automatic import of python-prefetch-generator

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diff --git a/.gitignore b/.gitignore
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+/prefetch_generator-1.0.3.tar.gz
diff --git a/python-prefetch-generator.spec b/python-prefetch-generator.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-prefetch-generator
+Version:	1.0.3
+Release:	1
+Summary:	a simple tool to compute arbitrary generator in a background thread
+License:	The Unlicense
+URL:		https://github.com/justheuristic/prefetch_generator
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/28/cf/f6c148289fd87832e9f20428b7e842921efac5313cb3ac5920a424543760/prefetch_generator-1.0.3.tar.gz
+BuildArch:	noarch
+
+
+%description
+Based on http://stackoverflow.com/questions/7323664/python-generator-pre-fetch
+
+This is a single-function package that makes it possible to transform any generator into a `BackgroundGenerator` which computes any number of elements from the generator ahead, in a background thread.
+
+It is quite lightweight, but not entirely weightless.
+
+The `BackgroundGenerator` is most useful when you have a GIL releasing task which might take a long time to complete (e.g. Disk I/O, Web Requests, pure C functions, GPU processing, ...), and another task which takes a similar amount of time, but is dependent on the results of the first task (e.g. Computationally intensive processing of data loaded from disk).
+
+Normally these two tasks will constantly wait for one another to finish. If you make one of these tasks a `BackgroundGenerator` (see examples below), they will work in parallel, potentially saving up to 50% of execution time (definitely less in practice).
+
+We personally use the `BackgroundGenerator` when iterating over minibatches of data for deep learning with tensorflow and theano ( lasagne, blocks, raw, etc.).
+
+Quick usage example (ipython notebook) - https://github.com/justheuristic/prefetch_generator/blob/master/example.ipynb
+
+This package contains two objects:
+ - The Class     `BackgroundGenerator(generator [,max_prefetch=1])`
+ - The decorator `@prefetch([max_prefetch=1])`
+
+the usage is either
+
+#for item in BackgroundGenerator(my_generator):
+#    do_stuff(item)
+
+or
+
+#@prefetch()
+#def my_generator(some_param):
+#    while True:
+#        X = read_heavy_file()
+#        y = wget_from_cornhub()
+#        do_pretty_much_anything(some_param)
+#        yield X, y
+
+
+More details are written in the `BackgroundGenerator` doc:
+See `help(BackgroundGenerator)`
+
+%package -n python3-prefetch-generator
+Summary:	a simple tool to compute arbitrary generator in a background thread
+Provides:	python-prefetch-generator
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-prefetch-generator
+Based on http://stackoverflow.com/questions/7323664/python-generator-pre-fetch
+
+This is a single-function package that makes it possible to transform any generator into a `BackgroundGenerator` which computes any number of elements from the generator ahead, in a background thread.
+
+It is quite lightweight, but not entirely weightless.
+
+The `BackgroundGenerator` is most useful when you have a GIL releasing task which might take a long time to complete (e.g. Disk I/O, Web Requests, pure C functions, GPU processing, ...), and another task which takes a similar amount of time, but is dependent on the results of the first task (e.g. Computationally intensive processing of data loaded from disk).
+
+Normally these two tasks will constantly wait for one another to finish. If you make one of these tasks a `BackgroundGenerator` (see examples below), they will work in parallel, potentially saving up to 50% of execution time (definitely less in practice).
+
+We personally use the `BackgroundGenerator` when iterating over minibatches of data for deep learning with tensorflow and theano ( lasagne, blocks, raw, etc.).
+
+Quick usage example (ipython notebook) - https://github.com/justheuristic/prefetch_generator/blob/master/example.ipynb
+
+This package contains two objects:
+ - The Class     `BackgroundGenerator(generator [,max_prefetch=1])`
+ - The decorator `@prefetch([max_prefetch=1])`
+
+the usage is either
+
+#for item in BackgroundGenerator(my_generator):
+#    do_stuff(item)
+
+or
+
+#@prefetch()
+#def my_generator(some_param):
+#    while True:
+#        X = read_heavy_file()
+#        y = wget_from_cornhub()
+#        do_pretty_much_anything(some_param)
+#        yield X, y
+
+
+More details are written in the `BackgroundGenerator` doc:
+See `help(BackgroundGenerator)`
+
+%package help
+Summary:	Development documents and examples for prefetch-generator
+Provides:	python3-prefetch-generator-doc
+%description help
+Based on http://stackoverflow.com/questions/7323664/python-generator-pre-fetch
+
+This is a single-function package that makes it possible to transform any generator into a `BackgroundGenerator` which computes any number of elements from the generator ahead, in a background thread.
+
+It is quite lightweight, but not entirely weightless.
+
+The `BackgroundGenerator` is most useful when you have a GIL releasing task which might take a long time to complete (e.g. Disk I/O, Web Requests, pure C functions, GPU processing, ...), and another task which takes a similar amount of time, but is dependent on the results of the first task (e.g. Computationally intensive processing of data loaded from disk).
+
+Normally these two tasks will constantly wait for one another to finish. If you make one of these tasks a `BackgroundGenerator` (see examples below), they will work in parallel, potentially saving up to 50% of execution time (definitely less in practice).
+
+We personally use the `BackgroundGenerator` when iterating over minibatches of data for deep learning with tensorflow and theano ( lasagne, blocks, raw, etc.).
+
+Quick usage example (ipython notebook) - https://github.com/justheuristic/prefetch_generator/blob/master/example.ipynb
+
+This package contains two objects:
+ - The Class     `BackgroundGenerator(generator [,max_prefetch=1])`
+ - The decorator `@prefetch([max_prefetch=1])`
+
+the usage is either
+
+#for item in BackgroundGenerator(my_generator):
+#    do_stuff(item)
+
+or
+
+#@prefetch()
+#def my_generator(some_param):
+#    while True:
+#        X = read_heavy_file()
+#        y = wget_from_cornhub()
+#        do_pretty_much_anything(some_param)
+#        yield X, y
+
+
+More details are written in the `BackgroundGenerator` doc:
+See `help(BackgroundGenerator)`
+
+%prep
+%autosetup -n prefetch-generator-1.0.3
+
+%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-prefetch-generator -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.3-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..814eec0
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+9a9956a6ced74f63cd7c55db4038d32d  prefetch_generator-1.0.3.tar.gz