automatic import of python-neural-semigroups

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+%global _empty_manifest_terminate_build 0
+Name:		python-neural-semigroups
+Version:	0.6.3
+Release:	1
+Summary:	Neural networks powered research of semigroups
+License:	Apache-2.0
+URL:		https://github.com/inpefess/neural-semigroups
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/f4/a1/1e9a7d27e62f24b735e459375c51978fc084c1056d3cab0b5f0ee4c3cf09/neural-semigroups-0.6.3.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-torch
+Requires:	python3-tqdm
+Requires:	python3-pytorch-ignite
+Requires:	python3-tensorboard
+
+%description
+The project is abandoned.
+If you want to reproduce results from the
+`paper <https://arxiv.org/abs/2103.07388>`__, please use `this
+notebook <https://colab.research.google.com/github/inpefess/neural-semigroups/blob/master/examples/train_a_model.ipynb>`__.
+Here we try to model Cayley tables of semigroups using neural networks.
+This work was inspired by `a sudoku
+solver <https://github.com/Kyubyong/sudoku>`__. A solved Sudoku puzzle
+is nothing more than a Cayley table of a quasigroup from 9 items with
+some well-known additional properties. So, one can imagine a puzzle made
+from a Cayley table of any other magma, e.g. a semigroup, by hiding part
+of its cells.
+There are two major differences between sudoku and puzzles based on
+semigroups:
+1) it’s easy to take a glance on a table to understand whether it is a
+   sudoku or not. That’s why it was possible to encode numbers in a
+   table cells as colour intensities. Sudoku is a picture, and a
+   semigroup is not. It’s difficult to check a Cayley table’s
+   associativity with a naked eye;
+2) Sudoku puzzles are solved by humans for fun and thus catalogued. When
+   solving a sudoku one knows for sure that there is a unique solution.
+   On the contrary, nobody guesses values in a partially filled Cayley
+   table of a semigroup as a form of amusement. As a result, one can
+   create a puzzle from a full Cayley table of a semigroup but there may
+   be many distinct solutions.
+
+%package -n python3-neural-semigroups
+Summary:	Neural networks powered research of semigroups
+Provides:	python-neural-semigroups
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-neural-semigroups
+The project is abandoned.
+If you want to reproduce results from the
+`paper <https://arxiv.org/abs/2103.07388>`__, please use `this
+notebook <https://colab.research.google.com/github/inpefess/neural-semigroups/blob/master/examples/train_a_model.ipynb>`__.
+Here we try to model Cayley tables of semigroups using neural networks.
+This work was inspired by `a sudoku
+solver <https://github.com/Kyubyong/sudoku>`__. A solved Sudoku puzzle
+is nothing more than a Cayley table of a quasigroup from 9 items with
+some well-known additional properties. So, one can imagine a puzzle made
+from a Cayley table of any other magma, e.g. a semigroup, by hiding part
+of its cells.
+There are two major differences between sudoku and puzzles based on
+semigroups:
+1) it’s easy to take a glance on a table to understand whether it is a
+   sudoku or not. That’s why it was possible to encode numbers in a
+   table cells as colour intensities. Sudoku is a picture, and a
+   semigroup is not. It’s difficult to check a Cayley table’s
+   associativity with a naked eye;
+2) Sudoku puzzles are solved by humans for fun and thus catalogued. When
+   solving a sudoku one knows for sure that there is a unique solution.
+   On the contrary, nobody guesses values in a partially filled Cayley
+   table of a semigroup as a form of amusement. As a result, one can
+   create a puzzle from a full Cayley table of a semigroup but there may
+   be many distinct solutions.
+
+%package help
+Summary:	Development documents and examples for neural-semigroups
+Provides:	python3-neural-semigroups-doc
+%description help
+The project is abandoned.
+If you want to reproduce results from the
+`paper <https://arxiv.org/abs/2103.07388>`__, please use `this
+notebook <https://colab.research.google.com/github/inpefess/neural-semigroups/blob/master/examples/train_a_model.ipynb>`__.
+Here we try to model Cayley tables of semigroups using neural networks.
+This work was inspired by `a sudoku
+solver <https://github.com/Kyubyong/sudoku>`__. A solved Sudoku puzzle
+is nothing more than a Cayley table of a quasigroup from 9 items with
+some well-known additional properties. So, one can imagine a puzzle made
+from a Cayley table of any other magma, e.g. a semigroup, by hiding part
+of its cells.
+There are two major differences between sudoku and puzzles based on
+semigroups:
+1) it’s easy to take a glance on a table to understand whether it is a
+   sudoku or not. That’s why it was possible to encode numbers in a
+   table cells as colour intensities. Sudoku is a picture, and a
+   semigroup is not. It’s difficult to check a Cayley table’s
+   associativity with a naked eye;
+2) Sudoku puzzles are solved by humans for fun and thus catalogued. When
+   solving a sudoku one knows for sure that there is a unique solution.
+   On the contrary, nobody guesses values in a partially filled Cayley
+   table of a semigroup as a form of amusement. As a result, one can
+   create a puzzle from a full Cayley table of a semigroup but there may
+   be many distinct solutions.
+
+%prep
+%autosetup -n neural-semigroups-0.6.3
+
+%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-neural-semigroups -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.3-1
+- Package Spec generated