automatic import of python-vectorhub

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+%global _empty_manifest_terminate_build 0
+Name:		python-vectorhub
+Version:	1.8.3
+Release:	1
+Summary:	One liner to encode data into vectors with state-of-the-art models using tensorflow, pytorch and other open source libraries. Word2Vec, Image2Vec, BERT, etc
+License:	Apache
+URL:		https://github.com/vector-ai/vectorhub
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/20/d5/03f2cf02eddd67439aef3e08bd54ca36f6d663c3ed092093b07998337411/vectorhub-1.8.3.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-PyYAML
+Requires:	python3-requests
+Requires:	python3-document-utils
+Requires:	python3-numpy
+Requires:	python3-Pillow
+Requires:	python3-PyYAML
+Requires:	python3-imageio
+Requires:	python3-soundfile
+Requires:	python3-tensorflow
+Requires:	python3-sphinx-rtd-theme
+Requires:	python3-requests
+Requires:	python3-sentence-transformers
+Requires:	python3-moviepy
+Requires:	python3-document-utils
+Requires:	python3-scikit-image
+Requires:	python3-PyYAML
+Requires:	python3-torch
+Requires:	python3-transformers
+Requires:	python3-fastai
+Requires:	python3-pytest
+Requires:	python3-torch
+Requires:	python3-appdirs
+Requires:	python3-mtcnn
+Requires:	python3-tf-models-official
+Requires:	python3-opencv-python
+Requires:	python3-tensorflow-text
+Requires:	python3-Pillow
+Requires:	python3-librosa
+Requires:	python3-tensorflow-hub
+Requires:	python3-fairseq
+Requires:	python3-numpy
+Requires:	python3-clip-by-openai
+Requires:	python3-bert-for-tf2
+Requires:	python3-appdirs
+Requires:	python3-librosa
+Requires:	python3-bert-for-tf2
+Requires:	python3-Pillow
+Requires:	python3-imageio
+Requires:	python3-scikit-image
+Requires:	python3-torch
+Requires:	python3-clip-by-openai
+Requires:	python3-opencv-python
+Requires:	python3-clip-by-openai
+Requires:	python3-PyYAML
+Requires:	python3-requests
+Requires:	python3-document-utils
+Requires:	python3-numpy
+Requires:	python3-document-utils
+Requires:	python3-fairseq
+Requires:	python3-torch
+Requires:	python3-tensorflow-hub
+Requires:	python3-soundfile
+Requires:	python3-tensorflow
+Requires:	python3-librosa
+Requires:	python3-transformers
+Requires:	python3-torch
+Requires:	python3-imageio
+Requires:	python3-scikit-image
+Requires:	python3-opencv-python
+Requires:	python3-fastai
+Requires:	python3-torch
+Requires:	python3-Pillow
+Requires:	python3-tensorflow
+Requires:	python3-appdirs
+Requires:	python3-mtcnn
+Requires:	python3-opencv-python
+Requires:	python3-imageio
+Requires:	python3-scikit-image
+Requires:	python3-tensorflow
+Requires:	python3-tensorflow-hub
+Requires:	python3-torch
+Requires:	python3-sentence-transformers
+Requires:	python3-transformers
+Requires:	python3-tensorflow
+Requires:	python3-tensorflow-text
+Requires:	python3-tensorflow
+Requires:	python3-tensorflow-hub
+Requires:	python3-tf-models-official
+Requires:	python3-bert-for-tf2
+Requires:	python3-tf-models-official
+Requires:	python3-tensorflow-hub
+Requires:	python3-tensorflow
+Requires:	python3-bert-for-tf2
+Requires:	python3-transformers
+Requires:	python3-torch
+Requires:	python3-moviepy
+Requires:	python3-opencv-python
+Requires:	python3-fairseq
+Requires:	python3-fastai
+Requires:	python3-imageio
+Requires:	python3-librosa
+Requires:	python3-moviepy
+Requires:	python3-mtcnn
+Requires:	python3-numpy
+Requires:	python3-opencv-python
+Requires:	python3-pytest
+Requires:	python3-requests
+Requires:	python3-scikit-image
+Requires:	python3-sentence-transformers
+Requires:	python3-soundfile
+Requires:	python3-sphinx-rtd-theme
+Requires:	python3-tensorflow-hub
+Requires:	python3-tensorflow-text
+Requires:	python3-tensorflow
+Requires:	python3-sphinx-rtd-theme
+Requires:	python3-pytest
+Requires:	python3-tf-models-official
+Requires:	python3-torch
+Requires:	python3-torch
+Requires:	python3-transformers
+
+%description
+<p align="center">
+    <a href="https://hub.getvectorai.com">
+        <img align="center" src="https://getvectorai.com/assets/vectorhub-goal.png" width="800"/>
+    </a>
+</p>
+<br>
+There are many ways to extract vectors from data. This library aims to bring in all the state of the art models in a simple manner to vectorise your data easily.
