# Copyright 2015 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.
# ==============================================================================
"""Tests for array_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import time

import numpy as np

from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import errors_impl
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import test_ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test as test_lib


class BatchMatrixTransposeTest(test_util.TensorFlowTestCase):

  def testNonBatchMatrix(self):
    matrix = [[1, 2, 3], [4, 5, 6]]  # Shape (2, 3)
    expected_transposed = [[1, 4], [2, 5], [3, 6]]  # Shape (3, 2)
    with self.test_session():
      transposed = array_ops.matrix_transpose(matrix)
      self.assertEqual((3, 2), transposed.get_shape())
      self.assertAllEqual(expected_transposed, transposed.eval())

  def testBatchMatrix(self):
    matrix_0 = [[1, 2, 3], [4, 5, 6]]
    matrix_0_t = [[1, 4], [2, 5], [3, 6]]
    matrix_1 = [[11, 22, 33], [44, 55, 66]]
    matrix_1_t = [[11, 44], [22, 55], [33, 66]]
    batch_matrix = [matrix_0, matrix_1]  # Shape (2, 2, 3)
    expected_transposed = [matrix_0_t, matrix_1_t]  # Shape (2, 3, 2)
    with self.test_session():
      transposed = array_ops.matrix_transpose(batch_matrix)
      self.assertEqual((2, 3, 2), transposed.get_shape())
      self.assertAllEqual(expected_transposed, transposed.eval())

  def testNonBatchMatrixDynamicallyDefined(self):
    matrix = [[1, 2, 3], [4, 5, 6]]  # Shape (2, 3)
    expected_transposed = [[1, 4], [2, 5], [3, 6]]  # Shape (3, 2)
    with self.test_session():
      matrix_ph = array_ops.placeholder(dtypes.int32)
      transposed = array_ops.matrix_transpose(matrix_ph)
      self.assertAllEqual(
          expected_transposed, transposed.eval(feed_dict={matrix_ph: matrix}))

  def testBatchMatrixDynamicallyDefined(self):
    matrix_0 = [[1, 2, 3], [4, 5, 6]]
    matrix_0_t = [[1, 4], [2, 5], [3, 6]]
    matrix_1 = [[11, 22, 33], [44, 55, 66]]
    matrix_1_t = [[11, 44], [22, 55], [33, 66]]
    batch_matrix = [matrix_0, matrix_1]  # Shape (2, 2, 3)
    expected_transposed = [matrix_0_t, matrix_1_t]  # Shape (2, 3, 2)
    with self.test_session():
      batch_matrix_ph = array_ops.placeholder(dtypes.int32)
      transposed = array_ops.matrix_transpose(batch_matrix_ph)
      self.assertAllEqual(
          expected_transposed,
          transposed.eval(feed_dict={batch_matrix_ph: batch_matrix}))

  def testTensorWithStaticRankLessThanTwoRaisesBecauseNotAMatrix(self):
    vector = [1, 2, 3]
    with self.test_session():
      with self.assertRaisesRegexp(ValueError, "should be a "):
        array_ops.matrix_transpose(vector)


class BooleanMaskTest(test_util.TensorFlowTestCase):

  def setUp(self):
    self.rng = np.random.RandomState(42)

  def CheckVersusNumpy(self, ndims_mask, arr_shape, ma
ke_mask=None):
    """Check equivalence between boolean_mask and numpy masking."""
    if make_mask is None:
      make_mask = lambda shape: self.rng.randint(0, 2, size=shape).astype(bool)
    arr = np.random.rand(*arr_shape)
    mask = make_mask(arr_shape[:ndims_mask])
    masked_arr = arr[mask]
    with self.test_session():
      masked_tensor = array_ops.boolean_mask(arr, mask)

