automatic import of python-miditokopeneuler20.03

1 files changed, 432 insertions, 0 deletions

diff --git a/python-miditok.spec b/python-miditok.spec
new file mode 100644
index 0000000..ba06ea5
--- /dev/null
+++ b/python-miditok.spec
@@ -0,0 +1,432 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-miditok
+Version:	2.0.6
+Release:	1
+Summary:	A convenient MIDI tokenizer for Deep Learning networks, with multiple encoding strategies
+License:	MIT
+URL:		https://github.com/Natooz/MidiTok
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/3c/58/587f75bd26a9717872bc5d1276ddcc10cedbf9344037fa56e062b1f0cbac/miditok-2.0.6.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-miditoolkit
+Requires:	python3-tqdm
+
+%description
+# MidiTok
+
+Python package to tokenize MIDI music files, presented at the ISMIR 2021 LBD.
+
+![MidiTok Logo](docs/assets/logo.png?raw=true "")
+
+[![PyPI version fury.io](https://badge.fury.io/py/miditok.svg)](https://pypi.python.org/pypi/miditok/)
+[![Python 3.7](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/release/)
+[![Documentation Status](https://readthedocs.org/projects/miditok/badge/?version=latest)](https://miditok.readthedocs.io/en/latest/?badge=latest)
+[![GitHub CI](https://github.com/Natooz/MidiTok/actions/workflows/pytest.yml/badge.svg)](https://github.com/Natooz/MidiTok/actions/workflows/pytest.yml)
+[![Codecov](https://img.shields.io/codecov/c/github/Natooz/MidiTok)](https://codecov.io/gh/Natooz/MidiTok)
+[![GitHub license](https://img.shields.io/github/license/Natooz/MidiTok.svg)](https://github.com/Natooz/MidiTok/blob/main/LICENSE)
+[![Downloads](https://pepy.tech/badge/MidiTok)](https://pepy.tech/project/MidiTok)
+[![Code style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+Using Deep Learning with symbolic music ? MidiTok can take care of converting (tokenizing) your MIDI files into tokens, ready to be fed to models such as Transformer, for any generation, transcription or MIR task.
+MidiTok features most known [MIDI tokenizations](https://miditok.readthedocs.io/en/latest/tokenizations.html) (e.g. [REMI](https://arxiv.org/abs/2002.00212), [Compound Word](https://arxiv.org/abs/2101.02402)...), and is built around the idea that they all share common parameters and methods. It supports [Byte Pair Encoding (BPE)](https://arxiv.org/abs/2301.11975) and data augmentation.
+
+**Documentation:** [miditok.readthedocs.com](https://miditok.readthedocs.io/en/latest/index.html)
+
+## Install
+
+```shell
+pip install miditok
+```
+MidiTok uses [MIDIToolkit](https://github.com/YatingMusic/miditoolkit), which itself uses [Mido](https://github.com/mido/mido) to read and write MIDI files, and BPE is backed by [Hugging Face 🤗tokenizers](https://github.com/huggingface/tokenizers) for super-fast encoding.
+
+## Usage example
+
+The most basic and useful methods are summarized here. And [here](colab-notebooks/Full_Example_HuggingFace_GPT2_Transformer.ipynb) is a simple notebook example showing how to use Hugging Face models to generate music, with MidiTok taking care of tokenizing MIDIs.
