text: # Copyright 2018 The TensorFlow Probability Authors.
  #
  # 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
  #
  #     http://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.
  # ============================================================================
  """Tests for the TF implementations of auto-batched VM variables."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  # Dependency imports
  
  import hypothesis as hp
  from hypothesis import strategies as hps
  from hypothesis.extra import numpy as hpnp
  
  import numpy as np
  import tensorflow.compat.v2 as tf
  
  from tensorflow_probability.python.experimental.auto_batching import backend_test_lib as backend_test
  from tensorflow_probability.python.experimental.auto_batching import instructions as inst
  from tensorflow_probability.python.experimental.auto_batching import tf_backend
  from tensorflow_probability.python.internal import hypothesis_testlib as tfp_hps
  from tensorflow_probability.python.internal import test_util
  
  # TODO(b/127689162): Restore testing complex dtypes.
  
  # TF_NP_DTYPES = [np.float32, np.float64, np.int32, np.complex64, np.bool_]
  TF_NP_DTYPES = [np.float32, np.float64, np.int32, np.bool_]
  TF_BACKEND = tf_backend.TensorFlowBackend()
  
  
  def var_init(max_stack_depth, initial_value):
    type_ = inst.TensorType(initial_value.dtype, initial_value.shape[1:])
    var = TF_BACKEND.create_variable(
        None, inst.VariableAllocation.FULL, type_,
        max_stack_depth, batch_size=initial_value.shape[0])
    return var.update(
        initial_value, TF_BACKEND.full_mask(initial_value.shape[0]))
  
  
  @test_util.test_all_tf_execution_regimes
  class TFVariableTest(test_util.TestCase, backend_test.VariableTestCase):
  
    def testTFSmoke(self):
      """Test the property on specific example, without relying on Hypothesis."""
      init = (12, np.random.randn(3, 2, 2).astype(np.float32))
      ops = [('pop', [False, False, True]),
             ('push', [True, False, True]),
             ('update', np.ones((3, 2, 2), dtype=np.float32),
              [True, True, False]),
             ('pop', [True, False, True])]
      self.check_same_results(
          init, ops, var_init, to_numpy_arrays=self.evaluate,
          exception_types=(ValueError, tf.errors.InvalidArgumentError))
  
    @hp.given(hps.data())
    @tfp_hps.tfp_hp_settings()
    def testTFVariableRandomOps(self, data):
      # Hypothesis strategy:
      # Generate a random max stack depth and value shape
      # Deduce the batch size from the value shape
      # Make a random dtype
      # Generate a random initial value of that dtype and shape
      # Generate ops, some of which write random values of that dtype and shape
      max_stack_depth = data.draw(hps.integers(min_value=1, max_value=100))
      value_shape = data.draw(hpnp.array_shapes(min_dims=1))
      batch_size = value_shape[0]
      dtype = data.draw(hps.one_of(*map(hps.just, TF_NP_DTYPES)))
      masks = hpnp.arrays(dtype=np.bool_, shape=[batch_size])
      values = hpnp.arrays(dtype, value_shape)
      init_val = data.draw(values)
      ops = data.draw(
          hps.lists(
              hps.one_of(
                  hps.tuples(hps.just('update'), values, masks),
                  hps.tuples(hps.just('push'), masks),
                  hps.tuples(hps.just('pop'), masks),  # preserve line break
                  hps.tuples(hps.just('read')))))
      init = (max_stack_depth, init_val)
      self.check_same_results(
          init, ops, var_init, to_numpy_arrays=self.evaluate,
          exception_types=(ValueError, tf.errors.InvalidArgumentError))
  
    def testClosingOverTensorDoesntRaise(self):
      x = tf.constant(0.)
      def f(y):
        return y * x
      arg_types = [inst.Type([inst.TensorType(shape=[], dtype=np.float32)])]
      TF_BACKEND.run_on_dummies(f, arg_types)
  
    def testDtypeMergingBoolsDoesntRaise(self):
      TF_BACKEND.merge_dtypes(np.bool_, np.bool_)
  
  
  if __name__ == '__main__':
    test_util.main()
  