"""
 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 mo.graph.graph import Node, Graph
from mo.ops.op import Op
from mo.utils.error import Error


def broadcastable(broadcast_from, broadcast_to):
    """Check if shape broadcast_from can be broadcasted to broadcast_to"""
    broadcast_to = np.array(broadcast_to, dtype=np.int64)
    broadcast_from = np.array(broadcast_from, dtype=np.int64)
    if broadcast_from.size > broadcast_to.size:
        return False
    broadcast_from = np.concatenate(
        (np.array([1] * (broadcast_to.size - broadcast_from.size), dtype=np.int64), broadcast_from))
    return np.all(np.logical_or(broadcast_from == 1, broadcast_from == broadcast_to))


def round_half_up(n):
    return np.floor(n + 0.5)


class FakeQuantize(Op):
    op = 'FakeQuantize'

    def __init__(self, graph: Graph, attrs: dict):
        mandatory_props = {
            'type': self.op,
            'op': self.op,
            'version': 'opset1',
            'levels': None,
            'is_eltwise': True,
            'infer': self.infer,
            'in_ports_count': 5,
            'out_ports_count': 1,
        }
        super().__init__(graph, mandatory_props, attrs)
        if self.attrs['levels'] is None:
            raise Error("FakeQuantize operation has no levels parameter")

    def supported_attrs(self):
        return [
            'levels',
        ]

    @staticmethod
    def infer(node: Node):
        assert len(node.in_nodes()) == 5
        assert len(node.out_nodes()) == 1
        inputs = [node.in_node(i) for i in range(5)]
        x, input_low, input_high, output_low, output_high = inputs
        assert x.has_valid('shape')
        # TODO Check all inputs[1..4] shapes are broadcastable to inputs[0] shape
        assert all([broadcastable(inputs[i].shape, inputs[0].shape) for i in range(1, 5)]), \
            "Not all shapes from FakeQuantize inputs can be broadcasted to input[0] for node {}".format(
                node.soft_get('name'))
        node.out_node().shape = x.shape.copy()

        if all([node.in_node(i).has_valid('value') for i in range(5)]):
            x, input_low, input_high, output_low, output_high = \
                [np.array(np.broadcast_to(node.value, x.value.shape), dtype=np.float32) for node in inputs]

            assert node.has_valid('levels')
            assert isinstance(node.levels, int)

            underflow_mask = x <= input_low
            overflow_mask = x > input_high
            # pylint: disable=assignment-from-no-return
            middle_mask = np.logical_not(np.logical_or(underflow_mask, overflow_mask))

            def middle_part(x, input_low, input_high, output_low, output_high):
                return round_half_up((x - input_low) / (input_high - input_low) * (node.levels - 1)) / \
                    (node.levels - 1) * (output_high - output_low) + output_low

            output = np.zeros_like(x)
            # pylint: disable=unsupported-assignment-operation
            output[middle_mask] = middle_part(
                x[middle_mask],
                input_low[middle_mask],
                input_high[middle_mask],
                output_low[middle_mask],
                output_high[middle_mask],
            )

            # pylint: disable=unsupported-assignment-operation
            output[overflow_mask] = output_high[overflow_mask]
            # pylint: disable=unsupported-assignment-operation
            output[underflow_mask] = output_low[underflow_mask]

            if not node.has_and_set('stop_value_propagation'):
                node.out_node().value = output