+
+```python
+from miditok import REMI
+from miditok.utils import get_midi_programs
+from miditoolkit import MidiFile
+from pathlib import Path
+
+# Creates the tokenizer and loads a MIDI
+tokenizer = REMI()  # using the default parameters, read the documentation to customize your tokenizer
+midi = MidiFile('path/to/your_midi.mid')
+
+# Converts MIDI to tokens, and back to a MIDI
+tokens = tokenizer(midi)  # calling it will automatically detect MIDIs, paths and tokens before the conversion
+converted_back_midi = tokenizer(tokens, get_midi_programs(midi))  # PyTorch / Tensorflow / Numpy tensors supported
+
+# Converts MIDI files to tokens saved as JSON files
+midi_paths = list(Path("path", "to", "dataset").glob("**/*.mid"))
+data_augmentation_offsets = [2, 1, 1]  # data augmentation on 2 pitch octaves, 1 velocity and 1 duration values
+tokenizer.tokenize_midi_dataset(midi_paths, Path("path", "to", "tokens_noBPE"),
+                                data_augment_offsets=data_augmentation_offsets)
+
+# Constructs the vocabulary with BPE, from the tokenized files
+tokenizer.learn_bpe(
+    vocab_size=500,
+    tokens_paths=list(Path("path", "to", "tokens_noBPE").glob("**/*.json")),
+    start_from_empty_voc=False,
+)
+
+# Saving our tokenizer, to retrieve it back later with the load_params method
+tokenizer.save_params(Path("path", "to", "save", "tokenizer"))
+
+# Converts the tokenized musics into tokens with BPE
+tokenizer.apply_bpe_to_dataset(Path('path', 'to', 'tokens_noBPE'), Path('path', 'to', 'tokens_BPE'))
+```
+
+## Tokenizations
+
+MidiTok implements the tokenizations: (links to original papers)
+* [REMI](https://dl.acm.org/doi/10.1145/3394171.3413671)
+* [REMI+](https://openreview.net/forum?id=NyR8OZFHw6i)
+* [MIDI-Like](https://link.springer.com/article/10.1007/s00521-018-3758-9)
+* [TSD](https://arxiv.org/abs/2301.11975)
+* [Structured](https://arxiv.org/abs/2107.05944)
+* [CPWord](https://ojs.aaai.org/index.php/AAAI/article/view/16091)
+* [Octuple](https://aclanthology.org/2021.findings-acl.70)
+* [MuMIDI](https://dl.acm.org/doi/10.1145/3394171.3413721)
+* [MMM](https://arxiv.org/abs/2008.06048)
+
+You can find short presentations in the [documentation](https://miditok.readthedocs.io/en/latest/tokenizations.html).
+
+## Limitations
+
+Tokenizations using Bar tokens (REMI, Compound Word and MuMIDI) **only considers a 4/x time signature** for now. This means that each bar is considered covering 4 beats.
+REMI+ and Octuple support it.
+
+## Contributions
+
+Contributions are gratefully welcomed, feel free to open an issue or send a PR if you want to add a tokenization or speed up the code. You can read the [contribution guide](CONTRIBUTING.md) for details.
+
+### Todos
+
+* Extend Time Signature to all tokenizations
+* Control Change messages
+* Option to represent pitch values as pitch intervals, as [it seems to improve performances](https://ismir2022program.ismir.net/lbd_369.html).
+* Speeding up MIDI read / load (Rust / C++ binding)
+* Data augmentation on duration values at the MIDI level
+
+## Citation
+
+If you use MidiTok for your research, a citation in your manuscript would be gladly appreciated. ❤️
+
+[**MidiTok paper**](https://archives.ismir.net/ismir2021/latebreaking/000005.pdf)
+```bibtex
+@inproceedings{miditok2021,
+    title={{MidiTok}: A Python package for {MIDI} file tokenization},
+    author={Fradet, Nathan and Briot, Jean-Pierre and Chhel, Fabien and El Fallah Seghrouchni, Amal and Gutowski, Nicolas},
+    booktitle={Extended Abstracts for the Late-Breaking Demo Session of the 22nd International Society for Music Information Retrieval Conference},
+    year={2021},
+    url={https://archives.ismir.net/ismir2021/latebreaking/000005.pdf},
+}
+```
+
+The BibTeX citations of all tokenizations can be found [in the documentation](https://miditok.readthedocs.io/en/latest/citations.html)
+
+
+## Acknowledgments
+
+Special thanks to all the contributors.
