text: # Copyright 2020 Huawei Technologies Co., Ltd
  #
  # 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.
  # ============================================================================
  """test tensor py"""
  import numpy as np
  from mindspore.nn import Cell
  from mindspore.ops import operations as P
  from mindspore.common.api import _executor
  import mindspore as ms
  from ..ut_filter import non_graph_engine
  
  
  def _attribute(tensor, shape_, size_, dtype_):
      result = (tensor.shape == shape_) and \
               (tensor.size() == size_) and \
               (tensor.dtype == dtype_)
      return result
  
  
  def test_tensor_init():
      nparray = np.ones([2, 2], np.float32)
      ms.Tensor(nparray)
  
      ms.Tensor(nparray, dtype=ms.float32)
  
  
  @non_graph_engine
  def test_tensor_add():
      a = ms.Tensor(np.ones([3, 3], np.float32))
      b = ms.Tensor(np.ones([3, 3], np.float32))
      a += b
  
  
  @non_graph_engine
  def test_tensor_sub():
      a = ms.Tensor(np.ones([2, 3]))
      b = ms.Tensor(np.ones([2, 3]))
      b -= a
  
  
  @non_graph_engine
  def test_tensor_mul():
      a = ms.Tensor(np.ones([3, 3]))
      b = ms.Tensor(np.ones([3, 3]))
      a *= b
  
  
  def test_tensor_dim():
      arr = np.ones((1, 6))
      b = ms.Tensor(arr)
      assert b.dim() == 2
  
  
  def test_tensor_size():
      arr = np.ones((1, 6))
      b = ms.Tensor(arr)
      assert arr.size == b.size()
  
  
  def test_dtype():
      a = ms.Tensor(np.ones((2, 3), dtype=np.int32))
      assert a.dtype() == ms.int32
  
  
  def test_asnumpy():
      npd = np.ones((2, 3))
      a = ms.Tensor(npd)
      a.set_dtype(ms.int32)
      assert a.asnumpy().all() == npd.all()
  
  
  def test_print():
      a = ms.Tensor(np.ones((2, 3)))
      a.set_dtype(ms.int32)
      print(a)
  
  
  def test_float():
      a = ms.Tensor(np.ones((2, 3)), ms.float16)
      assert a.dtype() == ms.float16
  
  
  def test_tensor_method_sub():
      """test_tensor_method_sub"""
      class Net(Cell):
          def __init__(self):
              super(Net, self).__init__()
              self.sub = P.Sub()
  
          def construct(self, x, y):
              out = x - y
              return out.transpose()
  
      net = Net()
  
      x = ms.Tensor(np.ones([5, 3], np.float32))
      y = ms.Tensor(np.ones([8, 5, 3], np.float32))
      _executor.compile(net, x, y)
  
  
  def test_tensor_method_mul():
      """test_tensor_method_mul"""
      class Net(Cell):
          def __init__(self):
              super(Net, self).__init__()
              self.sub = P.Sub()
  
          def construct(self, x, y):
              out = x * (-y)
              return out.transpose()
  
      net = Net()
  
      x = ms.Tensor(np.ones([5, 3], np.float32))
      y = ms.Tensor(np.ones([8, 5, 3], np.float32))
      _executor.compile(net, x, y)
  
  
  def test_tensor_method_div():
      """test_tensor_method_div"""
      class Net(Cell):
          def __init__(self):
              super(Net, self).__init__()
              self.sub = P.Sub()
  
          def construct(self, x, y):
              out = x / y
              return out.transpose()
  
      net = Net()
  
      x = ms.Tensor(np.ones([5, 3], np.float32))
      y = ms.Tensor(np.ones([8, 5, 3], np.float32))
      _executor.compile(net, x, y)
  