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+%global _empty_manifest_terminate_build 0
+Name:		python-onnxoptimizer
+Version:	0.3.10
+Release:	1
+Summary:	Open Neural Network Exchange
+License:	Apache License v2.0
+URL:		https://github.com/onnx/optimizer
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/90/d4/f0100c670fea4653dd3bdbde7848f71b19dc49696e5c73077a44099d9911/onnxoptimizer-0.3.10.tar.gz
+
+Requires:	python3-onnx
+Requires:	python3-mypy
+
+%description
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX Optimizer
+
+[![PyPI version](https://img.shields.io/pypi/v/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/)
+[![PyPI license](https://img.shields.io/pypi/l/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/onnx/optimizer/pulls)
+
+## Introduction
+
+ONNX provides a C++ library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes.
+
+The primary motivation is to share work between the many ONNX backend implementations. Not all possible optimizations can be directly implemented on ONNX graphs - some will need additional backend-specific information - but many can, and our aim is to provide all such passes along with ONNX so that they can be re-used with a single function call.
+
+You may be interested in invoking the provided passes, or in implementing new ones (or both).
+
+## Installation
+
+You can install onnxoptimizer from PyPI:
+
+```bash
+pip3 install onnxoptimizer
+```
+
+Note that you may need to upgrade your pip first if you have trouble:
+
+```bash
+pip3 install -U pip
+```
+
+If you want to build from source:
+
+```bash
+git clone --recursive https://github.com/onnx/optimizer onnxoptimizer
+cd onnxoptimizer
+pip3 install -e .
+```
+
+Note that you need to install protobuf before building from source.
+
+
+## Command-line API
+Now you can use command-line api in terminal instead of  python script.
+
+```
+python -m onnxoptimizer input_model.onnx output_model.onnx
+```
+
+Arguments list is following:
+```
+# python3 -m onnxoptimizer -h                                 
+usage: python -m onnxoptimizer input_model.onnx output_model.onnx 
+
+onnxoptimizer command-line api
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --print_all_passes    print all available passes
+  --print_fuse_elimination_passes
+                        print all fuse and elimination passes
+  -p [PASSES ...], --passes [PASSES ...]
+                        list of optimization passes name, if no set, fuse_and_elimination_passes will be used
+  --fixed_point         fixed point
+```
+## Roadmap
+
+* More built-in pass
+* Separate graph rewriting and constant folding (or a pure graph rewriting mode, see [issue #9](https://github.com/onnx/optimizer/issues/9) for the details)
+
+## Relevant tools
+
+* [onnx-simplifier](https://github.com/daquexian/onnx-simplifier): A handy and popular tool based on onnxoptimizer
+
+* [convertmodel.com](https://convertmodel.com/#outputFormat=onnx&inputFormat=onnx): onnx optimizer compiled as WebAssembly so that it can be used out-of-the-box
+
+## Code of Conduct
+
+[ONNX Open Source Code of Conduct](https://onnx.ai/codeofconduct.html)
+
+
+%package -n python3-onnxoptimizer
+Summary:	Open Neural Network Exchange
+Provides:	python-onnxoptimizer
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+BuildRequires:	python3-cffi
+BuildRequires:	gcc
+BuildRequires:	gdb
+%description -n python3-onnxoptimizer
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX Optimizer
+
+[![PyPI version](https://img.shields.io/pypi/v/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/)
+[![PyPI license](https://img.shields.io/pypi/l/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/onnx/optimizer/pulls)
+
+## Introduction
+
+ONNX provides a C++ library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes.
+
+The primary motivation is to share work between the many ONNX backend implementations. Not all possible optimizations can be directly implemented on ONNX graphs - some will need additional backend-specific information - but many can, and our aim is to provide all such passes along with ONNX so that they can be re-used with a single function call.
+
+You may be interested in invoking the provided passes, or in implementing new ones (or both).
+
+## Installation
+
+You can install onnxoptimizer from PyPI:
+
+```bash
+pip3 install onnxoptimizer
+```
+
+Note that you may need to upgrade your pip first if you have trouble:
+
+```bash
+pip3 install -U pip
+```
+
+If you want to build from source:
+
+```bash
+git clone --recursive https://github.com/onnx/optimizer onnxoptimizer
+cd onnxoptimizer
+pip3 install -e .
