text: """
   Copyright (C) 2018-2021 Intel Corporation
  
   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 numpy as np
  
  from extensions.front.tf.deconv_ext import Conv2DBackpropInputFrontExtractor
  from mo.utils.unittest.extractors import PB, BaseExtractorsTestingClass
  
  
  class DeconvolutionExtractorTest(BaseExtractorsTestingClass):
      @classmethod
      def setUpClass(cls):
          cls.strides = [1, 2, 3, 4]
  
      def test_deconv2d_defaults(self):
          node = PB({'pb': PB({'attr': {
              'data_format': PB({
                  's': b"NHWC"
              }),
              'strides': PB({
                  'list': PB({"i": self.strides})
              }),
              'padding': PB({
                  's': b'VALID'
              })
          }})})
          self.expected = {
              'bias_addable': True,
              'pad': None,  # will be inferred when input shape is known
              'pad_spatial_shape': None,
              'output_spatial_shape': None,
              'output_shape': None,
              'group': None,
          }
          Conv2DBackpropInputFrontExtractor.extract(node)
          self.res = node
          self.expected_call_args = (None, False)
          self.compare()
  
      def test_deconv2d_nhwc(self):
          node = PB({'pb': PB({'attr': {
              'data_format': PB({
                  's': b"NHWC"
              }),
              'strides': PB({
                  'list': PB({"i": self.strides})
              }),
              'padding': PB({
                  's': b'VALID'
              })
          }})})
  
          self.expected = {
              "spatial_dims": [1, 2],
              "channel_dims": [3],
              "batch_dims": [0],
              'stride': np.array(self.strides, dtype=np.int8),
          }
  
          Conv2DBackpropInputFrontExtractor.extract(node)
          self.res = node
          self.expected_call_args = (None, False)
          self.compare()
  
      def test_deconv2d_nchw(self):
          node = PB({'pb': PB({'attr': {
              'data_format': PB({
                  's': b"NCHW"
              }),
              'strides': PB({
                  'list': PB({"i": self.strides})
              }),
              'padding': PB({
                  's': b'VALID'
              })
          }})})
          self.expected = {
              "spatial_dims": [2, 3],
              "channel_dims": [1],
              "batch_dims": [0],
              'stride': np.array(self.strides, dtype=np.int8),
          }
  
          Conv2DBackpropInputFrontExtractor.extract(node)
          self.res = node
          self.expected_call_args = (None, False)
          self.compare()
  