Metadata-Version: 2.4
Name: num2words2
Version: 1.0.17
Summary: Modern, actively maintained fork of num2words optimized for LLM/AI/speech applications.
Home-page: https://github.com/jqueguiner/num2words
Author: Jean-Louis Queguiner
Author-email: Jean-Louis Queguiner <jean-louis.queguiner@gmail.com>
Maintainer: Jean-Louis Queguiner
Maintainer-email: Jean-Louis Queguiner <jean-louis.queguiner@gmail.com>
License-Expression: LGPL-2.1-only
Project-URL: Homepage, https://github.com/jqueguiner/num2words
Project-URL: Repository, https://github.com/jqueguiner/num2words.git
Project-URL: Bug Tracker, https://github.com/jqueguiner/num2words/issues
Keywords: number,word,numbers,words,convert,conversion,i18n,localisation,localization,internationalisation,internationalization
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Programming Language :: Python :: 3.15
Classifier: Topic :: Software Development :: Internationalization
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Software Development :: Localization
Classifier: Topic :: Text Processing :: Linguistic
Requires-Python: >=3.10
Description-Content-Type: text/x-rst
License-File: COPYING
Requires-Dist: docopt>=0.6.2
Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: maintainer

num2words2 library - Convert numbers to words in multiple languages
===================================================================

.. image:: https://img.shields.io/pypi/v/num2words2.svg
   :target: https://pypi.python.org/pypi/num2words2

.. image:: https://github.com/jqueguiner/num2words/workflows/CI/badge.svg
    :target: https://github.com/jqueguiner/num2words/actions

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    :target: https://coveralls.io/github/jqueguiner/num2words?branch=master


``num2words2`` is a modern, actively maintained fork of the original num2words library
that converts numbers like ``42`` to words like ``forty-two``. It supports multiple
languages (see the list below for full list of languages) and can even generate
ordinal numbers like ``forty-second``. This fork was created to address the maintenance
gap in the original project and optimize for modern AI/LLM/speech applications.

The project is hosted on GitHub_. Contributions are welcome.

.. _GitHub: https://github.com/jqueguiner/num2words

Installation
------------

The easiest way to install ``num2words2`` is to use pip::

    pip install num2words2

Otherwise, you can download the source package and then execute::

    python setup.py install



Development Setup
-----------------
The project uses pre-commit hooks to ensure code quality. To set up your development environment::

    # Install pre-commit
    pip install pre-commit

    # Install the git hook scripts
    pre-commit install

    # Run hooks on all files (optional, useful for initial setup)
    pre-commit run --all-files

This will automatically format and lint your code before each commit using:

* autopep8 - PEP 8 formatting
* autoflake - removes unused imports and variables
* isort - sorts imports
* flake8 - style and quality checks
* trailing-whitespace removal
* end-of-file fixing


Testing
-------

The library uses `pytest` for testing. First, install the development dependencies:

.. code-block:: bash

    make dev-install

Then, you can run the test suite using several methods:

*   **Run basic tests:** This runs tests with your current Python environment.

    .. code-block:: bash

        make test

*   **Run with Tox:** This runs tests against all supported Python versions, which is the standard for CI.

    .. code-block:: bash

        tox

Generating End-to-End Tests with LLMs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The repository includes a powerful script to generate high-quality, realistic test cases using Large Language Models (LLMs). This helps ensure accuracy across multiple languages and complex scenarios.

**What it does:** The ``tests/scripts/generate_llm_tests.py`` script uses an LLM (like GPT-4o) to create sentences containing numbers, dates, and currencies, and then generates the expected word-for-word conversion.

**Requirements:**

*   An OpenAI API key. You must set it as an environment variable: ``export OPENAI_API_KEY='your-key-here'``

**How to Use:**

To generate 10 new test sentences for French and Spanish, you can run:

.. code-block:: bash

    python tests/scripts/generate_llm_tests.py --languages fr,es --samples 10

The new tests will be appended to ``tests/data/e2e_test_sentences.csv``.

**Key Options:**

*   ``--languages``: Comma-separated list of language codes (e.g., ``en_IN,de,it``).
*   ``--samples``: Number of samples to generate per language.
*   ``--mode``: Use ``sentences`` for full sentences or ``numbers`` for direct number-to-word conversions.
*   ``--model``: The OpenAI model to use (e.g., ``gpt-4o``, ``gpt-4o-mini``).
*   ``--output``: Specify a different output file.
*   ``--overwrite``: Overwrite the output file instead of appending.

This tool is essential for expanding test coverage and ensuring the library's robustness.


