tensorflow_probability/python/bijectors/reshape.py
def _maybe_check_valid_shape(shape, validate_args):
  """Check that a shape Tensor is int-type and otherwise sane."""
  if not dtype_util.is_integer(shape.dtype):
    raise TypeError('{} dtype ({}) should be `int`-like.'.format(
        shape, dtype_util.name(shape.dtype)))

  assertions = []

  message = '`{}` rank should be <= 1.'
  if tensorshape_util.rank(shape.shape) is not None:
    if tensorshape_util.rank(shape.shape) > 1:
      raise ValueError(message.format(shape))
  elif validate_args:
    assertions.append(assert_util.assert_less(
        tf.rank(shape), 2, message=message.format(shape)))

  shape_ = tf.get_static_value(shape)

  message = '`{}` elements must have at most one `-1`.'
  if shape_ is not None:
    if sum(shape_ == -1) > 1:
      raise ValueError(message.format(shape))
  elif validate_args:
    assertions.append(assert_util.assert_less(
        tf.reduce_sum(input_tensor=tf.cast(tf.equal(shape, -1), tf.int32)),
        2,
        message=message.format(shape)))

  message = '`{}` elements must be either positive integers or `-1`.'
  if shape_ is not None:
    if np.any(shape_ < -1):
      raise ValueError(message.format(shape))
  elif validate_args:
    assertions.append(assert_util.assert_greater(
        shape, -2, message=message.format(shape)))

  return assertions
def _maybe_check_valid_shape(shape, validate_args):
  if not dtype_util.is_integer(shape.dtype):
    raise TypeError('{} dtype ({}) should be `int`-like.'.format(
        shape, dtype_util.name(shape.dtype)))

  assertions = []

  message = '`{}` rank should be <= 1.'
  if tensorshape_util.rank(shape.shape) is not None:
    if tensorshape_util.rank(shape.shape) > 1:
      raise ValueError(message.format(shape))
  elif validate_args:
    assertions.append(assert_util.assert_less(
        tf.rank(shape), 2, message=message.format(shape)))

  shape_ = tf.get_static_value(shape)

  message = '`{}` elements must have at most one `-1`.'
  if shape_ is not None:
    if sum(shape_ == -1) > 1:
      raise ValueError(message.format(shape))
  elif validate_args:
    assertions.append(assert_util.assert_less(
        tf.reduce_sum(input_tensor=tf.cast(tf.equal(shape, -1), tf.int32)),
        2,
        message=message.format(shape)))

  message = '`{}` elements must be either positive integers or `-1`.'
  if shape_ is not None:
    if np.any(shape_ < -1):
      raise ValueError(message.format(shape))
  elif validate_args:
    assertions.append(assert_util.assert_greater(
        shape, -2, message=message.format(shape)))

  return assertions
def _maybe_check_valid_shape ( shape , validate_args ) : if not dtype_util . is_integer ( shape . dtype ) : raise TypeError ( '{} dtype ({}) should be `int`-like.' . format ( shape , dtype_util . name ( shape . dtype ) ) ) assertions = [ ] message = '`{}` rank should be <= 1.' if tensorshape_util . rank ( shape . shape ) is not None : if tensorshape_util . rank ( shape . shape ) > 1 : raise ValueError ( message . format ( shape ) ) elif validate_args : assertions . append ( assert_util . assert_less ( tf . rank ( shape ) , 2 , message = message . format ( shape ) ) ) shape_ = tf . get_static_value ( shape ) message = '`{}` elements must have at most one `-1`.' if shape_ is not None : if sum ( shape_ == - 1 ) > 1 : raise ValueError ( message . format ( shape ) ) elif validate_args : assertions . append ( assert_util . assert_less ( tf . reduce_sum ( input_tensor = tf . cast ( tf . equal ( shape , - 1 ) , tf . int32 ) ) , 2 , message = message . format ( shape ) ) ) message = '`{}` elements must be either positive integers or `-1`.' if shape_ is not None : if np . any ( shape_ < - 1 ) : raise ValueError ( message . format ( shape ) ) elif validate_args : assertions . append ( assert_util . assert_greater ( shape , - 2 , message = message . format ( shape ) ) ) return assertions
Check that a shape Tensor is int-type and otherwise sane.
Check that a shape Tensor is int - type and otherwise sane .