automatic import of python-bert-for-tf2

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diff --git a/.gitignore b/.gitignore
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+/bert-for-tf2-0.14.9.tar.gz
diff --git a/python-bert-for-tf2.spec b/python-bert-for-tf2.spec
new file mode 100644
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+%global _empty_manifest_terminate_build 0
+Name:		python-bert-for-tf2
+Version:	0.14.9
+Release:	1
+Summary:	A TensorFlow 2.0 Keras implementation of BERT.
+License:	MIT
+URL:		https://github.com/kpe/bert-for-tf2/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/a5/a1/acb891630749c56901e770a34d6bac8a509a367dd74a05daf7306952e910/bert-for-tf2-0.14.9.tar.gz
+BuildArch:	noarch
+
+
+%description
+|Build Status| |Coverage Status| |Version Status| |Python Versions| |Downloads|
+This repo contains a `TensorFlow 2.0`_ `Keras`_ implementation of `google-research/bert`_
+with support for loading of the original `pre-trained weights`_,
+and producing activations **numerically identical** to the one calculated by the original model.
+`ALBERT`_ and `adapter-BERT`_ are also supported by setting the corresponding
+configuration parameters (``shared_layer=True``, ``embedding_size`` for `ALBERT`_
+and ``adapter_size`` for `adapter-BERT`_). Setting both will result in an adapter-ALBERT
+by sharing the BERT parameters across all layers while adapting every layer with layer specific adapter.
+The implementation is build from scratch using only basic tensorflow operations,
+following the code in `google-research/bert/modeling.py`_
+(but skipping dead code and applying some simplifications). It also utilizes `kpe/params-flow`_ to reduce
+common Keras boilerplate code (related to passing model and layer configuration arguments).
+`bert-for-tf2`_ should work with both `TensorFlow 2.0`_ and `TensorFlow 1.14`_ or newer.
+
+%package -n python3-bert-for-tf2
+Summary:	A TensorFlow 2.0 Keras implementation of BERT.
+Provides:	python-bert-for-tf2
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-bert-for-tf2
+|Build Status| |Coverage Status| |Version Status| |Python Versions| |Downloads|
+This repo contains a `TensorFlow 2.0`_ `Keras`_ implementation of `google-research/bert`_
+with support for loading of the original `pre-trained weights`_,
+and producing activations **numerically identical** to the one calculated by the original model.
+`ALBERT`_ and `adapter-BERT`_ are also supported by setting the corresponding
+configuration parameters (``shared_layer=True``, ``embedding_size`` for `ALBERT`_
+and ``adapter_size`` for `adapter-BERT`_). Setting both will result in an adapter-ALBERT
+by sharing the BERT parameters across all layers while adapting every layer with layer specific adapter.
+The implementation is build from scratch using only basic tensorflow operations,
+following the code in `google-research/bert/modeling.py`_
+(but skipping dead code and applying some simplifications). It also utilizes `kpe/params-flow`_ to reduce
+common Keras boilerplate code (related to passing model and layer configuration arguments).
+`bert-for-tf2`_ should work with both `TensorFlow 2.0`_ and `TensorFlow 1.14`_ or newer.
+
+%package help
+Summary:	Development documents and examples for bert-for-tf2
+Provides:	python3-bert-for-tf2-doc
+%description help
+|Build Status| |Coverage Status| |Version Status| |Python Versions| |Downloads|
+This repo contains a `TensorFlow 2.0`_ `Keras`_ implementation of `google-research/bert`_
+with support for loading of the original `pre-trained weights`_,
+and producing activations **numerically identical** to the one calculated by the original model.
+`ALBERT`_ and `adapter-BERT`_ are also supported by setting the corresponding
+configuration parameters (``shared_layer=True``, ``embedding_size`` for `ALBERT`_
+and ``adapter_size`` for `adapter-BERT`_). Setting both will result in an adapter-ALBERT
+by sharing the BERT parameters across all layers while adapting every layer with layer specific adapter.
+The implementation is build from scratch using only basic tensorflow operations,
+following the code in `google-research/bert/modeling.py`_
+(but skipping dead code and applying some simplifications). It also utilizes `kpe/params-flow`_ to reduce
+common Keras boilerplate code (related to passing model and layer configuration arguments).
+`bert-for-tf2`_ should work with both `TensorFlow 2.0`_ and `TensorFlow 1.14`_ or newer.
+
+%prep
+%autosetup -n bert-for-tf2-0.14.9
+
+%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-bert-for-tf2 -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.14.9-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..0c2711f
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+9a1c8d78b128caf434a2ba90105a9e8c  bert-for-tf2-0.14.9.tar.gz