Usage
-----
Command line::

    $ num2words2 10001
    ten thousand and one
    $ num2words2 24,120.10
    twenty-four thousand, one hundred and twenty point one
    $ num2words2 24,120.10 -l es
    veinticuatro mil ciento veinte punto uno
    $ num2words2 2.14 -l es --to currency
    dos euros con catorce céntimos

In code there's only one function to use::

    >>> from num2words2 import num2words
    >>> num2words(42)
    forty-two
    >>> num2words(42, to='ordinal')
    forty-second
    >>> num2words(42, lang='fr')
    quarante-deux

Besides the numerical argument, there are two main optional arguments, ``to:`` and ``lang:``

**to:** The converter to use. Supported values are:

* ``cardinal`` (default)
* ``ordinal``
* ``ordinal_num``
* ``year``
* ``currency``

**lang:** The language in which to convert the number. Supported values are:

* ``en`` (English, default)
* ``am`` (Amharic)
* ``ar`` (Arabic)
* ``az`` (Azerbaijani)
* ``be`` (Belarusian)
* ``bn`` (Bangladeshi)
* ``ca`` (Catalan)
* ``ce`` (Chechen)
* ``cs`` (Czech)
* ``cy`` (Welsh)
* ``da`` (Danish)
* ``de`` (German)
* ``en_GB`` (English - Great Britain)
* ``en_IN`` (English - India)
* ``en_NG`` (English - Nigeria)
* ``es`` (Spanish)
* ``es_CO`` (Spanish - Colombia)
* ``es_CR`` (Spanish - Costa Rica)
* ``es_GT`` (Spanish - Guatemala)
* ``es_VE`` (Spanish - Venezuela)
* ``eu`` (EURO)
* ``fa`` (Farsi)
* ``fi`` (Finnish)
* ``fr`` (French)
* ``fr_BE`` (French - Belgium)
* ``fr_CH`` (French - Switzerland)
* ``fr_DZ`` (French - Algeria)
* ``he`` (Hebrew)
* ``hi`` (Hindi)
* ``hu`` (Hungarian)
* ``hy`` (Armenian)
* ``id`` (Indonesian)
* ``is`` (Icelandic)
* ``it`` (Italian)
* ``ja`` (Japanese)
* ``kn`` (Kannada)
* ``ko`` (Korean)
* ``kz`` (Kazakh)
* ``mn`` (Mongolian)
* ``lt`` (Lithuanian)
* ``lv`` (Latvian)
* ``nl`` (Dutch)
* ``no`` (Norwegian)
* ``pl`` (Polish)
* ``pt`` (Portuguese)
* ``pt_BR`` (Portuguese - Brazilian)
* ``ro`` (Romanian)
* ``ru`` (Russian)
* ``sl`` (Slovene)
* ``sk`` (Slovak)
* ``sr`` (Serbian)
* ``sv`` (Swedish)
* ``te`` (Telugu)
* ``tet`` (Tetum)
* ``tg`` (Tajik)
* ``tr`` (Turkish)
* ``th`` (Thai)
* ``uk`` (Ukrainian)
* ``vi`` (Vietnamese)
* ``zh`` (Chinese - Traditional)
* ``zh_CN`` (Chinese - Simplified / Mainland China)
* ``zh_TW`` (Chinese - Traditional / Taiwan)
* ``zh_HK`` (Chinese - Traditional / Hong Kong)

You can supply values like ``fr_FR``; if the country doesn't exist but the
language does, the code will fall back to the base language (i.e. ``fr``). If
you supply an unsupported language, ``NotImplementedError`` is raised.
Therefore, if you want to call ``num2words`` with a fallback, you can do::

    try:
        return num2words(42, lang=mylang)
    except NotImplementedError:
        return num2words(42, lang='en')

Additionally, some converters and languages support other optional arguments
that are needed to make the converter useful in practice.

Wiki
----
For additional information on some localization please check the Wiki_.
And feel free to propose wiki enhancement.

.. _Wiki: https://github.com/jqueguiner/num2words/wiki

History
-------

``num2words`` is based on an old library, ``pynum2word``, created by Taro Ogawa
in 2003. Unfortunately, the library stopped being maintained and the author
can't be reached. There was another developer, Marius Grigaitis, who in 2011
added Lithuanian support, but didn't take over maintenance of the project.

Virgil Dupras from Savoir-faire Linux based himself on Marius Grigaitis' improvements
and re-published ``pynum2word`` as ``num2words``.

``num2words2`` Fork
-------------------

``num2words2`` is a modern fork of the original ``num2words`` library, created to address
the maintenance gap and optimize for modern AI/LLM/speech applications. This fork:

* Provides active maintenance aligned with rapidly evolving AI/ML ecosystem
* Fixes critical bugs affecting machine learning pipelines
* Adds enhanced language support for global AI applications
* Maintains backward compatibility with the original library

Jean-Louis Queguiner
