"""
 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 typing import Dict

from mo.front.tf.graph_utils import create_op_with_const_inputs
from mo.graph.graph import Graph, Node, rename_nodes
from mo.middle.replacement import MiddleReplacementPattern
from mo.ops.shape import Shape
from mo.ops.strided_slice import StridedSlice
from mo.utils.shape import get_shape_values_by_range_idxs, new_shape_node_from_shape_nodes, \
    get_shape_and_rank_nodes_by_port


class SliceLikeToStridedSlice(MiddleReplacementPattern):
    """
    Replace mxnet slice_like operation with StridedSlice in reshapable way.
    The begin parameter for StridedSlice is always a zero vector.
    The end parameter depends on the slice_like inputs and axes.

    1. If slice_like inputs has the same ranks, we can use second input shape (shape_like) as the end parameter for
       StridedSlice. Axes parameter will form end_mask, that allows to use slice only on the desired axes.
       Example:
       input_shape = [1, 64, 128, 256], shape_like = [1, 2, 3, 4], axes = [2, 3].
       In that case end = shape_like = [1, 2, 3, 4], but end_mask = [0, 0, 1, 1], so output_shape = [1, 64, 3, 4]

    2. Axes parameter has the last dimension of the first input shape (in that case shape_like >= input_shape).
       Here we can use only a part of shape_like as the end parameter.
       Example:
           input_shape = [1, 64, 128, 256], shape_like = [1, 2, 3, 4, 5], axes = [2, 3].
           end = shape_like[:4] = [1, 2, 3, 4], end_mask = [0, 0, 1, 1], output_shape = [1, 64, 3, 4]

    3. Usual case, where we form end parameter by concatenate parts of shape_like and input_shape.
       Examples:
           input_shape = [1, 64, 128, 256, 512], shape_like = [1, 2, 3, 4], axes = [2, 3].
           end = shape_like[:4] + input_shape[4:] = [1, 2, 3, 4, 512],
           end_mask = [0, 0, 1, 1, 0], output_shape = [1, 64, 3, 4, 512]

           input_shape = [1, 64, 128, 256], shape_like = [1, 2, 3, 4, 5], axes = [0, 2].
           end = shape_like[:3] + input_shape[3:] = [1, 2, 3, 256],
           end_mask = [1, 0, 1, 0], output_shape = [1, 64, 3, 256]
    """

    enabled = True
    graph_condition = [lambda graph: graph.graph['fw'] == 'mxnet']

    @staticmethod
    def pattern():
        return dict(
            nodes=[
                ('op', dict(kind='op', op='slice_like'))
            ],
            edges=[]
        )

    @staticmethod
    def replace_pattern(graph: Graph, match: Dict[str, Node]):
        node = match['op']
        name = node.soft_get('name', node.id)
        input_shape = node.in_port(0).data.get_shape()
        second_input_shape = node.in_port(1).data.get_shape()

        begin_mask = np.zeros(len(input_shape), dtype=np.int64)
        end_mask = np.zeros(len(input_shape), dtype=np.int64)

        for i in node.axes:
            end_mask[i] = np.int64(1)

        new_axis_mask = np.zeros(len(input_shape), dtype=np.int64)
        shrink_axis_mask = np.zeros(len(input_shape), dtype=np.int64)
        ellipsis_mask = np.zeros(len(input_shape), dtype=np.int64)

        ss = create_op_with_const_inputs(graph, StridedSlice,
                                         port_value_dict={1: np.zeros(len(input_shape), dtype=np.int64)},
                                         op_attrs={'name': 'StridedSlice', 'begin_mask': begin_mask,
                                                   'end_mask': end_mask, 'new_axis_mask': new_axis_mask,
                                                   'shrink_axis_mask': shrink_axis_mask,
                                                   'ellipsis_mask': ellipsis_mask})
        if input_shape.size == second_input_shape.size:
            end = Shape(graph, dict(name=name + '/End')).create_node()
            end.in_port(0).connect(node.in_port(1).get_source())
            ss.in_port(2).connect(end.out_port(0))
        else:
            shape_like, rank_like = get_shape_and_rank_nodes_by_port(node.in_port(1).get_source())
            end_first_part = get_shape_values_by_range_idxs(shape_like, rank_like, 0, node.axes[-1], include_end=True)
            if input_shape.size - 1 == node.axes[-1]:
                ss.in_port(2).connect(end_first_part.out_port(0))
            else:
                shape, rank = get_shape_and_rank_nodes_by_port(node.in_port(0).get_source())
                end_second_part = get_shape_values_by_range_idxs(shape, rank, node.axes[-1], -1, include_begin=False,
                                                                 include_end=True)
                end = new_shape_node_from_shape_nodes([end_first_part, end_second_part])
                ss.in_port(2).connect(end.out_port(0))

        node.in_port(0).get_connection().set_destination(ss.in_port(0))
        node.in_port(1).disconnect()
        node.out_port(0).get_connection().set_source(ss.out_port(0))

        rename_nodes([(node, name + '/ShouldBeDeleted'), (ss, name)])