automatic import of python-sruopeneuler20.03

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+/sru-2.6.0.tar.gz
diff --git a/python-sru.spec b/python-sru.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-sru
+Version:	2.6.0
+Release:	1
+Summary:	Simple Recurrent Units for Highly Parallelizable Recurrence
+License:	MIT
+URL:		https://github.com/taolei87/sru
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/40/ca/7537e0ef8c3361402b1787474f0960521d4de82673ab45c1f11909e1c7a1/sru-2.6.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-torch
+Requires:	python3-ninja
+
+%description
+
+## News
+SRU++, a new SRU variant, is released. [[tech report](https://arxiv.org/pdf/2102.12459.pdf)] [[blog](https://www.asapp.com/blog/reducing-the-high-cost-of-training-nlp-models-with-sru/)]
+
+The experimental code and SRU++ implementation are available on [the dev branch](https://github.com/asappresearch/sru/tree/3.0.0-dev/experiments/srupp_experiments) which will be merged into master later.
+
+## About
+
+**SRU** is a recurrent unit that can run over 10 times faster than cuDNN LSTM, without loss of accuracy tested on many tasks. 
+<p align="center">
+<img width=620 src="https://raw.githubusercontent.com/taolei87/sru/master/imgs/speed.png"><br>
+<i>Average processing time of LSTM, conv2d and SRU, tested on GTX 1070</i><br>
+</p>
+For example, the figure above presents the processing time of a single mini-batch of 32 samples. SRU achieves 10 to 16 times speed-up compared to LSTM, and operates as fast as (or faster than) word-level convolution using conv2d.
+
+#### Reference:
+Simple Recurrent Units for Highly Parallelizable Recurrence [[paper](https://arxiv.org/abs/1709.02755)]
+```
+@inproceedings{lei2018sru,
+  title={Simple Recurrent Units for Highly Parallelizable Recurrence},
+  author={Tao Lei and Yu Zhang and Sida I. Wang and Hui Dai and Yoav Artzi},
+  booktitle={Empirical Methods in Natural Language Processing (EMNLP)},
+  year={2018}
+}
+```
+
+When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute [[paper](https://arxiv.org/pdf/2102.12459)]
+```
+@article{lei2021srupp,
+  title={When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute},
+  author={Tao Lei},
+  journal={arXiv preprint arXiv:2102.12459},
+  year={2021}
+}
+```
+<br>
+
+## Requirements
+ - [PyTorch](http://pytorch.org/) >=1.6 recommended
+ - [ninja](https://ninja-build.org/)
+
+Install requirements via `pip install -r requirements.txt`.
+
+<br>
+
+## Installation
+
+#### From source:
+SRU can be installed as a regular package via `python setup.py install` or `pip install .`.
+
+#### From PyPi:
+`pip install sru`
+
+
+#### Directly use the source without installation:
+Make sure this repo and CUDA library can be found by the system, e.g. 
+```
+export PYTHONPATH=path_to_repo/sru
+export LD_LIBRARY_PATH=/usr/local/cuda/lib64
+```
+
+<br>
+
+## Examples
+The usage of SRU is similar to `nn.LSTM`. SRU likely requires more stacking layers than LSTM. We recommend starting by 2 layers and use more if necessary (see our report for more experimental details).
+```python
+import torch
+from sru import SRU, SRUCell
+
+# input has length 20, batch size 32 and dimension 128
+x = torch.FloatTensor(20, 32, 128).cuda()
+
+input_size, hidden_size = 128, 128
+
+rnn = SRU(input_size, hidden_size,
+    num_layers = 2,          # number of stacking RNN layers
+    dropout = 0.0,           # dropout applied between RNN layers
+    bidirectional = False,   # bidirectional RNN
+    layer_norm = False,      # apply layer normalization on the output of each layer
+    highway_bias = -2,        # initial bias of highway gate (<= 0)
+)
+rnn.cuda()
+
+output_states, c_states = rnn(x)      # forward pass
+
+# output_states is (length, batch size, number of directions * hidden size)
+# c_states is (layers, batch size, number of directions * hidden size)
+
+```
+
+<br>
+
+## Contributing
+Please read and follow the [guidelines](CONTRIBUTING.md).
+
+
+### Other Implementations
+
+[@musyoku](https://github.com/musyoku) had a very nice [SRU implementaion](https://github.com/musyoku/chainer-sru) in chainer.
