cleanup of deprecated test methods

PiperOrigin-RevId: 721043234

Author: tensorflower-gardener

tensorflow_model_optimization/python/core/internal/tensor_encoding/stages/research/kashin_test.py

@@ -204,49 +204,49 @@ def test_unknown_shape_raises(self):
     x = test_utils.get_tensor_with_random_shape()
     stage = self.default_encoding_stage()
     params, _ = stage.get_params()
-    with self.assertRaisesRegexp(ValueError, 'fully known'):
+    with self.assertRaisesRegex(ValueError, 'fully known'):
       stage.encode(x, params)
 
   @parameterized.parameters([((1, 1, 5),), ((1, 1, 1, 5),)])
   def test_more_than_two_ndims_raises(self, dims):
     x = tf.random.normal(dims)
     stage = self.default_encoding_stage()
     params, _ = stage.get_params()
-    with self.assertRaisesRegexp(ValueError, 'must be 1 or 2.'):
+    with self.assertRaisesRegex(ValueError, 'must be 1 or 2.'):
       stage.encode(x, params)
 
   @parameterized.parameters([0.0, 1.0, -1.0, 2.5])
   def test_eta_out_of_bounds_raises(self, eta):
-    with self.assertRaisesRegexp(ValueError, 'between 0 and 1'):
+    with self.assertRaisesRegex(ValueError, 'between 0 and 1'):
       kashin.KashinHadamardEncodingStage(eta=eta)
 
   @parameterized.parameters([0.0, -1.0])
   def test_delta_small_raises(self, delta):
-    with self.assertRaisesRegexp(ValueError, 'greater than 0'):
+    with self.assertRaisesRegex(ValueError, 'greater than 0'):
       kashin.KashinHadamardEncodingStage(delta=delta)
 
   @parameterized.parameters([0, -1, -10])
   def test_num_iters_small_raises(self, num_iters):
-    with self.assertRaisesRegexp(ValueError, 'positive'):
+    with self.assertRaisesRegex(ValueError, 'positive'):
       kashin.KashinHadamardEncodingStage(num_iters=num_iters)
 
   def test_num_iters_tensor_raises(self):
-    with self.assertRaisesRegexp(ValueError, 'num_iters'):
+    with self.assertRaisesRegex(ValueError, 'num_iters'):
       kashin.KashinHadamardEncodingStage(
           num_iters=tf.constant(2, dtype=tf.int32))
 
   def test_last_iter_clip_tensor_raises(self):
-    with self.assertRaisesRegexp(ValueError, 'last_iter_clip'):
+    with self.assertRaisesRegex(ValueError, 'last_iter_clip'):
       kashin.KashinHadamardEncodingStage(
           last_iter_clip=tf.constant(True, dtype=tf.bool))
 
   @parameterized.parameters([0, 1, 0.0, 1.0])
   def test_last_iter_clip_not_bool_raises(self, last_iter_clip):
-    with self.assertRaisesRegexp(ValueError, 'last_iter_clip must be a bool'):
+    with self.assertRaisesRegex(ValueError, 'last_iter_clip must be a bool'):
       kashin.KashinHadamardEncodingStage(last_iter_clip=last_iter_clip)
 
   def test_pad_extra_level_threshold_tensor_raises(self):
-    with self.assertRaisesRegexp(ValueError, 'pad_extra_level_threshold'):
+    with self.assertRaisesRegex(ValueError, 'pad_extra_level_threshold'):
       kashin.KashinHadamardEncodingStage(
           pad_extra_level_threshold=tf.constant(0.8, dtype=tf.float32))
 

tensorflow_model_optimization/python/core/internal/tensor_encoding/stages/research/misc_test.py

@@ -176,15 +176,15 @@ def test_empty_input_dynamic(self):
   @parameterized.parameters([tf.bool, tf.float32])
   def test_encode_unsupported_type_raises(self, dtype):
     stage = self.default_encoding_stage()
-    with self.assertRaisesRegexp(TypeError, 'Unsupported input type'):
+    with self.assertRaisesRegex(TypeError, 'Unsupported input type'):
       self.run_one_to_many_encode_decode(
           stage, lambda: tf.cast(self.default_input(), dtype))
 
   def test_encode_unsupported_input_shape_raises(self):
     x = tf.random.uniform((3, 4), maxval=10, dtype=tf.int32)
     stage = self.default_encoding_stage()
     params, _ = stage.get_params()
-    with self.assertRaisesRegexp(ValueError, 'Number of dimensions must be 1'):
+    with self.assertRaisesRegex(ValueError, 'Number of dimensions must be 1'):
       stage.encode(x, params)
 
 

tensorflow_model_optimization/python/core/internal/tensor_encoding/stages/research/quantization_test.py

@@ -112,7 +112,7 @@ def test_all_zero_input_works(self):
 
