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#
# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Utils used to manipulate tensor shapes."""

import tensorflow as tf, tf_keras


def assert_shape_equal(shape_a, shape_b):
  """Asserts that shape_a and shape_b are equal.

  If the shapes are static, raises a ValueError when the shapes
  mismatch.

  If the shapes are dynamic, raises a tf InvalidArgumentError when the shapes
  mismatch.

  Args:
    shape_a: a list containing shape of the first tensor.
    shape_b: a list containing shape of the second tensor.

  Returns:
    Either a tf.no_op() when shapes are all static and a tf.assert_equal() op
    when the shapes are dynamic.

  Raises:
    ValueError: When shapes are both static and unequal.
  """
  if (all(isinstance(dim, int) for dim in shape_a) and
      all(isinstance(dim, int) for dim in shape_b)):
    if shape_a != shape_b:
      raise ValueError('Unequal shapes {}, {}'.format(shape_a, shape_b))
    else:
      return tf.no_op()
  else:
    return tf.assert_equal(shape_a, shape_b)


def combined_static_and_dynamic_shape(tensor):
  """Returns a list containing static and dynamic values for the dimensions.

  Returns a list of static and dynamic values for shape dimensions. This is
  useful to preserve static shapes when available in reshape operation.

  Args:
    tensor: A tensor of any type.

  Returns:
    A list of size tensor.shape.ndims containing integers or a scalar tensor.
  """
  static_tensor_shape = tensor.shape.as_list()
  dynamic_tensor_shape = tf.shape(input=tensor)
  combined_shape = []
  for index, dim in enumerate(static_tensor_shape):
    if dim is not None:
      combined_shape.append(dim)
    else:
      combined_shape.append(dynamic_tensor_shape[index])
  return combined_shape


def pad_or_clip_nd(tensor, output_shape):
  """Pad or Clip given tensor to the output shape.

  Args:
    tensor: Input tensor to pad or clip.
    output_shape: A list of integers / scalar tensors (or None for dynamic dim)
      representing the size to pad or clip each dimension of the input tensor.

  Returns:
    Input tensor padded and clipped to the output shape.
  """
  tensor_shape = tf.shape(input=tensor)
  clip_size = [
      tf.where(tensor_shape[i] - shape > 0, shape, -1)
      if shape is not None else -1 for i, shape in enumerate(output_shape)
  ]
  clipped_tensor = tf.slice(
      tensor, begin=tf.zeros(len(clip_size), dtype=tf.int32), size=clip_size)

  # Pad tensor if the shape of clipped tensor is smaller than the expected
  # shape.
  clipped_tensor_shape = tf.shape(input=clipped_tensor)
  trailing_paddings = [
      shape - clipped_tensor_shape[i] if shape is not None else 0
      for i, shape in enumerate(output_shape)
  ]
  paddings = tf.stack(
      [tf.zeros(len(trailing_paddings), dtype=tf.int32), trailing_paddings],
      axis=1)
  padded_tensor = tf.pad(tensor=clipped_tensor, paddings=paddings)
  output_static_shape = [
      dim if not isinstance(dim, tf.Tensor) else None for dim in output_shape
  ]
  padded_tensor.set_shape(output_static_shape)
  return padded_tensor