automatic import of python-abdothebestopeneuler20.03

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+/abdoTheBest-1.0.0.tar.gz
diff --git a/python-abdothebest.spec b/python-abdothebest.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-abdoTheBest
+Version:	1.0.0
+Release:	1
+Summary:	A small example package
+License:	MIT
+URL:		https://github.com/gituser/example-pkg
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/55/f3/d948dfe7af193ea36a74b27f94c2c6d0490a6573a833982e13f3c207df03/abdoTheBest-1.0.0.tar.gz
+BuildArch:	noarch
+
+
+%description
+
+# GeNN
+[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/FahedSabellioglu/genn/blob/master/LICENSE.txt)
+
+GeNN (generative neural networks) is a high-level interface for text applications using PyTorch RNN's.
+
+
+## Features
+
+1.  Preprocessing: 
+	- Parsing txt, json, and csv files.
+	- NLTK, regex and spacy tokenization support.
+	- GloVe and fastText pretrained embeddings, with the ability to fine-tune for your data.
+2. Architectures and customization:
+	- GPT2 with small, medium, and large variants.
+	- LSTM and GRU, with variable size.
+	- Variable number of layers and batches.
+	- Dropout.
+3. Text generation:
+	- Random seed sampling from the n first tokens in all instances, or the most frequent token.
+	- Top-K sampling for next token prediction with variable K.
+	- Nucleus sampling for next token prediction with variable probability threshold.
+4. Text Summarization:
+	- All GPT2 variants can be trained to perform text summarization.
+
+## Getting started
+
+### How to install
+```bash
+pip install genn
+```
+### Prerequisites
+* PyTorch 1.4.0
+```bash
+pip install torch==1.4.0
+```
+* Pytorch Transformers
+```bash
+pip install pytorch_transformers
+```
+* NumPy
+```bash
+pip install numpy
+```
+* fastText
+```bash
+pip install fasttext
+```
+Use the package manager [pip](https://pypi.org/project/genn) to install genn.
+
+## Usage
+### Text Generation:
+##### RNNs (You can switch LSTMGenerator with GRUGenerator:
+```python
+from genn import Preprocessing, LSTMGenerator, GRUGenerator
+#LSTM example
+ds = Preprocessing("data.txt")
+gen = LSTMGenerator(ds, nLayers = 2,
+                        batchSize = 16,
+                        embSize = 64,
+                        lstmSize = 16,
+                        epochs = 20)
+			
+#Train the model
+gen.run()
+
+# Generate 5 new documents
+print(gen.generate_document(5))
+```
+##### GPT2 Generator:
+```python
+#GPT2 example
+gen = GPT2("data.txt",
+ 	    taskToken = "Movie:",
+	    epochs = 7,
+	    variant = "medium")
+#Train the model
+gen.run()
+
+#Generate 10 new documents
+print(gen.generate_document(10))
+```
+### Text Summarization:
+##### GPT2 Summarizer:
+```python
+#GPT2 Summarizer example
+from genn import GPT2Summarizer
+summ = GPT2Summarizer("data.txt",
+					epochs=3,
+					batch_size=8)
+
+#Train the model
+summ.run()
+
+#Create 5 summaries of a source document
+src_doc = "This is the source document to summarize"
+print(summ.summarize_document(n=5, setSeed = src_doc))
+```
+
+
+
+#### For more examples on how to use Preprocessing, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/preprocessing_examples.md).
+#### For more examples on how to use LSTMGenerator and GRUGenerator, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/generator_examples.md).
+#### For more examples on how to use GPT2, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/gpt2_examples.md).
+#### For more examples on how to use GPT2Summarizer, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/gpt2_summarizer_examples.md).
+## Contributing
+ Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
+## License
+Distributed under the MIT License. See [LICENSE](https://github.com/FahedSabellioglu/genn/blob/master/LICENSE.txt) for more information.
+
+
+
+
+%package -n python3-abdoTheBest
+Summary:	A small example package
+Provides:	python-abdoTheBest
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-abdoTheBest
+
+# GeNN
+[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/FahedSabellioglu/genn/blob/master/LICENSE.txt)
+
+GeNN (generative neural networks) is a high-level interface for text applications using PyTorch RNN's.
+
+
+## Features
+
+1.  Preprocessing: 
+	- Parsing txt, json, and csv files.
+	- NLTK, regex and spacy tokenization support.
+	- GloVe and fastText pretrained embeddings, with the ability to fine-tune for your data.
+2. Architectures and customization:
+	- GPT2 with small, medium, and large variants.
+	- LSTM and GRU, with variable size.
+	- Variable number of layers and batches.
+	- Dropout.
+3. Text generation:
+	- Random seed sampling from the n first tokens in all instances, or the most frequent token.
+	- Top-K sampling for next token prediction with variable K.
+	- Nucleus sampling for next token prediction with variable probability threshold.
+4. Text Summarization:
+	- All GPT2 variants can be trained to perform text summarization.
+
+## Getting started
+
+### How to install
+```bash
+pip install genn
+```
+### Prerequisites
+* PyTorch 1.4.0
+```bash
+pip install torch==1.4.0
+```
+* Pytorch Transformers
+```bash
+pip install pytorch_transformers
+```
+* NumPy
+```bash
+pip install numpy
+```
+* fastText
+```bash
+pip install fasttext
+```
+Use the package manager [pip](https://pypi.org/project/genn) to install genn.
