automatic import of python-kfp

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diff --git a/.gitignore b/.gitignore
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+/kfp-1.8.20.tar.gz
diff --git a/python-kfp.spec b/python-kfp.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-kfp
+Version:	1.8.20
+Release:	1
+Summary:	KubeFlow Pipelines SDK
+License:	Apache Software License
+URL:		https://github.com/kubeflow/pipelines
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/39/4f/a92391bd04ef76f7476b05366411e4c36d83eaa3fca18f1b643c7d5b75a3/kfp-1.8.20.tar.gz
+BuildArch:	noarch
+
+
+%description
+# `kfp`: Kubeflow Pipelines SDK
+
+[![PyPI Package version](https://badge.fury.io/py/kfp.svg)](https://badge.fury.io/py/kfp)
+[![PyPI Python Version](https://img.shields.io/pypi/pyversions/kfp.svg)](https://pypi.org/project/kfp/)
+[![PyPI Downloads](https://img.shields.io/pypi/dm/kfp)](https://pypi.org/project/kfp/)
+[![Documentation Status](https://readthedocs.org/projects/kubeflow-pipelines/badge/?version=latest)](https://kubeflow-pipelines.readthedocs.io/en/stable/?badge=latest)
+[![Code Style](https://img.shields.io/badge/code%20style-yapf-brightgreen.svg)](https://github.com/google/yapf)
+
+Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning workflows based on Docker containers within the [Kubeflow](https://www.kubeflow.org/) project.
+
+Use Kubeflow Pipelines to compose a multi-step workflow ([pipeline](https://www.kubeflow.org/docs/components/pipelines/concepts/pipeline/)) as a [graph](https://www.kubeflow.org/docs/components/pipelines/concepts/graph/) of containerized [tasks](https://www.kubeflow.org/docs/components/pipelines/concepts/step/) using [Python code](https://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/#getting-started-with-python-function-based-components) and/or [YAML](https://www.kubeflow.org/docs/components/pipelines/sdk/component-development/#creating-a-component-specification). Then, [run](https://www.kubeflow.org/docs/components/pipelines/concepts/run/) your pipeline with specified pipeline arguments, rerun your pipeline with new arguments or data, [schedule](https://www.kubeflow.org/docs/components/pipelines/concepts/run-trigger/) your pipeline to run on a recurring basis, organize your runs into [experiments](https://www.kubeflow.org/docs/components/pipelines/concepts/experiment/), save machine learning artifacts to compliant [artifact registries](https://www.kubeflow.org/docs/components/pipelines/concepts/metadata/), and visualize it all through the [Kubeflow Dashboard](https://www.kubeflow.org/docs/components/central-dash/overview/).
+
+## Documentation
+* [Kubeflow Pipelines Overview](https://www.kubeflow.org/docs/components/pipelines/introduction/)
+* [SDK Overview](https://www.kubeflow.org/docs/components/pipelines/sdk/sdk-overview/)
+* [SDK API Documentation](https://kubeflow-pipelines.readthedocs.io/en/stable/)
+
+## Installation
+
+To install the latest stable release, run:
+
+```sh
+pip install kfp
+```
+
+## Getting started
+
+The following is an example of a simple pipeline with one Python function-based component used in two separate tasks to do basic addition:
+
+```python
+import kfp
+from kfp.components import create_component_from_func
+import kfp.dsl as dsl
+
+def add(a: float, b: float) -> float:
+    '''Calculates sum of two arguments'''
+    return a + b
+
+
+# create a component using the add function
+add_op = create_component_from_func(add)
+
+# compose your pipeline using the dsl.pipeline decorator
+@dsl.pipeline(
+    name='Addition pipeline',
+    description='An example pipeline that performs addition calculations.')
+def add_pipeline(
+    a: float=1.0,
+    b: float=7.0,
+):
+    first_add_task = add_op(a=a, b=4.0)
+    second_add_task = add_op(a=first_add_task.output, b=b)
+
+# instantiate a client and submit your pipeline with arguments
+client = kfp.Client(host='<my-host-url>')
+client.create_run_from_pipeline_func(
+    add_pipeline, arguments={
+        'a': 7.0,
+        'b': 8.0
+    })
+
+```
+
+For more information, refer to [Building Python function-based components](https://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/).
