# Path: tensorflow_transform/beam/tft_unit.py
# def metadata_from_feature_spec(feature_spec, domains=None):
# def canonical_numeric_dtype(dtype):
# def make_feature_spec_wrapper(make_feature_spec, *args):
# def _format_example_as_numpy_dict(example, feature_shape_dict):
#     def has_ran(self):
#     def metrics(self):
#     def __exit__(self, exc_type, exc_val, exc_tb):
#   def _makeTestPipeline(self):
#   def assertMetricsCounterEqual(self, metrics, name, expected_count,
#                                 namespaces_list=None):
#   def assertAnalyzerOutputs(self,
#                             input_data,
#                             input_metadata,
#                             analyzer_fn,
#                             expected_outputs,
#                             test_data=None,
#                             desired_batch_size=None,
#                             beam_pipeline=None,
#                             force_tf_compat_v1=False,
#                             output_record_batches=False):
#     def preprocessing_fn(inputs):
#   def assertAnalyzeAndTransformResults(self,
#                                        input_data,
#                                        input_metadata,
#                                        preprocessing_fn,
#                                        expected_data=None,
#                                        expected_metadata=None,
#                                        expected_vocab_file_contents=None,
#                                        test_data=None,
#                                        desired_batch_size=None,
#                                        beam_pipeline=None,
#                                        temp_dir=None,
#                                        force_tf_compat_v1=False,
#                                        output_record_batches=False):
# class TransformTestCase(test_case.TransformTestCase):
#   class _TestPipeline(beam.Pipeline):


import tensorflow as tf
import simple_example
from tensorflow_transform.beam import tft_unit
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for simple_example."""



_EXPECTED_TRANSFORMED_OUTPUT = [
    {
        'x_centered': 1.0,
        'y_normalized': 1.0,
        'x_centered_times_y_normalized': 1.0,
        's_integerized': 0,
    },
    {
        'x_centered': 0.0,
        'y_normalized': 0.5,
        'x_centered_times_y_normalized': 0.0,
        's_integerized': 1,
    },
    {
        'x_centered': -1.0,
        'y_normalized': 0.0,
        'x_centered_times_y_normalized': -0.0,
        's_integerized': 0,
    },
]



# Path: tensorflow_transform/beam/tft_unit.py
# def metadata_from_feature_spec(feature_spec, domains=None):
# def canonical_numeric_dtype(dtype):
# def make_feature_spec_wrapper(make_feature_spec, *args):
# def _format_example_as_numpy_dict(example, feature_shape_dict):
#     def has_ran(self):
#     def metrics(self):
#     def __exit__(self, exc_type, exc_val, exc_tb):
#   def _makeTestPipeline(self):
#   def assertMetricsCounterEqual(self, metrics, name, expected_count,
#                                 namespaces_list=None):
#   def assertAnalyzerOutputs(self,
#                             input_data,
#                             input_metadata,
#                             analyzer_fn,
#                             expected_outputs,
#                             test_data=None,
#                             desired_batch_size=None,
#                             beam_pipeline=None,
#                             force_tf_compat_v1=False,
#                             output_record_batches=False):
#     def preprocessing_fn(inputs):
#   def assertAnalyzeAndTransformResults(self,
#                                        input_data,
#                                        input_metadata,
#                                        preprocessing_fn,
#                                        expected_data=None,
#                                        expected_metadata=None,
#                                        expected_vocab_file_contents=None,
#                                        test_data=None,
#                                        desired_batch_size=None,
#                                        beam_pipeline=None,
#                                        temp_dir=None,
#                                        force_tf_compat_v1=False,
#                                        output_record_batches=False):
# class TransformTestCase(test_case.TransformTestCase):
#   class _TestPipeline(beam.Pipeline):


# Path: examples/simple_example_test.py
import tensorflow as tf
import simple_example
from tensorflow_transform.beam import tft_unit

# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for simple_example."""



_EXPECTED_TRANSFORMED_OUTPUT = [
    {
        'x_centered': 1.0,
        'y_normalized': 1.0,
        'x_centered_times_y_normalized': 1.0,
        's_integerized': 0,
    },
    {
        'x_centered': 0.0,
        'y_normalized': 0.5,
        'x_centered_times_y_normalized': 0.0,
        's_integerized': 1,
    },
    {
        'x_centered': -1.0,
        'y_normalized': 0.0,
        'x_centered_times_y_normalized': -0.0,
        's_integerized': 0,
    },
]



class SimpleExampleTest(tft_unit.TransformTestCase):