semvec/pypi-packages huggingface dataset:
ebexten 0.1
AWS utility package

<h1> AWS Elastic Beanstalk </h1>

**EBExtensions Option Settings Validator**

########################### Akash #########################

########################## AWS Tools ##############################


Usage:

python ebXparser.py ebx_config_file


Release Notes:

-- Validates the formatting and option settings of ebextensions general option_settings

"http://docs.aws.amazon.com/elasticbeanstalk/latest/dg/command-options-general.html"

-- Works with both the general and shorthand formats. eg:

General::

option_settings:
  - namespace: aws:autoscaling:asg
    option_name: Cooldown
    value: 500


Shorthand::

option_settings:
  aws:autoscaling:asg:
    Cooldown: 500


-- Sub Directory "configs" contains all the files for different namespaces that contain the option settings and allowed values that serve as reference.

-- aws:elb:policies:policy_name has not been implemented yet.

-- aws:elasticbeanstalk:environment:process:default -> HealthCheckInterval has 2 sets of thresholds for ALB and NLB. Only the wider set has been implemented yet.

-- Options like "InstanceType" might be considered valid if they are entered as any string. Will update this later to allow only valid values.

-- Files under the "config" directory can be modified to add/delete/update option settings.

-- YAML lint does not work very well with the shorthand version.
ic-loss 1.0.1
Implementation of inverse contrastive loss

## Inverse Contrastive Loss

Implementations of inverse contrastive loss from [Learning Invariant Representations using Inverse Contrastive Loss](https://arxiv.org/abs/2102.08343). The model architecture used on the ADNI dataset in the paper is also included along with PyTorch and Tensorflow implementations of the loss function.

## Installation

```bash
$ pip install ic-loss
```

## Usage

```python
import torch
from ic_loss.losses import icl, icl_tf # icl - pytorch, icl_tf - tensorflow
from ic_loss.models import ADNIResNet # ADNIResNet - pytroch model used in the paper

model = ADNIResNet()

x = torch.randn([1, 1, 512, 512])
logits, latent = model(x)

loss = icl(latent, c) # c - extraneous attribute 

```
pyStuffing 0.0.4
A Python package for performing Bit Stuffing and Byte Stuffing operations
Requires Python: >=3.6

# Python library to perform __Bit Stuffing__ and __Byte Stuffing__

## To install
`pip install pyStuffing`

## 1. Bit Stuffing usage example.

```PYTHON
from pyStuffing import BitStuffing
from pyStuffing import clear

if __name__ == "__main__":
    stuff = BitStuffing()
    stuff.startStuffing() # START STUFFING
    stuff.startUnstuffing()

    clear() # CLEAR SCREEN

    # PRINT SEQUENCES TO SCREEN
    print("Main Sequence:       {}".format(stuff.sequence))
    print("Stuffed Sequence:    {}".format(stuff.stuffed))
    print("Un-Stuffed Sequence: {}".format(stuff.unStuffed))
    print("Stuffed Sequence:    {}".format(stuff.getStuffedColored()))
```

### Explanation
1. Inititallise the class `BitStuffing()` You can pass the bit sequence to the class as a list.
  - `BitStuffing([0,0,1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0])`
  - If nothing is passed on to the class, you will be prompted for input.
2. Start the bit stuffing by calling the `.startStuffing()` method.
3. The input sequence is obtained as `.sequence` object.
4. The bit stuffed sequence is available as the `.stuffed` object.
5. The un-stuffed sequence can be obtained as the `.unStuffed` object.
6. The `.getStuffedColored()` returns the stuffed sequence along with the added flags, when printed, explicitly differentiates between the colors of the main sequence, bits stuffed and the flag.
![Example Colored Output](./Images/colored-bit-stuffing.png)

---

## 2. Byte Stuffing usage example.

```PYTHON
from pyStuffing import ByteStuffing
from pyStuffing import clear


if __name__ == "__main__":
    stuff = ByteStuffing(list("abcdefghijklmnopqrstuvwxyz".upper()), escape_character="E", flag_character="F")
    # stuff = ByteStuffing()
    stuff.startStuffing()
    stuff.startUnStuffing()

    clear()

    print("Sequence:  {}".format(stuff.sequence))
    print("Stuffed:   {}".format(stuff.stuffed))
    print("Un-stuffed: {}".format(stuff.unStuffed))
```
### Explanation
1. Initiallise the class `ByteStuffing()` You can pass the byte sequence to the class as a list.
  - `ByteStuffing(list("abcdefghijklmnopqrstuvwxyz".upper()))`
  - If nothing is passed on to the class, you will be prompted for input.
2. Start the bit stuffing by calling the `.startStuffing()` method.
3. The input sequence is obtained as `.sequence` object.
4. The bytes stuffed sequence is available as the `.stuffed` object.
5. The un-stuffed sequence can be obtained as the `.unStuffed` object.

## Note:
You can set the `.stuffed` object to the stuffed sequence and call the `.startUnstuffing()` to unstuff the sequence and be obtained sa the `.unStuffed` object.