+
+[@adrianbg](https://github.com/adrianbg) implemented the first [CPU version](https://github.com/taolei87/sru/pull/42).
+
+<br>
+
+
+
+
+
+
+%package -n python3-sru
+Summary:	Simple Recurrent Units for Highly Parallelizable Recurrence
+Provides:	python-sru
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-sru
+
+## News
+SRU++, a new SRU variant, is released. [[tech report](https://arxiv.org/pdf/2102.12459.pdf)] [[blog](https://www.asapp.com/blog/reducing-the-high-cost-of-training-nlp-models-with-sru/)]
+
+The experimental code and SRU++ implementation are available on [the dev branch](https://github.com/asappresearch/sru/tree/3.0.0-dev/experiments/srupp_experiments) which will be merged into master later.
+
+## About
+
+**SRU** is a recurrent unit that can run over 10 times faster than cuDNN LSTM, without loss of accuracy tested on many tasks. 
+<p align="center">
+<img width=620 src="https://raw.githubusercontent.com/taolei87/sru/master/imgs/speed.png"><br>
+<i>Average processing time of LSTM, conv2d and SRU, tested on GTX 1070</i><br>
+</p>
+For example, the figure above presents the processing time of a single mini-batch of 32 samples. SRU achieves 10 to 16 times speed-up compared to LSTM, and operates as fast as (or faster than) word-level convolution using conv2d.
+
+#### Reference:
+Simple Recurrent Units for Highly Parallelizable Recurrence [[paper](https://arxiv.org/abs/1709.02755)]
+```
+@inproceedings{lei2018sru,
+  title={Simple Recurrent Units for Highly Parallelizable Recurrence},
+  author={Tao Lei and Yu Zhang and Sida I. Wang and Hui Dai and Yoav Artzi},
+  booktitle={Empirical Methods in Natural Language Processing (EMNLP)},
+  year={2018}
+}
+```
+
+When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute [[paper](https://arxiv.org/pdf/2102.12459)]
+```
+@article{lei2021srupp,
+  title={When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute},
+  author={Tao Lei},
+  journal={arXiv preprint arXiv:2102.12459},
+  year={2021}
+}
+```
+<br>
+
+## Requirements
+ - [PyTorch](http://pytorch.org/) >=1.6 recommended
+ - [ninja](https://ninja-build.org/)
+
+Install requirements via `pip install -r requirements.txt`.
+
+<br>
+
+## Installation
+
+#### From source:
+SRU can be installed as a regular package via `python setup.py install` or `pip install .`.
+
+#### From PyPi:
+`pip install sru`
+
+
+#### Directly use the source without installation:
+Make sure this repo and CUDA library can be found by the system, e.g. 
+```
+export PYTHONPATH=path_to_repo/sru
+export LD_LIBRARY_PATH=/usr/local/cuda/lib64
+```
+
+<br>
+
+## Examples
+The usage of SRU is similar to `nn.LSTM`. SRU likely requires more stacking layers than LSTM. We recommend starting by 2 layers and use more if necessary (see our report for more experimental details).
+```python
+import torch
+from sru import SRU, SRUCell
+
+# input has length 20, batch size 32 and dimension 128
+x = torch.FloatTensor(20, 32, 128).cuda()
+
+input_size, hidden_size = 128, 128
+
+rnn = SRU(input_size, hidden_size,
+    num_layers = 2,          # number of stacking RNN layers
+    dropout = 0.0,           # dropout applied between RNN layers
+    bidirectional = False,   # bidirectional RNN
+    layer_norm = False,      # apply layer normalization on the output of each layer
+    highway_bias = -2,        # initial bias of highway gate (<= 0)
+)
+rnn.cuda()
+
+output_states, c_states = rnn(x)      # forward pass
+
+# output_states is (length, batch size, number of directions * hidden size)
+# c_states is (layers, batch size, number of directions * hidden size)
+
+```
+
+<br>
+
+## Contributing
+Please read and follow the [guidelines](CONTRIBUTING.md).
+
+
+### Other Implementations
+
+[@musyoku](https://github.com/musyoku) had a very nice [SRU implementaion](https://github.com/musyoku/chainer-sru) in chainer.
+
+[@adrianbg](https://github.com/adrianbg) implemented the first [CPU version](https://github.com/taolei87/sru/pull/42).
