automatic import of python-tflite-model-maker-nightly

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+/tflite-model-maker-nightly-0.4.3.dev202304110509.tar.gz
diff --git a/python-tflite-model-maker-nightly.spec b/python-tflite-model-maker-nightly.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-tflite-model-maker-nightly
+Version:	0.4.3.dev202304110509
+Release:	1
+Summary:	TFLite Model Maker: a model customization library for on-device applications.
+License:	Apache 2.0
+URL:		http://github.com/tensorflow/examples
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/fd/71/1bea458e05411b3e964598b51518df4f503e054c46e2f946d98735225f59/tflite-model-maker-nightly-0.4.3.dev202304110509.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-tf-models-official
+Requires:	python3-numpy
+Requires:	python3-pillow
+Requires:	python3-sentencepiece
+Requires:	python3-tensorflow-datasets
+Requires:	python3-fire
+Requires:	python3-flatbuffers
+Requires:	python3-absl-py
+Requires:	python3-urllib3
+Requires:	python3-tflite-support-nightly
+Requires:	python3-tensorflow
+Requires:	python3-numba
+Requires:	python3-librosa
+Requires:	python3-lxml
+Requires:	python3-PyYAML
+Requires:	python3-matplotlib
+Requires:	python3-six
+Requires:	python3-tfa-nightly
+Requires:	python3-neural-structured-learning
+Requires:	python3-tensorflow-model-optimization
+Requires:	python3-Cython
+Requires:	python3-scann
+Requires:	python3-tensorflowjs
+Requires:	python3-tensorflow-hub
+Requires:	python3-tensorflow-hub
+
+%description
+# TFLite Model Maker
+
+## Overview
+
+The TFLite Model Maker library simplifies the process of adapting and converting
+a TensorFlow neural-network model to particular input data when deploying this
+model for on-device ML applications.
+
+## Requirements
+
+*   Refer to
+    [requirements.txt](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/requirements.txt)
+    for dependent libraries that're needed to use the library and run the demo
+    code.
+*   Note that you might also need to install `sndfile` for Audio tasks.
+On Debian/Ubuntu, you can do so by `sudo apt-get install libsndfile1`
+
+## Installation
+
+There are two ways to install Model Maker.
+
+*   Install a prebuilt pip package:
+    [`tflite-model-maker`](https://pypi.org/project/tflite-model-maker/).
+
+```shell
+pip install tflite-model-maker
+```
+
+If you want to install nightly version
+[`tflite-model-maker-nightly`](https://pypi.org/project/tflite-model-maker-nightly/),
+please follow the command:
+
+```shell
+pip install tflite-model-maker-nightly
+```
+
+*   Clone the source code from GitHub and install.
+
+```shell
+git clone https://github.com/tensorflow/examples
+cd examples/tensorflow_examples/lite/model_maker/pip_package
+pip install -e .
+```
+
+TensorFlow Lite Model Maker depends on TensorFlow
+[pip package](https://www.tensorflow.org/install/pip). For GPU support, please
+refer to TensorFlow's [GPU guide](https://www.tensorflow.org/install/gpu) or
+[installation guide](https://www.tensorflow.org/install).
+
+## End-to-End Example
+
+For instance, it could have an end-to-end image classification example that
+utilizes this library with just 4 lines of code, each of which representing one
+step of the overall process. For more detail, you could refer to
+[Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb).
+
+*   Step 1. Import the required modules.
+
+```python
+from tflite_model_maker import image_classifier
+from tflite_model_maker.image_classifier import DataLoader
+```
+
+*   Step 2. Load input data specific to an on-device ML app.
+
+```python
+data = DataLoader.from_folder('flower_photos/')
+```
+
+*   Step 3. Customize the TensorFlow model.
+
+```python
+model = image_classifier.create(data)
+```
+
+*   Step 4. Evaluate the model.
+
+```python
+loss, accuracy = model.evaluate()
+```
+
+*   Step 5. Export to Tensorflow Lite model and label file in `export_dir`.
+
+```python
+model.export(export_dir='/tmp/')
+```
+
+## Notebook
+
+Currently, we support image classification, text classification and question
+answer tasks. Meanwhile, we provide demo code for each of them in demo folder.
+
+*   [Overview for TensorFlow Lite Model Maker](https://www.tensorflow.org/lite/guide/model_maker)
+*   [Python API Reference](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker)
+*   [Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb)
+*   [Colab for text classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb)
+*   [Colab for BERT question answer](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb)
+*   [Colab for object detection](https://www.tensorflow.org/lite/tutorials/model_maker_object_detection)
+
+
+%package -n python3-tflite-model-maker-nightly
+Summary:	TFLite Model Maker: a model customization library for on-device applications.
+Provides:	python-tflite-model-maker-nightly
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-tflite-model-maker-nightly
+# TFLite Model Maker
+
+## Overview
+
+The TFLite Model Maker library simplifies the process of adapting and converting
+a TensorFlow neural-network model to particular input data when deploying this
+model for on-device ML applications.
+
+## Requirements
+
+*   Refer to
+    [requirements.txt](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/requirements.txt)
+    for dependent libraries that're needed to use the library and run the demo
+    code.
+*   Note that you might also need to install `sndfile` for Audio tasks.
+On Debian/Ubuntu, you can do so by `sudo apt-get install libsndfile1`
+
+## Installation
+
+There are two ways to install Model Maker.
+
+*   Install a prebuilt pip package:
+    [`tflite-model-maker`](https://pypi.org/project/tflite-model-maker/).
