"""
 Copyright (C) 2018-2020 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.
"""

import numpy as np

from extensions.front.standalone_const_eraser import StandaloneConstEraser
from extensions.ops.DetectionOutput import DetectionOutput
from mo.front.common.partial_infer.utils import int64_array
from mo.front.subgraph_matcher import SubgraphMatch
from mo.front.tf.replacement import FrontReplacementFromConfigFileSubGraph
from mo.graph.graph import Graph
from mo.ops.const import Const
from mo.ops.op import PermuteAttrs
from mo.ops.reshape import Reshape
from mo.ops.result import Result


class SSDToolboxDetectionOutputReplacement(FrontReplacementFromConfigFileSubGraph):
    replacement_id = 'SSDToolboxDetectionOutput'

    def run_before(self):
        return [StandaloneConstEraser]

    def nodes_to_remove(self, graph: Graph, match: SubgraphMatch):
        return []

    def generate_sub_graph(self, graph: Graph, match: SubgraphMatch):
        # IE DetectionOutput layer consumes flattened confidences and locations tensors.
        # That is why we add reshapes before them.
        locs_node = match.single_input_node(0)
        conf_node = match.single_input_node(1)
        prior_boxes_node = match.single_input_node(2)

        locs_out_nodes = locs_node[0].out_nodes()
        assert len(locs_out_nodes) == 1
        locs_out_node = locs_out_nodes[list(locs_out_nodes.keys())[0]]
        assert locs_out_node.op == "Result", locs_out_node.op
        graph.remove_node(locs_out_node.id)

        conf_out_nodes = conf_node[0].out_nodes()
        assert len(conf_out_nodes) == 1
        conf_out_node = conf_out_nodes[list(conf_out_nodes.keys())[0]]
        assert conf_out_node.op == "Result", conf_out_node.op
        graph.remove_node(conf_out_node.id)

        # reshape operation to flatten confidence tensor
        const = Const(graph, {'value': int64_array([0, -1])}).create_node()
        reshape_loc_node = Reshape(graph, {}).create_node([locs_node, const], dict(name='DetectionOutput_Reshape_loc_'))

        # reshape operation to flatten confidence tensor
        reshape_conf_node = Reshape(graph, {}).create_node([conf_node, const], dict(name='DetectionOutput_Reshape_conf_'))

        # remove the Result node after the priors node
        assert prior_boxes_node[0].out_node().op == "Result"
        graph.remove_node(prior_boxes_node[0].out_node().id)

        # reshape operation for prior boxes tensor
        const = Const(graph, {'value': int64_array([1, 2, -1])}).create_node()
        reshape_priors_node = Reshape(graph, {}).create_node([prior_boxes_node, const],
                                                             dict(name='DetectionOutput_Reshape_priors_'))
        # create Detection Output node with three inputs: locations, confidences and prior boxes
        detection_output_op = DetectionOutput(graph, match.custom_replacement_desc.custom_attributes)
        detection_output_node = detection_output_op.create_node(
            [reshape_loc_node, reshape_conf_node, reshape_priors_node],
            dict(name=detection_output_op.attrs['type'] + '_'))
        PermuteAttrs.set_permutation(reshape_priors_node, detection_output_node, None)

        # create Output node to mark DetectionOutput as a graph output operation
        output_op = Result(graph)
        output_op.create_node([detection_output_node], dict(name='sink_'))
        return {}
