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+%global _empty_manifest_terminate_build 0
+Name:		python-e3nn
+Version:	0.5.1
+Release:	1
+Summary:	Equivariant convolutional neural networks for the group E(3) of 3 dimensional rotations, translations, and mirrors.
+License:	MIT
+URL:		https://e3nn.org
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/a1/e5/fd5a05004fa4367511bc05b573773fe59031e20d7eb1a21743fda3eb5db5/e3nn-0.5.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-sympy
+Requires:	python3-scipy
+Requires:	python3-torch
+Requires:	python3-opt-einsum-fx
+Requires:	python3-pytest
+Requires:	python3-pre-commit
+
+%description
+# Euclidean neural networks
+[![Coverage Status](https://coveralls.io/repos/github/e3nn/e3nn/badge.svg?branch=main)](https://coveralls.io/github/e3nn/e3nn?branch=main)
+[![DOI](https://zenodo.org/badge/237431920.svg)](https://zenodo.org/badge/latestdoi/237431920)
+
+**[Documentation](https://docs.e3nn.org)** | **[Code](https://github.com/e3nn/e3nn)** | **[ChangeLog](https://github.com/e3nn/e3nn/blob/main/ChangeLog.md)** | **[Colab](https://colab.research.google.com/drive/1Gps7mMOmzLe3Rt_b012xsz4UyuexTKAf?usp=sharing)**
+
+The aim of this library is to help the development of [E(3)](https://en.wikipedia.org/wiki/Euclidean_group) equivariant neural networks.
+It contains fundamental mathematical operations such as [tensor products](https://docs.e3nn.org/en/stable/api/o3/o3_tp.html) and [spherical harmonics](https://docs.e3nn.org/en/stable/api/o3/o3_sh.html).
+
+![](https://user-images.githubusercontent.com/333780/79220728-dbe82c00-7e54-11ea-82c7-b3acbd9b2246.gif)
+
+## Installation
+
+**Important:** install pytorch and only then run the command
+
+```
+pip install --upgrade pip
+pip install --upgrade e3nn
+```
+
+For details and optional dependencies, see [INSTALL.md](https://github.com/e3nn/e3nn/blob/main/INSTALL.md)
+
+### Breaking changes
+e3nn is under development.
+It is recommanded to install using pip. The main branch is considered as unstable.
+The second version number is incremented every time a breaking change is made to the code.
+```
+0.(increment when backwards incompatible release).(increment for backwards compatible release)
+```
+
+## Help
+We are happy to help! The best way to get help on `e3nn` is to submit a [Question](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=question&template=question.md&title=%E2%9D%93+%5BQUESTION%5D) or [Bug Report](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=bug&template=bug-report.md&title=%F0%9F%90%9B+%5BBUG%5D).
+
+## Want to get involved? Great!
+If you want to get involved in and contribute to the development, improvement, and application of `e3nn`, introduce yourself in the [discussions](https://github.com/e3nn/e3nn/discussions/new).
+
+## Code of conduct
+Our community abides by the [Contributor Covenant Code of Conduct](https://github.com/e3nn/e3nn/blob/main/code_of_conduct.md).
+
+## Citing
+```
+@misc{e3nn_paper,
+    doi = {10.48550/ARXIV.2207.09453},
+    url = {https://arxiv.org/abs/2207.09453},
+    author = {Geiger, Mario and Smidt, Tess},
+    keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences}, 
+    title = {e3nn: Euclidean Neural Networks},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+
+@software{e3nn,
+  author       = {Mario Geiger and
+                  Tess Smidt and
+                  Alby M. and
+                  Benjamin Kurt Miller and
+                  Wouter Boomsma and
+                  Bradley Dice and
+                  Kostiantyn Lapchevskyi and
+                  Maurice Weiler and
+                  Michał Tyszkiewicz and
+                  Simon Batzner and
+                  Dylan Madisetti and
+                  Martin Uhrin and
+                  Jes Frellsen and
+                  Nuri Jung and
+                  Sophia Sanborn and
+                  Mingjian Wen and
+                  Josh Rackers and
+                  Marcel Rød and
+                  Michael Bailey},
+  title        = {Euclidean neural networks: e3nn},
+  month        = apr,
+  year         = 2022,
+  publisher    = {Zenodo},
+  version      = {0.5.0},
+  doi          = {10.5281/zenodo.6459381},
+  url          = {https://doi.org/10.5281/zenodo.6459381}
+}
+```
+
+### Copyright
+
+Euclidean neural networks (e3nn) Copyright (c) 2020, The Regents of the
+University of California, through Lawrence Berkeley National Laboratory
+(subject to receipt of any required approvals from the U.S. Dept. of Energy),
+Ecole Polytechnique Federale de Lausanne (EPFL), Free University of Berlin
+and Kostiantyn Lapchevskyi. All rights reserved.
+
+If you have questions about your rights to use or distribute this software,
+please contact Berkeley Lab's Intellectual Property Office at
+IPO@lbl.gov.
