semvec/pypi-packages huggingface dataset:
configmate-pydantic-validator 0.1.0
Extends configmate with pydantic validation logic.
Requires Python: >=3.8,<4.0

# configmate-pydantic-validator
Extends `configmate` to levarage Pydantic for config validation.

## Installation
```bash
pip install configmate-pydanticvalidator
```
OR directly with the extras of `configmate`:
```bash
pip install configmate[pydantic]
```

## Usage
By loading this plugin, `configmate` will use Pydantic to validate the config. 
Under the hood, a new validator is registered to `configmate.components.validators.TypeValidatorFactory`.
This validator will be used for any non-function passed to `validation` in configmate.get_config.

```python
from typing import Dict
import dataclasses

import configmate

@dataclasses.dataclass
class Config:
    host: str
    port: int

config = configmate.get_config(
    "config.json",  # main config
    validation=Dict[str, str], # pydantic will handle validation
)
## >> {'host': 'localhost', 'port': '8080'} # note that port is a string due to pydantic

config = configmate.get_config(
    "config.json",  # main config
    validation=Config, # pydantic will handle validation
) 
## >> Config(host='localhost', port=8080) # port is an int as expected

config = configmate.get_config(
    "config.json",  # main config
    validation=lambda config_dict: ..., # callables will be called with the config dict (as without this plugin)
)
## >> ... # whatever the callable returns
```
myrepos-utils 0.0.4.2
Additional utilities for myrepos

# myrepos-utils

## Usage

### config
Configures a new repository, checks it out by default, and configures
additional git remotes and git configurations as specified

For example:
```
$ mr-utils config centos/hyperscale/emacs "git clone -b c9s-sig-hyperscale https://git.centos.org/rpms/emacs.git" --extra-git-remotes fedora https://src.fedoraproject.org/rpms/emacs.git --git-configs user.email salimma@centosproject.org

```

### find
Let's say you have the following repositories configured in `~/.mrconfig`:

```
[src/github/owner1/projA]
...

[src/github/owner2/projB]
...
```

This will let you quickly switch to `~/src/github/owner1/projA`:
```
cd (mr-utils find github projA)
```

If there are multiple matches, they will be printed out.

### sort
This will sort `~/.mrconfig` based on sections and write it back out
```
mr-utils sort
```

### version
Displays the version of the current `myrepos-utils`
twosigma-memento 0.28
Two Sigma Memento Core
Requires Python: >=3.8

# Two Sigma Memento
Memento is a framework and lightweight set of standards that encourage discipline in the way data
is incrementally transformed through code. The goal of Memento is to ensure that data is
reproducible and that accurate provenance is maintained in metadata. The Memento Framework
does not pin itself to a specific programming language, back-end storage technology or compute
framework. Rather, it focuses on a technique for generating and accessing data that preserves
accurate metadata throughout.

Memento can be extended with plugins which customize where memoized data is stored and how
distributed compute is executed.

This codebase hosts several independent products, which are all synchronized in their version.
This product is the core memento framework, which is usable by itself or in combination with
various plugins.

The core framework includes three storage backends, filesystem (the default), memory, and
null (never store). The core framework also includes two runner backends, local (in-process),
and null (never run). To use memento for distributed computation or shared storage, other
plugins can be used.


# Quick Start
The following instructions should get you from a git clone to a working build of Memento.
These instructions are tested on Linux and Windows but should work in other environments
as well.

## Prerequisites
You need a Python environment with hatch installed:

```bash
$ python --version
Python 3.11.1
$ pip install hatch
```

## Build Memento
To build, simply run the following command. You will get a dist directory with a pip package.

```bash
$ hatch build
```


## Test Memento
Memento has an extensive suite of unit tests. To run the tests to ensure the build is working,
run the following.

```bash
$ hatch run cov
```

There are some tests that will not run by default because they require a lot of RAM and are slow
to run. To include those tests, include the `--runslow` parameter.

## Run Memento
If you have an existing environment, you can install memento with:

```bash
$ pip install -e .
```

If you prefer to start an isolated environment with a barebones Python with just Memento installed:

```bash
$ hatch shell
$ python
```

# Quick example
Try a simple cached function definition in a python repl:

```bash
$ hatch run python
Python 3.11.1 (tags/v3.11.1:a7a450f, Dec  6 2022, 19:58:39) [MSC v.1934 64 bit (AMD64)] on win32                                                                                                                                                                                                               
Type "help", "copyright", "credits" or "license" for more information.
>>> from twosigma.memento import memento_function
>>> @memento_function
... def f(x):
...     print("eval f")
...     return x + 1
...
>>> f(1)
eval f
2
>>> # The second time we run it should not print "eval f" because it reads the cached result
>>> f(1)
2
>>>
```
autodistill-siglip 0.1.0
SigLIP base model 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"
      >
    </a>
  </p>
</div>

# Autodistill SigLIP Module

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

[CLIP](https://github.com/openai/CLIP), developed by OpenAI, is a computer vision model trained using pairs of images and text. You can use CLIP with autodistill for image classification.

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

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

## Installation

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


```bash
pip3 install autodistill-clip
```

## Quickstart

```python
from autodistill_siglip import SigLIP
from autodistill.detection import CaptionOntology

# define an ontology to map class names to our SigLIP 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
labels = ["person", "a forklift"]
base_model = SigLIP(
    ontology=CaptionOntology({item: item for item in labels})
)

results = base_model.predict("image.jpeg", confidence=0.1)

top_1 = results.get_top_k(1)

# show top label
print(labels[top_1[0][0]])

# label folder of images
base_model.label("./context_images", extension=".jpeg")
```


## License

The SigLIP model is licensed under an [Apache 2.0 license](https://huggingface.co/google/siglip-base-patch16-224).

## 🏆 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!
opinionated-mixins 0.0.1
Opinionated set of mixins. Implemented in Pydantic, SQLAlchemy, MongoEngine, ODMantic, etc.
Requires Python: >=3.8

# opinionated-mixins

[![PyPI - Version](https://img.shields.io/pypi/v/opinionated-mixins.svg)](https://pypi.org/project/opinionated-mixins)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/opinionated-mixins.svg)](https://pypi.org/project/opinionated-mixins)

Opinionated set of mixins. Implemented in Pydantic, SQLAlchemy, MongoEngine, ODMantic, etc.

-----

**Table of Contents**

- [opinionated-mixins](#opinionated-mixins)
  - [Installation](#installation)
  - [License](#license)

## Installation

```console
pip install opinionated-mixins
```

## License

`opinionated-mixins` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.