# Copyright 2016 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.
# ==============================================================================
"""Internal utilities for `LinearOperator` classes."""

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

from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops


def assert_no_entries_with_modulus_zero(
    x, message=None, name="assert_no_entries_with_modulus_zero"):
  """Returns `Op` that asserts Tensor `x` has no entries with modulus zero.

  Args:
    x:  Numeric `Tensor`, real, integer, or complex.
    message:  A string message to prepend to failure message.
    name:  A name to give this `Op`.

  Returns:
    An `Op` that asserts `x` has no entries with modulus zero.
  """
  with ops.name_scope(name, values=[x]):
    x = ops.convert_to_tensor(x, name="x")
    dtype = x.dtype.base_dtype
    should_be_nonzero = math_ops.abs(x)
    zero = ops.convert_to_tensor(0, dtype=dtype.real_dtype)
    return check_ops.assert_less(zero, should_be_nonzero, message=message)


def assert_zero_imag_part(x, message=None, name="assert_zero_imag_part"):
  """Returns `Op` that asserts Tensor `x` has no non-zero imaginary parts.

  Args:
    x:  Numeric `Tensor`, real, integer, or complex.
    message:  A string message to prepend to failure message.
    name:  A name to give this `Op`.

  Returns:
    An `Op` that asserts `x` has no entries with modulus zero.
  """
  with ops.name_scope(name, values=[x]):
    x = ops.convert_to_tensor(x, name="x")
    dtype = x.dtype.base_dtype

    if dtype.is_floating:
      return control_flow_ops.no_op()

    zero = ops.convert_to_tensor(0, dtype=dtype.real_dtype)
    return check_ops.assert_equal(zero, math_ops.imag(x), message=message)


def assert_compatible_matrix_dimensions(operator, x):
  """Assert that an argument to solve/apply has proper domain dimension.

  If `operator.shape[-2:] = [M, N]`, and `x.shape[-2:] = [Q, R]`, then
  `operator.apply(x)` is defined only if `N = Q`.  This `Op` returns an
  `Assert` that "fires" if this is not the case.  Static checks are already
  done by the base class `LinearOperator`.

  Args:
    operator:  `LinearOperator`.
    x:  `Tensor`.

  Returns:
    `Assert` `Op`.
  """
  # Static checks are done in the base class.  Only tensor asserts here.
  assert_same_dd = check_ops.assert_equal(
      array_ops.shape(x)[-2],
      operator.domain_dimension_tensor(),
      message=(
          "Incompatible matrix dimensions.  "
          "shape[-2] of argument to be the same as this operator"))

  return assert_same_dd


def shape_tensor(shape, name=None):
  """Convert Tensor using default type, unless empty list or tuple."""
  # Works just like random_ops._ShapeTensor.
  if isinstance(shape, (tuple, list)) and not shape:
    dtype = dtypes.int32
  else:
    dtype = None
  return ops.convert_to_tensor(shape, dtype=dtype, name=name)
