reapxdev/pypi-scraper huggingface dataset:
freezegun 1.5.5 — Let your Python tests travel through time
Author: Steve Pulec
License: Apache-2.0 
Requires Python: >=3.8
Dependencies: python-dateutil>=2.7

FreezeGun: Let your Python tests travel through time
====================================================

.. image:: https://img.shields.io/pypi/v/freezegun.svg
   :target: https://pypi.python.org/pypi/freezegun/
.. image:: https://github.com/spulec/freezegun/workflows/CI/badge.svg
   :target: https://github.com/spulec/freezegun/actions
.. image:: https://coveralls.io/repos/spulec/freezegun/badge.svg?branch=master
   :target: https://coveralls.io/r/spulec/freezegun

FreezeGun is a library that allows your Python tests to travel through time by mocking the datetime module.

Usage
-----

Once the decorator or context manager have been invoked, all calls to datetime.datetime.now(), datetime.datetime.utcnow(), datetime.date.today(), time.time(), time.localtime(), time.gmtime(), and time.strftime() will return the time that has been frozen. time.monotonic() and time.perf_counter() will also be frozen, but as usual it makes no guarantees about their absolute value, only their changes over time.

Decorator
~~~~~~~~~

.. code-block:: python

    from freezegun import freeze_time
    import datetime
    import unittest

    # Freeze time for a pytest style test:

    @freeze_time("2012-01-14")
    def test():
        assert datetime.datetime.now() == datetime.datetime(2012, 1, 14)

    # Or a unittest TestCase - freezes for every test, and set up and tear down code

    @freeze_time("1955-11-12")
    class MyTests(unittest.TestCase):
        def test_the_class(self):
            assert datetime.datetime.now() == datetime.datetime(1955, 11, 12)

    # Or any other class - freezes around each callable (may not work in every case)

    @freeze_time("2012-01-14")
    class Tester(object):
        def test_the_class(self):
            assert datetime.datetime.now() == datetime.datetime(2012, 1, 14)

    # Or method decorator, might also pass frozen time object as kwarg

    class TestUnitTestMethodDecorator(unittest.TestCase):
        @freeze_time('2013-04-09')
        def test_
tortoise-orm 1.1.7 — Easy async ORM for python, built with relations in mind
License: Apache-2.0 
Keywords: sql, mysql, postgres, psql, sqlite, aiosqlite, asyncpg, relational, database, rdbms, orm, object mapper, async, asyncio, aio, psycopg
Requires Python: >=3.10
Dependencies: pypika-tortoise<1.0.0,>=0.6.5, iso8601<3.0.0,>=2.1.0; python_version < "4.0", aiosqlite<1.0.0,>=0.16.0, anyio, tomlkit<1.0.0,>=0.11.4; python_version < "3.11", typing-extensions>=4.1.0; python_version < "3.11", ipython; extra == "ipython", nest-asyncio>=1.6.0; extra == "ipython", ptpython>=3.0.0; extra == "ptpython", ciso8601; sys_platform != "win32" and implementation_name == "cpython" and extra == "accel", uvloop; sys_platform != "win32" and implementation_name == "cpython" and extra == "accel", orjson; extra == "accel", asyncpg; extra == "asyncpg", aiomysql; extra == "aiomysql", asyncmy<1.0.0,>=0.2.11; extra == "asyncmy", psycopg[binary,pool]<4.0.0,>=3.0.12; extra == "psycopg", asyncodbc<1.0.0,>=0.1.1; python_version < "4.0" and extra == "asyncodbc"

============
Tortoise ORM
============

.. image:: https://img.shields.io/pypi/v/tortoise-orm.svg?style=flat
   :target: https://pypi.python.org/pypi/tortoise-orm
.. image:: https://pepy.tech/badge/tortoise-orm/month
   :target: https://pepy.tech/project/tortoise-orm
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   :target: https://github.com/tortoise/tortoise-orm/actions?query=workflow:gh-pages
.. image:: https://github.com/tortoise/tortoise-orm/actions/workflows/ci.yml/badge.svg?branch=develop
   :target: https://github.com/tortoise/tortoise-orm/actions?query=workflow:ci
.. image:: https://coveralls.io/repos/github/tortoise/tortoise-orm/badge.svg
   :target: https://coveralls.io/github/tortoise/tortoise-orm

Introduction
============

Tortoise ORM is an easy-to-use ``asyncio`` ORM *(Object Relational Mapper)* inspired by Django.

You can find the docs at `Documentation <https://tortoise.github.io>`_

