automatic import of python-hydra-core

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diff --git a/.gitignore b/.gitignore
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+/hydra-core-1.3.2.tar.gz
diff --git a/python-hydra-core.spec b/python-hydra-core.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-hydra-core
+Version:	1.3.2
+Release:	1
+Summary:	A framework for elegantly configuring complex applications
+License:	MIT
+URL:		https://github.com/facebookresearch/hydra
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/6d/8e/07e42bc434a847154083b315779b0a81d567154504624e181caf2c71cd98/hydra-core-1.3.2.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-omegaconf
+Requires:	python3-antlr4-python3-runtime
+Requires:	python3-packaging
+Requires:	python3-importlib-resources
+
+%description
+### Releases
+#### Stable
+**Hydra 1.3** is the stable version of Hydra.
+- [Documentation](https://hydra.cc/docs/1.3/intro/)
+- Installation : `pip install hydra-core --upgrade`
+See the [NEWS.md](NEWS.md) file for a summary of recent changes to Hydra.
+### License
+Hydra is licensed under [MIT License](LICENSE).
+## Hydra Ecosystem
+#### Check out these third-party libraries that build on Hydra's functionality:
+* [hydra-zen](https://github.com/mit-ll-responsible-ai/hydra-zen): Pythonic utilities for working with Hydra. Dynamic config generation capabilities, enhanced config store features, a Python API for launching Hydra jobs, and more.
+* [lightning-hydra-template](https://github.com/ashleve/lightning-hydra-template): user-friendly template combining Hydra with [Pytorch-Lightning](https://github.com/Lightning-AI/lightning) for ML experimentation.
+* [hydra-torch](https://github.com/pytorch/hydra-torch): [configen](https://github.com/facebookresearch/hydra/tree/main/tools/configen)-generated configuration classes enabling type-safe PyTorch configuration for Hydra apps.
+* NVIDIA's DeepLearningExamples repository contains a Hydra Launcher plugin, the [distributed_launcher](https://github.com/NVIDIA/DeepLearningExamples/tree/9c34e35c218514b8607d7cf381d8a982a01175e9/Tools/PyTorch/TimeSeriesPredictionPlatform/distributed_launcher), which makes use of the pytorch [distributed.launch](https://pytorch.org/docs/stable/distributed.html#launch-utility) API.
+#### Ask questions in Github Discussions or StackOverflow (Use the tag #fb-hydra or #omegaconf):
+* [Github Discussions](https://github.com/facebookresearch/hydra/discussions)
+* [StackOverflow](https://stackexchange.com/filters/391828/hydra-questions)
+* [Twitter](https://twitter.com/Hydra_Framework)
+Check out the Meta AI [blog post](https://ai.facebook.com/blog/reengineering-facebook-ais-deep-learning-platforms-for-interoperability/) to learn about how Hydra fits into Meta's efforts to reengineer deep learning platforms for interoperability.
+### Citing Hydra
+If you use Hydra in your research please use the following BibTeX entry:
+```BibTeX
+@Misc{Yadan2019Hydra,
+  author =       {Omry Yadan},
+  title =        {Hydra - A framework for elegantly configuring complex applications},
+  howpublished = {Github},
+  year =         {2019},
+  url =          {https://github.com/facebookresearch/hydra}
+}
+```
+
+%package -n python3-hydra-core
+Summary:	A framework for elegantly configuring complex applications
+Provides:	python-hydra-core
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-hydra-core
+### Releases
+#### Stable
+**Hydra 1.3** is the stable version of Hydra.
+- [Documentation](https://hydra.cc/docs/1.3/intro/)
+- Installation : `pip install hydra-core --upgrade`
+See the [NEWS.md](NEWS.md) file for a summary of recent changes to Hydra.
+### License
+Hydra is licensed under [MIT License](LICENSE).
+## Hydra Ecosystem
+#### Check out these third-party libraries that build on Hydra's functionality:
+* [hydra-zen](https://github.com/mit-ll-responsible-ai/hydra-zen): Pythonic utilities for working with Hydra. Dynamic config generation capabilities, enhanced config store features, a Python API for launching Hydra jobs, and more.
+* [lightning-hydra-template](https://github.com/ashleve/lightning-hydra-template): user-friendly template combining Hydra with [Pytorch-Lightning](https://github.com/Lightning-AI/lightning) for ML experimentation.
+* [hydra-torch](https://github.com/pytorch/hydra-torch): [configen](https://github.com/facebookresearch/hydra/tree/main/tools/configen)-generated configuration classes enabling type-safe PyTorch configuration for Hydra apps.
