text: # Copyright 2020 The FastEstimator 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.
  # ==============================================================================
  import unittest
  
  import numpy as np
  import tensorflow as tf
  import torch
  
  from fastestimator.backend.feed_forward import feed_forward
  from fastestimator.op.tensorop.gradient import GradientOp
  from fastestimator.test.unittest_util import OneLayerTorchModel, is_equal, one_layer_tf_model
  
  
  class TestGradientOp(unittest.TestCase):
      @classmethod
      def setUpClass(cls):
          cls.tf_data = tf.Variable([1.0, 2.0, 4.0])
          cls.tf_output = [tf.constant([2, 4, 8])]
          cls.torch_data = torch.tensor([1.0, 2.0, 4.0], requires_grad=True)
          cls.torch_output = [torch.tensor([2, 4, 8], dtype=torch.float32)]
          cls.tf_model = one_layer_tf_model()
          cls.torch_model = OneLayerTorchModel()
  
      def test_tf_input(self):
          def update_gradient(gradient):
              with tf.GradientTape(persistent=True) as tape:
                  x = self.tf_data * self.tf_data
                  output = gradient.forward(data=[self.tf_data, x], state={'tape': tape})
                  self.assertTrue(is_equal(output, self.tf_output))
  
          gradient = GradientOp(inputs='x', finals='x', outputs='y')
          gradient.build("tf")
          strategy = tf.distribute.get_strategy()
          if isinstance(strategy, tf.distribute.MirroredStrategy):
              strategy.run(update_gradient, args=(gradient, ))
          else:
              update_gradient(gradient)
  
      def test_torch_input(self):
          gradient = GradientOp(inputs='x', finals='x', outputs='y')
          gradient.build("torch")
          x = self.torch_data * self.torch_data
          output = gradient.forward(data=[self.torch_data, x], state={'tape': None})
          self.assertTrue(is_equal(output, self.torch_output))
  
      def test_tf_model_input(self):
          def update_gradient(gradient):
              with tf.GradientTape(persistent=True) as tape:
                  pred = feed_forward(model=self.tf_model, x=tf.constant([[1.0, 1.0, 1.0], [1.0, -1.0, -0.5]]))
                  output = gradient.forward(data=[pred], state={"tape": tape})
                  self.assertTrue(is_equal(output[0][0].numpy(), np.array([[2.0], [0.0], [0.5]], dtype="float32")))
  
          gradient = GradientOp(finals="pred", outputs="grad", model=self.tf_model)
          gradient.build("tf")
          strategy = tf.distribute.get_strategy()
          if isinstance(strategy, tf.distribute.MirroredStrategy):
              strategy.run(update_gradient, args=(gradient, ))
          else:
              update_gradient(gradient)
  
      def test_torch_model_input(self):
          gradient = GradientOp(finals="pred", outputs="grad", model=self.torch_model)
          gradient.build("torch")
          with tf.GradientTape(persistent=True) as tape:
              pred = feed_forward(model=self.torch_model, x=torch.tensor([[1.0, 1.0, 1.0], [1.0, -1.0, -0.5]]))
              output = gradient.forward(data=[pred], state={"tape": tape})
          self.assertTrue(is_equal(output[0][0].numpy(), np.array([[2.0, 0.0, 0.5]], dtype="float32")))
  