A toy compiler that can convert Python scripts to pickle bytecode ๐Ÿฅ’

Overview

Pickora ๐Ÿฐ

A small compiler that can convert Python scripts to pickle bytecode.

Requirements

  • Python 3.8+

No third-party modules are required.

Usage

usage: pickora.py [-h] [-d] [-r] [-l {none,python,pickle}] [-o OUTPUT] file

A toy compiler that can convert Python scripts to pickle bytecode.

positional arguments:
  file                  the Python script to compile

optional arguments:
  -h, --help            show this help message and exit
  -d, --dis             disassamble compiled pickle bytecode
  -r, --eval, --run     run the pickle bytecode
  -l {none,python,pickle}, --lambda {none,python,pickle}
                        choose lambda compiling mode
  -o OUTPUT, --output OUTPUT
                        write compiled pickle to file

Lambda syntax is disabled (--lambda=none) by default.

For exmple, you can run:

python3 pickora.py -d samples/hello.py -o output.pkl

to compile samples/hello.py to output.pkl and show the disassamble result of the compiled pickle bytecode.

But this won't run the pickle for you. If you want you should add -r option, or execute the following command after compile:

python3 -m pickle output.pkl

Special Syntax

RETURN

RETURN is a keyword reserved for specifying pickle.load(s) result. This keyword should only be put in the last statement alone, and you can assign any value / expression to it.

For example, after you compile the following code and use pickle.loads to load the compiled pickle, it returns a string 'INT_MAX=2147483647'.

# source.py
n = pow(2, 31) - 1
RETURN = "INT_MAX=%d" % n

It might look like this:

$ python3 pickora.py source.py -o output.pkl
Saving pickle to output.pkl

$ python3 -m pickle output.pkl
'INT_MAX=2147483647'

Todos

  • Operators (compare, unary, binary, subscript)
  • Unpacking assignment
  • Augmented assignment
  • Macros (directly using GLOBAL, OBJECT bytecodes)
  • Lambda (I don't want to support normal function, because it seems not "picklic" for me)
    • Python bytecode mode
    • Pickle bytecode mode

Impracticable

  • Function call with kwargs
    • NEWOBJ_EX only support type object (it calls __new__)

FAQ

What is pickle?

RTFM.

Why?

It's cool.

Is it useful?

No, not at all, it's definitely useless.

So, is this garbage?

Yep, it's cool garbage.

Would it support syntaxes like if / while / for ?

No. All pickle can do is just simply define a variable or call a function, so this kind of syntax wouldn't exist.

But if you want to do things like:

ans = input("Yes/No: ")
if ans == 'Yes':
  print("Great!")
elif ans == 'No':
  exit()

It's still achievable! You can rewrite your code to this:

from functools import partial
condition = {'Yes': partial(print, 'Great!'), 'No': exit}
ans = input("Yes/No: ")
condition.get(ans, repr)()

ta-da!

For the loop syntax, you can try to use map / reduce ... .

And yes, you are right, it's functional programming time!

Owner
๊Œ—แ–˜๊’’๊€ค๊“„๊’’๊€ค๊ˆค๊Ÿ
I hate coding.
๊Œ—แ–˜๊’’๊€ค๊“„๊’’๊€ค๊ˆค๊Ÿ
View model summaries in PyTorch!

torchinfo (formerly torch-summary) Torchinfo provides information complementary to what is provided by print(your_model) in PyTorch, similar to Tensor

Tyler Yep 1.5k Jan 05, 2023
A standard framework for modelling Deep Learning Models for tabular data

PyTorch Tabular aims to make Deep Learning with Tabular data easy and accessible to real-world cases and research alike.

801 Jan 08, 2023
Implementation for Curriculum DeepSDF

Curriculum-DeepSDF This repository is an implementation for Curriculum DeepSDF. Full paper is available here. Preparation Please follow original setti

Haidong Zhu 69 Dec 29, 2022
Code for Multiple Instance Active Learning for Object Detection, CVPR 2021

Language: ็ฎ€ไฝ“ไธญๆ–‡ | English Introduction This is the code for Multiple Instance Active Learning for Object Detection, CVPR 2021. Installation A Linux pla

Tianning Yuan 269 Dec 21, 2022
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

deepface Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid

Sefik Ilkin Serengil 5.2k Jan 02, 2023
TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

YeongHyeon Park 7 Aug 28, 2022
TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors

TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors This package provides a simulator for vision-based

Facebook Research 255 Dec 27, 2022
Tensorflow implementation of MIRNet for Low-light image enhancement

MIRNet Tensorflow implementation of the MIRNet architecture as proposed by Learning Enriched Features for Real Image Restoration and Enhancement. Lanu

Soumik Rakshit 91 Jan 06, 2023
Omniverse sample scripts - A guide for developing with Python scripts on NVIDIA Ominverse

Omniverse sample scripts ใ“ใ“ใงใฏใ€NVIDIA Omniverse ( https://www.nvidia.com/ja-jp/om

ft-lab (Yutaka Yoshisaka) 37 Nov 17, 2022
JugLab 33 Dec 30, 2022
(AAAI2020)Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing

Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing This repository contains pytorch source code for AAAI2020 oral paper: Grapy-ML

54 Aug 04, 2022
Official repo of the paper "Surface Form Competition: Why the Highest Probability Answer Isn't Always Right"

Surface Form Competition This is the official repo of the paper "Surface Form Competition: Why the Highest Probability Answer Isn't Always Right" We p

Peter West 46 Dec 23, 2022
A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".

Improved Adversarial Systems for 3D Object Generation and Reconstruction: This is a repository for the paper "Improved Adversarial Systems for 3D Obje

Edward Smith 188 Dec 25, 2022
Reinforcement Learning Theory Book (rus)

Reinforcement Learning Theory Book (rus)

qbrick 206 Nov 27, 2022
Rainbow DQN implementation that outperforms the paper's results on 40% of games using 20x less data ๐ŸŒˆ

Rainbow ๐ŸŒˆ An implementation of Rainbow DQN which reaches a median HNS of 205.7 after only 10M frames (the original Rainbow from Hessel et al. 2017 re

Dominik Schmidt 31 Dec 21, 2022
LoL Runes Recommender With Python

LoL-Runes-Recommender Para ejecutar la aplicaciรณn se debe llamar a execute_app.p

Sebastiรกn Salinas 1 Jan 10, 2022
Official code for On Path Integration of Grid Cells: Group Representation and Isotropic Scaling (NeurIPS 2021)

On Path Integration of Grid Cells: Group Representation and Isotropic Scaling This repo contains the official implementation for the paper On Path Int

Ruiqi Gao 39 Nov 10, 2022
"SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image", Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, Zhangyang Wang

SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image [Paper] [Website] Pipeline Code Environment pip install -r requirements

VITA 250 Jan 05, 2023
Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles ๐Ÿš—

urban_road_filter: a real-time LIDAR-based urban road and sidewalk detection algorithm for autonomous vehicles Dependency ROS (tested with Kinetic and

JKK - Vehicle Industry Research Center 180 Dec 12, 2022
Discovering and Achieving Goals via World Models

Discovering and Achieving Goals via World Models [Project Website] [Benchmark Code] [Video (2min)] [Oral Talk (13min)] [Paper] Russell Mendonca*1, Ole

Oleg Rybkin 71 Dec 22, 2022