"""
 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])
