automatic import of python-params-flow

3 files changed, 188 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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+/params-flow-0.8.2.tar.gz
diff --git a/python-params-flow.spec b/python-params-flow.spec
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+%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
diff --git a/sources b/sources
new file mode 100644
index 0000000..0dd0b2b
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+6be1c75d140491fb9b17644ae22867f0  params-flow-0.8.2.tar.gz