old_contents: # Copyright 2022 DeepMind Technologies Limited.
  #
  # 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.
  
  """Tests for ferminet.envelopes."""
  
  from absl.testing import absltest
  from absl.testing import parameterized
  from ferminet import envelopes
  import jax.numpy as jnp
  import numpy as np
  
  
  def _shape_options():
    shapes = [[(3, 4, 5), (5, 6, 4, 2)], [(3, 4, 1), (1, 6, 4, 2)],
              [(3, 1, 5), (5, 6, 1, 2)], [(3, 1, 1), (1, 6, 1, 2)]]
    for shape1, shape2 in shapes:
      yield {
          'testcase_name': f'_shape1={shape1}_shape2={shape2}',
          'shapes': (shape1, shape2),
      }
  
  
  class ApplyCovarianceTest(parameterized.TestCase):
  
    @parameterized.named_parameters(_shape_options())
    def test_apply_covariance(self, shapes):
      rng = np.random.RandomState(0).standard_normal
      dtype = np.float32
      x = rng(shapes[0]).astype(dtype)
      y = rng(shapes[1]).astype(dtype)
      np.testing.assert_allclose(
          envelopes._apply_covariance(x, y),
          jnp.einsum('ijk,kmjn->ijmn', x, y))
  
    @parameterized.named_parameters(_shape_options())
    def test_reduced_apply_covariance(self, shapes):
      rng = np.random.RandomState(0).standard_normal
      dtype = np.float32
      x = rng(shapes[0]).astype(dtype)
      y = rng(shapes[1]).astype(dtype)
      np.testing.assert_allclose(
          jnp.squeeze(
              envelopes._apply_covariance(x, jnp.expand_dims(y, -1)), axis=-1),
          jnp.einsum('ijk,klj->ijl', x, y))
  
  
  if __name__ == '__main__':
    absltest.main()
  
new_contents: # Copyright 2022 DeepMind Technologies Limited.
  #
  # 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.
  
  """Tests for ferminet.envelopes."""
  
  from absl.testing import absltest
  from absl.testing import parameterized
  from ferminet import envelopes
  import jax.numpy as jnp
  import numpy as np
  
  
  def _shape_options(dim2=None):
    shapes = [[(3, 4, 5), (5, 6, 4, 2)], [(3, 4, 1), (1, 6, 4, 2)],
              [(3, 1, 5), (5, 6, 1, 2)], [(3, 1, 1), (1, 6, 1, 2)]]
    for shape1, shape2 in shapes:
      if dim2:
        shape2 = shape2[:dim2]
      yield {
          'testcase_name': f'_shape1={shape1}_shape2={shape2}',
          'shapes': (shape1, shape2),
      }
  
  
  class ApplyCovarianceTest(parameterized.TestCase):
  
    @parameterized.named_parameters(_shape_options())
    def test_apply_covariance(self, shapes):
      rng = np.random.RandomState(0).standard_normal
      dtype = np.float32
      x = rng(shapes[0]).astype(dtype)
      y = rng(shapes[1]).astype(dtype)
      np.testing.assert_allclose(
          envelopes._apply_covariance(x, y),
          jnp.einsum('ijk,kmjn->ijmn', x, y))
  
    @parameterized.named_parameters(_shape_options(dim2=3))
    def test_reduced_apply_covariance(self, shapes):
      rng = np.random.RandomState(0).standard_normal
      dtype = np.float32
      x = rng(shapes[0]).astype(dtype)
      y = rng(shapes[1]).astype(dtype)
      np.testing.assert_allclose(
          jnp.squeeze(
              envelopes._apply_covariance(x, jnp.expand_dims(y, -1)), axis=-1),
          jnp.einsum('ijk,klj->ijl', x, y))
  
  
  if __name__ == '__main__':
    absltest.main()
  
subject: Correct bug in shapes used in envelope test introduced in a refactor.