+We acknowledge [Aubay](https://blog.aubay.com/index.php/language/en/home/?lang=en), the [LIP6](https://www.lip6.fr/?LANG=en), [LERIA](http://blog.univ-angers.fr/leria/n) and [ESEO](https://eseo.fr/en) for the initial financing and support.
+
+
+%package -n python3-miditok
+Summary:	A convenient MIDI tokenizer for Deep Learning networks, with multiple encoding strategies
+Provides:	python-miditok
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-miditok
+# MidiTok
+
+Python package to tokenize MIDI music files, presented at the ISMIR 2021 LBD.
+
+![MidiTok Logo](docs/assets/logo.png?raw=true "")
+
+[![PyPI version fury.io](https://badge.fury.io/py/miditok.svg)](https://pypi.python.org/pypi/miditok/)
+[![Python 3.7](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/release/)
+[![Documentation Status](https://readthedocs.org/projects/miditok/badge/?version=latest)](https://miditok.readthedocs.io/en/latest/?badge=latest)
+[![GitHub CI](https://github.com/Natooz/MidiTok/actions/workflows/pytest.yml/badge.svg)](https://github.com/Natooz/MidiTok/actions/workflows/pytest.yml)
+[![Codecov](https://img.shields.io/codecov/c/github/Natooz/MidiTok)](https://codecov.io/gh/Natooz/MidiTok)
+[![GitHub license](https://img.shields.io/github/license/Natooz/MidiTok.svg)](https://github.com/Natooz/MidiTok/blob/main/LICENSE)
+[![Downloads](https://pepy.tech/badge/MidiTok)](https://pepy.tech/project/MidiTok)
+[![Code style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+Using Deep Learning with symbolic music ? MidiTok can take care of converting (tokenizing) your MIDI files into tokens, ready to be fed to models such as Transformer, for any generation, transcription or MIR task.
+MidiTok features most known [MIDI tokenizations](https://miditok.readthedocs.io/en/latest/tokenizations.html) (e.g. [REMI](https://arxiv.org/abs/2002.00212), [Compound Word](https://arxiv.org/abs/2101.02402)...), and is built around the idea that they all share common parameters and methods. It supports [Byte Pair Encoding (BPE)](https://arxiv.org/abs/2301.11975) and data augmentation.
+
+**Documentation:** [miditok.readthedocs.com](https://miditok.readthedocs.io/en/latest/index.html)
+
+## Install
+
+```shell
+pip install miditok
+```
+MidiTok uses [MIDIToolkit](https://github.com/YatingMusic/miditoolkit), which itself uses [Mido](https://github.com/mido/mido) to read and write MIDI files, and BPE is backed by [Hugging Face 🤗tokenizers](https://github.com/huggingface/tokenizers) for super-fast encoding.
+
+## Usage example
+
+The most basic and useful methods are summarized here. And [here](colab-notebooks/Full_Example_HuggingFace_GPT2_Transformer.ipynb) is a simple notebook example showing how to use Hugging Face models to generate music, with MidiTok taking care of tokenizing MIDIs.
