semvec/pypi-packages huggingface dataset:
config-json 0.0.3
An Easier Way to work with json files
Requires Python: >=3.6

# Config-Json
### An Easier Way to work with JSON configs
this library provide some top level method to make it easy to work with json
`Link: https://pypi.org/project/config-json/`
1. How to Use? 
```python
from jconf.config import Config

# u can either do
c = Config(config_name="main.json") # default name is main.json
# or
with Config(config_name="main.json")  as c:
    pass

```
2. functions available
```python
from jconf.config import Config

c = Config(config_name="main.json")
# returns all the keys from a json file
c.keys()
# returns all the values from a json file
c.values()
# gets the key from a json file
c.get('car')
# deletes a key from a json file
c.delete('car')
# update a value in a json file
c.update('car', 'tesla')
# sets a new key in json file
c.set('car2', 'ferari')
```
3. instead of using functions you can also use:
```python
from jconf.config import Config

c = Config('main.json')
# to/set or change a value
c['car'] = "name"
c['number_plate'] = ['3131', '3134', '3132']
# delete a key
del c['car']
# loop over a json file
for i in c:
    print(i) # returns a list of tuple with (key, value)
# updates / add to an existing key
c['car'] += {'model', 'year'}
# get a value
car = c['car']
```

* Automatically creates the passed config_name if it doesn't exists
pytorch-clip-guided-loss 2021.12.25.0
Pytorch implementation of the CLIP guided loss.

# pytorch_clip_guided_loss: Pytorch implementation of the CLIP guided loss for Text-To-Image, Image-To-Image, or Image-To-Text generation.

A simple library that implements CLIP guided loss in PyTorch.

<p align="center">
  <img src="resources/preview.png"/>
</p>

[![Downloads](https://pepy.tech/badge/pytorch_clip_guided_loss)](https://pepy.tech/project/pytorch_clip_guided_loss)
[![Downloads](https://pepy.tech/badge/pytorch_clip_guided_loss/month)](https://pepy.tech/project/pytorch_clip_guided_loss)
[![Downloads](https://pepy.tech/badge/pytorch_clip_guided_loss/week)](https://pepy.tech/project/pytorch_clip_guided_loss)


## Install package

```bash
pip install pytorch_clip_guided_loss
```

## Install the latest version

```bash
pip install --upgrade git+https://github.com/bes-dev/pytorch_clip_guided_loss.git
```

## Features
- The library supports multiple prompts (images or texts) as targets for optimization.
- The library automatically detects the language of the input text, and multilingual translate it via google translate.
- The library supports the original CLIP model by OpenAI and ruCLIP model by SberAI.

## Usage

### Simple code

```python
import torch
from pytorch_clip_guided_loss import get_clip_guided_loss

loss_fn = get_clip_guided_loss(clip_type="ruclip", input_range = (-1, 1)).eval().requires_grad_(False)
# text prompt
loss_fn.add_prompt(text="text description of the what we would like to generate")
# image prompt
loss_fn.add_prompt(image=torch.randn(1, 3, 224, 224))

# variable
var = torch.randn(1, 3, 224, 224).requires_grad_(True)
loss = loss_fn.image_loss(image=var)["loss"]
loss.backward()
print(var.grad)
```

### VQGAN-CLIP

We provide our tiny implementation of the VQGAN-CLIP pipeline for image generation as an example of the usage of our library.
To start using our implementation of the VQGAN-CLIP please follow by [documentation](examples/vqgan_clip).

### Zero-shot Object Detection

We provide our tiny implementation of the object detector based on Selective Search region proposals and CLIP guided loss.
To start using our implementation of the ClipRCNN please follow by [documentation](examples/object_detection).
package1001 0.4.0
A small example package
Requires Python: >=3.7

# package1001

This is sample package created for educational reasons.

![pytest](https://github.com/tbrus/package1001/actions/workflows/run-tests-with-actions.yml/badge.svg)
![tox](https://github.com/tbrus/package1001/actions/workflows/run-tests-with-tox.yml/badge.svg)

[![codecov](https://codecov.io/gh/tbrus/my-own-package/branch/master/graph/badge.svg?token=Z0DK3WEFAR)](https://codecov.io/gh/tbrus/package1001)

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package1001.

```bash
pip install package1001
```

## Usage

To use this package:

```python
import package1001

...

```

## License

This project is released under [MIT License](https://github.com/tbrus/package1001/blob/master/LICENSE).
zoomlog 1.1
Simple python logger
Requires Python: >=3.6

<h1>ZOOM LOG</h1>
<h3>Simple python logger</h3>

***Quick start:***

```python
from zoomlog import Logger, DEBUG, INFO

logger = Logger("test.log", name="test", level=DEBUG)
logger.debug("My logger is work!")
name = "Sergey"
logger.addLevel("NEW USER", INFO)
logger.log("NEW USER", "Created new user - %s" % name)
```

```
00:00:00 01.01.22  DEBUG:test >>  My logger is work!
00:00:01 01.01.22  NEW USER:test >>  Created new user - Sergey
```
*test.log*
```
00:00:00 01.01.22


00:00:00 01.01.22  DEBUG:test >>  My logger is work!
00:00:01 01.01.22  NEW USER:test >>  Created new user - Sergey
```