automatic import of python-opennmt-tf

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+/OpenNMT-tf-2.31.0.tar.gz
diff --git a/python-opennmt-tf.spec b/python-opennmt-tf.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-OpenNMT-tf
+Version:	2.31.0
+Release:	1
+Summary:	Neural machine translation and sequence learning using TensorFlow
+License:	MIT
+URL:		https://opennmt.net
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/5d/8d/4b12ae213b41eb63e19b1d0a7f4e71287dc0ff5b3e13ef8b098fbd8ab169/OpenNMT-tf-2.31.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-ctranslate2
+Requires:	python3-packaging
+Requires:	python3-pyonmttok
+Requires:	python3-pyyaml
+Requires:	python3-rouge
+Requires:	python3-sacrebleu
+Requires:	python3-tensorflow-addons
+Requires:	python3-myst-parser
+Requires:	python3-sphinx-rtd-theme
+Requires:	python3-sphinx
+Requires:	python3-tensorflow
+Requires:	python3-tensorflow-text
+Requires:	python3-black
+Requires:	python3-flake8
+Requires:	python3-isort
+Requires:	python3-parameterized
+Requires:	python3-pytest-cov
+
+%description
+OpenNMT-tf also implements most of the techniques commonly used to train and evaluate sequence models, such as:
+* automatic evaluation during the training
+* multiple decoding strategy: greedy search, beam search, random sampling
+* N-best rescoring
+* gradient accumulation
+* scheduled sampling
+* checkpoint averaging
+* ... and more!
+*See the [documentation](https://opennmt.net/OpenNMT-tf/) to learn how to use these features.*
+## Usage
+OpenNMT-tf requires:
+* Python 3.7 or above
+* TensorFlow 2.6, 2.7, 2.8, 2.9, 2.10, or 2.11
+We recommend installing it with `pip`:
+```bash
+pip install --upgrade pip
+pip install OpenNMT-tf
+```
+*See the [documentation](https://opennmt.net/OpenNMT-tf/installation.html) for more information.*
+### Command line
+OpenNMT-tf comes with several command line utilities to prepare data, train, and evaluate models.
+For all tasks involving a model execution, OpenNMT-tf uses a unique entrypoint: `onmt-main`. A typical OpenNMT-tf run consists of 3 elements:
+* the **model** type
+* the **parameters** described in a YAML file
+* the **run** type such as `train`, `eval`, `infer`, `export`, `score`, `average_checkpoints`, or `update_vocab`
+that are passed to the main script:
+```
+onmt-main --model_type <model> --config <config_file.yml> --auto_config <run_type> <run_options>
+```
+*For more information and examples on how to use OpenNMT-tf, please visit [our documentation](https://opennmt.net/OpenNMT-tf).*
+### Library
+OpenNMT-tf also exposes [well-defined and stable APIs](https://opennmt.net/OpenNMT-tf/package/overview.html), from high-level training utilities to low-level model layers and dataset transformations.
+For example, the `Runner` class can be used to train and evaluate models with few lines of code:
+```python
+import opennmt
+config = {
+    "model_dir": "/data/wmt-ende/checkpoints/",
+    "data": {
+        "source_vocabulary": "/data/wmt-ende/joint-vocab.txt",
+        "target_vocabulary": "/data/wmt-ende/joint-vocab.txt",
+        "train_features_file": "/data/wmt-ende/train.en",
+        "train_labels_file": "/data/wmt-ende/train.de",
+        "eval_features_file": "/data/wmt-ende/valid.en",
+        "eval_labels_file": "/data/wmt-ende/valid.de",
+    }
+}
+model = opennmt.models.TransformerBase()
+runner = opennmt.Runner(model, config, auto_config=True)
+runner.train(num_devices=2, with_eval=True)
+```
+Here is another example using OpenNMT-tf to run efficient beam search with a self-attentional decoder:
+```python
+decoder = opennmt.decoders.SelfAttentionDecoder(num_layers=6, vocab_size=32000)
+initial_state = decoder.initial_state(
+    memory=memory, memory_sequence_length=memory_sequence_length
+)
+batch_size = tf.shape(memory)[0]
+start_ids = tf.fill([batch_size], opennmt.START_OF_SENTENCE_ID)
+decoding_result = decoder.dynamic_decode(
+    target_embedding,
+    start_ids=start_ids,
+    initial_state=initial_state,
+    decoding_strategy=opennmt.utils.BeamSearch(4),
+)
+```
+More examples using OpenNMT-tf as a library can be found online:
+* The directory [examples/library](https://github.com/OpenNMT/OpenNMT-tf/tree/master/examples/library) contains additional examples that use OpenNMT-tf as a library
+* [nmt-wizard-docker](https://github.com/OpenNMT/nmt-wizard-docker) uses the high-level `opennmt.Runner` API to wrap OpenNMT-tf with a custom interface for training, translating, and serving
+*For a complete overview of the APIs, see the [package documentation](https://opennmt.net/OpenNMT-tf/package/overview.html).*
+## Additional resources
+* [Documentation](https://opennmt.net/OpenNMT-tf)
+* [Forum](https://forum.opennmt.net)
+* [Gitter](https://gitter.im/OpenNMT/OpenNMT-tf)
+
+%package -n python3-OpenNMT-tf
+Summary:	Neural machine translation and sequence learning using TensorFlow
+Provides:	python-OpenNMT-tf
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-OpenNMT-tf
+OpenNMT-tf also implements most of the techniques commonly used to train and evaluate sequence models, such as:
+* automatic evaluation during the training
+* multiple decoding strategy: greedy search, beam search, random sampling
+* N-best rescoring
+* gradient accumulation
+* scheduled sampling
+* checkpoint averaging
+* ... and more!
