text: #   Copyright (c) 2018 PaddlePaddle 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
  from op_test import OpTest
  
  
  def crop(data, offsets, crop_shape):
      def indexOf(shape, index):
          result = []
          for dim in reversed(shape):
              result.append(index % dim)
              index = index / dim
          return result[::-1]
  
      result = []
      for i, value in enumerate(data.flatten()):
          index = indexOf(data.shape, i)
          selected = True
          if len(index) == len(offsets):
              for j, offset in enumerate(offsets):
                  selected = selected and index[j] >= offset and index[
                      j] < crop_shape[j] + offset
              if selected:
                  result.append(value)
      return np.array(result).reshape(crop_shape)
  
  
  class TestCropOp(OpTest):
      def setUp(self):
          self.op_type = "crop"
          self.crop_by_input = False
          self.offset_by_input = False
          self.attrs = {}
          self.initTestCase()
          if self.crop_by_input:
              self.inputs = {
                  'X': np.random.random(self.x_shape).astype("float32"),
                  'Y': np.random.random(self.crop_shape).astype("float32")
              }
          else:
              self.attrs['shape'] = self.crop_shape
              self.inputs = {
                  'X': np.random.random(self.x_shape).astype("float32"),
              }
          if self.offset_by_input:
              self.inputs['Offsets'] = np.array(self.offsets).astype('int32')
          else:
              self.attrs['offsets'] = self.offsets
          self.outputs = {
              'Out': crop(self.inputs['X'], self.offsets, self.crop_shape)
          }
  
      def initTestCase(self):
          self.x_shape = (8, 8)
          self.crop_shape = (2, 2)
          self.offsets = [1, 2]
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad_normal(self):
          self.check_grad(['X'], 'Out', max_relative_error=0.006)
  
  
  class TestCase1(TestCropOp):
      def initTestCase(self):
          self.x_shape = (16, 8, 32)
          self.crop_shape = [2, 2, 3]
          self.offsets = [1, 5, 3]
  
  
  class TestCase2(TestCropOp):
      def initTestCase(self):
          self.x_shape = (4, 8)
          self.crop_shape = [4, 8]
          self.offsets = [0, 0]
  
  
  class TestCase3(TestCropOp):
      def initTestCase(self):
          self.x_shape = (4, 8, 16)
          self.crop_shape = [2, 2, 3]
          self.offsets = [1, 5, 3]
          self.crop_by_input = True
  
  
  class TestCase4(TestCropOp):
      def initTestCase(self):
          self.x_shape = (4, 4)
          self.crop_shape = [4, 4]
          self.offsets = [0, 0]
          self.crop_by_input = True
  
  
  class TestCase5(TestCropOp):
      def initTestCase(self):
          self.x_shape = (3, 4, 5)
          self.crop_shape = [2, 2, 3]
          self.offsets = [1, 0, 2]
          self.offset_by_input = True
  
  
  class TestCase6(TestCropOp):
      def initTestCase(self):
          self.x_shape = (10, 9, 14)
          self.crop_shape = [3, 3, 5]
          self.offsets = [3, 5, 4]
          self.crop_by_input = True
          self.offset_by_input = True
  
  
  if __name__ == '__main__':
      unittest.main()
  