+
+%package -n python3-kfp
+Summary:	KubeFlow Pipelines SDK
+Provides:	python-kfp
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-kfp
+# `kfp`: Kubeflow Pipelines SDK
+
+[![PyPI Package version](https://badge.fury.io/py/kfp.svg)](https://badge.fury.io/py/kfp)
+[![PyPI Python Version](https://img.shields.io/pypi/pyversions/kfp.svg)](https://pypi.org/project/kfp/)
+[![PyPI Downloads](https://img.shields.io/pypi/dm/kfp)](https://pypi.org/project/kfp/)
+[![Documentation Status](https://readthedocs.org/projects/kubeflow-pipelines/badge/?version=latest)](https://kubeflow-pipelines.readthedocs.io/en/stable/?badge=latest)
+[![Code Style](https://img.shields.io/badge/code%20style-yapf-brightgreen.svg)](https://github.com/google/yapf)
+
+Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning workflows based on Docker containers within the [Kubeflow](https://www.kubeflow.org/) project.
+
+Use Kubeflow Pipelines to compose a multi-step workflow ([pipeline](https://www.kubeflow.org/docs/components/pipelines/concepts/pipeline/)) as a [graph](https://www.kubeflow.org/docs/components/pipelines/concepts/graph/) of containerized [tasks](https://www.kubeflow.org/docs/components/pipelines/concepts/step/) using [Python code](https://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/#getting-started-with-python-function-based-components) and/or [YAML](https://www.kubeflow.org/docs/components/pipelines/sdk/component-development/#creating-a-component-specification). Then, [run](https://www.kubeflow.org/docs/components/pipelines/concepts/run/) your pipeline with specified pipeline arguments, rerun your pipeline with new arguments or data, [schedule](https://www.kubeflow.org/docs/components/pipelines/concepts/run-trigger/) your pipeline to run on a recurring basis, organize your runs into [experiments](https://www.kubeflow.org/docs/components/pipelines/concepts/experiment/), save machine learning artifacts to compliant [artifact registries](https://www.kubeflow.org/docs/components/pipelines/concepts/metadata/), and visualize it all through the [Kubeflow Dashboard](https://www.kubeflow.org/docs/components/central-dash/overview/).
+
+## Documentation
+* [Kubeflow Pipelines Overview](https://www.kubeflow.org/docs/components/pipelines/introduction/)
+* [SDK Overview](https://www.kubeflow.org/docs/components/pipelines/sdk/sdk-overview/)
+* [SDK API Documentation](https://kubeflow-pipelines.readthedocs.io/en/stable/)
+
+## Installation
+
+To install the latest stable release, run:
+
+```sh
+pip install kfp
+```
+
+## Getting started
+
+The following is an example of a simple pipeline with one Python function-based component used in two separate tasks to do basic addition:
+
+```python
+import kfp
+from kfp.components import create_component_from_func
+import kfp.dsl as dsl
+
+def add(a: float, b: float) -> float:
+    '''Calculates sum of two arguments'''
+    return a + b
+
+
+# create a component using the add function
+add_op = create_component_from_func(add)
+
+# compose your pipeline using the dsl.pipeline decorator
+@dsl.pipeline(
+    name='Addition pipeline',
+    description='An example pipeline that performs addition calculations.')
+def add_pipeline(
+    a: float=1.0,
+    b: float=7.0,
+):
+    first_add_task = add_op(a=a, b=4.0)
+    second_add_task = add_op(a=first_add_task.output, b=b)
+
+# instantiate a client and submit your pipeline with arguments
+client = kfp.Client(host='<my-host-url>')
+client.create_run_from_pipeline_func(
+    add_pipeline, arguments={
+        'a': 7.0,
+        'b': 8.0
+    })
+
+```
+
+For more information, refer to [Building Python function-based components](https://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/).
