text: """
   Copyright (C) 2020 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.
  """
  
  from extensions.ops.ctc_greedy_decoder_seq_len import CTCGreedyDecoderSeqLenOp
  from extensions.ops.ctc_loss import CTCLoss
  from extensions.ops.transpose import Transpose
  from mo.front.common.partial_infer.utils import int64_array
  from mo.front.common.replacement import FrontReplacementSubgraph
  from mo.front.tf.graph_utils import create_op_with_const_inputs
  from mo.graph.graph import Graph, rename_nodes
  
  
  class CTCLossReplacement(FrontReplacementSubgraph):
      """
      The CTCLoss appears along with CTCGreedyDecoder operation in particular. Since the TensorFlow* CTCGreedyDecoder
      outputs sparse tensor format, the OpenVINO CTCGreedyDecoderSeqLen has a different format and the CTCLoss is also affected
      in terms of different format for its inputs. So the corresponding sub-graph with CTCGreedyDecoding and CTCLoss
      must be transformed properly.
      """
      enabled = True
  
      def run_before(self):
          from extensions.front.tf.CTCGreedyDecoderReplacement import CTCGreedyDecoderReplacement
          return [CTCGreedyDecoderReplacement]
  
      def pattern(self):
          return dict(
              nodes=[
                  ('transpose', dict(op='Transpose')),
                  ('ctc_greedy_decoder', dict(op='CTCGreedyDecoderSeqLen')),
                  ('cast', dict(op='Cast')),
                  ('sparse_to_dense', dict(op='SparseToDense')),
                  ('const', dict(op='Const')),
                  ('ctc_loss', dict(op='CTCLoss')),
              ],
              edges=[
                  ('transpose', 'ctc_greedy_decoder', {'out': 0, 'in': 0}),
                  ('transpose', 'ctc_loss', {'out': 0, 'in': 0}),
                  ('ctc_greedy_decoder', 'sparse_to_dense', {'out': 0, 'in': 0}),
                  ('ctc_greedy_decoder', 'sparse_to_dense', {'out': 2, 'in': 1}),
                  ('ctc_greedy_decoder', 'sparse_to_dense', {'out': 1, 'in': 2}),
                  ('const', 'sparse_to_dense', {'out': 0, 'in': 3}),
                  ('ctc_greedy_decoder', 'cast', {'out': 1, 'in': 0}),
                  ('ctc_greedy_decoder', 'ctc_loss', {'out': 0, 'in': 1}),
                  ('cast', 'ctc_loss', {'out': 0, 'in': 2})
              ])
  
      def replace_sub_graph(self, graph: Graph, match: dict):
          transpose_tf = match['transpose']
          ctc_greedy_decoder_tf = match['ctc_greedy_decoder']
          cast_tf = match['cast']
          ctc_loss_tf = match['ctc_loss']
          sparse_to_dense_tf = match['sparse_to_dense']
          output_sparse_to_dense_name = sparse_to_dense_tf.soft_get('name', sparse_to_dense_tf.id)
          ctc_data_permute = create_op_with_const_inputs(graph, Transpose, {1: int64_array([1, 0, 2])},
                                                         {'name': ctc_greedy_decoder_tf.name + '/ctc_data_permute'})
          ctc_data_permute.in_port(0).connect(transpose_tf.out_port(0))
  
          ctc_greedy_decoder_tf_name = ctc_greedy_decoder_tf.soft_get('name', ctc_greedy_decoder_tf.id)
          assert ctc_greedy_decoder_tf.has_valid('merge_repeated'), \
              'The CTCGreedyDecoderSeqLen node "{}" misses "merge_repeated" attribute'.format(ctc_greedy_decoder_tf_name)
          merge_repeated_tf = ctc_greedy_decoder_tf.merge_repeated
          ctc_greedy_decoder = CTCGreedyDecoderSeqLenOp(graph, {'name': output_sparse_to_dense_name,
                                                                'merge_repeated': merge_repeated_tf}).create_node()
          rename_nodes([(sparse_to_dense_tf, output_sparse_to_dense_name + '/AbandonedName'),
                        (ctc_greedy_decoder, output_sparse_to_dense_name)])
          ctc_greedy_decoder.in_port(0).connect(ctc_data_permute.out_port(0))
          ctc_greedy_decoder.in_port(1).connect(ctc_greedy_decoder_tf.in_port(1).get_connection().get_source())
  
          # set output of the new sub-graph as a source for SparseToDense consumer
          output_ctc_loss_name = ctc_loss_tf.soft_get('name', ctc_loss_tf.id)
          assert ctc_loss_tf.has_valid('preprocess_collapse_repeated'), \
              'The CTCLoss node "{}" misses "preprocess_collapse_repeated" attribute'.format(output_ctc_loss_name)
          assert ctc_loss_tf.has_valid('ctc_merge_repeated'), \
              'The CTCLoss node "{}" misses "ctc_merge_repeated" attribute'.format(output_ctc_loss_name)
          assert ctc_loss_tf.has_valid('unique'), \
              'The CTCLoss node "{}" misses "unique" attribute'.format(output_ctc_loss_name)
          preprocess_collapse_repeated = ctc_loss_tf.preprocess_collapse_repeated
          ctc_merge_repeated = ctc_loss_tf.ctc_merge_repeated
          unique = ctc_loss_tf.unique
          ctc_loss = CTCLoss(graph, {'name': output_ctc_loss_name,
                                     'preprocess_collapse_repeated': preprocess_collapse_repeated,
                                     'ctc_merge_repeated': ctc_merge_repeated,
                                     'unique': unique}).create_node()
          rename_nodes([(ctc_loss_tf, output_ctc_loss_name + '/AbandonedName'), (ctc_loss, output_ctc_loss_name)])
          ctc_loss_tf.out_port(0).get_connection().set_source(ctc_loss.out_port(0))
          if ctc_loss_tf.logits_time_major:
              ctc_loss.in_port(0).connect(ctc_data_permute.out_port(0))
          else:
              ctc_loss.in_port(0).connect(transpose_tf.out_port(0))
          ctc_loss.in_port(1).connect(ctc_greedy_decoder_tf.in_port(1).get_connection().get_source())
          ctc_loss.in_port(2).connect(ctc_greedy_decoder.out_port(0))
          ctc_loss.in_port(3).connect(ctc_greedy_decoder.out_port(1))
  
          # remove no longer needed nodes
          graph.remove_nodes_from([sparse_to_dense_tf.id, cast_tf.id, ctc_loss_tf.id, ctc_greedy_decoder_tf.id])
  