229 lines
8.1 KiB
Python
229 lines
8.1 KiB
Python
# Copyright 2018 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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"""Tests for ragged_util."""
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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 absl.testing import parameterized
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import numpy as np
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops.ragged import ragged_util
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from tensorflow.python.platform import googletest
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# Example 3d tensor for test cases. Has shape [4, 2, 3].
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TENSOR_3D = [[[('%d%d%d' % (i, j, k)).encode('utf-8')
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for k in range(3)]
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for j in range(2)]
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for i in range(4)]
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# Example 4d tensor for test cases. Has shape [4, 2, 3, 5].
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TENSOR_4D = [[[[('%d%d%d%d' % (i, j, k, l)).encode('utf-8')
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for l in range(5)]
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for k in range(3)]
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for j in range(2)]
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for i in range(4)]
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@test_util.run_all_in_graph_and_eager_modes
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class RaggedUtilTest(test_util.TensorFlowTestCase,
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parameterized.TestCase):
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@parameterized.parameters([
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# Docstring examples
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dict(
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data=['a', 'b', 'c'],
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repeats=[3, 0, 2],
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axis=0,
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expected=[b'a', b'a', b'a', b'c', b'c']),
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dict(
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data=[[1, 2], [3, 4]],
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repeats=[2, 3],
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axis=0,
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expected=[[1, 2], [1, 2], [3, 4], [3, 4], [3, 4]]),
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dict(
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data=[[1, 2], [3, 4]],
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repeats=[2, 3],
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axis=1,
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expected=[[1, 1, 2, 2, 2], [3, 3, 4, 4, 4]]),
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# Scalar repeats value
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dict(
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data=['a', 'b', 'c'],
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repeats=2,
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axis=0,
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expected=[b'a', b'a', b'b', b'b', b'c', b'c']),
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dict(
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data=[[1, 2], [3, 4]],
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repeats=2,
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axis=0,
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expected=[[1, 2], [1, 2], [3, 4], [3, 4]]),
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dict(
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data=[[1, 2], [3, 4]],
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repeats=2,
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axis=1,
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expected=[[1, 1, 2, 2], [3, 3, 4, 4]]),
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# data & repeats are broadcast to have at least one dimension,
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# so these are all equivalent:
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dict(data=3, repeats=4, axis=0, expected=[3, 3, 3, 3]),
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dict(data=[3], repeats=4, axis=0, expected=[3, 3, 3, 3]),
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dict(data=3, repeats=[4], axis=0, expected=[3, 3, 3, 3]),
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dict(data=[3], repeats=[4], axis=0, expected=[3, 3, 3, 3]),
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# Empty tensor
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dict(data=[], repeats=[], axis=0, expected=[]),
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])
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def testRepeat(self, data, repeats, expected, axis=None):
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result = ragged_util.repeat(data, repeats, axis)
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self.assertAllEqual(result, expected)
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@parameterized.parameters([
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dict(mode=mode, **args)
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for mode in ['constant', 'dynamic', 'unknown_shape']
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for args in [
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# data & repeats are broadcast to have at least one dimension,
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# so these are all equivalent:
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dict(data=3, repeats=4, axis=0),
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dict(data=[3], repeats=4, axis=0),
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dict(data=3, repeats=[4], axis=0),
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dict(data=[3], repeats=[4], axis=0),
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# 1-dimensional data tensor.
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dict(data=[], repeats=5, axis=0),
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dict(data=[1, 2, 3], repeats=5, axis=0),
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dict(data=[1, 2, 3], repeats=[3, 0, 2], axis=0),
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dict(data=[1, 2, 3], repeats=[3, 0, 2], axis=-1),
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dict(data=[b'a', b'b', b'c'], repeats=[3, 0, 2], axis=0),
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# 2-dimensional data tensor.
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dict(data=[[1, 2, 3], [4, 5, 6]], repeats=3, axis=0),
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dict(data=[[1, 2, 3], [4, 5, 6]], repeats=3, axis=1),
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dict(data=[[1, 2, 3], [4, 5, 6]], repeats=[3, 5], axis=0),
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dict(data=[[1, 2, 3], [4, 5, 6]], repeats=[3, 5, 7], axis=1),
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# 3-dimensional data tensor: shape=[4, 2, 3].
