eager: Initial support for iteration over tf.contrib.data.Dataset objects.
TODO: - Support function-valued operation attributes in eager (Required for MapDataset, FilterDataset etc. which encode the per-element computation in a TensorFlow function) PiperOrigin-RevId: 168418250
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@ -2,13 +2,14 @@ licenses(["notice"]) # Apache 2.0
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package(default_visibility = ["//tensorflow:internal"])
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load("//tensorflow:tensorflow.bzl", "cuda_py_test")
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load("//tensorflow:tensorflow.bzl", "py_test", "cuda_py_test")
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py_library(
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name = "tfe",
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srcs = ["tfe.py"],
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srcs_version = "PY2AND3",
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deps = [
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":datasets",
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":saver",
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"//tensorflow/python:framework_ops",
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"//tensorflow/python:util",
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@ -31,6 +32,34 @@ cuda_py_test(
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],
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)
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py_library(
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name = "datasets",
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srcs = ["datasets.py"],
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srcs_version = "PY2AND3",
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visibility = ["//tensorflow:internal"],
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deps = [
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"//tensorflow/contrib/data/python/ops:dataset_ops",
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"//tensorflow/contrib/data/python/util:nest",
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"//tensorflow/python:dataset_ops_gen",
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"//tensorflow/python:errors",
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"//tensorflow/python:resource_variable_ops",
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"//tensorflow/python/eager:context",
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],
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)
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py_test(
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name = "datasets_test",
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srcs = ["datasets_test.py"],
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srcs_version = "PY2AND3",
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deps = [
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":datasets",
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"//tensorflow/contrib/data",
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"//tensorflow/python:math_ops",
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"//tensorflow/python/eager:test",
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"//third_party/py/numpy",
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],
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)
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py_library(
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name = "saver",
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srcs = ["saver.py"],
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96
tensorflow/contrib/eager/python/datasets.py
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96
tensorflow/contrib/eager/python/datasets.py
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@ -0,0 +1,96 @@
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Support for tf.contrib.data when eager execution is enabled."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import threading
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from tensorflow.contrib.data.python.util import nest
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from tensorflow.python.eager import context
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from tensorflow.python.framework import errors
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from tensorflow.python.ops import gen_dataset_ops
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from tensorflow.python.ops import resource_variable_ops
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_uid_counter = 0
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_uid_lock = threading.Lock()
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def _iterator_shared_name():
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with _uid_lock:
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global _uid_counter
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uid = _uid_counter
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_uid_counter += 1
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return "eager_iterator_{}".format(uid)
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class Iterator(object):
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"""An iterator producing tf.Tensor objects from a tf.contrib.data.Dataset."""
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def __init__(self, dataset):
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"""Creates a new iterator over the given dataset.
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For example:
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```python
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dataset = tf.contrib.data.Dataset.range(4)
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for x in Iterator(dataset):
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print(x)
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```
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Args:
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dataset: A `tf.contrib.data.Dataset` object.
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Raises:
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RuntimeError: When invoked without eager execution enabled.
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"""
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if not context.in_eager_mode():
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raise RuntimeError(
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"{} objects only make sense when eager execution is enabled".format(
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type(self)))
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ds_variant = dataset.make_dataset_resource()
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self._output_types = dataset.output_types
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self._flat_output_types = nest.flatten(dataset.output_types)
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self._flat_output_shapes = nest.flatten(dataset.output_shapes)
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self._resource = gen_dataset_ops.iterator(
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container="",
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shared_name=_iterator_shared_name(),
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output_types=self._flat_output_types,
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output_shapes=self._flat_output_shapes)
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gen_dataset_ops.make_iterator(ds_variant, self._resource)
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def __del__(self):
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if self._resource is not None:
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resource_variable_ops.destroy_resource_op(self._resource)
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self._resource = None
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def __iter__(self):
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return self
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def __next__(self): # For Python 3 compatibility
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return self.next()
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def next(self):
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"""Return the next tf.Tensor from the dataset."""
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try:
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ret = gen_dataset_ops.iterator_get_next(
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self._resource,
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output_types=self._flat_output_types,
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output_shapes=self._flat_output_shapes)
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return nest.pack_sequence_as(self._output_types, ret)
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except errors.OutOfRangeError:
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raise StopIteration
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69
tensorflow/contrib/eager/python/datasets_test.py
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69
tensorflow/contrib/eager/python/datasets_test.py
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@ -0,0 +1,69 @@
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.data import Dataset
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from tensorflow.contrib.eager.python import datasets
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from tensorflow.python.eager import test
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from tensorflow.python.ops import math_ops
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class IteratorTest(test.TestCase):
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def testBasic(self):
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got = []
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for t in datasets.Iterator(Dataset.range(4)):
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got.append(t.numpy())
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self.assertAllEqual([0, 1, 2, 3], got)
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def testMultipleIteratorsOnTheSameDataset(self):
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ds = Dataset.range(4)
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it1 = datasets.Iterator(ds)
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it2 = datasets.Iterator(ds)
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got = [x.numpy() for x in it1]
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self.assertAllEqual([0, 1, 2, 3], got)
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got = [x.numpy() for x in it2]
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self.assertAllEqual([0, 1, 2, 3], got)
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def testNestedOutputs(self):
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ds = Dataset.zip((Dataset.range(4), Dataset.zip((Dataset.range(4),
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Dataset.range(4)))))
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total = 0
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# The Iterator will return a nested structure of Tensor objects.
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# Some funkiness to compare against simple integers.
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for (i, x) in enumerate(datasets.Iterator(ds)):
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want = (i, (i, i))
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got = (x[0].numpy(), (x[1][0].numpy(), x[1][1].numpy()))
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self.assertEqual(got, want)
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total += 1
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self.assertEqual(4, total)
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def testMapAndFilter(self):
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# TODO(ashankar): Address this
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self.skipTest('Not working yet, requires function attribute support')
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def even(x):
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return math_ops.equal(math_ops.mod(x, 2), 0)
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it = datasets.Iterator(Dataset.range(8).map(math_ops.square).filter(even))
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got = [x.numpy() for x in it]
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self.assertAllEqual([0, 4, 16, 36], got)
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if __name__ == '__main__':
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test.main()
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