text: """
   Copyright (c) 2017-2019 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 logging as log
  
  from extensions.front.squared_difference import SquaredDifference
  from extensions.front.sub import Sub
  from mo.front.common.replacement import FrontReplacementSubgraph
  from mo.graph.graph import Node, Graph
  from extensions.front.div import Div
  from mo.ops.op import Op
  
  
  class MVNUnrolled(FrontReplacementSubgraph):
      enabled = True
  
      def run_before(self):
          return [SquaredDifference, Div, Sub]
  
      def pattern(self):
          log.debug('Enabled MVN replacement')
          return dict(
              nodes=[
                  ('mean', dict(kind='op', op='Mean')),
                  ('stop_grad', dict(kind='op', op='StopGradient')),
                  ('sqdiff', dict(kind='op', op='SquaredDifference')),
                  ('variance', dict(kind='op', op='Mean')),
                  ('add', dict(kind='op', op='Add')),
                  ('pow', dict(kind='op', op='Pow')),
                  ('sub', dict(kind='op', op='Sub')),
                  ('truediv', dict(kind='op', op='Div')),
              ],
              edges=[
                  ('mean', 'stop_grad', {'in': 0}),
                  ('stop_grad', 'sqdiff', {'in': 1}),
                  ('sqdiff', 'variance', {'in': 0}),
                  ('mean', 'sub', {'in': 1}),
                  ('variance', 'add'),
                  ('add', 'pow', {'in': 0}),
                  ('pow', 'truediv', {'in': 1}),
                  ('sub', 'truediv', {'in': 0}),
              ])
  
      @staticmethod
      def replace_sub_graph(graph: Graph, match: dict):
          MVN = Op.get_op_class_by_name('MVN')
  
          mvn = MVN(graph, dict(
              name=match['truediv'].name + '/MVN_',
              required_reduction_indices=[1, 2] if graph.graph['layout'] == 'NHWC' else [2, 3]
          ))
          mvn.attrs['old_infer'] = mvn.attrs['infer']
          mvn.attrs['infer'] = __class__.infer
  
          mean_reduction = match['mean'].in_node(1)
          variance_reduction = match['variance'].in_node(1)
          pow2 = match['pow'].in_node(1)
          eps = match['add'].in_node(0 if match['add'].in_node(0).id != match['variance'].id else 1)
  
          new_subgraph = mvn.create_node([match['mean'].in_node(0), mean_reduction, variance_reduction, pow2, eps])
  
          match['truediv'].replace_node(new_subgraph)
  
      @staticmethod
      def infer(node: Node):
          if not (node.in_node(1).has_valid('value') and node.in_node(2).has_valid('value')):
              log.warning('Reduction indices for mean and variance for MVN node {} are not constants'.format(node.name))
              return
  
          if not (all(node.in_node(1).value == node.required_reduction_indices) and
                      all(node.in_node(2).value == node.required_reduction_indices)):
              log.warning('Reduction indices for mean {} and variance {} do not match required ones {}'.format(
                  node.in_node(1).value,
                  node.in_node(2).value,
                  node.required_reduction_indices
              ))
              return
          
          if not (node.in_node(3).has_valid('value') and node.in_node(4).has_valid('value')):
              log.warning('Power or/and epsilon values for MVN node {} are not constants'.format(node.name))
              return
  
          if node.in_node(3).value != 0.5:
              log.warning('Power for MVN node {} ({}) is not equal to 0.5'.format(node.name, node.in_node(3).value))
              return
  
          node['eps'] = node.in_node(4).value
  
          for i in range(1, 5):
              node.graph.remove_edge(node.in_node(i).id, node.id)
          node.old_infer(node)
  