"""
 Copyright (c) 2017-2018 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 networkx as nx
import numpy as np

from mo.front.caffe.extractors.utils import get_canonical_axis_index
from mo.graph.graph import Node
from mo.ops.op import Op


class ArgMaxOp(Op):
    op = 'ArgMax'

    def __init__(self, graph: nx.MultiDiGraph, attrs: dict):
        mandatory_props = {
            'type': __class__.op,
            'op': __class__.op,
            'infer': ArgMaxOp.argmax_infer
        }
        super().__init__(graph, mandatory_props, attrs)

    def supported_attrs(self):
        return [
            'out_max_val',
            'top_k',
            'axis',
        ]

    @staticmethod
    def argmax_infer(node: Node):
        shape = node.in_node(0).shape
        if shape is None:
            return

        # there are two inputs in TensorFlow. The second input is the axis for ArgMax
        if len(node.in_nodes()) == 2:
            if node.in_node(1).value is None:
                log.debug('The second argument to ArgMax is None')
                return
            node.axis = node.in_node(1).value.item()
            # remove the unnecessary input
            node.graph.remove_edge(node.in_node(1).id, node.id)

        num_top_axes = shape.size
        if num_top_axes < 3:
            num_top_axes = 3

        out_shape = np.ones(num_top_axes, dtype=int)

        if node.has_valid('axis'):
            axis = get_canonical_axis_index(shape, node.axis)
            node.axis = axis
            out_shape = np.array(shape)
            out_shape[axis] = node.top_k
        else:
            out_shape[0] = shape[0]
            out_shape[2] = node.top_k
            if node.out_max_val:
                out_shape[1] = 2

        node.out_node().shape = out_shape
