# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# 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
#
#     http://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.
# ======================================
"""XLA Shape utilities."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as _np  # Avoids becoming a part of public Tensorflow API.

from six.moves import xrange

from tensorflow.compiler.xla import xla_data_pb2
from tensorflow.compiler.xla.python_api import types


class Shape(object):
  """Wraps a xla_data_pb2.ShapeProto message with a convenient Python type.

  Provides direct access to the underlying xla_data_pb2.ShapeProto message in
  the
  message attribute, along with accessor wrappers to the message's fields.
  Avoid direct access to .message unless interacting directly with protobuf APIs
  like CopyFrom. In other words, prefer hauling the shape around in a Shape, and
  only access .message when strictly required by the protobuf API.
  """

  def __init__(self, element_type, dimensions, layout=None):
    """Creates a new XLA Shape.

    Args:
      element_type: element type from xla_data_pb2.
      dimensions: sequence of dimensions sizes (integers), or sequence
        of Shapes in the case of a tuple, i.e. when element_type is
        TUPLE.
      layout: optional minor_to_major sequence for layout. If not given, the
        default major-to-minor layout is used.

    Raises:
      ValueError: if element_type is TUPLE but dimensions are not Shape objects.
    """
    self.message = xla_data_pb2.ShapeProto()
    self.message.element_type = element_type
    if element_type == xla_data_pb2.TUPLE:
      if not all(isinstance(subshape, Shape) for subshape in dimensions):
        raise ValueError(
            'XLA tuple requires sequence of Shape objects as dimensions')
      self._tuple_shapes = tuple(dimensions)
      for component_shape in self._tuple_shapes:
        component_message = self.message.tuple_shapes.add()
        component_message.CopyFrom(component_shape.message)
    else:
      self.message.dimensions.extend(dimensions)
      if layout is None:
        layout = list(reversed(range(len(dimensions))))
      self.message.layout.format = xla_data_pb2.DENSE
      self.message.layout.minor_to_major.extend(layout)

  def element_type(self):
    return self.message.element_type

  def is_tuple(self):
    return self.element_type() == xla_data_pb2.TUPLE

  def dimensions(self):
    if self.is_tuple():
      raise ValueError('Tuple shape has no dimensions. Try tuple_shapes()?')
    return self.message.dimensions

  def tuple_shapes(self):
    """If this is a tuple, returns its sequence of constituent Shape objects.

    Returns:
      Tuple sub-shapes.

    Raises:
      ValueError: if this is not a tuple.
    """
    if not self.is_tuple():
      raise ValueError('tuple_shapes() called on a non-tuple shape')
    return self._tuple_shapes

  def layout(self):
    return self.message.layout

  @staticmethod
  def from_pyval(pyval):
    return CreateShapeFromNumpy(pyval)


def _CreateShapeFromNumpy(ndarray):  # pylint: disable=invalid-name
  """Create a Shape from a given Numpy array.

  Args:
    ndarray: Numpy array.

  Returns:
    A Shape object.
  """
  element_type = types.MAP_DTYPE_TO_RECORD[str(ndarray.dtype)].primitive_type
  dimensions = ndarray.shape

  # Set the shape's layout based on the ordering of ndarray.
  # Numpy arrays come in two orders: Fortran (column-major) and C (row-major).
  if _np.isfortran(ndarray):
    # Column-major layout. This corresponds to a "dimension order is
    # minor-to-major" layout in XLA.
    layout = range(ndarray.ndim)
  else:
    # Row-major layout. This corresponds to a "dimension order is
    # major-to-minor" layout int XLA.
    layout = list(reversed(xrange(ndarray.ndim)))

  return Shape(element_type, dimensions, layout)


def CreateShapeFromNumpy(value):  # pylint: disable=invalid-name
  """Create a Shape from a Numpy array or a nested tuple structure thereof.

  Args:
    value: Numpy array or (possibly nested) tuple structure that bottoms out in
      Numpy arrays.

  Returns:
    A Shape object.
  """
  if isinstance(value, tuple):
    return Shape(
        xla_data_pb2.TUPLE,
        [CreateShapeFromNumpy(component) for component in value])
  else:
    return _CreateShapeFromNumpy(value)


def CreateShapeFromDtypeAndTuple(dtype, shape_tuple):  # pylint: disable=invalid-name
  """Create a shape from a Numpy dtype and a sequence of nonnegative integers.

  Args:
    dtype: a numpy dtype, e.g. np.dtype('int32').
    shape_tuple: a sequence of nonnegative integers.

  Returns:
    A Shape object.
  """
  element_type = types.MAP_DTYPE_TO_RECORD[str(dtype)].primitive_type
  return Shape(element_type, shape_tuple)