+*See the [documentation](https://opennmt.net/OpenNMT-tf/) to learn how to use these features.*
+## Usage
+OpenNMT-tf requires:
+* Python 3.7 or above
+* TensorFlow 2.6, 2.7, 2.8, 2.9, 2.10, or 2.11
+We recommend installing it with `pip`:
+```bash
+pip install --upgrade pip
+pip install OpenNMT-tf
+```
+*See the [documentation](https://opennmt.net/OpenNMT-tf/installation.html) for more information.*
+### Command line
+OpenNMT-tf comes with several command line utilities to prepare data, train, and evaluate models.
+For all tasks involving a model execution, OpenNMT-tf uses a unique entrypoint: `onmt-main`. A typical OpenNMT-tf run consists of 3 elements:
+* the **model** type
+* the **parameters** described in a YAML file
+* the **run** type such as `train`, `eval`, `infer`, `export`, `score`, `average_checkpoints`, or `update_vocab`
+that are passed to the main script:
+```
+onmt-main --model_type <model> --config <config_file.yml> --auto_config <run_type> <run_options>
+```
+*For more information and examples on how to use OpenNMT-tf, please visit [our documentation](https://opennmt.net/OpenNMT-tf).*
+### Library
+OpenNMT-tf also exposes [well-defined and stable APIs](https://opennmt.net/OpenNMT-tf/package/overview.html), from high-level training utilities to low-level model layers and dataset transformations.
+For example, the `Runner` class can be used to train and evaluate models with few lines of code:
+```python
+import opennmt
+config = {
+    "model_dir": "/data/wmt-ende/checkpoints/",
+    "data": {
+        "source_vocabulary": "/data/wmt-ende/joint-vocab.txt",
+        "target_vocabulary": "/data/wmt-ende/joint-vocab.txt",
+        "train_features_file": "/data/wmt-ende/train.en",
+        "train_labels_file": "/data/wmt-ende/train.de",
+        "eval_features_file": "/data/wmt-ende/valid.en",
+        "eval_labels_file": "/data/wmt-ende/valid.de",
+    }
+}
+model = opennmt.models.TransformerBase()
+runner = opennmt.Runner(model, config, auto_config=True)
+runner.train(num_devices=2, with_eval=True)
+```
+Here is another example using OpenNMT-tf to run efficient beam search with a self-attentional decoder:
+```python
+decoder = opennmt.decoders.SelfAttentionDecoder(num_layers=6, vocab_size=32000)
+initial_state = decoder.initial_state(
+    memory=memory, memory_sequence_length=memory_sequence_length
+)
+batch_size = tf.shape(memory)[0]
+start_ids = tf.fill([batch_size], opennmt.START_OF_SENTENCE_ID)
+decoding_result = decoder.dynamic_decode(
+    target_embedding,
+    start_ids=start_ids,
+    initial_state=initial_state,
+    decoding_strategy=opennmt.utils.BeamSearch(4),
+)
+```
+More examples using OpenNMT-tf as a library can be found online:
+* The directory [examples/library](https://github.com/OpenNMT/OpenNMT-tf/tree/master/examples/library) contains additional examples that use OpenNMT-tf as a library
+* [nmt-wizard-docker](https://github.com/OpenNMT/nmt-wizard-docker) uses the high-level `opennmt.Runner` API to wrap OpenNMT-tf with a custom interface for training, translating, and serving
+*For a complete overview of the APIs, see the [package documentation](https://opennmt.net/OpenNMT-tf/package/overview.html).*
+## Additional resources
+* [Documentation](https://opennmt.net/OpenNMT-tf)
+* [Forum](https://forum.opennmt.net)
+* [Gitter](https://gitter.im/OpenNMT/OpenNMT-tf)
+
+%package help
+Summary:	Development documents and examples for OpenNMT-tf
+Provides:	python3-OpenNMT-tf-doc
+%description help
+OpenNMT-tf also implements most of the techniques commonly used to train and evaluate sequence models, such as:
+* automatic evaluation during the training
+* multiple decoding strategy: greedy search, beam search, random sampling
+* N-best rescoring
+* gradient accumulation
+* scheduled sampling
+* checkpoint averaging
+* ... and more!
