%global _empty_manifest_terminate_build 0
Name:		python-gym-saturation
Version:	0.10.1
Release:	1
Summary:	Gymnasium environments for saturation provers
License:	Apache-2.0
URL:		https://github.com/inpefess/gym-saturation
Source0:	https://mirrors.aliyun.com/pypi/web/packages/36/e5/8c195f8b3201af95c3b83ffec32b62a008fe533879582ab89ec7c740a52a/gym_saturation-0.10.1.tar.gz
BuildArch:	noarch

Requires:	python3-importlib_resources
Requires:	python3-pexpect
Requires:	python3-gymnasium

%description
``gym-saturation`` is a collection of `Gymnasium
<https://gymnasium.farama.org/>`__ environments for reinforcement learning
(RL) agents striving to prove theorems. Currently, only theorems
written in `TPTP library <https://tptp.org>`__ formal language are
supported.
There are two environments in ``gym-saturation`` following the same API: `SaturationEnv <https://gym-saturation.readthedocs.io/en/latest/environments/saturation-env.html>`__: ``VampireEnv`` is a wrapper around a recent `Vampire
<https://github.com/vprover/vampire>`__ prover, and ``IProverEnv``
relies on a stable version of `iProver
<https://gitlab.com/korovin/iprover/>`__.
In contrast to monolithic architecture of a typical Automated Theorem
Prover (ATP), ``gym-saturation`` gives different agents opportunities
to select clauses themselves and train from their experience.
Combined with a particular agent, ``gym-saturation`` can work as an
ATP.
``gym-saturation`` can be interesting for RL practitioners willing to
apply their experience to theorem proving without coding all the
logic-related stuff themselves. It also can be useful for automated
deduction researchers who want to create an RL-empowered ATP.

%package -n python3-gym-saturation
Summary:	Gymnasium environments for saturation provers
Provides:	python-gym-saturation
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-gym-saturation
``gym-saturation`` is a collection of `Gymnasium
<https://gymnasium.farama.org/>`__ environments for reinforcement learning
(RL) agents striving to prove theorems. Currently, only theorems
written in `TPTP library <https://tptp.org>`__ formal language are
supported.
There are two environments in ``gym-saturation`` following the same API: `SaturationEnv <https://gym-saturation.readthedocs.io/en/latest/environments/saturation-env.html>`__: ``VampireEnv`` is a wrapper around a recent `Vampire
<https://github.com/vprover/vampire>`__ prover, and ``IProverEnv``
relies on a stable version of `iProver
<https://gitlab.com/korovin/iprover/>`__.
In contrast to monolithic architecture of a typical Automated Theorem
Prover (ATP), ``gym-saturation`` gives different agents opportunities
to select clauses themselves and train from their experience.
Combined with a particular agent, ``gym-saturation`` can work as an
ATP.
``gym-saturation`` can be interesting for RL practitioners willing to
apply their experience to theorem proving without coding all the
logic-related stuff themselves. It also can be useful for automated
deduction researchers who want to create an RL-empowered ATP.

%package help
Summary:	Development documents and examples for gym-saturation
Provides:	python3-gym-saturation-doc
%description help
``gym-saturation`` is a collection of `Gymnasium
<https://gymnasium.farama.org/>`__ environments for reinforcement learning
(RL) agents striving to prove theorems. Currently, only theorems
written in `TPTP library <https://tptp.org>`__ formal language are
supported.
There are two environments in ``gym-saturation`` following the same API: `SaturationEnv <https://gym-saturation.readthedocs.io/en/latest/environments/saturation-env.html>`__: ``VampireEnv`` is a wrapper around a recent `Vampire
<https://github.com/vprover/vampire>`__ prover, and ``IProverEnv``
relies on a stable version of `iProver
<https://gitlab.com/korovin/iprover/>`__.
In contrast to monolithic architecture of a typical Automated Theorem
Prover (ATP), ``gym-saturation`` gives different agents opportunities
to select clauses themselves and train from their experience.
Combined with a particular agent, ``gym-saturation`` can work as an
ATP.
``gym-saturation`` can be interesting for RL practitioners willing to
apply their experience to theorem proving without coding all the
logic-related stuff themselves. It also can be useful for automated
deduction researchers who want to create an RL-empowered ATP.

%prep
%autosetup -n gym_saturation-0.10.1

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
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}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-gym-saturation -f filelist.lst
%dir %{python3_sitelib}/*

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

%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.10.1-1
- Package Spec generated