"""
 Copyright (c) 2018-2019 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 logging as log

import numpy as np

from extensions.front.Pack import Pack
from extensions.front.tf.nearest_neighbor_upsampling import NearestNeighborUpsampling
from mo.front.common.partial_infer.utils import int64_array
from mo.front.common.replacement import FrontReplacementSubgraph
from mo.graph.graph import Graph


def is_value_is_constant(val: np.ndarray, const: [int, float]):
    if val.ndim > 1:
        return False
    if val.ndim == 1 and len(val) > 1:
        return False
    return val.item() == const


class FlattenToReshapeableReshape(FrontReplacementSubgraph):
    """
    The TensorFlow implementation of the Flatten operation is not reshape-able because the batch size is hardcoded
    during te constant propagation. This transform sets the 'dim' attribute for the Reshape to [0, -1].
    """
    enabled = True

    def run_after(self):
        return [NearestNeighborUpsampling]

    def run_before(self):
        return [Pack]

    def pattern(self):
        return dict(
            nodes=[
                ('shape', dict(op='Shape')),
                ('strided_slice', dict(op='StridedSlice')),
                ('pack', dict(op='Pack')),
                ('const', dict(op='Const')),
                ('reshape', dict(op='Reshape')),
            ],
            edges=[
                ('shape', 'strided_slice', {'in': 0}),
                ('strided_slice', 'pack', {'in': 0}),
                ('const', 'pack', {'in': 1}),
                ('pack', 'reshape', {'in': 1}),
            ])

    @staticmethod
    def replace_sub_graph(graph: Graph, match: dict):
        strided_slice_node = match['strided_slice']
        const_node = match['const']
        reshape_node = match['reshape']
        pack_node = match['pack']

        if not const_node.has_valid('value') or not is_value_is_constant(const_node.value, -1):
            log.debug('The pattern does not correspond to flatten. The second reshape dimension is not -1. It is {}'.
                      format(const_node.soft_get('value')))
            return
        if len(pack_node.in_nodes()) != 2:
            log.debug('The pattern does not correspond to flatten. The "Pack" operation produces tensor with 3 items '
                      'but should produce just 2.')
            return

        expected_values = [0, 1, 1]  # expected values to a StridedSlice to get the batch size
        for ind in range(3):
            if not strided_slice_node.in_node(ind + 1).has_valid('value') or \
                    not is_value_is_constant(strided_slice_node.in_node(ind + 1).value, expected_values[ind]):
                log.debug('The pattern does not correspond to flatten because of the input with index {}. The value is '
                          '"{}".'.format(ind, strided_slice_node.soft_get('value')))
                return

        graph.remove_edge(pack_node.id, reshape_node.id)
        reshape_node['dim'] = int64_array([0, -1])
        log.debug('The node "{}" is actually a Flatten node'.format(reshape_node.soft_get('name')))