+
+```python
+from miditok import REMI
+from miditok.utils import get_midi_programs
+from miditoolkit import MidiFile
+from pathlib import Path
+
+# Creates the tokenizer and loads a MIDI
+tokenizer = REMI()  # using the default parameters, read the documentation to customize your tokenizer
+midi = MidiFile('path/to/your_midi.mid')
+
+# Converts MIDI to tokens, and back to a MIDI
+tokens = tokenizer(midi)  # calling it will automatically detect MIDIs, paths and tokens before the conversion
+converted_back_midi = tokenizer(tokens, get_midi_programs(midi))  # PyTorch / Tensorflow / Numpy tensors supported
+
+# Converts MIDI files to tokens saved as JSON files
+midi_paths = list(Path("path", "to", "dataset").glob("**/*.mid"))
+data_augmentation_offsets = [2, 1, 1]  # data augmentation on 2 pitch octaves, 1 velocity and 1 duration values
+tokenizer.tokenize_midi_dataset(midi_paths, Path("path", "to", "tokens_noBPE"),
+                                data_augment_offsets=data_augmentation_offsets)
+
+# Constructs the vocabulary with BPE, from the tokenized files
+tokenizer.learn_bpe(
+    vocab_size=500,
+    tokens_paths=list(Path("path", "to", "tokens_noBPE").glob("**/*.json")),
+    start_from_empty_voc=False,
+)
+
+# Saving our tokenizer, to retrieve it back later with the load_params method
+tokenizer.save_params(Path("path", "to", "save", "tokenizer"))
+
+# Converts the tokenized musics into tokens with BPE
+tokenizer.apply_bpe_to_dataset(Path('path', 'to', 'tokens_noBPE'), Path('path', 'to', 'tokens_BPE'))
+```
+
+## Tokenizations
+
+MidiTok implements the tokenizations: (links to original papers)
+* [REMI](https://dl.acm.org/doi/10.1145/3394171.3413671)
+* [REMI+](https://openreview.net/forum?id=NyR8OZFHw6i)
+* [MIDI-Like](https://link.springer.com/article/10.1007/s00521-018-3758-9)
+* [TSD](https://arxiv.org/abs/2301.11975)
+* [Structured](https://arxiv.org/abs/2107.05944)
+* [CPWord](https://ojs.aaai.org/index.php/AAAI/article/view/16091)
+* [Octuple](https://aclanthology.org/2021.findings-acl.70)
+* [MuMIDI](https://dl.acm.org/doi/10.1145/3394171.3413721)
+* [MMM](https://arxiv.org/abs/2008.06048)
+
+You can find short presentations in the [documentation](https://miditok.readthedocs.io/en/latest/tokenizations.html).
+
+## Limitations
+
+Tokenizations using Bar tokens (REMI, Compound Word and MuMIDI) **only considers a 4/x time signature** for now. This means that each bar is considered covering 4 beats.
+REMI+ and Octuple support it.
+
+## Contributions
+
+Contributions are gratefully welcomed, feel free to open an issue or send a PR if you want to add a tokenization or speed up the code. You can read the [contribution guide](CONTRIBUTING.md) for details.
+
+### Todos
+
+* Extend Time Signature to all tokenizations
+* Control Change messages
+* Option to represent pitch values as pitch intervals, as [it seems to improve performances](https://ismir2022program.ismir.net/lbd_369.html).
+* Speeding up MIDI read / load (Rust / C++ binding)
+* Data augmentation on duration values at the MIDI level
+
+## Citation
+
+If you use MidiTok for your research, a citation in your manuscript would be gladly appreciated. ❤️
+
+[**MidiTok paper**](https://archives.ismir.net/ismir2021/latebreaking/000005.pdf)
+```bibtex
+@inproceedings{miditok2021,
+    title={{MidiTok}: A Python package for {MIDI} file tokenization},
+    author={Fradet, Nathan and Briot, Jean-Pierre and Chhel, Fabien and El Fallah Seghrouchni, Amal and Gutowski, Nicolas},
+    booktitle={Extended Abstracts for the Late-Breaking Demo Session of the 22nd International Society for Music Information Retrieval Conference},
+    year={2021},
+    url={https://archives.ismir.net/ismir2021/latebreaking/000005.pdf},
+}
+```
+
+The BibTeX citations of all tokenizations can be found [in the documentation](https://miditok.readthedocs.io/en/latest/citations.html)
+
+
+## Acknowledgments
+
+Special thanks to all the contributors.