+* NVIDIA's DeepLearningExamples repository contains a Hydra Launcher plugin, the [distributed_launcher](https://github.com/NVIDIA/DeepLearningExamples/tree/9c34e35c218514b8607d7cf381d8a982a01175e9/Tools/PyTorch/TimeSeriesPredictionPlatform/distributed_launcher), which makes use of the pytorch [distributed.launch](https://pytorch.org/docs/stable/distributed.html#launch-utility) API.
+#### Ask questions in Github Discussions or StackOverflow (Use the tag #fb-hydra or #omegaconf):
+* [Github Discussions](https://github.com/facebookresearch/hydra/discussions)
+* [StackOverflow](https://stackexchange.com/filters/391828/hydra-questions)
+* [Twitter](https://twitter.com/Hydra_Framework)
+Check out the Meta AI [blog post](https://ai.facebook.com/blog/reengineering-facebook-ais-deep-learning-platforms-for-interoperability/) to learn about how Hydra fits into Meta's efforts to reengineer deep learning platforms for interoperability.
+### Citing Hydra
+If you use Hydra in your research please use the following BibTeX entry:
+```BibTeX
+@Misc{Yadan2019Hydra,
+  author =       {Omry Yadan},
+  title =        {Hydra - A framework for elegantly configuring complex applications},
+  howpublished = {Github},
+  year =         {2019},
+  url =          {https://github.com/facebookresearch/hydra}
+}
+```
+
+%package help
+Summary:	Development documents and examples for hydra-core
+Provides:	python3-hydra-core-doc
+%description help
+### Releases
+#### Stable
+**Hydra 1.3** is the stable version of Hydra.
+- [Documentation](https://hydra.cc/docs/1.3/intro/)
+- Installation : `pip install hydra-core --upgrade`
+See the [NEWS.md](NEWS.md) file for a summary of recent changes to Hydra.
+### License
+Hydra is licensed under [MIT License](LICENSE).
+## Hydra Ecosystem
+#### Check out these third-party libraries that build on Hydra's functionality:
+* [hydra-zen](https://github.com/mit-ll-responsible-ai/hydra-zen): Pythonic utilities for working with Hydra. Dynamic config generation capabilities, enhanced config store features, a Python API for launching Hydra jobs, and more.
+* [lightning-hydra-template](https://github.com/ashleve/lightning-hydra-template): user-friendly template combining Hydra with [Pytorch-Lightning](https://github.com/Lightning-AI/lightning) for ML experimentation.
+* [hydra-torch](https://github.com/pytorch/hydra-torch): [configen](https://github.com/facebookresearch/hydra/tree/main/tools/configen)-generated configuration classes enabling type-safe PyTorch configuration for Hydra apps.
+* NVIDIA's DeepLearningExamples repository contains a Hydra Launcher plugin, the [distributed_launcher](https://github.com/NVIDIA/DeepLearningExamples/tree/9c34e35c218514b8607d7cf381d8a982a01175e9/Tools/PyTorch/TimeSeriesPredictionPlatform/distributed_launcher), which makes use of the pytorch [distributed.launch](https://pytorch.org/docs/stable/distributed.html#launch-utility) API.
+#### Ask questions in Github Discussions or StackOverflow (Use the tag #fb-hydra or #omegaconf):
+* [Github Discussions](https://github.com/facebookresearch/hydra/discussions)
+* [StackOverflow](https://stackexchange.com/filters/391828/hydra-questions)
+* [Twitter](https://twitter.com/Hydra_Framework)
+Check out the Meta AI [blog post](https://ai.facebook.com/blog/reengineering-facebook-ais-deep-learning-platforms-for-interoperability/) to learn about how Hydra fits into Meta's efforts to reengineer deep learning platforms for interoperability.
+### Citing Hydra
+If you use Hydra in your research please use the following BibTeX entry:
+```BibTeX
+@Misc{Yadan2019Hydra,
+  author =       {Omry Yadan},
+  title =        {Hydra - A framework for elegantly configuring complex applications},
+  howpublished = {Github},
+  year =         {2019},
+  url =          {https://github.com/facebookresearch/hydra}
+}
+```
+
+%prep
+%autosetup -n hydra-core-1.3.2
+
+%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-hydra-core -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.3.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..ce75e65
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+14f283e037901a5a2c91978eefe6bc2b  hydra-core-1.3.2.tar.gz