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.

AI-Broad-casting - AI Broad casting with python

Basic Code 1. Use The Code Configuration Environment conda create -n code_base p

NLP Core Library and Model Zoo based on PaddlePaddle 2.0

PaddleNLP 2.0拥有丰富的模型库、简洁易用的API与高性能的分布式训练的能力,旨在为飞桨开发者提升文本建模效率,并提供基于PaddlePaddle 2.0的NLP领域最佳实践。

6.9k Jan 01, 2023
Finds snippets in iambic pentameter in English-language text and tries to combine them to a rhyming sonnet.

Sonnet finder Finds snippets in iambic pentameter in English-language text and tries to combine them to a rhyming sonnet. Usage This is a Python scrip

Marcel Bollmann 11 Sep 25, 2022
Machine learning classifiers to predict American Sign Language .

ASL-Classifiers American Sign Language (ASL) is a natural language that serves as the predominant sign language of Deaf communities in the United Stat

Tarek idrees 0 Feb 08, 2022
Task-based datasets, preprocessing, and evaluation for sequence models.

SeqIO: Task-based datasets, preprocessing, and evaluation for sequence models. SeqIO is a library for processing sequential data to be fed into downst

Google 290 Dec 26, 2022
Paddlespeech Streaming ASR GUI

Paddlespeech-Streaming-ASR-GUI Introduction A paddlespeech Streaming ASR GUI. Us

Niek Zhen 3 Jan 05, 2022
Transformer training code for sequential tasks

Sequential Transformer This is a code for training Transformers on sequential tasks such as language modeling. Unlike the original Transformer archite

Meta Research 578 Dec 13, 2022
Stanford CoreNLP provides a set of natural language analysis tools written in Java

Stanford CoreNLP Stanford CoreNLP provides a set of natural language analysis tools written in Java. It can take raw human language text input and giv

Stanford NLP 8.8k Jan 07, 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
Unsupervised Language Model Pre-training for French

FlauBERT and FLUE FlauBERT is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the n

GETALP 212 Dec 10, 2022
Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning and Hydra.

🤗 Contributing to OpenSpeech 🤗 OpenSpeech provides reference implementations of various ASR modeling papers and three languages recipe to perform ta

Openspeech TEAM 513 Jan 03, 2023
Pipeline for chemical image-to-text competition

BMS-Molecular-Translation Introduction This is a pipeline for Bristol-Myers Squibb – Molecular Translation by Vadim Timakin and Maksim Zhdanov. We got

Maksim Zhdanov 7 Sep 20, 2022
🍊 PAUSE (Positive and Annealed Unlabeled Sentence Embedding), accepted by EMNLP'2021 🌴

PAUSE: Positive and Annealed Unlabeled Sentence Embedding Sentence embedding refers to a set of effective and versatile techniques for converting raw

EQT 21 Dec 15, 2022
2021 AI CUP Competition on Traditional Chinese Scene Text Recognition - Intermediate Contest

繁體中文場景文字辨識 程式碼說明 組別:這就是我 成員:蔣明憲 唐碩謙 黃玥菱 林冠霆 蕭靖騰 目錄 環境套件 安裝方式 資料夾布局 前處理-製作偵測訓練註解檔 前處理-製作分類訓練樣本 part.py : 從 json 裁切出分類訓練樣本 Class.py : 將切出來的樣本按照文字分類到各資料夾

HuanyueTW 3 Jan 14, 2022
A very simple framework for state-of-the-art Natural Language Processing (NLP)

A very simple framework for state-of-the-art NLP. Developed by Humboldt University of Berlin and friends. Flair is: A powerful NLP library. Flair allo

flair 12.3k Jan 02, 2023
This repo contains simple to use, pretrained/training-less models for speaker diarization.

PyDiar This repo contains simple to use, pretrained/training-less models for speaker diarization. Supported Models Binary Key Speaker Modeling Based o

12 Jan 20, 2022
Easy to use, state-of-the-art Neural Machine Translation for 100+ languages

EasyNMT - Easy to use, state-of-the-art Neural Machine Translation This package provides easy to use, state-of-the-art machine translation for more th

Ubiquitous Knowledge Processing Lab 748 Jan 06, 2023
Sploitus - Command line search tool for sploitus.com. Think searchsploit, but with more POCs

Sploitus Command line search tool for sploitus.com. Think searchsploit, but with

watchdog2000 5 Mar 07, 2022
Asr abc - Automatic speech recognition(ASR),中文语音识别

语音识别的简单示例,主要在课堂演示使用 创建python虚拟环境 在linux 和macos 上验证通过 # 如果已经有pyhon3.6 环境,跳过该步骤,使用

LIyong.Guo 8 Nov 11, 2022
Contact Extraction with Question Answering.

contactsQA Extraction of contact entities from address blocks and imprints with Extractive Question Answering. Goal Input: Dr. Max Mustermann Hauptstr

Jan 2 Apr 20, 2022