automatic import of python-gokart

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diff --git a/.gitignore b/.gitignore
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--- a/.gitignore
+++ b/.gitignore
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+/gokart-1.2.2.tar.gz
diff --git a/python-gokart.spec b/python-gokart.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-gokart
+Version:	1.2.2
+Release:	1
+Summary:	Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline. [Documentation](https://gokart.readthedocs.io/en/latest/)
+License:	MIT
+URL:		https://github.com/m3dev/gokart
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/a9/59/d3e333f2c6b6871187a7b15d078cbdddb72fe2f052696e6e43386a699e1b/gokart-1.2.2.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-luigi
+Requires:	python3-boto3
+Requires:	python3-slack-sdk
+Requires:	python3-pandas
+Requires:	python3-numpy
+Requires:	python3-tqdm
+Requires:	python3-google-auth
+Requires:	python3-pyarrow
+Requires:	python3-uritemplate
+Requires:	python3-google-api-python-client
+Requires:	python3-APScheduler
+Requires:	python3-redis
+Requires:	python3-matplotlib
+
+%description
+# gokart
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/m3dev/gokart/master/docs/gokart_logo_side_isolation.svg" width="90%">
+<p>
+
+[![Test](https://github.com/m3dev/gokart/workflows/Test/badge.svg)](https://github.com/m3dev/gokart/actions?query=workflow%3ATest)
+[![](https://readthedocs.org/projects/gokart/badge/?version=latest)](https://gokart.readthedocs.io/en/latest/)
+[![Python Versions](https://img.shields.io/pypi/pyversions/gokart.svg)](https://pypi.org/project/gokart/)
+[![](https://img.shields.io/pypi/v/gokart)](https://pypi.org/project/gokart/)
+![](https://img.shields.io/pypi/l/gokart)
+
+Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.
+
+
+[Documentation](https://gokart.readthedocs.io/en/latest/) for the latest release is hosted on readthedocs.
+
+
+# About gokart
+
+Here are some good things about gokart.
+
+- The following meta data for each Task is stored separately in a `pkl` file with hash value
+    - task output data
+    - imported all module versions
+    - task processing time
+    - random seed in task
+    - displayed log
+    - all parameters set as class variables in the task
+- Automatically rerun the pipeline if parameters of Tasks are changed.
+- Support GCS and S3 as a data store for intermediate results of Tasks in the pipeline.
+- The above output is exchanged between tasks as an intermediate file, which is memory-friendly
+- `pandas.DataFrame` type and column checking during I/O
+- Directory structure of saved files is automatically determined from structure of script
+- Seeds for numpy and random are automatically fixed
+- Can code while adhering to [SOLID](https://en.wikipedia.org/wiki/SOLID) principles as much as possible
+- Tasks are locked via redis even if they run in parallel
+
+**All the functions above are created for constructing Machine Learning batches. Provides an excellent environment for reproducibility and team development.**
+
+
+Here are some non-goal / downside of the gokart.
+- Batch execution in parallel is supported, but parallel and concurrent execution of task in memory.
+- Gokart is focused on reproducibility. So, I/O and capacity of data storage can become a bottleneck.
+- No support for task visualize.
+- Gokart is not an experiment management tool. The management of the execution result is cut out as [Thunderbolt](https://github.com/m3dev/thunderbolt).
+- Gokart does not recommend writing pipelines in toml, yaml, json, and more. Gokart is preferring to write them in Python.
+
+# Getting Started
+
+Within the activated Python environment, use the following command to install gokart.
+
+```
+pip install gokart
+```
+
+
+# Quickstart
+
+A minimal gokart tasks looks something like this:
+
+
+```python
+import gokart
+
+class Example(gokart.TaskOnKart):
+    def run(self):
+        self.dump('Hello, world!')
+
+task = Example()
+output = gokart.build(task)
+print(output)
+```
+
+`gokart.build` return the result of dump by `gokart.TaskOnKart`. The example will output the following.
+
+
+```
+Hello, world!
+```
+
+
+This is an introduction to some of the gokart.
+There are still more useful features.
+
+Please See [Documentation](https://gokart.readthedocs.io/en/latest/) .
