old_contents: # Lint as: python3
  # Copyright 2019 Google LLC
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      https://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # 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.
  
  # This test is the same as keras_lstm_test, but all shapes are static.
  # This stresses the TensorList lowering more specifically.
  
  import numpy as np
  from pyiree.tf.support import tf_test_utils
  import tensorflow.compat.v2 as tf
  
  NUM_UNITS = 10
  NUM_TIMESTEPS = 24
  NUM_BATCH = 7
  INPUT_SHAPE = [NUM_BATCH, NUM_TIMESTEPS, NUM_UNITS]
  
  
  def lstm_module():
    tf_test_utils.set_random_seed()
    inputs = tf.keras.layers.Input(batch_size=NUM_BATCH, shape=INPUT_SHAPE[1:])
    outputs = tf.keras.layers.LSTM(units=NUM_UNITS, return_sequences=True)(inputs)
    model = tf.keras.Model(inputs, outputs)
    module = tf.Module()
    module.m = model
    module.predict = tf.function(
        input_signature=[tf.TensorSpec(INPUT_SHAPE, tf.float32)])(
            model.call)
    return module
  
  
  # TODO(silvasean): Get this test working on other backends.
  @tf_test_utils.compile_modules(
      backends=["tf", "iree_vmla"], lstm=(lstm_module, ["predict"]))
  class LstmTest(tf_test_utils.SavedModelTestCase):
  
    def test_lstm(self):
      m = self.modules.lstm.all
      m.predict(
          tf.constant(
              np.arange(NUM_BATCH * NUM_TIMESTEPS * NUM_UNITS,
                        dtype=np.float32).reshape(
                            [NUM_BATCH, NUM_TIMESTEPS, NUM_UNITS]),
              shape=[NUM_BATCH, NUM_TIMESTEPS,
                     NUM_UNITS])).print().assert_all_close()
  
  
  if __name__ == "__main__":
    if hasattr(tf, "enable_v2_behavior"):
      tf.enable_v2_behavior()
    tf.test.main()
  
new_contents: # Lint as: python3
  # Copyright 2019 Google LLC
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      https://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # 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.
  
  # This test is the same as keras_lstm_test, but all shapes are static.
  # This stresses the TensorList lowering more specifically.
  
  import numpy as np
  from pyiree.tf.support import tf_test_utils
  import tensorflow.compat.v2 as tf
  
  NUM_UNITS = 10
  NUM_TIMESTEPS = 24
  NUM_BATCH = 7
  INPUT_SHAPE = [NUM_BATCH, NUM_TIMESTEPS, NUM_UNITS]
  
  
  def lstm_module():
    tf_test_utils.set_random_seed()
    inputs = tf.keras.layers.Input(batch_size=NUM_BATCH, shape=INPUT_SHAPE[1:])
    outputs = tf.keras.layers.LSTM(units=NUM_UNITS, return_sequences=True)(inputs)
    model = tf.keras.Model(inputs, outputs)
    module = tf.Module()
    module.m = model
    module.predict = tf.function(
        input_signature=[tf.TensorSpec(INPUT_SHAPE, tf.float32)])(
            model.call)
    return module
  
  
  # TODO(silvasean): Get this test working on other backends.
  @tf_test_utils.compile_modules(
      backends=["tf", "iree_vmla"], lstm=(lstm_module, ["predict"]))
  class LstmTest(tf_test_utils.SavedModelTestCase):
  
    def test_lstm(self):
      m = self.modules.lstm.all
      m.predict(
          tf.constant(
              np.arange(NUM_BATCH * NUM_TIMESTEPS * NUM_UNITS,
                        dtype=np.float32).reshape(
                            [NUM_BATCH, NUM_TIMESTEPS, NUM_UNITS]),
              shape=[NUM_BATCH, NUM_TIMESTEPS,
                     NUM_UNITS])).print().assert_all_close(1e-5, 1e-5)
  
  
  if __name__ == "__main__":
    if hasattr(tf, "enable_v2_behavior"):
      tf.enable_v2_behavior()
    tf.test.main()
  
subject: Decrease test precision for keras_lstm_static_test.