+
+## Usage
+### Text Generation:
+##### RNNs (You can switch LSTMGenerator with GRUGenerator:
+```python
+from genn import Preprocessing, LSTMGenerator, GRUGenerator
+#LSTM example
+ds = Preprocessing("data.txt")
+gen = LSTMGenerator(ds, nLayers = 2,
+                        batchSize = 16,
+                        embSize = 64,
+                        lstmSize = 16,
+                        epochs = 20)
+			
+#Train the model
+gen.run()
+
+# Generate 5 new documents
+print(gen.generate_document(5))
+```
+##### GPT2 Generator:
+```python
+#GPT2 example
+gen = GPT2("data.txt",
+ 	    taskToken = "Movie:",
+	    epochs = 7,
+	    variant = "medium")
+#Train the model
+gen.run()
+
+#Generate 10 new documents
+print(gen.generate_document(10))
+```
+### Text Summarization:
+##### GPT2 Summarizer:
+```python
+#GPT2 Summarizer example
+from genn import GPT2Summarizer
+summ = GPT2Summarizer("data.txt",
+					epochs=3,
+					batch_size=8)
+
+#Train the model
+summ.run()
+
+#Create 5 summaries of a source document
+src_doc = "This is the source document to summarize"
+print(summ.summarize_document(n=5, setSeed = src_doc))
+```
+
+
+
+#### For more examples on how to use Preprocessing, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/preprocessing_examples.md).
+#### For more examples on how to use LSTMGenerator and GRUGenerator, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/generator_examples.md).
+#### For more examples on how to use GPT2, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/gpt2_examples.md).
+#### For more examples on how to use GPT2Summarizer, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/gpt2_summarizer_examples.md).
+## Contributing
+ Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
+## License
+Distributed under the MIT License. See [LICENSE](https://github.com/FahedSabellioglu/genn/blob/master/LICENSE.txt) for more information.
+
+
+
+
+%package help
+Summary:	Development documents and examples for abdoTheBest
+Provides:	python3-abdoTheBest-doc
+%description help
+
+# GeNN
+[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/FahedSabellioglu/genn/blob/master/LICENSE.txt)
+
+GeNN (generative neural networks) is a high-level interface for text applications using PyTorch RNN's.
+
+
+## Features
+
+1.  Preprocessing: 
+	- Parsing txt, json, and csv files.
+	- NLTK, regex and spacy tokenization support.
+	- GloVe and fastText pretrained embeddings, with the ability to fine-tune for your data.
+2. Architectures and customization:
+	- GPT2 with small, medium, and large variants.
+	- LSTM and GRU, with variable size.
+	- Variable number of layers and batches.
+	- Dropout.
+3. Text generation:
+	- Random seed sampling from the n first tokens in all instances, or the most frequent token.
+	- Top-K sampling for next token prediction with variable K.
+	- Nucleus sampling for next token prediction with variable probability threshold.
+4. Text Summarization:
+	- All GPT2 variants can be trained to perform text summarization.
+
+## Getting started
+
+### How to install
+```bash
+pip install genn
+```
+### Prerequisites
+* PyTorch 1.4.0
+```bash
+pip install torch==1.4.0
+```
+* Pytorch Transformers
+```bash
+pip install pytorch_transformers
+```
+* NumPy
+```bash
+pip install numpy
+```
+* fastText
+```bash
+pip install fasttext
+```
+Use the package manager [pip](https://pypi.org/project/genn) to install genn.
+
+## Usage
+### Text Generation:
+##### RNNs (You can switch LSTMGenerator with GRUGenerator:
+```python
+from genn import Preprocessing, LSTMGenerator, GRUGenerator
+#LSTM example
+ds = Preprocessing("data.txt")
+gen = LSTMGenerator(ds, nLayers = 2,
+                        batchSize = 16,
+                        embSize = 64,
+                        lstmSize = 16,
+                        epochs = 20)
+			
+#Train the model
+gen.run()
+
+# Generate 5 new documents
+print(gen.generate_document(5))
+```
+##### GPT2 Generator:
+```python
+#GPT2 example
+gen = GPT2("data.txt",
+ 	    taskToken = "Movie:",
+	    epochs = 7,
+	    variant = "medium")
+#Train the model
+gen.run()
+
+#Generate 10 new documents
+print(gen.generate_document(10))
+```
+### Text Summarization:
+##### GPT2 Summarizer:
+```python
+#GPT2 Summarizer example
+from genn import GPT2Summarizer
+summ = GPT2Summarizer("data.txt",
+					epochs=3,
+					batch_size=8)
+
+#Train the model
+summ.run()
+
+#Create 5 summaries of a source document
+src_doc = "This is the source document to summarize"
+print(summ.summarize_document(n=5, setSeed = src_doc))
+```
+
+
+
+#### For more examples on how to use Preprocessing, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/preprocessing_examples.md).
+#### For more examples on how to use LSTMGenerator and GRUGenerator, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/generator_examples.md).
+#### For more examples on how to use GPT2, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/gpt2_examples.md).
+#### For more examples on how to use GPT2Summarizer, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/gpt2_summarizer_examples.md).
+## Contributing
+ Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
+## License
+Distributed under the MIT License. See [LICENSE](https://github.com/FahedSabellioglu/genn/blob/master/LICENSE.txt) for more information.
+
+
+
+
+%prep
+%autosetup -n abdoTheBest-1.0.0
+
+%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-abdoTheBest -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.0-1
+- Package Spec generated
diff --git a/sources b/sources
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+0dd86edb8f36368eed6a1ce29f19f883  abdoTheBest-1.0.0.tar.gz