+*See the [documentation](https://opennmt.net/OpenNMT-tf/) to learn how to use these features.*
+## Usage
+OpenNMT-tf requires:
+* Python 3.7 or above
+* TensorFlow 2.6, 2.7, 2.8, 2.9, 2.10, or 2.11
+We recommend installing it with `pip`:
+```bash
+pip install --upgrade pip
+pip install OpenNMT-tf
+```
+*See the [documentation](https://opennmt.net/OpenNMT-tf/installation.html) for more information.*
+### Command line
+OpenNMT-tf comes with several command line utilities to prepare data, train, and evaluate models.
+For all tasks involving a model execution, OpenNMT-tf uses a unique entrypoint: `onmt-main`. A typical OpenNMT-tf run consists of 3 elements:
+* the **model** type
+* the **parameters** described in a YAML file
+* the **run** type such as `train`, `eval`, `infer`, `export`, `score`, `average_checkpoints`, or `update_vocab`
+that are passed to the main script:
+```
+onmt-main --model_type <model> --config <config_file.yml> --auto_config <run_type> <run_options>
+```
+*For more information and examples on how to use OpenNMT-tf, please visit [our documentation](https://opennmt.net/OpenNMT-tf).*
+### Library
+OpenNMT-tf also exposes [well-defined and stable APIs](https://opennmt.net/OpenNMT-tf/package/overview.html), from high-level training utilities to low-level model layers and dataset transformations.
+For example, the `Runner` class can be used to train and evaluate models with few lines of code:
+```python
+import opennmt
+config = {
+    "model_dir": "/data/wmt-ende/checkpoints/",
+    "data": {
+        "source_vocabulary": "/data/wmt-ende/joint-vocab.txt",
+        "target_vocabulary": "/data/wmt-ende/joint-vocab.txt",
+        "train_features_file": "/data/wmt-ende/train.en",
+        "train_labels_file": "/data/wmt-ende/train.de",
+        "eval_features_file": "/data/wmt-ende/valid.en",
+        "eval_labels_file": "/data/wmt-ende/valid.de",
+    }
+}
+model = opennmt.models.TransformerBase()
+runner = opennmt.Runner(model, config, auto_config=True)
+runner.train(num_devices=2, with_eval=True)
+```
+Here is another example using OpenNMT-tf to run efficient beam search with a self-attentional decoder:
+```python
+decoder = opennmt.decoders.SelfAttentionDecoder(num_layers=6, vocab_size=32000)
+initial_state = decoder.initial_state(
+    memory=memory, memory_sequence_length=memory_sequence_length
+)
+batch_size = tf.shape(memory)[0]
+start_ids = tf.fill([batch_size], opennmt.START_OF_SENTENCE_ID)
+decoding_result = decoder.dynamic_decode(
+    target_embedding,
+    start_ids=start_ids,
+    initial_state=initial_state,
+    decoding_strategy=opennmt.utils.BeamSearch(4),
+)
+```
+More examples using OpenNMT-tf as a library can be found online:
+* The directory [examples/library](https://github.com/OpenNMT/OpenNMT-tf/tree/master/examples/library) contains additional examples that use OpenNMT-tf as a library
+* [nmt-wizard-docker](https://github.com/OpenNMT/nmt-wizard-docker) uses the high-level `opennmt.Runner` API to wrap OpenNMT-tf with a custom interface for training, translating, and serving
+*For a complete overview of the APIs, see the [package documentation](https://opennmt.net/OpenNMT-tf/package/overview.html).*
+## Additional resources
+* [Documentation](https://opennmt.net/OpenNMT-tf)
+* [Forum](https://forum.opennmt.net)
+* [Gitter](https://gitter.im/OpenNMT/OpenNMT-tf)
+
+%prep
+%autosetup -n OpenNMT-tf-2.31.0
+
+%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-OpenNMT-tf -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 2.31.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..22251ee
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+bd979a3289f0f311a220a385ccf531fa  OpenNMT-tf-2.31.0.tar.gz