+```
+
+Note that you need to install protobuf before building from source.
+
+
+## Command-line API
+Now you can use command-line api in terminal instead of  python script.
+
+```
+python -m onnxoptimizer input_model.onnx output_model.onnx
+```
+
+Arguments list is following:
+```
+# python3 -m onnxoptimizer -h                                 
+usage: python -m onnxoptimizer input_model.onnx output_model.onnx 
+
+onnxoptimizer command-line api
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --print_all_passes    print all available passes
+  --print_fuse_elimination_passes
+                        print all fuse and elimination passes
+  -p [PASSES ...], --passes [PASSES ...]
+                        list of optimization passes name, if no set, fuse_and_elimination_passes will be used
+  --fixed_point         fixed point
+```
+## Roadmap
+
+* More built-in pass
+* Separate graph rewriting and constant folding (or a pure graph rewriting mode, see [issue #9](https://github.com/onnx/optimizer/issues/9) for the details)
+
+## Relevant tools
+
+* [onnx-simplifier](https://github.com/daquexian/onnx-simplifier): A handy and popular tool based on onnxoptimizer
+
+* [convertmodel.com](https://convertmodel.com/#outputFormat=onnx&inputFormat=onnx): onnx optimizer compiled as WebAssembly so that it can be used out-of-the-box
+
+## Code of Conduct
+
+[ONNX Open Source Code of Conduct](https://onnx.ai/codeofconduct.html)
+
+
+%package help
+Summary:	Development documents and examples for onnxoptimizer
+Provides:	python3-onnxoptimizer-doc
+%description help
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# ONNX Optimizer
+
+[![PyPI version](https://img.shields.io/pypi/v/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/)
+[![PyPI license](https://img.shields.io/pypi/l/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/onnx/optimizer/pulls)
+
+## Introduction
+
+ONNX provides a C++ library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes.
+
+The primary motivation is to share work between the many ONNX backend implementations. Not all possible optimizations can be directly implemented on ONNX graphs - some will need additional backend-specific information - but many can, and our aim is to provide all such passes along with ONNX so that they can be re-used with a single function call.
+
+You may be interested in invoking the provided passes, or in implementing new ones (or both).
+
+## Installation
+
+You can install onnxoptimizer from PyPI:
+
+```bash
+pip3 install onnxoptimizer
+```
+
+Note that you may need to upgrade your pip first if you have trouble:
+
+```bash
+pip3 install -U pip
+```
+
+If you want to build from source:
+
+```bash
+git clone --recursive https://github.com/onnx/optimizer onnxoptimizer
+cd onnxoptimizer
+pip3 install -e .
+```
+
+Note that you need to install protobuf before building from source.
+
+
+## Command-line API
+Now you can use command-line api in terminal instead of  python script.
+
+```
+python -m onnxoptimizer input_model.onnx output_model.onnx
+```
+
+Arguments list is following:
+```
+# python3 -m onnxoptimizer -h                                 
+usage: python -m onnxoptimizer input_model.onnx output_model.onnx 
+
+onnxoptimizer command-line api
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --print_all_passes    print all available passes
+  --print_fuse_elimination_passes
+                        print all fuse and elimination passes
+  -p [PASSES ...], --passes [PASSES ...]
+                        list of optimization passes name, if no set, fuse_and_elimination_passes will be used
+  --fixed_point         fixed point
+```
+## Roadmap
+
+* More built-in pass
+* Separate graph rewriting and constant folding (or a pure graph rewriting mode, see [issue #9](https://github.com/onnx/optimizer/issues/9) for the details)
+
+## Relevant tools
+
+* [onnx-simplifier](https://github.com/daquexian/onnx-simplifier): A handy and popular tool based on onnxoptimizer
+
+* [convertmodel.com](https://convertmodel.com/#outputFormat=onnx&inputFormat=onnx): onnx optimizer compiled as WebAssembly so that it can be used out-of-the-box
+
+## Code of Conduct
+
+[ONNX Open Source Code of Conduct](https://onnx.ai/codeofconduct.html)
+
+
+%prep
+%autosetup -n onnxoptimizer-0.3.10
+
+%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-onnxoptimizer -f filelist.lst
+%dir %{python3_sitearch}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.10-1
+- Package Spec generated