semvec/pypi-packages huggingface dataset:
queueCrypt 0.0.2
A library that creates a queue and secures it better.
License: MIT

# queueCrypt.
"queueCrypt" is a library that is library that creates a queue and secures it better.
Basically this library encrypts the data that is been added to the queue and saves the data 
securely and safely.

## Libraries Needed:
`pip install cryptography`

this library is needed to encrypt the data. Without this
library the library won't work!

## How To Call The Library?
```python
from queueCrypt import Queue
```

### To Call The Errors Exceptions Use:
```python
from queueCrypt import ErrorQueueSizeNotValid, ErrorRequestedHigherThanExpected
#----OR----#
from queueCrypt.errors import *
```

### To Call The Encryption Handler Use:
```python
from queueCrypt.ext import Encryption
#----OR----#
from queueCrypt.ext import *
```

## First Thing That Needed Before Using...
```python
from queueCrypt import *

q = Queue(5)
#----OR----#
q = Queue()
```
Every time that you create the Queue function you can set `Queue(number)` or use `Queue()` which will leave the queue with no limit. By setting a number, 
you set the queue length, that can be changed if you need by using `q.change_queue_length(number)`.

## Examples:
* Let's create a secret input from the user that takes passwords
and encrypts them. The user will type the passwords until he will type `"quit"`. After adding them
to the queue, print the encrypted list, and the decrypted list.
```python
from queueCrypt import *

q = Queue()

while True:
    user = input("Type passwords: ")
    if user == "quit":
        break
    # When putting the user data you need to convert it to bytes!
    q.put(bytes(user, 'utf8'))
        
# returns in bytes form.
print(q.get_queue_encrypted())
print(q.get_queue_decrypted())
```

* Now let's say that you want to encrypt the data again
but after printing the inputted data, you return the encrypted data and delete it.
```python
from queueCrypt import *

q = Queue()

while True:
    user = input("Type passwords: ")
    if user == "quit":
        break
        
    # When putting the user data you need to convert it to bytes!
    q.put(bytes(user, 'utf8'))
    print(q.get_queue_encrypted()[0].decode())
    # destroys the first element in the queue list with timeout of 0.1.
    q.next_and_destroy(0.1)
```
dl4dp 0.9.1
A Python library for morphological tagging, lemmatization and dependency parsing
License: MIT
Requires Python: >=3.6

# dl4dp

**dl4dp** is a Python NLP library, which provides tools for morphological tagging, lemmatization and dependency parsing.
The main motivation for this library is to provide state-of-the-art tools for Slovak language, but the models can be build
for any language with training data in [Universal Dependencies](https://universaldependencies.org).

### Installation

The library supports Python 3.6 and later.

#### pip

The dl4dp is available on [PyPi](https://pypi.python.org/pypi) and can be installed via `pip`. To install simply
run:
```
pip install dl4dp
```

To upgrade the previous installation to the newest release, use:
```
pip install dl4dp -U
```

#### From source

Alternatively, you can also install library from this git repository, which will give you more flexibility and allows
you to start contributing to the dl4dp code. For this option, run:
```
git clone https://github.com/peterbednar/dl4dp.git
cd dl4dp
pip install -e .
```

### Getting started with dl4dp

The library provides a command-line interface which allows you to train own model, create pipeline package for production deployment, or parse data. The following command will download [Universal Dependencies](https://universaldependencies.org)  archive and train morphological tagger model on English EWT treebank:

```
python -m dl4dp train tagger -t en_ewt
```

Similarly, the following command will train a model for dependency parser:

```
python -m dl4dp train parser -t en_ewt
```

Subsequently, you can create and install a pipeline package:

```
python -m dl4dp package install -t en_ewt
```

After the installation of the pipeline, it can be used for the parsing of input data in CoNLL-U format:

```
python -m dl4dp parse ~/.dl4dp/treebanks/en_ewt/en_ewt-ud-test.conllu output.conllu -m en_ewt-0.1.0
```

### LICENSE

dl4dp is released under the MIT License. See the [LICENSE](https://github.com/peterbednar/dl4dp/blob/master/LICENSE)
file for more details.