   @parameterized.parameters([0, 17, -1, 1.5])
   def test_bits_out_of_range_raises(self, bits):
-    with self.assertRaisesRegexp(ValueError, 'integer between 1 and 16'):
+    with self.assertRaisesRegex(ValueError, 'integer between 1 and 16'):
       quantization.PRNGUniformQuantizationEncodingStage(bits=bits)
 
   def test_dynamic_input_shape(self):
@@ -226,11 +226,11 @@ def test_all_zero_input_works(self):
 
   @parameterized.parameters([0, 17, -1, 1.5])
   def test_bits_out_of_range_raises(self, bits):
-    with self.assertRaisesRegexp(ValueError, 'integer between 1 and 16'):
+    with self.assertRaisesRegex(ValueError, 'integer between 1 and 16'):
       quantization.PerChannelUniformQuantizationEncodingStage(bits=bits)
 
   def test_stochastic_tensor_raises(self):
-    with self.assertRaisesRegexp(TypeError, 'stochastic'):
+    with self.assertRaisesRegex(TypeError, 'stochastic'):
       quantization.PerChannelUniformQuantizationEncodingStage(
           stochastic=tf.constant(True, dtype=tf.bool))
 
@@ -343,7 +343,7 @@ def test_all_zero_input_works(self):
 
   @parameterized.parameters([0, 17, -1, 1.5])
   def test_bits_out_of_range_raises(self, bits):
-    with self.assertRaisesRegexp(ValueError, 'integer between 1 and 16'):
+    with self.assertRaisesRegex(ValueError, 'integer between 1 and 16'):
       quantization.PerChannelPRNGUniformQuantizationEncodingStage(bits=bits)
 
   def test_dynamic_input_shape(self):

tensorflow_model_optimization/python/core/internal/tensor_encoding/stages/stages_impl_test.py

@@ -192,15 +192,15 @@ def test_unknown_shape_raises(self):
     x = test_utils.get_tensor_with_random_shape()
     stage = self.default_encoding_stage()
     params, _ = stage.get_params()
-    with self.assertRaisesRegexp(ValueError, 'fully known'):
+    with self.assertRaisesRegex(ValueError, 'fully known'):
       stage.encode(x, params)
 
   @parameterized.parameters([((1, 1, 5),), ((1, 1, 1, 5),)])
   def test_more_than_two_ndims_raises(self, dims):
     x = tf.random.normal(dims)
     stage = self.default_encoding_stage()
     params, _ = stage.get_params()
-    with self.assertRaisesRegexp(ValueError, 'must be 1 or 2.'):
+    with self.assertRaisesRegex(ValueError, 'must be 1 or 2.'):
       stage.encode(x, params)
 
 
@@ -336,27 +336,27 @@ def test_commutes_with_sum_given_min_max(self):
 
   @parameterized.parameters([0, 17, -1, 1.5])
   def test_bits_out_of_range_raises(self, bits):
-    with self.assertRaisesRegexp(ValueError, 'integer between 1 and 16'):
+    with self.assertRaisesRegex(ValueError, 'integer between 1 and 16'):
       stages_impl.UniformQuantizationEncodingStage(bits=bits)
 
   @parameterized.parameters([1.0, ([1.0, 2.0, 3.0],)])
   def test_bad_min_max_tensor_raises(self, bad_min_max):
-    with self.assertRaisesRegexp(ValueError, r'shape \(2\)'):
+    with self.assertRaisesRegex(ValueError, r'shape \(2\)'):
       stages_impl.UniformQuantizationEncodingStage(
           min_max=tf.constant(bad_min_max))
 
   @parameterized.parameters([([1.0],), ([1.0, 2.0, 3.0],)])
   def test_bad_min_max_python_shape_raises(self, bad_min_max):
-    with self.assertRaisesRegexp(ValueError, 'list with two elements'):
+    with self.assertRaisesRegex(ValueError, 'list with two elements'):
       stages_impl.UniformQuantizationEncodingStage(min_max=bad_min_max)
 
   @parameterized.parameters([([1.0, 1.0],), ([2.0, 1.0],)])
   def test_bad_min_max_python_values_raises(self, bad_min_max):
-    with self.assertRaisesRegexp(ValueError, 'smaller than the second'):
+    with self.assertRaisesRegex(ValueError, 'smaller than the second'):
       stages_impl.UniformQuantizationEncodingStage(min_max=bad_min_max)
 
   def test_stochastic_tensor_raises(self):
-    with self.assertRaisesRegexp(TypeError, 'stochastic'):
+    with self.assertRaisesRegex(TypeError, 'stochastic'):
       stages_impl.UniformQuantizationEncodingStage(
           stochastic=tf.constant(True, dtype=tf.bool))
 
