%global _empty_manifest_terminate_build 0
Name:		python-params-flow
Version:	0.8.2
Release:	1
Summary:	Tensorflow Keras utilities for reducing boilerplate code.
License:	MIT
URL:		https://github.com/kpe/params-flow/
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/a9/95/ff49f5ebd501f142a6f0aaf42bcfd1c192dc54909d1d9eb84ab031d46056/params-flow-0.8.2.tar.gz
BuildArch:	noarch


%description
|Build Status| |Coverage Status| |Version Status| |Python Versions| |Downloads|
`params-flow`_ provides an alternative style for defining your `Keras`_ model
or layer configuration in order to reduce the boilerplate code related to
passing and (de)serializing your model/layer configuration arguments.
`params-flow`_ encourages this:
   import params_flow as pf
   class MyDenseLayer(pf.Layer):      # using params_flow Layer/Model instead of Keras ones
     class Params(pf.Layer.Params):   # extend one or more base Params configurations
       num_outputs = None             # declare all configuration arguments
       activation = "gelu"            #   provide or override super() defaults
                                      # do not define an __init__()
     def build(self, in_shape):
       self.kernel = self.add_variable("kernel",
                                       [int(in_shape[-1]),
                                        self.params.num_outputs])     # access config arguments
which would be sufficient to pass the right configuration arguments to the
super layer/model, as well as take care of (de)serialization, so you can concentrate
on the ``build()`` or ``call()`` implementations, instead of writing boilerplate
code like this:
    from tf.keras.layers import Layer
    class MyDenseLayer(Layer):
      def __init__(self,
                   num_outputs,            # put all of the layer configuration in the constructor
                   activation = "gelu",    #     provide defaults
                   **kwargs):              # allow base layer configuration to be passed to super
        self.num_outputs = num_outputs
        self.activation = activation
        super().__init__(**kwargs)
      def build(self, in_shape):
        self.kernel = self.add_variable("kernel",
                                        [int(in_shape[-1]),
                                         self.num_outputs])      # access config arguments
      def get_config(self):                # serialize layer configuration, __init__() is the deserializer
        config = {
          'num_outputs': self.num_outputs,
          'activation': self.activation
        }
        base_config = super().get_config()
        return dict(list(base_config.items())) + list(config.items())

%package -n python3-params-flow
Summary:	Tensorflow Keras utilities for reducing boilerplate code.
Provides:	python-params-flow
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-params-flow
|Build Status| |Coverage Status| |Version Status| |Python Versions| |Downloads|
`params-flow`_ provides an alternative style for defining your `Keras`_ model
or layer configuration in order to reduce the boilerplate code related to
passing and (de)serializing your model/layer configuration arguments.
`params-flow`_ encourages this:
   import params_flow as pf
   class MyDenseLayer(pf.Layer):      # using params_flow Layer/Model instead of Keras ones
     class Params(pf.Layer.Params):   # extend one or more base Params configurations
       num_outputs = None             # declare all configuration arguments
       activation = "gelu"            #   provide or override super() defaults
                                      # do not define an __init__()
     def build(self, in_shape):
       self.kernel = self.add_variable("kernel",
                                       [int(in_shape[-1]),
                                        self.params.num_outputs])     # access config arguments
which would be sufficient to pass the right configuration arguments to the
super layer/model, as well as take care of (de)serialization, so you can concentrate
on the ``build()`` or ``call()`` implementations, instead of writing boilerplate
code like this:
    from tf.keras.layers import Layer
    class MyDenseLayer(Layer):
      def __init__(self,
                   num_outputs,            # put all of the layer configuration in the constructor
                   activation = "gelu",    #     provide defaults
                   **kwargs):              # allow base layer configuration to be passed to super
        self.num_outputs = num_outputs
        self.activation = activation
        super().__init__(**kwargs)
      def build(self, in_shape):
        self.kernel = self.add_variable("kernel",
                                        [int(in_shape[-1]),
                                         self.num_outputs])      # access config arguments
      def get_config(self):                # serialize layer configuration, __init__() is the deserializer
        config = {
          'num_outputs': self.num_outputs,
          'activation': self.activation
        }
        base_config = super().get_config()
        return dict(list(base_config.items())) + list(config.items())

%package help
Summary:	Development documents and examples for params-flow
Provides:	python3-params-flow-doc
%description help
|Build Status| |Coverage Status| |Version Status| |Python Versions| |Downloads|
`params-flow`_ provides an alternative style for defining your `Keras`_ model
or layer configuration in order to reduce the boilerplate code related to
passing and (de)serializing your model/layer configuration arguments.
`params-flow`_ encourages this:
   import params_flow as pf
   class MyDenseLayer(pf.Layer):      # using params_flow Layer/Model instead of Keras ones
     class Params(pf.Layer.Params):   # extend one or more base Params configurations
       num_outputs = None             # declare all configuration arguments
       activation = "gelu"            #   provide or override super() defaults
                                      # do not define an __init__()
     def build(self, in_shape):
       self.kernel = self.add_variable("kernel",
                                       [int(in_shape[-1]),
                                        self.params.num_outputs])     # access config arguments
which would be sufficient to pass the right configuration arguments to the
super layer/model, as well as take care of (de)serialization, so you can concentrate
on the ``build()`` or ``call()`` implementations, instead of writing boilerplate
code like this:
    from tf.keras.layers import Layer
    class MyDenseLayer(Layer):
      def __init__(self,
                   num_outputs,            # put all of the layer configuration in the constructor
                   activation = "gelu",    #     provide defaults
                   **kwargs):              # allow base layer configuration to be passed to super
        self.num_outputs = num_outputs
        self.activation = activation
        super().__init__(**kwargs)
      def build(self, in_shape):
        self.kernel = self.add_variable("kernel",
                                        [int(in_shape[-1]),
                                         self.num_outputs])      # access config arguments
      def get_config(self):                # serialize layer configuration, __init__() is the deserializer
        config = {
          'num_outputs': self.num_outputs,
          'activation': self.activation
        }
        base_config = super().get_config()
        return dict(list(base_config.items())) + list(config.items())

%prep
%autosetup -n params-flow-0.8.2

%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-params-flow -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.8.2-1
- Package Spec generated