Add conversion for count_nonzero outside of package
PiperOrigin-RevId: 229158255
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@ -1339,6 +1339,8 @@ def reduce_sum(input_tensor, axis=None, keepdims=False, name=None):
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@tf_export(v1=["math.count_nonzero", "count_nonzero"])
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@deprecation.deprecated_args(
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None, "keep_dims is deprecated, use keepdims instead", "keep_dims")
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@deprecation.deprecated_args(
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None, "reduction_indices is deprecated, use axis instead", "axis")
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def count_nonzero(input_tensor,
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axis=None,
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keepdims=None,
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@ -121,7 +121,6 @@ renames = {
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'tf.container': 'tf.compat.v1.container',
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'tf.convert_to_tensor_or_indexed_slices': 'tf.compat.v1.convert_to_tensor_or_indexed_slices',
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'tf.convert_to_tensor_or_sparse_tensor': 'tf.compat.v1.convert_to_tensor_or_sparse_tensor',
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'tf.count_nonzero': 'tf.compat.v1.count_nonzero',
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'tf.count_up_to': 'tf.compat.v1.count_up_to',
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'tf.create_partitioned_variables': 'tf.compat.v1.create_partitioned_variables',
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'tf.cross': 'tf.linalg.cross',
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@ -119,6 +119,11 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
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"tf.load_file_system_library": {
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"library_filename": "library_location",
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},
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"tf.count_nonzero": {
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"input_tensor": "input",
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"keep_dims": "keepdims",
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"reduction_indices": "axis",
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},
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"tf.math.count_nonzero": {
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"input_tensor": "input",
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"keep_dims": "keepdims",
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@ -511,6 +516,8 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
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"tf.sort",
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"tf.contrib.framework.argsort":
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"tf.argsort",
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"tf.count_nonzero":
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"tf.math.count_nonzero",
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"tf.manip.batch_to_space_nd":
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"tf.batch_to_space",
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"tf.quantize_v2":
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@ -506,7 +506,7 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
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"tf.nn.dropout(x, 1 - (1 - func(3 + 4.)), name=\"foo\")\n",
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)
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def testCountNonZeroChanges(self):
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def testMathCountNonZeroChanges(self):
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text = (
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"tf.math.count_nonzero(input_tensor=input, dtype=dtype, name=name, "
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"reduction_indices=axis, keep_dims=keepdims)\n"
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@ -518,6 +518,18 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
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)
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self.assertEqual(new_text, expected_text)
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def testCountNonZeroChanges(self):
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text = (
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"tf.count_nonzero(input_tensor=input, dtype=dtype, name=name, "
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"reduction_indices=axis, keep_dims=keepdims)\n"
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)
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_, unused_report, unused_errors, new_text = self._upgrade(text)
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expected_text = (
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"tf.math.count_nonzero(input=input, dtype=dtype, name=name, "
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"axis=axis, keepdims=keepdims)\n"
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)
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self.assertEqual(new_text, expected_text)
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def testRandomMultinomialToRandomCategorical(self):
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text = (
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"tf.random.multinomial(logits, samples, seed, name, output_dtype)\n"
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