semvec/pypi-packages huggingface dataset:
mentord 1.2.0
A sync library for creating self bots in python!


### Example use:

```python
from mentord import *

User_client: Client = Client()
User_client.connect(token='YOUR TOKEN')
```

### Use decorators:

```python
@on_ready(client=User_client, delay=5.0)
def client_on_ready() -> None:
    print('Self-bot is started!')

@on_message(client=User_client, delay=0.5)
def client_on_message(message: Message) -> None:
    utils.channel_opers.ChannelOperations(channel=message.channel_id, client=User_client).send_message("hello!")
```
cleanphi 0.2.0
Natural language processing framework to clean sentences and texts.
Requires Python: >=3.6

# CleanPhi

CleanPhi is a powerful Python framework designed to enhance text processing by effectively removing unwanted elements such as extraneous characters and unicodes. Leveraging the capabilities of natural language processing, CleanPhi provides a comprehensive set of functionalities, making it an invaluable tool for text cleaning and related tasks.

```python
from CleanPhi import clean

clean("some input",
    unicode=True,               # fix various unicode errors
    to_ascii=True,                  # transliterate to closest ASCII representation
    to_lower=True,                     # to_lowercase text
    no_line_breaks=False,           # fully strip line breaks as opposed to only normalizing them
    remove_url=False,                  # replace all URLs with a special token
    remove_email=False,                # replace all email addresses with a special token
    remove_ph=False,         # replace all phone numbers with a special token
    remove_nums=False,               # replace all numbers with a special token
    remove_digits=False,                # replace all digits with a special token
    remove_currency=False,      # replace all currency symbols with a special token
    remove_punct=False,                 # remove punctuations
    replace_with_punct="",          # instead of removing punctuations you may replace them
    replace_with_url="<URL>",
    replace_with_email="<EMAIL>",
    replace_with_phone_number="<PHONE>",
    replace_with_number="<NUMBER>",
    replace_with_digit="0",
    replace_with_currency_symbol="<CUR>",
    lang="en"                       # set to 'de' for German special handling
)
```

Choose an arguement and use the **clean** function in your code:

```python
import CleanPhi
text = "Hello, world!  Hello...\t \tworld?\n\nHello:\r\n\n\nWorld. "
proc_text = "Hello, world! Hello... world?\nHello:\nWorld."
assert CleanPhi.remove_whitespace(text, no_line_breaks=False) == proc_text
assert CleanPhi.remove_whitespace(" dd\nd  ", no_line_breaks=True) == "dd d"
```

### To install CleanPhi in >=Python3.6

```powershell
pip install CleanPhi
```

### Use CleanPhi with Scikit

```python
from CleanPhi.scikit import PhiTransformer

cleaner = PhiTransformer(remove_punct=False, to_lower=False)
cleaner.transform(['Clean text.', 'Natural language processing!'])
```

# Version 0.2.0 

- Bugs fixed
autodistill-albef 0.1.0
ALBEF module for use with Autodistill
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png?4"
      >
    </a>
  </p>
</div>

# Autodistill ALBEF Module

This repository contains the code supporting the ALBEF base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[ALBEF](https://github.com/salesforce/LAVIS), developed by Salesforce, is a computer vision model that supports a range of tasks, including image-text pre-training, image-text retrieval, visual question anserting, and zero-shot classification. You can classify images using ALBEF with Autodistill.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [ALBEF Autodistill documentation](https://autodistill.github.io/autodistill/base_models/albef/).

## Installation

To use ALBEF with autodistill, you need to install the following dependency:


```bash
pip3 install autodistill-albef
```

## Quickstart

```python
from autodistill_albef import ALBEF

# define an ontology to map class names to our ALBEF prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
base_model = ALBEF(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)
base_model.label("./context_images", extension=".jpeg")
```


## License

This project is licensed under a [3-Clause BSD license](LICENSE).

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
pallet-sim 0.0.22
Pallet simulation library developed at THWS

# Pallet Simulator

Pallet Simulator is a Python package that provides functionalities for visualizing pallets inside orders and simulating
their stability.

## Installation

You can install the Pallet Simulator package using pip:

``` 
pip install pallet-sim
```

## Usage

Please refer to the examples folder for detailed examples on how to use the Pallet Simulator. The examples demonstrate
how to visualize pallets within orders and simulate their stability.

## Data Structures

The Pallet Simulator uses a JSON-based data structure to represent orders. An order object contains the following
fields:

``` json
{
    "orderID": "order_id",
    "pallets": [
        {
            "palletNr": "palletNr",
            "palletType": "EURO_PALLET",
            "palletDimension": {
                "x": 800.0,
                "y": 1200.0
            },
            "boxes": [
                {
                    "product_code": "0",
                    "anchor": {
                        "x": 313.0,
                        "y": 606.0,
                        "z": 0.0
                    },
                    "dimension": {
                        "x": 159.0,
                        "y": 298.0,
                        "z": 211.0
                    },
                    "approach": "9"
                }
            ]
        }
    ],
    "articles": {
        "0": {
            "description": "description",
            "weight": 5054.0,
            "shippingGroup": "A"
        }
    }
}
```

In this structure, an order consists of multiple pallets, each identified by a palletNr. Each pallet has a type
(palletType), dimensions (palletDimension), and a list of boxes (boxes). Each box is defined by a product code
(product_code), anchor coordinates (anchor), dimensions (dimension), and an approach value (approach).

The articles field represents the articles or products included in the order. Each article is identified by a numeric
key (e.g., "0") and has a description, weight, and shipping group.

The Pallet Simulator utilizes this order structure to generate visual representations of pallets and perform stability
simulations.

Feel free to modify and adapt this structure based on your specific requirements and use cases.

For more details on how to use the Pallet Simulator, please refer to the documentation and examples provided with the
package.

Example files are located under pallet_data/generic_data.

## License

This project is licensed under the MIT License. See the LICENSE file for details.

The MIT License is a permissive open-source license that allows you to use, modify, and distribute the software for both
commercial and non-commercial purposes. It provides you with the freedom to adapt the codebase to suit your needs while
providing attribution to the original authors.

For more information about the MIT License, please visit opensource.org/licenses/MIT.