Tortoise ORM supports CPython 3.10 and later for SQLite, MySQL, PostgreSQL, Microsoft SQL Server, and Oracle.

Why was Tortoise ORM built?
---------------------------

Tortoise ORM was built to provide a lightweight, async-native Object-Relational Mapper for Python with a familiar Django-like API.

Tortoise ORM performs well when compared to other Python ORMs. Here are `our benchmarks <https://github.com/tortoise/orm-benchmarks>`_ on PostgreSQL 17, where we measure different read and write operations (rows/sec, more is better):

.. image:: https://raw.githubusercontent.com/tortoise/tortoise-orm/develop/docs/ORM_Perf.png
    :target: https://github.com/tortoise/orm-benchmarks

How is an ORM useful?
---------------------

An Object-Relational Mapper (ORM) abstracts database interactions, allowing developers to work with databases using high-level, object-oriented code instead of raw SQL.

* Reduces boilerplate SQL, allowing faster development with cleaner, more readable code.
* Helps prevent SQL injection by using parameterized queries
xgboost 3.3.0 — XGBoost Python Package
License: Apache-2.0 
Requires Python: >=3.12
Dependencies: numpy, nvidia-nccl-cu12; platform_system == "Linux", scipy, dask; extra == "dask", distributed; extra == "dask", pandas; extra == "dask", pandas>=1.2; extra == "pandas", graphviz; extra == "plotting", matplotlib; extra == "plotting", cloudpickle; extra == "pyspark", pyspark>=4.0; extra == "pyspark", scikit-learn; extra == "pyspark", scikit-learn; extra == "scikit-learn"

======================
XGBoost Python Package
======================

|PyPI version|

Installation
============

From `PyPI <https://pypi.python.org/pypi/xgboost>`_
---------------------------------------------------

For a stable version, install using ``pip``::

    pip install xgboost

.. |PyPI version| image:: https://badge.fury.io/py/xgboost.svg
   :target: http://badge.fury.io/py/xgboost

For building from source, see `build <https://xgboost.readthedocs.io/en/latest/build.html>`_.
timm 1.0.28 — PyTorch Image Models
License: Apache-2.0 
Keywords: pytorch, image-classification
Requires Python: >=3.8
Dependencies: torch, torchvision, pyyaml, huggingface_hub, safetensors

# PyTorch Image Models
- [What's New](#whats-new)
- [Introduction](#introduction)
- [Models](#models)
- [Features](#features)
- [Results](#results)
- [Getting Started (Documentation)](#getting-started-documentation)
- [Train, Validation, Inference Scripts](#train-validation-inference-scripts)
- [Awesome PyTorch Resources](#awesome-pytorch-resources)
- [Licenses](#licenses)
- [Citing](#citing)

## What's New

## July 10, 2026
* Improve optimizer `torch.compile` and tensor learning-rate support.
* Extend NaFlexViT patch-layout (for NaFlex-CLAP), and `forward_intermediates` (NaFlex dict input) support.
* Harden pickle loading and improve custom-label inference.
* Release 1.0.28

## May 27, 2026
* Add model defs and pretrained weights for EUPE ViT (DINOv3-style) and ConvNeXt models. See the [Efficient Universal Perception Encoder paper](https://arxiv.org/abs/2603.22387).
* Add TIPSv2 model defs and pretrained weights for (DINOv2-style) ViTs. See the [TIPSv2 paper](https://arxiv.org/abs/2604.12012).

## May 8, 2026
* Release 1.0.27

## April 23, 2026
* Add Gemma4 ViT encoders w/ NaFlex pipeline support (variable aspect/size per image). Thanks [Yonghye Kwon](https://github.com/developer0hye)
* Support DINOv3 weights in NaFlexVit. Thanks [Yonghye Kwon](https://github.com/developer0hye)
* Some improvements to Muon fallback (AdamW/NadamW) lr behavior

## March 23, 2026
* Improve pickle checkpoint handling security. Default all loading to `weights_only=True`, add safe_global for ArgParse.
* Improve attention mask handling for core ViT/EVA models & layers. Resolve bool masks, pass `is_causal` through for SSL tasks.
* Fix class & register token uses with ViT and no pos embed enabled.
* Add Patch Representation Refinement (PRR) as a pooling option in ViT. Thanks Sina (https://github.com/sinahmr).
* Improve consistency of output projection / MLP dimensions for attention pooling layers.
* Hiera model F.SDPA optimization to allow Flash Attention kernel use.
* Caution added to SGDP