+Vector Hub provides:
+- A low barrier of entry for practitioners (using common methods)
+- Vectorise rich and complex data types like: text, image, audio, etc in 3 lines of code
+- Retrieve and find information about a model
+- An easy way to handle dependencies easily for different models
+- Universal format of installation and encoding (using a simple `encode` method).
+In order to provide an easy way for practitioners to quickly experiment, research and build new models and feature vectors, we provide a streamlined way to obtain vectors through our universal `encode` API.
+Every model has the following: 
+- `encode` allows you to turn raw data into a vector
+- `bulk_encode` allows you to turn multiple objects into multiple vectors
+- `encode_documents` returns a list of dictionaries with with an encoded field
+For bi-modal models:
+Question Answering encoders will have:
+- `encode_question`
+- `encode_answer`
+- `bulk_encode_question`
+- `bulk_encode_answer`
+Text Image Bi-encoders will have: 
+- `encode_image`
+- `encode_text`
+- `bulk_encode_image`
+- `bulk_encode_text`
+
+%package -n python3-vectorhub
+Summary:	One liner to encode data into vectors with state-of-the-art models using tensorflow, pytorch and other open source libraries. Word2Vec, Image2Vec, BERT, etc
+Provides:	python-vectorhub
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-vectorhub
+<p align="center">
+    <a href="https://hub.getvectorai.com">
+        <img align="center" src="https://getvectorai.com/assets/vectorhub-goal.png" width="800"/>
+    </a>
+</p>
+<br>
+There are many ways to extract vectors from data. This library aims to bring in all the state of the art models in a simple manner to vectorise your data easily.
+Vector Hub provides:
+- A low barrier of entry for practitioners (using common methods)
+- Vectorise rich and complex data types like: text, image, audio, etc in 3 lines of code
+- Retrieve and find information about a model
+- An easy way to handle dependencies easily for different models
+- Universal format of installation and encoding (using a simple `encode` method).
+In order to provide an easy way for practitioners to quickly experiment, research and build new models and feature vectors, we provide a streamlined way to obtain vectors through our universal `encode` API.
+Every model has the following: 
+- `encode` allows you to turn raw data into a vector
+- `bulk_encode` allows you to turn multiple objects into multiple vectors
+- `encode_documents` returns a list of dictionaries with with an encoded field
+For bi-modal models:
+Question Answering encoders will have:
+- `encode_question`
+- `encode_answer`
+- `bulk_encode_question`
+- `bulk_encode_answer`
+Text Image Bi-encoders will have: 
+- `encode_image`
+- `encode_text`
+- `bulk_encode_image`
+- `bulk_encode_text`
+
+%package help
+Summary:	Development documents and examples for vectorhub
+Provides:	python3-vectorhub-doc
+%description help
+<p align="center">
+    <a href="https://hub.getvectorai.com">
+        <img align="center" src="https://getvectorai.com/assets/vectorhub-goal.png" width="800"/>
+    </a>
+</p>
+<br>
+There are many ways to extract vectors from data. This library aims to bring in all the state of the art models in a simple manner to vectorise your data easily.
+Vector Hub provides:
+- A low barrier of entry for practitioners (using common methods)
+- Vectorise rich and complex data types like: text, image, audio, etc in 3 lines of code
+- Retrieve and find information about a model
+- An easy way to handle dependencies easily for different models
+- Universal format of installation and encoding (using a simple `encode` method).
+In order to provide an easy way for practitioners to quickly experiment, research and build new models and feature vectors, we provide a streamlined way to obtain vectors through our universal `encode` API.
+Every model has the following: 
+- `encode` allows you to turn raw data into a vector
+- `bulk_encode` allows you to turn multiple objects into multiple vectors
+- `encode_documents` returns a list of dictionaries with with an encoded field
+For bi-modal models:
+Question Answering encoders will have:
+- `encode_question`
+- `encode_answer`
+- `bulk_encode_question`
+- `bulk_encode_answer`
+Text Image Bi-encoders will have: 
+- `encode_image`
+- `encode_text`
+- `bulk_encode_image`
+- `bulk_encode_text`
+
+%prep
+%autosetup -n vectorhub-1.8.3
+
+%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-vectorhub -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 1.8.3-1
+- Package Spec generated