File python-tokenizers.spec of Package python-tokenizers

#
# spec file for package python-tokenizers
#
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%{?!python_module:%define python_module() python-%{**} python3-%{**}}

Name:           python-tokenizers
Version:        0.19.1
Release:        0
Summary:        Provides an implementation of today's most used tokenizers
License:        Apache-2.0
URL:            https://github.com/huggingface/tokenizers
Source0:        https://github.com/huggingface/tokenizers/archive/refs/tags/v%{version}.tar.gz#/tokenizers-%{version}.tar.gz
Source1:        vendor.tar.gz
BuildRequires:  %{python_module devel}
BuildRequires:  %{python_module maturin}
BuildRequires:  %{python_module pip}
BuildRequires:  %{python_module tomli}
BuildRequires:  %{python_module setuptools}
BuildRequires:  cargo-packaging
BuildRequires:  gcc-c++
BuildRequires:  fdupes
BuildRequires:  python-rpm-macros
BuildRequires:  python-rpm-macros
%python_subpackages

%description
Provides an implementation of today's most used tokenizers, with a focus on
performance and versatility.
* Train new vocabularies and tokenize, using today's most used tokenizers.
* Extremely fast (both training and tokenization), thanks to the Rust
  implementation. Takes less than 20 seconds to tokenize a GB of text on a
  server's CPU.
* Easy to use, but also extremely versatile.
* Designed for research and production.
* Normalization comes with alignments tracking. It's always possible to get the
  part of the original sentence that corresponds to a given token.
* Does all the pre-processing: Truncate, Pad, add the special tokens your model
  needs.

%prep
%autosetup -p1 -n tokenizers-%{version}
cd bindings/python
tar xzf %{S:1}

%build
cd bindings/python
%pyproject_wheel

%install
cd bindings/python
%pyproject_install
%python_expand %fdupes %{buildroot}/%{$python_sitearch}/*

%check

%files %{python_files}
%license LICENSE
%doc README.md
%{python_sitearch}/tokenizers*

%changelog