+
+```shell
+pip install tflite-model-maker
+```
+
+If you want to install nightly version
+[`tflite-model-maker-nightly`](https://pypi.org/project/tflite-model-maker-nightly/),
+please follow the command:
+
+```shell
+pip install tflite-model-maker-nightly
+```
+
+*   Clone the source code from GitHub and install.
+
+```shell
+git clone https://github.com/tensorflow/examples
+cd examples/tensorflow_examples/lite/model_maker/pip_package
+pip install -e .
+```
+
+TensorFlow Lite Model Maker depends on TensorFlow
+[pip package](https://www.tensorflow.org/install/pip). For GPU support, please
+refer to TensorFlow's [GPU guide](https://www.tensorflow.org/install/gpu) or
+[installation guide](https://www.tensorflow.org/install).
+
+## End-to-End Example
+
+For instance, it could have an end-to-end image classification example that
+utilizes this library with just 4 lines of code, each of which representing one
+step of the overall process. For more detail, you could refer to
+[Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb).
+
+*   Step 1. Import the required modules.
+
+```python
+from tflite_model_maker import image_classifier
+from tflite_model_maker.image_classifier import DataLoader
+```
+
+*   Step 2. Load input data specific to an on-device ML app.
+
+```python
+data = DataLoader.from_folder('flower_photos/')
+```
+
+*   Step 3. Customize the TensorFlow model.
+
+```python
+model = image_classifier.create(data)
+```
+
+*   Step 4. Evaluate the model.
+
+```python
+loss, accuracy = model.evaluate()
+```
+
+*   Step 5. Export to Tensorflow Lite model and label file in `export_dir`.
+
+```python
+model.export(export_dir='/tmp/')
+```
+
+## Notebook
+
+Currently, we support image classification, text classification and question
+answer tasks. Meanwhile, we provide demo code for each of them in demo folder.
+
+*   [Overview for TensorFlow Lite Model Maker](https://www.tensorflow.org/lite/guide/model_maker)
+*   [Python API Reference](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker)
+*   [Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb)
+*   [Colab for text classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb)
+*   [Colab for BERT question answer](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb)
+*   [Colab for object detection](https://www.tensorflow.org/lite/tutorials/model_maker_object_detection)
+
+
+%package help
+Summary:	Development documents and examples for tflite-model-maker-nightly
+Provides:	python3-tflite-model-maker-nightly-doc
+%description help
+# TFLite Model Maker
+
+## Overview
+
+The TFLite Model Maker library simplifies the process of adapting and converting
+a TensorFlow neural-network model to particular input data when deploying this
+model for on-device ML applications.
+
+## Requirements
+
+*   Refer to
+    [requirements.txt](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/requirements.txt)
+    for dependent libraries that're needed to use the library and run the demo
+    code.
+*   Note that you might also need to install `sndfile` for Audio tasks.
+On Debian/Ubuntu, you can do so by `sudo apt-get install libsndfile1`
+
+## Installation
+
+There are two ways to install Model Maker.
+
+*   Install a prebuilt pip package:
+    [`tflite-model-maker`](https://pypi.org/project/tflite-model-maker/).
+
+```shell
+pip install tflite-model-maker
+```
+
+If you want to install nightly version
+[`tflite-model-maker-nightly`](https://pypi.org/project/tflite-model-maker-nightly/),
+please follow the command:
+
+```shell
+pip install tflite-model-maker-nightly
+```
+
+*   Clone the source code from GitHub and install.
+
+```shell
+git clone https://github.com/tensorflow/examples
+cd examples/tensorflow_examples/lite/model_maker/pip_package
+pip install -e .
+```
+
+TensorFlow Lite Model Maker depends on TensorFlow
+[pip package](https://www.tensorflow.org/install/pip). For GPU support, please
+refer to TensorFlow's [GPU guide](https://www.tensorflow.org/install/gpu) or
+[installation guide](https://www.tensorflow.org/install).
+
+## End-to-End Example
+
+For instance, it could have an end-to-end image classification example that
+utilizes this library with just 4 lines of code, each of which representing one
+step of the overall process. For more detail, you could refer to
+[Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb).
+
+*   Step 1. Import the required modules.
+
+```python
+from tflite_model_maker import image_classifier
+from tflite_model_maker.image_classifier import DataLoader
+```
+
+*   Step 2. Load input data specific to an on-device ML app.
+
+```python
+data = DataLoader.from_folder('flower_photos/')
+```
+
+*   Step 3. Customize the TensorFlow model.
+
+```python
+model = image_classifier.create(data)
+```
+
+*   Step 4. Evaluate the model.
+
+```python
+loss, accuracy = model.evaluate()
+```
+
+*   Step 5. Export to Tensorflow Lite model and label file in `export_dir`.
+
+```python
+model.export(export_dir='/tmp/')
+```
+
+## Notebook
+
+Currently, we support image classification, text classification and question
+answer tasks. Meanwhile, we provide demo code for each of them in demo folder.
+
+*   [Overview for TensorFlow Lite Model Maker](https://www.tensorflow.org/lite/guide/model_maker)
+*   [Python API Reference](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker)
+*   [Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb)
+*   [Colab for text classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb)
+*   [Colab for BERT question answer](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb)
+*   [Colab for object detection](https://www.tensorflow.org/lite/tutorials/model_maker_object_detection)
+
+
+%prep
+%autosetup -n tflite-model-maker-nightly-0.4.3.dev202304110509
+
+%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-tflite-model-maker-nightly -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.3.dev202304110509-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..6a2e49a
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+036a2e0ca6864d8903949fe4ecb82844  tflite-model-maker-nightly-0.4.3.dev202304110509.tar.gz