+
+<br>
+
+
+
+
+
+
+%package help
+Summary:	Development documents and examples for sru
+Provides:	python3-sru-doc
+%description help
+
+## News
+SRU++, a new SRU variant, is released. [[tech report](https://arxiv.org/pdf/2102.12459.pdf)] [[blog](https://www.asapp.com/blog/reducing-the-high-cost-of-training-nlp-models-with-sru/)]
+
+The experimental code and SRU++ implementation are available on [the dev branch](https://github.com/asappresearch/sru/tree/3.0.0-dev/experiments/srupp_experiments) which will be merged into master later.
+
+## About
+
+**SRU** is a recurrent unit that can run over 10 times faster than cuDNN LSTM, without loss of accuracy tested on many tasks. 
+<p align="center">
+<img width=620 src="https://raw.githubusercontent.com/taolei87/sru/master/imgs/speed.png"><br>
+<i>Average processing time of LSTM, conv2d and SRU, tested on GTX 1070</i><br>
+</p>
+For example, the figure above presents the processing time of a single mini-batch of 32 samples. SRU achieves 10 to 16 times speed-up compared to LSTM, and operates as fast as (or faster than) word-level convolution using conv2d.
+
+#### Reference:
+Simple Recurrent Units for Highly Parallelizable Recurrence [[paper](https://arxiv.org/abs/1709.02755)]
+```
+@inproceedings{lei2018sru,
+  title={Simple Recurrent Units for Highly Parallelizable Recurrence},
+  author={Tao Lei and Yu Zhang and Sida I. Wang and Hui Dai and Yoav Artzi},
+  booktitle={Empirical Methods in Natural Language Processing (EMNLP)},
+  year={2018}
+}
+```
+
+When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute [[paper](https://arxiv.org/pdf/2102.12459)]
+```
+@article{lei2021srupp,
+  title={When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute},
+  author={Tao Lei},
+  journal={arXiv preprint arXiv:2102.12459},
+  year={2021}
+}
+```
+<br>
+
+## Requirements
+ - [PyTorch](http://pytorch.org/) >=1.6 recommended
+ - [ninja](https://ninja-build.org/)
+
+Install requirements via `pip install -r requirements.txt`.
+
+<br>
+
+## Installation
+
+#### From source:
+SRU can be installed as a regular package via `python setup.py install` or `pip install .`.
+
+#### From PyPi:
+`pip install sru`
+
+
+#### Directly use the source without installation:
+Make sure this repo and CUDA library can be found by the system, e.g. 
+```
+export PYTHONPATH=path_to_repo/sru
+export LD_LIBRARY_PATH=/usr/local/cuda/lib64
+```
+
+<br>
+
+## Examples
+The usage of SRU is similar to `nn.LSTM`. SRU likely requires more stacking layers than LSTM. We recommend starting by 2 layers and use more if necessary (see our report for more experimental details).
+```python
+import torch
+from sru import SRU, SRUCell
+
+# input has length 20, batch size 32 and dimension 128
+x = torch.FloatTensor(20, 32, 128).cuda()
+
+input_size, hidden_size = 128, 128
+
+rnn = SRU(input_size, hidden_size,
+    num_layers = 2,          # number of stacking RNN layers
+    dropout = 0.0,           # dropout applied between RNN layers
+    bidirectional = False,   # bidirectional RNN
+    layer_norm = False,      # apply layer normalization on the output of each layer
+    highway_bias = -2,        # initial bias of highway gate (<= 0)
+)
+rnn.cuda()
+
+output_states, c_states = rnn(x)      # forward pass
+
+# output_states is (length, batch size, number of directions * hidden size)
+# c_states is (layers, batch size, number of directions * hidden size)
+
+```
+
+<br>
+
+## Contributing
+Please read and follow the [guidelines](CONTRIBUTING.md).
+
+
+### Other Implementations
+
+[@musyoku](https://github.com/musyoku) had a very nice [SRU implementaion](https://github.com/musyoku/chainer-sru) in chainer.
+
+[@adrianbg](https://github.com/adrianbg) implemented the first [CPU version](https://github.com/taolei87/sru/pull/42).
+
+<br>
+
+
+
+
+
+
+%prep
+%autosetup -n sru-2.6.0
+
+%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-sru -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2.6.0-1
+- Package Spec generated
diff --git a/sources b/sources
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+e67408b94e399b972e5544fb2d655bb2  sru-2.6.0.tar.gz