semvec/pypi-packages huggingface dataset:
pysr-container 0.1.1
Реализация контейнера внедрения зависимостей pysr-abc-container 1.0
Requires Python: <4.0,>=3.12

# Внедрение зависимостей

Для управления зависимостями внутри приложения используется контейнер pysr-container

- Cовместим с [pysr-abc-container](https://qbrick-framework.ru/python/standard/pysr-abc-container).
- Обнаруживает циклические ссылки.
- Принимает определения в виде словаря.


## Типы определений

Контейнер в версии 0.1.0 поддерживает следующие типы определений:

### Значение

Определение типа `Value` вернет установленное значение как есть. В данном случае значение переменной obj - тип SomeClass:

```python
from pysr_container import Container
from pysr_container.definitions import Value

class SomeClass: ...

container = Container({
	SomeClass: Value(SomeClass)
})

obj = container.get(SomeClass)
```

### Ссылка

Один ключ контейнера может предоставлять значение, сохраненной в контейнер по другому ключу. Для этого используется определение `Reference`. В данном случае значение переменной obj - тип SomeClass:

```python
from pysr_container import Container
from pysr_container.definitions import Value, Reference

class SomeClass: ...

container = Container({
	SomeClass: Value(SomeClass),
    'ref': Reference(SomeClass)
})

obj = container.get('ref')
```

### Объект

Определение типа `Object` при получении из контейнера предпримет попытку создать объект заданного класса с заданными параметрами. Если конструктор получаемого класса содержит параметры, для которых определен тип и которые не переданы в явном виде, будет предпринята попытка получить объекты этих типов из контейнера:

```python
from pysr_container import Container
from pysr_container.definitions import Object

class Foo:
    @property
    def name(self):
        return 'Foo'

class Bar:
    def __init__(self, name, foo: Foo):
        self.__foo = foo
        self.__name = name
    @property
    def name(self):
        return self.__foo.name + self.__name

container = Container({
    Foo: Object(Foo),
    Bar: Object(Bar, {'name': 'Bar'})
})

obj = container.get(Bar)

print(obj.name)  # выведет FooBar
```
namelov 0.2.0
A lovely package that's describes your name in a lovely way
Requires Python: >=3.5

# Lovely Name Adjective Generator 💖✨

### Welcome to the most **AWESOME** name describer on the internet! 🎉

## What is this magical thing? 🧙♂️

Ever wondered what your name REALLY means? No, not the boring dictionary stuff — we’re talking about **awesome adjectives** that make you sound like a superhero, a genius, or someone who definitely deserves a cookie 🍪.

This little script takes your name, looks at each letter, and tells you exactly how *FANTASTIC* you are. Because let's be honest, who doesn't want to hear they are **Xenodochial**? (Yeah, we had to look that one up too.)

## How to use it? 👇

1. Type your name.
2. Get showered with compliments.
3. Repeat as often as needed to boost your ego.

Example:

```python
hello("Alice")
```

Output:

```
Hello Alice!
Let me describe your Lovely name:
A is for Awesome
L is for Lively
I is for Inspiring
C is for Creative
E is for Excellent
```

See? You basically just won the personality lottery! 🎰

## Features 🚀

- Transforms plain old letters into shining adjectives.
- Works with any name, even "ZXQ" — because why not?
- Makes your friends jealous with your instantly upgraded name.
- Absolutely no sarcasm detected. (Promise.)

## Why did we build this? 🤔

Because we believe everyone deserves to feel like the **Brilliant, Zealous, and Joyful** human they are. And because the world could use more compliments, one letter at a time.

## Warning ⚠️

- May cause sudden bursts of confidence.
- May result in random smiling and shouting "I'm amazing!" in public.
- May lead to excessive use of fancy words like "Xenodochial" in normal conversation.
- Use responsibly. We are not responsible for any new fans you attract.

## Test it yourself! 🧪

Run the tests to make sure the magic is working:

```bash
pytest test_lovely.py
```

## The big goodbye 👋

When you’re done basking in the glory of your lovely name, say goodbye with *style*:

```python
goodbye()
```

Output:

```
Goodbye love!
See you again in the world of love!
```

Because even goodbyes should feel warm and fuzzy. ❤️

## Contributors

- You (because every name deserves an adjective)
- Us, the humble adjective wizards

## License

Feel free to share the love! ❤️

Now go forth, and make the world a more **Awesome, Brilliant, Creative, Diligent…** place! 🎈

If your name ever changes, just rerun the script. We got your back, letter by letter. 😉

**Stay lovely, stay weird.** ✌️

Would you like me to help you generate a README in markdown format or any other particular style?
ommx-da4-adapter 0.1.1
An adapter for the Digital Annealer V4 from/to OMMX.
Requires Python: >=3.9

# ommx-da4-adapter
This package provides an adapter for [Fujitsu Digital Annealer(DA4)](https://www.fujitsu.com/jp/digitalannealer/) from [OMMX](https://github.com/Jij-Inc/ommx). It allows you to solve optimization problems defined in OMMX format using DA4's powerful solver.

## Installation
The `ommx-da4-adapter` can be installed from PyPI as follows:

```bash
pip install ommx-da4-adapter
```

## Usage
Here's a simple example of how to use the adapter directly:

```python
from ommx.v1 import Instance, DecisionVariable
from ommx_da4_adapter import OMMXDA4Adapter

x_0 = DecisionVariable.binary(id=0, name="x_0")
x_1 = DecisionVariable.binary(id=1, name="x_1")

ommx_instance = Instance.from_components(
    decision_variables=[x_0, x_1],
    objective=x_0 * x_1 + x_0 - x_1 + 1,
    constraints=[x_0 + x_1 == 1],
    sense=Instance.MINIMIZE,
)

ommx_sampleset = OMMXDA4Adapter.sample(
    ommx_instance=ommx_instance,
    token="*** your da4 api token ***",
    url="*** da4 url ***",
)
```

You can also use the adapter and client separately:

```python
from ommx_da4_adapter import OMMXDA4Adapter, DA4Client

# Assuming ommx_instance is already defined as above
adapter = OMMXDA4Adapter(ommx_instance)

qubo_request = adapter.sampler_input

client = DA4Client(
    token="*** your da4 api token ***",
    url="*** da4 url ***",
)

qubo_response = client.sample(qubo_request=qubo_request)

ommx_sampleset = adapter.decode_to_sampleset(qubo_response)
```
RRPad 0.0.2
ReplicationReflectionPad combines ReplicationPad and ReflectionPad to prevent errors from being reported due to small input sizes when using non-constant padding types. 
Requires Python: >=3.8
Keywords: RRPad, ReplicationReflectionPad, Pad, PyTorch, AI, neural, NN, Padding

# RRPad
ReplicationReflectionPad combines ReplicationPad and ReflectionPad to prevent errors from being reported due to small input sizes when using non-constant padding types.

## Install
```bash
pip install RRPad
```

## Use
```python
from RRPad import ReplicationReflectionPad1d,ReplicationReflectionPad2d
...
pad=ReplicationReflectionPad1d(paddingSize)
input=torch.randn((1,2,3))
output=pad(input)
```

## HomePage
<https://github.com/PsycheHalo/RRPad/>