%global _empty_manifest_terminate_build 0
Name:		python-textgenrnn
Version:	2.0.0
Release:	1
Summary:	Easily train your own text-generating neural network of any size and complexity
License:	MIT
URL:		https://github.com/minimaxir/textgenrnn
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/27/60/419daf7e2d87bcafc6f0f65736ce76e5cc83cebbae758dd59b4c1fae99cd/textgenrnn-2.0.0.tar.gz
BuildArch:	noarch


%description
Easily train your own text-generating neural network of
any size and complexity on any text dataset with a few lines
of code, or quickly train on a text using a pretrained model.

- A modern neural network architecture which utilizes new techniques as
attention-weighting and skip-embedding to accelerate training
and improve model quality.
- Able to train on and generate text at either the
character-level or word-level.
- Able to configure RNN size, the number of RNN layers,
and whether to use bidirectional RNNs.
- Able to train on any generic input text file, including large files.
- Able to train models on a GPU and then use them with a CPU.
- Able to utilize a powerful CuDNN implementation of RNNs
when trained on the GPU, which massively speeds up training time as
opposed to normal LSTM implementations.
- Able to train the model using contextual labels,
allowing it to learn faster and produce better results in some cases.
- Able to generate text interactively for customized stories.

%package -n python3-textgenrnn
Summary:	Easily train your own text-generating neural network of any size and complexity
Provides:	python-textgenrnn
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-textgenrnn
Easily train your own text-generating neural network of
any size and complexity on any text dataset with a few lines
of code, or quickly train on a text using a pretrained model.

- A modern neural network architecture which utilizes new techniques as
attention-weighting and skip-embedding to accelerate training
and improve model quality.
- Able to train on and generate text at either the
character-level or word-level.
- Able to configure RNN size, the number of RNN layers,
and whether to use bidirectional RNNs.
- Able to train on any generic input text file, including large files.
- Able to train models on a GPU and then use them with a CPU.
- Able to utilize a powerful CuDNN implementation of RNNs
when trained on the GPU, which massively speeds up training time as
opposed to normal LSTM implementations.
- Able to train the model using contextual labels,
allowing it to learn faster and produce better results in some cases.
- Able to generate text interactively for customized stories.

%package help
Summary:	Development documents and examples for textgenrnn
Provides:	python3-textgenrnn-doc
%description help
Easily train your own text-generating neural network of
any size and complexity on any text dataset with a few lines
of code, or quickly train on a text using a pretrained model.

- A modern neural network architecture which utilizes new techniques as
attention-weighting and skip-embedding to accelerate training
and improve model quality.
- Able to train on and generate text at either the
character-level or word-level.
- Able to configure RNN size, the number of RNN layers,
and whether to use bidirectional RNNs.
- Able to train on any generic input text file, including large files.
- Able to train models on a GPU and then use them with a CPU.
- Able to utilize a powerful CuDNN implementation of RNNs
when trained on the GPU, which massively speeds up training time as
opposed to normal LSTM implementations.
- Able to train the model using contextual labels,
allowing it to learn faster and produce better results in some cases.
- Able to generate text interactively for customized stories.

%prep
%autosetup -n textgenrnn-2.0.0

%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-textgenrnn -f filelist.lst
%dir %{python3_sitelib}/*

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

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.0-1
- Package Spec generated