+
+NOTICE.  This Software was developed under funding from the U.S. Department
+of Energy and the U.S. Government consequently retains certain rights.  As
+such, the U.S. Government has been granted for itself and others acting on
+its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the
+Software to reproduce, distribute copies to the public, prepare derivative
+works, and perform publicly and display publicly, and to permit others to do so.
+
+
+
+
+%package -n python3-e3nn
+Summary:	Equivariant convolutional neural networks for the group E(3) of 3 dimensional rotations, translations, and mirrors.
+Provides:	python-e3nn
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-e3nn
+# Euclidean neural networks
+[![Coverage Status](https://coveralls.io/repos/github/e3nn/e3nn/badge.svg?branch=main)](https://coveralls.io/github/e3nn/e3nn?branch=main)
+[![DOI](https://zenodo.org/badge/237431920.svg)](https://zenodo.org/badge/latestdoi/237431920)
+
+**[Documentation](https://docs.e3nn.org)** | **[Code](https://github.com/e3nn/e3nn)** | **[ChangeLog](https://github.com/e3nn/e3nn/blob/main/ChangeLog.md)** | **[Colab](https://colab.research.google.com/drive/1Gps7mMOmzLe3Rt_b012xsz4UyuexTKAf?usp=sharing)**
+
+The aim of this library is to help the development of [E(3)](https://en.wikipedia.org/wiki/Euclidean_group) equivariant neural networks.
+It contains fundamental mathematical operations such as [tensor products](https://docs.e3nn.org/en/stable/api/o3/o3_tp.html) and [spherical harmonics](https://docs.e3nn.org/en/stable/api/o3/o3_sh.html).
+
+![](https://user-images.githubusercontent.com/333780/79220728-dbe82c00-7e54-11ea-82c7-b3acbd9b2246.gif)
+
+## Installation
+
+**Important:** install pytorch and only then run the command
+
+```
+pip install --upgrade pip
+pip install --upgrade e3nn
+```
+
+For details and optional dependencies, see [INSTALL.md](https://github.com/e3nn/e3nn/blob/main/INSTALL.md)
+
+### Breaking changes
+e3nn is under development.
+It is recommanded to install using pip. The main branch is considered as unstable.
+The second version number is incremented every time a breaking change is made to the code.
+```
+0.(increment when backwards incompatible release).(increment for backwards compatible release)
+```
+
+## Help
+We are happy to help! The best way to get help on `e3nn` is to submit a [Question](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=question&template=question.md&title=%E2%9D%93+%5BQUESTION%5D) or [Bug Report](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=bug&template=bug-report.md&title=%F0%9F%90%9B+%5BBUG%5D).
+
+## Want to get involved? Great!
+If you want to get involved in and contribute to the development, improvement, and application of `e3nn`, introduce yourself in the [discussions](https://github.com/e3nn/e3nn/discussions/new).
+
+## Code of conduct
+Our community abides by the [Contributor Covenant Code of Conduct](https://github.com/e3nn/e3nn/blob/main/code_of_conduct.md).
+
+## Citing
+```
+@misc{e3nn_paper,
+    doi = {10.48550/ARXIV.2207.09453},
+    url = {https://arxiv.org/abs/2207.09453},
+    author = {Geiger, Mario and Smidt, Tess},
+    keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences}, 
+    title = {e3nn: Euclidean Neural Networks},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+
+@software{e3nn,
+  author       = {Mario Geiger and
+                  Tess Smidt and
+                  Alby M. and
+                  Benjamin Kurt Miller and
+                  Wouter Boomsma and
+                  Bradley Dice and
+                  Kostiantyn Lapchevskyi and
+                  Maurice Weiler and
+                  Michał Tyszkiewicz and
+                  Simon Batzner and
+                  Dylan Madisetti and
+                  Martin Uhrin and
+                  Jes Frellsen and
+                  Nuri Jung and
+                  Sophia Sanborn and
+                  Mingjian Wen and
+                  Josh Rackers and
+                  Marcel Rød and
+                  Michael Bailey},
+  title        = {Euclidean neural networks: e3nn},
+  month        = apr,
+  year         = 2022,
+  publisher    = {Zenodo},
+  version      = {0.5.0},
+  doi          = {10.5281/zenodo.6459381},
+  url          = {https://doi.org/10.5281/zenodo.6459381}
+}
+```
+
+### Copyright
+
+Euclidean neural networks (e3nn) Copyright (c) 2020, The Regents of the
+University of California, through Lawrence Berkeley National Laboratory
+(subject to receipt of any required approvals from the U.S. Dept. of Energy),
+Ecole Polytechnique Federale de Lausanne (EPFL), Free University of Berlin
+and Kostiantyn Lapchevskyi. All rights reserved.
+
+If you have questions about your rights to use or distribute this software,
+please contact Berkeley Lab's Intellectual Property Office at
+IPO@lbl.gov.
+
+NOTICE.  This Software was developed under funding from the U.S. Department
+of Energy and the U.S. Government consequently retains certain rights.  As
+such, the U.S. Government has been granted for itself and others acting on
+its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the
+Software to reproduce, distribute copies to the public, prepare derivative
+works, and perform publicly and display publicly, and to permit others to do so.