+
+Have a good gokart life.
+
+# Achievements
+
+Gokart is a proven product.
+
+- It's actually been used by [m3.inc](https://corporate.m3.com/en) for over 3 years
+- Natural Language Processing Competition by [Nishika.inc](https://nishika.com) 2nd prize : [Solution Repository](https://github.com/vaaaaanquish/nishika_akutagawa_2nd_prize)
+
+
+# Thanks
+
+gokart is a wrapper for luigi. Thanks to luigi and dependent projects!
+
+- [luigi](https://github.com/spotify/luigi)
+
+
+%package -n python3-gokart
+Summary:	Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline. [Documentation](https://gokart.readthedocs.io/en/latest/)
+Provides:	python-gokart
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-gokart
+# gokart
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/m3dev/gokart/master/docs/gokart_logo_side_isolation.svg" width="90%">
+<p>
+
+[![Test](https://github.com/m3dev/gokart/workflows/Test/badge.svg)](https://github.com/m3dev/gokart/actions?query=workflow%3ATest)
+[![](https://readthedocs.org/projects/gokart/badge/?version=latest)](https://gokart.readthedocs.io/en/latest/)
+[![Python Versions](https://img.shields.io/pypi/pyversions/gokart.svg)](https://pypi.org/project/gokart/)
+[![](https://img.shields.io/pypi/v/gokart)](https://pypi.org/project/gokart/)
+![](https://img.shields.io/pypi/l/gokart)
+
+Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.
+
+
+[Documentation](https://gokart.readthedocs.io/en/latest/) for the latest release is hosted on readthedocs.
+
+
+# About gokart
+
+Here are some good things about gokart.
+
+- The following meta data for each Task is stored separately in a `pkl` file with hash value
+    - task output data
+    - imported all module versions
+    - task processing time
+    - random seed in task
+    - displayed log
+    - all parameters set as class variables in the task
+- Automatically rerun the pipeline if parameters of Tasks are changed.
+- Support GCS and S3 as a data store for intermediate results of Tasks in the pipeline.
+- The above output is exchanged between tasks as an intermediate file, which is memory-friendly
+- `pandas.DataFrame` type and column checking during I/O
+- Directory structure of saved files is automatically determined from structure of script
+- Seeds for numpy and random are automatically fixed
+- Can code while adhering to [SOLID](https://en.wikipedia.org/wiki/SOLID) principles as much as possible
+- Tasks are locked via redis even if they run in parallel
+
+**All the functions above are created for constructing Machine Learning batches. Provides an excellent environment for reproducibility and team development.**
+
+
+Here are some non-goal / downside of the gokart.
+- Batch execution in parallel is supported, but parallel and concurrent execution of task in memory.
+- Gokart is focused on reproducibility. So, I/O and capacity of data storage can become a bottleneck.
+- No support for task visualize.
+- Gokart is not an experiment management tool. The management of the execution result is cut out as [Thunderbolt](https://github.com/m3dev/thunderbolt).
+- Gokart does not recommend writing pipelines in toml, yaml, json, and more. Gokart is preferring to write them in Python.
+
+# Getting Started
+
+Within the activated Python environment, use the following command to install gokart.
+
+```
+pip install gokart
+```
+
+
+# Quickstart
+
+A minimal gokart tasks looks something like this:
+
+
+```python
+import gokart
+
+class Example(gokart.TaskOnKart):
+    def run(self):
+        self.dump('Hello, world!')
+
+task = Example()
+output = gokart.build(task)
+print(output)
+```
+
+`gokart.build` return the result of dump by `gokart.TaskOnKart`. The example will output the following.
+
+
+```
+Hello, world!
+```
+
+
+This is an introduction to some of the gokart.
+There are still more useful features.
+
+Please See [Documentation](https://gokart.readthedocs.io/en/latest/) .
+
+Have a good gokart life.
+
+# Achievements
+
+Gokart is a proven product.
+
+- It's actually been used by [m3.inc](https://corporate.m3.com/en) for over 3 years
+- Natural Language Processing Competition by [Nishika.inc](https://nishika.com) 2nd prize : [Solution Repository](https://github.com/vaaaaanquish/nishika_akutagawa_2nd_prize)
+
+
+# Thanks
+
+gokart is a wrapper for luigi. Thanks to luigi and dependent projects!
