text: """
   Copyright (c) 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 numpy as np
  
  from extensions.back.FuseReshapesSequence import FuseReshapesSequence
  from extensions.back.FuseTransposesSequence import FuseTransposesSequence
  from extensions.back.TransposeToPermute import TransposeToPermute
  from mo.back.replacement import BackReplacementPattern
  from mo.front.common.partial_infer.utils import int64_array
  from mo.graph.graph import Graph
  
  
  class ShuffleChannelPatternOptimization(BackReplacementPattern):
      enabled = True
      force_clean_up = True
  
      def run_after(self):
          return [FuseTransposesSequence, FuseReshapesSequence]
  
      def run_before(self):
          return [TransposeToPermute]
  
      @staticmethod
      def pattern():
          return dict(
              nodes=[
                  ('t_start_order', {'type': 'Const'}),
                  ('t_start_order_d', {'value': lambda value: value is not None and np.array_equal(value, [0, 2, 3, 1])}),
                  ('t_start', {'type': 'Transpose'}),
                  ('t_start_d', {}),
  
                  ('reshape_dim', {'type': 'Const'}),
                  ('reshape_dim_d', {'value': lambda value: value is not None and value.size == 5 and value[0] == -1}),
                  ('reshape_start', {'type': 'Reshape'}),
                  ('reshape_start_d', {}),
  
                  ('t_5d_order', {'type': 'Const'}),
                  ('t_5d_order_d', {'value': lambda value: value is not None and np.array_equal(value, [0, 1, 2, 4, 3])}),
                  ('t_5d', {'type': 'Transpose'}),
                  ('t_5d_d', {}),
  
                  ('reshape_1_dim', {'type': 'Const'}),
                  ('reshape_1_dim_d', {'value': lambda value: value is not None and value.size == 4 and value[0] == -1}),
                  ('reshape_end', {'type': 'Reshape'}),
                  ('reshape_end_d', {}),
  
                  ('t_end_order', {'type': 'Const'}),
                  ('t_end_order_d', {'value': lambda value: value is not None and np.array_equal(value, [0, 3, 1, 2])}),
                  ('t_end', {'type': 'Transpose'}),
              ],
              edges=[
                  ('t_start_order', 't_start_order_d'),
                  ('t_start_order_d', 't_start', {'in': 1}),
                  ('t_start', 't_start_d'),
  
                  ('reshape_dim', 'reshape_dim_d'),
                  ('t_start_d', 'reshape_start', {'in': 0}),
                  ('reshape_dim_d', 'reshape_start', {'in': 1}),
                  ('reshape_start', 'reshape_start_d'),
  
                  ('t_5d_order', 't_5d_order_d'),
                  ('reshape_start_d', 't_5d', {'in': 0}),
                  ('t_5d_order_d', 't_5d', {'in': 1}),
                  ('t_5d', 't_5d_d'),
  
                  ('reshape_1_dim', 'reshape_1_dim_d'),
                  ('t_5d_d', 'reshape_end', {'in': 0}),
                  ('reshape_1_dim_d', 'reshape_end', {'in': 1}),
                  ('reshape_end', 'reshape_end_d'),
  
                  ('t_end_order', 't_end_order_d'),
                  ('reshape_end_d', 't_end', {'in': 0}),
                  ('t_end_order_d', 't_end', {'in': 1}),
              ],
          )
  
      @staticmethod
      def feature_dim_splitted(short_shape, long_shape):
          return all([short_shape[i] == long_shape[i] for i in range(len(short_shape) - 1)]) and \
                 short_shape[-1] == long_shape[-1] * long_shape[-2]
  
      @staticmethod
      def replace_pattern(graph: Graph, match: dict):
          reshape_5d = match['reshape_start']
          if not ShuffleChannelPatternOptimization.feature_dim_splitted(
                  short_shape=reshape_5d.in_port(0).data.get_shape(), long_shape=reshape_5d.out_port(0).data.get_shape()):
              return
  
          reshape_4d = match['reshape_end']
          if not ShuffleChannelPatternOptimization.feature_dim_splitted(
                  short_shape=reshape_4d.out_port(0).data.get_shape(), long_shape=reshape_4d.in_port(0).data.get_shape()):
              return
  
          start = match['t_start']
          end = match['t_end']
  
          new_start = match['reshape_start']
          new_end = match['reshape_end']
  
          start_source = start.in_port(0).get_connection().get_source()
          end_connection = end.out_port(0).get_connection()
  
          new_end.out_port(0).disconnect()
          end_connection.set_source(new_end.out_port(0))
  
          start.in_port(0).disconnect()
          new_start.in_port(0).disconnect()
  
          new_start.in_port(0).connect(start_source)
  
          match['reshape_dim']['value'] = int64_array(np.take(new_start.in_port(1).data.get_value(), [0, 3, 4, 1, 2]))
          match['reshape_dim'].infer(match['reshape_dim'])
          new_start.infer(new_start)
  
          match['t_5d_order']['value'] = int64_array([0, 2, 1, 3, 4])
          match['t_5d_order'].infer(match['t_5d_order'])
          match['t_5d'].infer(match['t_5d'])
  
          match['reshape_1_dim']['value'] = int64_array(np.take(new_start.in_port(1).data.get_value(), [0, 3, 1, 2]))
          match['reshape_1_dim'].infer(match['reshape_1_dim'])
  