@@ -441,16 +441,16 @@ def test_bad_input_executes(self):
   @parameterized.parameters([tf.bool, tf.int32])
   def test_encode_unsupported_type_raises(self, dtype):
     stage = self.default_encoding_stage()
-    with self.assertRaisesRegexp(TypeError, 'Unsupported packing type'):
+    with self.assertRaisesRegex(TypeError, 'Unsupported packing type'):
       self.run_one_to_many_encode_decode(
           stage, lambda: tf.cast(self.default_input(), dtype))
 
   def test_bad_input_bits_raises(self):
-    with self.assertRaisesRegexp(TypeError, 'cannot be a TensorFlow value'):
+    with self.assertRaisesRegex(TypeError, 'cannot be a TensorFlow value'):
       stages_impl.BitpackingEncodingStage(tf.constant(1, dtype=tf.int32))
-    with self.assertRaisesRegexp(ValueError, 'between 1 and 16'):
+    with self.assertRaisesRegex(ValueError, 'between 1 and 16'):
       stages_impl.BitpackingEncodingStage(0)
-    with self.assertRaisesRegexp(ValueError, 'between 1 and 16'):
+    with self.assertRaisesRegex(ValueError, 'between 1 and 16'):
       stages_impl.BitpackingEncodingStage(17)
 
 

tensorflow_model_optimization/python/core/internal/tensor_encoding/utils/tf_utils_test.py

@@ -57,16 +57,18 @@ def test_apply_twice_equals_identity(self, first_dim):
   def test_illegal_inputs_shape(self, *dims):
     """Tests incorrect rank of the input."""
     x = tf.random.normal(dims)
-    with self.assertRaisesRegexp(ValueError,
-                                 'Number of dimensions of x must be 2.'):
+    with self.assertRaisesRegex(
+        ValueError, 'Number of dimensions of x must be 2.'
+    ):
       tf_utils.fast_walsh_hadamard_transform(x)
 
   @parameterized.parameters([[1, 3], [1, 7], [1, 9], [4, 3]])
   def test_illegal_inputs_static_power_of_two(self, *dims):
     """Tests incorrect static shape of the rank 2 input."""
     x = tf.random.normal(dims)
-    with self.assertRaisesRegexp(ValueError,
-                                 'The dimension of x must be a power of two.'):
+    with self.assertRaisesRegex(
+        ValueError, 'The dimension of x must be a power of two.'
+    ):
       tf_utils.fast_walsh_hadamard_transform(x)
 
   def test_illegal_inputs_dynamic_power_of_two(self):
@@ -170,20 +172,20 @@ def test_approximately_uniform_distribution(self):
     self.assertAllLessEqual(bucket_counts, 10250)
 
   def test_tensor_num_elements_raises(self):
-    with self.assertRaisesRegexp(TypeError, 'must be a Python integer'):
+    with self.assertRaisesRegex(TypeError, 'must be a Python integer'):
       tf_utils._cmwc_random_sequence(
           tf.constant(10), tf.constant(123, tf.int64))
 
   def test_negative_num_elements_raises(self):
-    with self.assertRaisesRegexp(ValueError, 'must be positive'):
+    with self.assertRaisesRegex(ValueError, 'must be positive'):
       tf_utils._cmwc_random_sequence(-10, tf.constant(123, tf.int64))
 
   def test_python_seed_raises(self):
-    with self.assertRaisesRegexp(TypeError, 'tf.int64 Tensor'):
+    with self.assertRaisesRegex(TypeError, 'tf.int64 Tensor'):
       tf_utils._cmwc_random_sequence(10, 123)
 
   def test_tf_int32_seed_raises(self):
-    with self.assertRaisesRegexp(TypeError, 'tf.int64 Tensor'):
+    with self.assertRaisesRegex(TypeError, 'tf.int64 Tensor'):
       tf_utils._cmwc_random_sequence(10, tf.constant(123, tf.int32))
 
 
@@ -232,9 +234,9 @@ def test_expected_dtype(self, dtype):
 
   @parameterized.parameters([tf.int32, tf.int64])
   def test_type_error_raises(self, dtype):
-    with self.assertRaisesRegexp(
-        TypeError, 'Supported types are tf.float32 and '
-        'tf.float64 values'):
+    with self.assertRaisesRegex(
+        TypeError, 'Supported types are tf.float32 and tf.float64 values'
+    ):
       tf_utils.random_floats_cmwc(10, tf.constant(456, tf.int64), dtype)
 
   def test_differs_given_different_seed(self):
@@ -291,9 +293,9 @@ def test_expected_dtype(self, dtype):
 
   @parameterized.parameters([tf.int32, tf.int64])
   def test_type_error_raises(self, dtype):
-    with self.assertRaisesRegexp(TypeError,
-                                 'Supported types are tf.float32 and '
-                                 'tf.float64 values'):
+    with self.assertRaisesRegex(
+        TypeError, 'Supported types are tf.float32 and tf.float64 values'
+    ):
       tf_utils.random_floats(10, tf.constant([456, 123], tf.int64), dtype)
 
   def test_differs_given_different_seed(self):