"""
 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.
"""

from extensions.ops.ReduceOps import ReduceProd
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_node_with_second_input
from mo.graph.graph import Graph
from mo.ops.const import Const
from mo.ops.reshape import Reshape
from mo.ops.shape import Shape
from mo.utils.shape import node_to_get_shape_value_of_indices, new_shape_node_from_shape_nodes


class FlattenONNXToReshape(FrontReplacementSubgraph):
    """
    ONNX Flatten operation flattens the input tensor into a 2D matrix by given axis:

    Input of shape [d_0, d_1, ... d_n]
    Output of shape [d_0 X d_1 ... d_(axis-1),  d_axis X d_(axis+1) ... X dn]

    Corner case with axis=0: output shape will be [1, d_0 X d_1 ... X dn]
    """
    enabled = True

    def run_before(self):
        from extensions.front.LogSoftmax import LogSoftmaxFrontReplacer
        return [LogSoftmaxFrontReplacer]

    def pattern(self):
        return dict(nodes=[('flatten', dict(op='FlattenONNX'))],
                    edges=[])

    def replace_sub_graph(self, graph: Graph, match: dict):
        node = match['flatten']
        name = node.soft_get('name', node.id)

        assert node.has_valid('axis'), 'Flatten {} should have `axis` attribute extracted, but it\'s not'.format(name)
        axis = node.axis

        if axis == 0:
            dim = Const(graph, {'value': int64_array([1, -1])}).create_node()
        elif axis == 1:
            dim = Const(graph, {'value': int64_array([0, -1])}).create_node()
        else:
            shape = Shape(graph, {'name': name + '/input_shape'}).create_node()

            idxs = list(range(axis)) if axis > 0 else list(range(axis, 0))

            axis_shape_portion = node_to_get_shape_value_of_indices(shape, idxs)
            first_dims = create_op_node_with_second_input(graph, ReduceProd, int64_array([0]),
                                                          {'keep_dims': True})
            second_dims = Const(graph, {'value': int64_array([-1])}).create_node()

            node.in_port(0).get_source().connect(shape.in_port(0))
            axis_shape_portion.out_port(0).connect(first_dims.in_port(0))

            order_of_dims = [first_dims, second_dims] if axis > 0 else [second_dims, first_dims]

            dim = new_shape_node_from_shape_nodes(order_of_dims)

        reshape_node = Reshape(graph, {'name': node.id + '/Reshape'}).create_node()
        reshape_node.in_port(1).connect(dim.out_port(0))

        node.out_port(0).get_connection().set_source(reshape_node.out_port(0))
        node.in_port(0).get_connection().set_destination(reshape_node.in_port(0))
