tensorflow_probability/python/distributions/blockwise.py
def _maybe_validate_distributions(distributions, dtype_override, validate_args):
  """Checks that `distributions` satisfies all assumptions."""
  assertions = []

  if not _is_iterable(distributions) or not distributions:
    raise ValueError('`distributions` must be a list of one or more '
                     'distributions.')

  if dtype_override is None:
    dts = [
        dtype_util.base_dtype(d.dtype)
        for d in distributions
        if d.dtype is not None
    ]
    if dts[1:] != dts[:-1]:
      raise TypeError('Distributions must have same dtype; found: {}.'.format(
          set(dtype_util.name(dt) for dt in dts)))

  # Validate event_ndims.
  for d in distributions:
    if tensorshape_util.rank(d.event_shape) is not None:
      if tensorshape_util.rank(d.event_shape) != 1:
        raise ValueError('`Distribution` must be vector variate, '
                         'found event nimds: {}.'.format(
                             tensorshape_util.rank(d.event_shape)))
    elif validate_args:
      assertions.append(
          assert_util.assert_equal(
              1, tf.size(input=d.event_shape_tensor()),
              message='`Distribution` must be vector variate.'))

  batch_shapes = [d.batch_shape for d in distributions]
  if all(tensorshape_util.is_fully_defined(b) for b in batch_shapes):
    if batch_shapes[1:] != batch_shapes[:-1]:
      raise ValueError('Distributions must have the same `batch_shape`; '
                       'found: {}.'.format(batch_shapes))
  elif validate_args:
    batch_shapes = [
        tensorshape_util.as_list(d.batch_shape)  # pylint: disable=g-complex-comprehension
        if tensorshape_util.is_fully_defined(d.batch_shape) else
        d.batch_shape_tensor() for d in distributions
    ]
    assertions.extend(
        assert_util.assert_equal(  # pylint: disable=g-complex-comprehension
            b1, b2,
            message='Distribution `batch_shape`s must be identical.')
        for b1, b2 in zip(batch_shapes[1:], batch_shapes[:-1]))

  return assertions
def _maybe_validate_distributions(distributions, dtype_override, validate_args):
  """Checks that `distributions` satisfies all assumptions."""
  assertions = []

  if not _is_iterable(distributions) or not distributions:
    raise ValueError('`distributions` must be a list of one or more '
                     'distributions.')

  if dtype_override is None:
    dts = [
        dtype_util.base_dtype(d.dtype)
        for d in distributions
        if d.dtype is not None
    ]
    if dts[1:] != dts[:-1]:
      raise TypeError('Distributions must have same dtype; found: {}.'.format(
          set(dtype_util.name(dt) for dt in dts)))

  # Validate event_ndims.
  for d in distributions:
    if tensorshape_util.rank(d.event_shape) is not None:
      if tensorshape_util.rank(d.event_shape) != 1:
        raise ValueError('`Distribution` must be vector variate, '
                         'found event nimds: {}.'.format(
                             tensorshape_util.rank(d.event_shape)))
    elif validate_args:
      assertions.append(
          assert_util.assert_equal(
              1, tf.size(input=d.event_shape_tensor()),
              message='`Distribution` must be vector variate.'))

  batch_shapes = [d.batch_shape for d in distributions]
  if all(tensorshape_util.is_fully_defined(b) for b in batch_shapes):
    if batch_shapes[1:] != batch_shapes[:-1]:
      raise ValueError('Distributions must have the same `batch_shape`; '
                       'found: {}.'.format(batch_shapes))
  elif validate_args:
    batch_shapes = [
        tensorshape_util.as_list(d.batch_shape)  # pylint: disable=g-complex-comprehension
        if tensorshape_util.is_fully_defined(d.batch_shape) else
        d.batch_shape_tensor() for d in distributions
    ]
    assertions.extend(
        assert_util.assert_equal(  # pylint: disable=g-complex-comprehension
            b1, b2,
            message='Distribution `batch_shape`s must be identical.')
        for b1, b2 in zip(batch_shapes[1:], batch_shapes[:-1]))

  return assertions
Checks that `distributions` satisfies all assumptions.