+
+%package help
+Summary:	Development documents and examples for kfp
+Provides:	python3-kfp-doc
+%description help
+# `kfp`: Kubeflow Pipelines SDK
+
+[![PyPI Package version](https://badge.fury.io/py/kfp.svg)](https://badge.fury.io/py/kfp)
+[![PyPI Python Version](https://img.shields.io/pypi/pyversions/kfp.svg)](https://pypi.org/project/kfp/)
+[![PyPI Downloads](https://img.shields.io/pypi/dm/kfp)](https://pypi.org/project/kfp/)
+[![Documentation Status](https://readthedocs.org/projects/kubeflow-pipelines/badge/?version=latest)](https://kubeflow-pipelines.readthedocs.io/en/stable/?badge=latest)
+[![Code Style](https://img.shields.io/badge/code%20style-yapf-brightgreen.svg)](https://github.com/google/yapf)
+
+Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning workflows based on Docker containers within the [Kubeflow](https://www.kubeflow.org/) project.
+
+Use Kubeflow Pipelines to compose a multi-step workflow ([pipeline](https://www.kubeflow.org/docs/components/pipelines/concepts/pipeline/)) as a [graph](https://www.kubeflow.org/docs/components/pipelines/concepts/graph/) of containerized [tasks](https://www.kubeflow.org/docs/components/pipelines/concepts/step/) using [Python code](https://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/#getting-started-with-python-function-based-components) and/or [YAML](https://www.kubeflow.org/docs/components/pipelines/sdk/component-development/#creating-a-component-specification). Then, [run](https://www.kubeflow.org/docs/components/pipelines/concepts/run/) your pipeline with specified pipeline arguments, rerun your pipeline with new arguments or data, [schedule](https://www.kubeflow.org/docs/components/pipelines/concepts/run-trigger/) your pipeline to run on a recurring basis, organize your runs into [experiments](https://www.kubeflow.org/docs/components/pipelines/concepts/experiment/), save machine learning artifacts to compliant [artifact registries](https://www.kubeflow.org/docs/components/pipelines/concepts/metadata/), and visualize it all through the [Kubeflow Dashboard](https://www.kubeflow.org/docs/components/central-dash/overview/).
+
+## Documentation
+* [Kubeflow Pipelines Overview](https://www.kubeflow.org/docs/components/pipelines/introduction/)
+* [SDK Overview](https://www.kubeflow.org/docs/components/pipelines/sdk/sdk-overview/)
+* [SDK API Documentation](https://kubeflow-pipelines.readthedocs.io/en/stable/)
+
+## Installation
+
+To install the latest stable release, run:
+
+```sh
+pip install kfp
+```
+
+## Getting started
+
+The following is an example of a simple pipeline with one Python function-based component used in two separate tasks to do basic addition:
+
+```python
+import kfp
+from kfp.components import create_component_from_func
+import kfp.dsl as dsl
+
+def add(a: float, b: float) -> float:
+    '''Calculates sum of two arguments'''
+    return a + b
+
+
+# create a component using the add function
+add_op = create_component_from_func(add)
+
+# compose your pipeline using the dsl.pipeline decorator
+@dsl.pipeline(
+    name='Addition pipeline',
+    description='An example pipeline that performs addition calculations.')
+def add_pipeline(
+    a: float=1.0,
+    b: float=7.0,
+):
+    first_add_task = add_op(a=a, b=4.0)
+    second_add_task = add_op(a=first_add_task.output, b=b)
+
+# instantiate a client and submit your pipeline with arguments
+client = kfp.Client(host='<my-host-url>')
+client.create_run_from_pipeline_func(
+    add_pipeline, arguments={
+        'a': 7.0,
+        'b': 8.0
+    })
+
+```
+
+For more information, refer to [Building Python function-based components](https://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/).
+
+%prep
+%autosetup -n kfp-1.8.20
+
+%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-kfp -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.8.20-1
+- Package Spec generated
diff --git a/sources b/sources
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+e324e03c314c3f579719c7d933248588  kfp-1.8.20.tar.gz