%global _empty_manifest_terminate_build 0

Name:		python-pyro-ppl
Version:	1.8.6
Release:	1
Summary:	Deep universal probabilistic programming with Python and PyTorch
License:	Apache-2.0
URL:		https://github.com/pyro-ppl/pyro
Source0:	https://github.com/pyro-ppl/pyro/archive/refs/tags/%{version}.tar.gz#/%{name}-%{version}.tar.gz

Requires:	python3-numpy
Requires:	python3-opt-einsum
Requires:	python3-pytorch
Requires:	python3-tqdm

%description
Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notably, it was designed with these principles in mind:
- Universal: Pyro is a universal PPL - it can represent any computable probability distribution.
- Scalable: Pyro scales to large data sets with little overhead compared to hand-written code.
- Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions.
- Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level
 abstractions to express generative and inference models, while allowing experts easy-access to customize inference.

%package -n python3-pyro-ppl
Summary:	Deep universal probabilistic programming with Python and PyTorch
Provides:	python-pyro-ppl

BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-setuptools_scm
BuildRequires:	python3-pbr
BuildRequires:	python3-pip
BuildRequires:	python3-wheel

%description -n python3-pyro-ppl
Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notably, it was designed with these principles in mind:
- Universal: Pyro is a universal PPL - it can represent any computable probability distribution.
- Scalable: Pyro scales to large data sets with little overhead compared to hand-written code.
- Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions.
- Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level
 abstractions to express generative and inference models, while allowing experts easy-access to customize inference.

%package help
Summary:	Development documents and examples for python-pyro-ppl
Provides:	python3-pyro-ppl-doc

%description help
Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notably, it was designed with these principles in mind:
- Universal: Pyro is a universal PPL - it can represent any computable probability distribution.
- Scalable: Pyro scales to large data sets with little overhead compared to hand-written code.
- Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions.
- Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level
 abstractions to express generative and inference models, while allowing experts easy-access to customize inference.

%prep
%autosetup -p1 -n pyro-%{version}

%build
%pyproject_build

%install
%pyproject_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/doclist.lst .

%files -n python3-pyro-ppl
%doc *.md
%license LICENSE.md
%{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Sun Jan 28 2024 Binshuo Zu <274620705z@gmail.com> - 1.8.6-1
- Package init