      # Le
ading dimension size of masked_tensor is always unknown until runtime
      # since we don't how many elements will be kept.
      self.assertAllEqual(masked_tensor.get_shape()[1:], masked_arr.shape[1:])

      self.assertAllClose(masked_arr, masked_tensor.eval())

  def testMaskDim1ArrDim1(self):
    ndims_mask = 1
    for arr_shape in [(1,), (2,), (3,), (10,)]:
      self.CheckVersusNumpy(ndims_mask, arr_shape)

  def testMaskDim1ArrDim2(self):
    ndims_mask = 1
    for arr_shape in [(1, 1), (2, 2), (2, 5)]:
      self.CheckVersusNumpy(ndims_mask, arr_shape)

  def testMaskDim2ArrDim2(self):
    ndims_mask = 2
    for arr_shape in [(1, 1), (2, 2), (2, 5)]:
      self.CheckVersusNumpy(ndims_mask, arr_shape)

  def testMaskDim2ArrDim3(self):
    ndims_mask = 2
    for arr_shape in [(1, 1, 1), (1, 2, 2), (2, 2, 1)]:
      self.CheckVersusNumpy(ndims_mask, arr_shape)

  def testEmptyInput2D(self):
    mask = np.array([True, False])
    arr = np.array([[], []]).astype(np.float32)
    numpy_result = arr[mask]
    tf_result = array_ops.boolean_mask(arr, mask)
    self.assertAllEqual(numpy_result.shape[1:], tf_result.get_shape()[1:])
    with self.test_session():
      self.assertAllClose(numpy_result, tf_result.eval())

  def testEmptyInput1D(self):
    mask = np.array([]).astype(bool)
    arr = np.array([]).astype(np.float32)
    numpy_result = arr[mask]
    tf_result = array_ops.boolean_mask(arr, mask)
    self.assertAllEqual(numpy_result.shape[1:], tf_result.get_shape()[1:])
    with self.test_session():
      self.assertAllClose(numpy_result, tf_result.eval())

  def testEmptyOutput(self):
    make_mask = lambda shape: np.zeros(shape, dtype=bool)
    for ndims_mask in range(1, 4):
      for ndims_arr in range(ndims_mask, ndims_mask + 3):
        for _ in range(3):
          arr_shape = np.random.randint(1, 5, size=ndims_arr)
          self.CheckVersusNumpy(ndims_mask, arr_shape, make_mask=make_mask)

  def testWorksWithDimensionsEqualToNoneDuringGraphBuild(self):
    # The rank of the mask tensor must be specified. This is explained
    # in the docstring as well.
    with self.test_session() as sess:
      ph_tensor = array_ops.placeholder(dtypes.int32, shape=None)
      ph_mask = array_ops.placeholder(dtypes.bool, shape=[None])

      arr = np.array([[1, 2], [3, 4]])
      mask = np.array([False, True])

      masked_tensor = sess.run(array_ops.boolean_mask(ph_tensor, ph_mask),
                               feed_dict={ph_tensor: arr,
                                          ph_mask: mask})
      np.testing.assert_allclose(masked_tensor, arr[mask])

  def testMaskDimensionsSetToNoneRaises(self):
    # The rank of the mask tensor must be specified. This is explained
    # in the docstring as well.
    with self.test_session():
      tensor = array_ops.placeholder(dtypes.int32, shape=[None, 2])
      mask = array_ops.placeholder(dtypes.bool, shape=None)
      with self.assertRaisesRegexp(ValueError, "dimensions must be specified"):
        array_ops.boolean_mask(tensor, mask)

  def testMaskHasMoreDimsThanTensorRaises(self):
    mask = [[True, True], [False, False]]
    tensor = [1, 2, 3, 4]
    with self.test_session():
      with self.assertRaisesRegexp(ValueError, "incompatible"):
        array_ops.boolean_mask(tensor, mask).eval()

  def testMaskIsScalarRaises(self):
    mask = True
    tensor = 1
    with self.test_session():
      with self.assertRaisesRegexp(ValueError, "mask.*scalar"):
        array_ops.boolean_mask(tensor, mask).eval()

  def testMaskShapeDifferentThanFirstPartOfTensorShapeRaises(self):
    mask = [True, True, True]
    tensor = [[1, 2], [3, 4]]
    with self.test_session():
      with self.assertRaisesRegexp(ValueError, "incompatible"):
        array_ops.boolean_mask(tensor, mask).eval()


class OperatorShapeTest(test_util.Ten