+
+
+
+
+%package help
+Summary:	Development documents and examples for e3nn
+Provides:	python3-e3nn-doc
+%description help
+# Euclidean neural networks
+[![Coverage Status](https://coveralls.io/repos/github/e3nn/e3nn/badge.svg?branch=main)](https://coveralls.io/github/e3nn/e3nn?branch=main)
+[![DOI](https://zenodo.org/badge/237431920.svg)](https://zenodo.org/badge/latestdoi/237431920)
+
+**[Documentation](https://docs.e3nn.org)** | **[Code](https://github.com/e3nn/e3nn)** | **[ChangeLog](https://github.com/e3nn/e3nn/blob/main/ChangeLog.md)** | **[Colab](https://colab.research.google.com/drive/1Gps7mMOmzLe3Rt_b012xsz4UyuexTKAf?usp=sharing)**
+
+The aim of this library is to help the development of [E(3)](https://en.wikipedia.org/wiki/Euclidean_group) equivariant neural networks.
+It contains fundamental mathematical operations such as [tensor products](https://docs.e3nn.org/en/stable/api/o3/o3_tp.html) and [spherical harmonics](https://docs.e3nn.org/en/stable/api/o3/o3_sh.html).
+
+![](https://user-images.githubusercontent.com/333780/79220728-dbe82c00-7e54-11ea-82c7-b3acbd9b2246.gif)
+
+## Installation
+
+**Important:** install pytorch and only then run the command
+
+```
+pip install --upgrade pip
+pip install --upgrade e3nn
+```
+
+For details and optional dependencies, see [INSTALL.md](https://github.com/e3nn/e3nn/blob/main/INSTALL.md)
+
+### Breaking changes
+e3nn is under development.
+It is recommanded to install using pip. The main branch is considered as unstable.
+The second version number is incremented every time a breaking change is made to the code.
+```
+0.(increment when backwards incompatible release).(increment for backwards compatible release)
+```
+
+## Help
+We are happy to help! The best way to get help on `e3nn` is to submit a [Question](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=question&template=question.md&title=%E2%9D%93+%5BQUESTION%5D) or [Bug Report](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=bug&template=bug-report.md&title=%F0%9F%90%9B+%5BBUG%5D).
+
+## Want to get involved? Great!
+If you want to get involved in and contribute to the development, improvement, and application of `e3nn`, introduce yourself in the [discussions](https://github.com/e3nn/e3nn/discussions/new).
+
+## Code of conduct
+Our community abides by the [Contributor Covenant Code of Conduct](https://github.com/e3nn/e3nn/blob/main/code_of_conduct.md).
+
+## Citing
+```
+@misc{e3nn_paper,
+    doi = {10.48550/ARXIV.2207.09453},
+    url = {https://arxiv.org/abs/2207.09453},
+    author = {Geiger, Mario and Smidt, Tess},
+    keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences}, 
+    title = {e3nn: Euclidean Neural Networks},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+
+@software{e3nn,
+  author       = {Mario Geiger and
+                  Tess Smidt and
+                  Alby M. and
+                  Benjamin Kurt Miller and
+                  Wouter Boomsma and
+                  Bradley Dice and
+                  Kostiantyn Lapchevskyi and
+                  Maurice Weiler and
+                  Michał Tyszkiewicz and
+                  Simon Batzner and
+                  Dylan Madisetti and
+                  Martin Uhrin and
+                  Jes Frellsen and
+                  Nuri Jung and
+                  Sophia Sanborn and
+                  Mingjian Wen and
+                  Josh Rackers and
+                  Marcel Rød and
+                  Michael Bailey},
+  title        = {Euclidean neural networks: e3nn},
+  month        = apr,
+  year         = 2022,
+  publisher    = {Zenodo},
+  version      = {0.5.0},
+  doi          = {10.5281/zenodo.6459381},
+  url          = {https://doi.org/10.5281/zenodo.6459381}
+}
+```
+
+### Copyright
+
+Euclidean neural networks (e3nn) Copyright (c) 2020, The Regents of the
+University of California, through Lawrence Berkeley National Laboratory
+(subject to receipt of any required approvals from the U.S. Dept. of Energy),
+Ecole Polytechnique Federale de Lausanne (EPFL), Free University of Berlin
+and Kostiantyn Lapchevskyi. All rights reserved.
+
+If you have questions about your rights to use or distribute this software,
+please contact Berkeley Lab's Intellectual Property Office at
+IPO@lbl.gov.
+
+NOTICE.  This Software was developed under funding from the U.S. Department
+of Energy and the U.S. Government consequently retains certain rights.  As
+such, the U.S. Government has been granted for itself and others acting on
+its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the
+Software to reproduce, distribute copies to the public, prepare derivative
+works, and perform publicly and display publicly, and to permit others to do so.
+
+
+
+
+%prep
+%autosetup -n e3nn-0.5.1
+
+%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-e3nn -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.1-1
+- Package Spec generated