+We acknowledge [Aubay](https://blog.aubay.com/index.php/language/en/home/?lang=en), the [LIP6](https://www.lip6.fr/?LANG=en), [LERIA](http://blog.univ-angers.fr/leria/n) and [ESEO](https://eseo.fr/en) for the initial financing and support.
+
+
+%package help
+Summary:	Development documents and examples for miditok
+Provides:	python3-miditok-doc
+%description help
+# MidiTok
+
+Python package to tokenize MIDI music files, presented at the ISMIR 2021 LBD.
+
+![MidiTok Logo](docs/assets/logo.png?raw=true "")
+
+[![PyPI version fury.io](https://badge.fury.io/py/miditok.svg)](https://pypi.python.org/pypi/miditok/)
+[![Python 3.7](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/release/)
+[![Documentation Status](https://readthedocs.org/projects/miditok/badge/?version=latest)](https://miditok.readthedocs.io/en/latest/?badge=latest)
+[![GitHub CI](https://github.com/Natooz/MidiTok/actions/workflows/pytest.yml/badge.svg)](https://github.com/Natooz/MidiTok/actions/workflows/pytest.yml)
+[![Codecov](https://img.shields.io/codecov/c/github/Natooz/MidiTok)](https://codecov.io/gh/Natooz/MidiTok)
+[![GitHub license](https://img.shields.io/github/license/Natooz/MidiTok.svg)](https://github.com/Natooz/MidiTok/blob/main/LICENSE)
+[![Downloads](https://pepy.tech/badge/MidiTok)](https://pepy.tech/project/MidiTok)
+[![Code style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+Using Deep Learning with symbolic music ? MidiTok can take care of converting (tokenizing) your MIDI files into tokens, ready to be fed to models such as Transformer, for any generation, transcription or MIR task.
+MidiTok features most known [MIDI tokenizations](https://miditok.readthedocs.io/en/latest/tokenizations.html) (e.g. [REMI](https://arxiv.org/abs/2002.00212), [Compound Word](https://arxiv.org/abs/2101.02402)...), and is built around the idea that they all share common parameters and methods. It supports [Byte Pair Encoding (BPE)](https://arxiv.org/abs/2301.11975) and data augmentation.
+
+**Documentation:** [miditok.readthedocs.com](https://miditok.readthedocs.io/en/latest/index.html)
+
+## Install
+
+```shell
+pip install miditok
+```
+MidiTok uses [MIDIToolkit](https://github.com/YatingMusic/miditoolkit), which itself uses [Mido](https://github.com/mido/mido) to read and write MIDI files, and BPE is backed by [Hugging Face 🤗tokenizers](https://github.com/huggingface/tokenizers) for super-fast encoding.
+
+## Usage example
+
+The most basic and useful methods are summarized here. And [here](colab-notebooks/Full_Example_HuggingFace_GPT2_Transformer.ipynb) is a simple notebook example showing how to use Hugging Face models to generate music, with MidiTok taking care of tokenizing MIDIs.
