semvec/pypi-packages huggingface dataset:
egor 1.0.1
A Task automation tool for competitive programmers
License: MIT

# Egor

[![Build Status](https://travis-ci.com/chermehdi/egor.svg?token=3C8Yf6qqmy7FqR6ZT4UY&branch=master)](https://travis-ci.com/chermehdi/egor)

## Description

- as most Competitive programmers don't have the luxuary of using IDE's such as intellij to benifit from the
  `CHelper` tool (maybe because they don't like the added overhead of IDE's or the prefere eclipse or, as most of the people i know, they use `C++`, parsing tasks can become tedious, and here where **egor** can help, by providing a simple 
  cli for parsing and running your tasks.


## Usage

- Just install egor by doing `pip install egor`
- You should also install *(if you don't already have it)* [Competitive companion](https://github.com/jmerle/competitive-companion)
- And voila, you can start using it as any other command line app, just type egor to see the help, and there is help for
every subcommand provided by `egor`

![Egor Command](https://raw.githubusercontent.com/chermehdi/egor/master/screenshots/egor.png)
- Open a codeforces or a spoj problem, run `egor task parse` and press the plus button of `Competitive companion`, a new directory with 
the name of task will be generated, with the `input` and `output` files, and a sample `source` file.
- When you write the solution for your problem you  can type `egor task test` to test your solution against the provided sample `input`.
![Egor Test](https://raw.githubusercontent.com/chermehdi/egor/master/screenshots/egor-test.png)
- You can run `egor task copy` to copy the source code of your solution to the clipboard
- The location of the configuration file to configure some default values is `${home_dir}/.egor/configuration` and it's in the `reStructuredText` format,This is sample fo a configuration file:
```
    [egor]
    egor_port = 6174
    egor_default_lang=cpp

    [core]
    first_run = yes
```
- in doubt you can always run `egor task -h` to see the help of commands.
![Egor Help](https://raw.githubusercontent.com/chermehdi/egor/master/screenshots/egor-help.png)
## Developer guide

- You should have pip installed
- Clone the current repository, and run `pip install -e .` to have 
a version of 'egor' installed in your machine so you can test it
- Unit testing is important, any added feature should be associated with a unit/integration test
- At least one reviewer should see your code before merging to master.

## Contribution
- You can contribute to the project by raising issues, writing tests, suggesting features, and making pull requests.
configure-gpu 0.1
a package for allocating tensorflow gpu memory use in python
License: MIT

# Gpu allocator

## install
```bash
$ pip install configure_gpu
```

This is a package for allocating gpu memory to prevent clogging the gpu memory.

## Usage
```python
# tensorflow import
import tensorflow as tf

# gpu allocator package
from configure_gpu import gpu_allocator

# configuring gpu
gpu_memory_allocator(tf, percentage_allocation=0.95)

```
pandoc-code-attribute 0.2.0
Pandoc filter to add attributes to code blocks based on their classes
License: MIT

pandoc-code-attribute
=====================

Pandoc filter to add attributes to code blocks based on their classes.

Installation
------------

First install python and python-pip.

Then use pip to install:

::

   pip3 install --user pandoc-code-attribute

Usage
-----

Configuration
~~~~~~~~~~~~~

By default, this filter won’t add style attributes to prevent undefined
errors.

You can enable it by setting ``code-attribute`` field in metadata.

To add attributes to all classes, use:

::

   code-attribute: true

To add attributes to specific classes, use:

::

   code-attribute:
       - cpp

Example
~~~~~~~

This pandoc filter will add attributes to code blocks based on their
classes.

For example, it can be very useful to use different styles for different
language in ``listings`` :

::

   ---
   code-attribute:
       - cpp
       - python
   header-includes: |
       \usepackage{listings}
       \usepackage[usenames,dvipsnames]{color}

       \lstset{ % General settings
           numbers=left,
           numberstyle=\tiny
       }

       \lstdefinestyle{cpp}{ % Only for C++
           keywordstyle=\color{Green}
       }

       \lstdefinestyle{python}{ % Only for Python
           keywordstyle=\color{Magenta}
       }
   ---

   C++:

   ```cpp
   int main(int argc, char *argv[])
   {
       return 0;
   }
   ```

   Python:

   ```python
   def main():
       print('Hello')

   if __name__ == '__main__':
       main()
   ```

Then compile the example (``--listings`` is needed only for this
example):

::

   pandoc input.md --filter pandoc-code-attribute --listings -o output.pdf

Results:

Command
~~~~~~~

In general, to use this filter, just add this filter to pandoc command:

::

   pandoc input.md --filter pandoc-code-attribute -o output.pdf

License
-------

MIT License