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dict(data=TENSOR_3D, repeats=2, axis=0),
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dict(data=TENSOR_3D, repeats=2, axis=1),
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dict(data=TENSOR_3D, repeats=2, axis=2),
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dict(data=TENSOR_3D, repeats=[2, 0, 4, 1], axis=0),
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dict(data=TENSOR_3D, repeats=[3, 2], axis=1),
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dict(data=TENSOR_3D, repeats=[1, 3, 1], axis=2),
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# 4-dimensional data tensor: shape=[4, 2, 3, 5].
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dict(data=TENSOR_4D, repeats=2, axis=0),
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dict(data=TENSOR_4D, repeats=2, axis=1),
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dict(data=TENSOR_4D, repeats=2, axis=2),
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dict(data=TENSOR_4D, repeats=2, axis=3),
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dict(data=TENSOR_4D, repeats=[2, 0, 4, 1], axis=0),
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dict(data=TENSOR_4D, repeats=[3, 2], axis=1),
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dict(data=TENSOR_4D, repeats=[1, 3, 1], axis=2),
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dict(data=TENSOR_4D, repeats=[1, 3, 0, 0, 2], axis=3),
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]
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])
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def testValuesMatchesNumpy(self, mode, data, repeats, axis):
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# Exception: we can't handle negative axis if data.ndims is unknown.
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if axis < 0 and mode == 'unknown_shape':
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return
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expected = np.repeat(data, repeats, axis)
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if mode == 'constant':
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data = constant_op.constant(data)
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repeats = constant_op.constant(repeats)
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elif mode == 'dynamic':
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data = constant_op.constant(data)
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repeats = constant_op.constant(repeats)
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data = array_ops.placeholder_with_default(data, data.shape)
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repeats = array_ops.placeholder_with_default(repeats, repeats.shape)
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elif mode == 'unknown_shape':
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data = array_ops.placeholder_with_default(data, None)
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repeats = array_ops.placeholder_with_default(repeats, None)
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result = ragged_util.repeat(data, repeats, axis)
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self.assertAllEqual(result, expected)
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@parameterized.parameters([
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dict(
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descr='axis >= rank(data)',
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mode='dynamic',
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data=[1, 2, 3],
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repeats=[3, 0, 2],
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axis=1,
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error='axis=1 out of bounds: expected -1<=axis<1'),
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dict(
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descr='axis < -rank(data)',
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mode='dynamic',
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data=[1, 2, 3],
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repeats=[3, 0, 2],
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axis=-2,
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error='axis=-2 out of bounds: expected -1<=axis<1'),
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dict(
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descr='len(repeats) != data.shape[axis]',
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mode='dynamic',
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data=[[1, 2, 3], [4, 5, 6]],
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repeats=[2, 3],
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axis=1,
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error='Dimensions 3 and 2 are not compatible'),
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dict(
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descr='rank(repeats) > 1',
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mode='dynamic',
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data=[[1, 2, 3], [4, 5, 6]],
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repeats=[[3], [5]],
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axis=1,
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error=r'Shape \(2, 1\) must have rank at most 1'),
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dict(
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descr='non-integer axis',
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mode='constant',
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data=[1, 2, 3],
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repeats=2,
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axis='foo',
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exception=TypeError,
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error='axis must be an int'),
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])
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def testError(self,
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descr,
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mode,
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data,
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repeats,
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axis,
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exception=ValueError,
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error=None):
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# Make sure that this is also an error case for numpy.
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with self.assertRaises(exception):
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np.repeat(data, repeats, axis)
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if mode == 'constant':
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data = constant_op.constant(data)
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repeats = constant_op.constant(repeats)
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elif mode == 'dynamic':
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data = constant_op.constant(data)
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repeats = constant_op.constant(repeats)
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data = array_ops.placeholder_with_default(data, data.shape)
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repeats = array_ops.placeholder_with_default(repeats, repeats.shape)
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elif mode == 'unknown_shape':
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data = array_ops.placeholder_with_default(data, None)
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repeats = array_ops.placeholder_with_default(repeats, None)
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with self.assertRaisesRegexp(exception, error):
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ragged_util.repeat(data, repeats, axis)
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if __name__ == '__main__':
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googletest.main()
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