+
+```python
+from miditok import REMI
+from miditok.utils import get_midi_programs
+from miditoolkit import MidiFile
+from pathlib import Path
+
+# Creates the tokenizer and loads a MIDI
+tokenizer = REMI()  # using the default parameters, read the documentation to customize your tokenizer
+midi = MidiFile('path/to/your_midi.mid')
+
+# Converts MIDI to tokens, and back to a MIDI
+tokens = tokenizer(midi)  # calling it will automatically detect MIDIs, paths and tokens before the conversion
+converted_back_midi = tokenizer(tokens, get_midi_programs(midi))  # PyTorch / Tensorflow / Numpy tensors supported
+
+# Converts MIDI files to tokens saved as JSON files
+midi_paths = list(Path("path", "to", "dataset").glob("**/*.mid"))
+data_augmentation_offsets = [2, 1, 1]  # data augmentation on 2 pitch octaves, 1 velocity and 1 duration values
+tokenizer.tokenize_midi_dataset(midi_paths, Path("path", "to", "tokens_noBPE"),
+                                data_augment_offsets=data_augmentation_offsets)
+
+# Constructs the vocabulary with BPE, from the tokenized files
+tokenizer.learn_bpe(
+    vocab_size=500,
+    tokens_paths=list(Path("path", "to", "tokens_noBPE").glob("**/*.json")),
+    start_from_empty_voc=False,
+)
+
+# Saving our tokenizer, to retrieve it back later with the load_params method
+tokenizer.save_params(Path("path", "to", "save", "tokenizer"))
+
+# Converts the tokenized musics into tokens with BPE
+tokenizer.apply_bpe_to_dataset(Path('path', 'to', 'tokens_noBPE'), Path('path', 'to', 'tokens_BPE'))
+```
+
+## Tokenizations
+
+MidiTok implements the tokenizations: (links to original papers)
+* [REMI](https://dl.acm.org/doi/10.1145/3394171.3413671)
+* [REMI+](https://openreview.net/forum?id=NyR8OZFHw6i)
+* [MIDI-Like](https://link.springer.com/article/10.1007/s00521-018-3758-9)
+* [TSD](https://arxiv.org/abs/2301.11975)
+* [Structured](https://arxiv.org/abs/2107.05944)
+* [CPWord](https://ojs.aaai.org/index.php/AAAI/article/view/16091)
+* [Octuple](https://aclanthology.org/2021.findings-acl.70)
+* [MuMIDI](https://dl.acm.org/doi/10.1145/3394171.3413721)
+* [MMM](https://arxiv.org/abs/2008.06048)
+
+You can find short presentations in the [documentation](https://miditok.readthedocs.io/en/latest/tokenizations.html).
+
+## Limitations
+
+Tokenizations using Bar tokens (REMI, Compound Word and MuMIDI) **only considers a 4/x time signature** for now. This means that each bar is considered covering 4 beats.
+REMI+ and Octuple support it.
+
+## Contributions
+
+Contributions are gratefully welcomed, feel free to open an issue or send a PR if you want to add a tokenization or speed up the code. You can read the [contribution guide](CONTRIBUTING.md) for details.
+
+### Todos
+
+* Extend Time Signature to all tokenizations
+* Control Change messages
+* Option to represent pitch values as pitch intervals, as [it seems to improve performances](https://ismir2022program.ismir.net/lbd_369.html).
+* Speeding up MIDI read / load (Rust / C++ binding)
+* Data augmentation on duration values at the MIDI level
+
+## Citation
+
+If you use MidiTok for your research, a citation in your manuscript would be gladly appreciated. ❤️
+
+[**MidiTok paper**](https://archives.ismir.net/ismir2021/latebreaking/000005.pdf)
+```bibtex
+@inproceedings{miditok2021,
+    title={{MidiTok}: A Python package for {MIDI} file tokenization},
+    author={Fradet, Nathan and Briot, Jean-Pierre and Chhel, Fabien and El Fallah Seghrouchni, Amal and Gutowski, Nicolas},
+    booktitle={Extended Abstracts for the Late-Breaking Demo Session of the 22nd International Society for Music Information Retrieval Conference},
+    year={2021},
+    url={https://archives.ismir.net/ismir2021/latebreaking/000005.pdf},
+}
+```
+
+The BibTeX citations of all tokenizations can be found [in the documentation](https://miditok.readthedocs.io/en/latest/citations.html)
+
+
+## Acknowledgments
+
+Special thanks to all the contributors.
+We acknowledge [Aubay](https://blog.aubay.com/index.php/language/en/home/?lang=en), the [LIP6](https://www.lip6.fr/?LANG=en), [LERIA](http://blog.univ-angers.fr/leria/n) and [ESEO](https://eseo.fr/en) for the initial financing and support.
+
+
+%prep
+%autosetup -n miditok-2.0.6
+
+%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-miditok -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.6-1
+- Package Spec generated