+
+- [luigi](https://github.com/spotify/luigi)
+
+
+%package help
+Summary:	Development documents and examples for gokart
+Provides:	python3-gokart-doc
+%description help
+# gokart
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/m3dev/gokart/master/docs/gokart_logo_side_isolation.svg" width="90%">
+<p>
+
+[![Test](https://github.com/m3dev/gokart/workflows/Test/badge.svg)](https://github.com/m3dev/gokart/actions?query=workflow%3ATest)
+[![](https://readthedocs.org/projects/gokart/badge/?version=latest)](https://gokart.readthedocs.io/en/latest/)
+[![Python Versions](https://img.shields.io/pypi/pyversions/gokart.svg)](https://pypi.org/project/gokart/)
+[![](https://img.shields.io/pypi/v/gokart)](https://pypi.org/project/gokart/)
+![](https://img.shields.io/pypi/l/gokart)
+
+Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.
+
+
+[Documentation](https://gokart.readthedocs.io/en/latest/) for the latest release is hosted on readthedocs.
+
+
+# About gokart
+
+Here are some good things about gokart.
+
+- The following meta data for each Task is stored separately in a `pkl` file with hash value
+    - task output data
+    - imported all module versions
+    - task processing time
+    - random seed in task
+    - displayed log
+    - all parameters set as class variables in the task
+- Automatically rerun the pipeline if parameters of Tasks are changed.
+- Support GCS and S3 as a data store for intermediate results of Tasks in the pipeline.
+- The above output is exchanged between tasks as an intermediate file, which is memory-friendly
+- `pandas.DataFrame` type and column checking during I/O
+- Directory structure of saved files is automatically determined from structure of script
+- Seeds for numpy and random are automatically fixed
+- Can code while adhering to [SOLID](https://en.wikipedia.org/wiki/SOLID) principles as much as possible
+- Tasks are locked via redis even if they run in parallel
+
+**All the functions above are created for constructing Machine Learning batches. Provides an excellent environment for reproducibility and team development.**
+
+
+Here are some non-goal / downside of the gokart.
+- Batch execution in parallel is supported, but parallel and concurrent execution of task in memory.
+- Gokart is focused on reproducibility. So, I/O and capacity of data storage can become a bottleneck.
+- No support for task visualize.
+- Gokart is not an experiment management tool. The management of the execution result is cut out as [Thunderbolt](https://github.com/m3dev/thunderbolt).
+- Gokart does not recommend writing pipelines in toml, yaml, json, and more. Gokart is preferring to write them in Python.
+
+# Getting Started
+
+Within the activated Python environment, use the following command to install gokart.
+
+```
+pip install gokart
+```
+
+
+# Quickstart
+
+A minimal gokart tasks looks something like this:
+
+
+```python
+import gokart
+
+class Example(gokart.TaskOnKart):
+    def run(self):
+        self.dump('Hello, world!')
+
+task = Example()
+output = gokart.build(task)
+print(output)
+```
+
+`gokart.build` return the result of dump by `gokart.TaskOnKart`. The example will output the following.
+
+
+```
+Hello, world!
+```
+
+
+This is an introduction to some of the gokart.
+There are still more useful features.
+
+Please See [Documentation](https://gokart.readthedocs.io/en/latest/) .
+
+Have a good gokart life.
+
+# Achievements
+
+Gokart is a proven product.
+
+- It's actually been used by [m3.inc](https://corporate.m3.com/en) for over 3 years
+- Natural Language Processing Competition by [Nishika.inc](https://nishika.com) 2nd prize : [Solution Repository](https://github.com/vaaaaanquish/nishika_akutagawa_2nd_prize)
+
+
+# Thanks
+
+gokart is a wrapper for luigi. Thanks to luigi and dependent projects!
+
+- [luigi](https://github.com/spotify/luigi)
+
+
+%prep
+%autosetup -n gokart-1.2.2
+
+%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-gokart -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..ef66d88
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+4acca03920140bb382808f7610fcde10  gokart-1.2.2.tar.gz