semvec/pypi-packages huggingface dataset:
accessmysqlconverter 2.0.0
A package to convert MS Access (.mdb, .accdb) into a SQL file (Optionally with It's data)

[![Version](https://img.shields.io/badge/version-2.0.0-brightgreen)](https://github.com/miguel93041/accessmysqlconverter)
[![License](https://img.shields.io/badge/license-GPL-blue.svg?style=flat)](https://github.com/miguel93041/accessmysqlconverter/blob/master/LICENSE)
[![paypal](https://www.paypalobjects.com/en_US/ES/i/btn/btn_donateCC_LG.gif)](https://www.paypal.com/donate?hosted_button_id=N8EGU933CPFV8)

# AccessMySQLConverter
AccessMySQLConverter aims to provide a tool which converts MS Access database files (.mdb, .accdb) into a SQL file (compatible with PostgreSQL, MariaDB and MySQL) that can be run, generating It's structure (tables, ERM...) and It's data

## Installation
To install it you must have Python 3.x installed and run in the command prompt

**`pip install --no-cache-dir --upgrade accessmysqlconverter`**

## Run it
For executing the program run in the command prompt the following instruction

**`python -m accessmysqlconverter.application`**

![Application Image](https://i.gyazo.com/12b7d363894abfd367e06c048710d4a2.png)

The tool is limited by the driver so after converting an Access Database you should look for:
* Nullability of columns and Default values
* Check ERM for missing 1:1, 1:N or N:N
* Collation, character set
* Foreign Keys ON DELETE actions
## License
See [LICENSE](LICENSE.gpl) for more information

## Donations
[![paypal](https://www.paypalobjects.com/en_US/ES/i/btn/btn_donateCC_LG.gif)](https://www.paypal.com/donate?hosted_button_id=N8EGU933CPFV8)
meowsay 1.0.2
Cowsay, but better.
Requires Python: >=3.6

# meowsay

Cowsay, but better.

## Installation

```console
pip install meowsay
```

## Usage

```console
> meowsay meow
 ______
< meow >
 ------
        \      |\---/|
         \     | ,_, |
                \_`_/-..----.
             ___/ `   ' ,\"\"+ \  sk
            (__...'   __\    |`.___.';
              (_,...'(_,.`__)/'.....+

> fortune | meowsay
 _____________________________________
/ "I looked all over the place for an \
| explanation. Elves and Gremlins."   |
|                                     |
\ - Mike "Heisen who?" Galbraith      /
 -------------------------------------
        \      |\---/|
         \     | ,_, |
                \_`_/-..----.
             ___/ `   ' ,\"\"+ \  sk
            (__...'   __\    |`.___.';
              (_,...'(_,.`__)/'.....+
```
highres 1.0.4
A script to download hi-resolution images using google search by image feature

========
highres
========

-----------------
About The Project
-----------------

'highres' script to download hi-resolution images using google search by image feature

Prerequisites
=============
* Web browser (Chrome or Firefox or Edge or Safari)
* Webdriver for the browser

Installation
============

Install highres using pip

``pip install highres``

Usage
=====

* If not available already, install a browser such or Chrome or Firefox or Safari or Edge
* Download webdriver for your Browser
  1 Chrome https://chromedriver.chromium.org/downloads/
  2 Firefox https://github.com/mozilla/geckodriver/releases/
  3 Edge https://developer.microsoft.com/en-us/microsoft-edge/tools/webdriver/
  4 Safari https://developer.apple.com/documentation/webkit/testing_with_webdriver_in_safari/
* Add Webdrivers's folder path to system path
* place your images in a folder, ex: ``C:\users\dr\Pictures\low-res-images\``

execute as follows

``C:\>highres C:\users\dr\Pictures\low-res-images``
soco-encoders 0.2.9.2
Neural encoders for embedding diverse objects

# Encoders
A library of neural encoders to convert any types of data into embeddings, dense or sparse. 


## Build a package
    python setup.py bdist_wheel
    twine upload dist/*

## Use Locally
    model = EncoderLoader.load_model('pretrain-models', <model_id>, use_gpu=True, region='cn')
    model.encode(['I am a good man'], show_progress_bar=True, batch_size=batch_size,)

## Use as RESTful API
Start a server
    
    uvicorn soco_encoders.http.main:app --host 0.0.0.0 --port 8000 --workers 4

Start a client

    res = requests.post(url='http://localhost:8000/encoder/v1/encode',
                          json={
                              "model_id": model_id,
                              "text": [x1] * batch_size,
                              'batch_size': batch_size,
                              "mode": "default",
                              "kwargs": {}
                          })

## Use as GRPC API
Start a server
    
    python -m soco_encoders.grpc.server --host 0.0.0.0 --port 8000 --workers 4
    
Start a client
       
       check out example in bench_grpc.py
Strive 0.0.7
Make use of Tkinter in XML.
Requires Python: >=3.7

Make use of Tkinter in XML.

Release notes 0.0.7
===
* Added `convert` function to the Window and the Widget class

Documentation
===

Coming soon. Try reading the source code if you want to use this module, the documentation will release sometime soon, join the Discord for notifications: https://discord.gg/DQTRTWQ7yd
pytorch_revgrad 0.2.0
A pytorch module (and function) to reverse gradients.
Requires Python: >=3.5

# `pytorch-revgrad`

[![PyPI version](https://badge.fury.io/py/pytorch-revgrad.svg)](https://badge.fury.io/py/pytorch-revgrad)
[![Coverage Status](https://coveralls.io/repos/github/janfreyberg/pytorch-revgrad/badge.svg?branch=master)](https://coveralls.io/github/janfreyberg/pytorch-revgrad?branch=master)
[![ci status](https://travis-ci.org/janfreyberg/pytorch-revgrad.svg?branch=master)](https://travis-ci.com/janfreyberg/pytorch-revgrad)
![python version](https://camo.githubusercontent.com/0a3ef56c3f80aca9bc6bafeab605803d81fe2284/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d332e352532422d626c75652e737667)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

This package implements a gradient reversal layer for pytorch modules.

## Example usage

```python
import torch

from pytorch_revgrad import RevGrad

model = torch.nn.Sequential(
    torch.nn.Linear(10, 5),
    torch.nn.Linear(5, 2),
    RevGrad()
)
```