab notebook! Installation Use the packa

Dan Carroll 16 Nov 07, 2022