"""
 Copyright (c) 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


class EmbeddingBagBase(Op):
    enabled = False

    op = op_type = None
    version = None
    in_ports_count = None

    def __init__(self, graph: Graph, attrs: dict):
        super().__init__(graph, {
            'op': self.op,
            'type': self.op_type,
            'version': self.version,

            'infer': self.infer,

            'in_ports_count': self.in_ports_count,
            'out_ports_count': 1,
        }, attrs)

    @staticmethod
    def infer(node: Node):
        raise NotImplementedError('Please use specialized EmbeddingBag operation class, EmbeddingBagBase is base class')


class EmbeddingBagOffsetsSum(EmbeddingBagBase):
    op = op_type = 'EmbeddingBagOffsetsSum'
    version = 'opset3'
    in_ports_count = 5

    @staticmethod
    def infer(node: Node):
        name = node.soft_get('name', node.id)

        connected_in_ports = {idx: port for idx, port in node.in_ports().items() if not port.disconnected()}
        assert len(connected_in_ports) >= 3 and all(p in connected_in_ports for p in [0, 1, 2]), \
            "EmbeddingBagOffsetsSum should have at least 3 connected input port, but it doesn't " \
            "for node: `{}`. Ports: {}".format(name, connected_in_ports)

        weights_shape = node.in_port(0).data.get_shape()
        assert len(weights_shape) >= 2,\
            "EmbeddingBagOffsetsSum should have at least 2D weights for node: `{}`".format(name)
        offsets_shape = node.in_port(2).data.get_shape()
        assert offsets_shape is not None and len(offsets_shape) == 1,\
            "Rank of the offsets in EmbeddingBagOffsetsSum should be equal to 1 for node: `{}`".format(name)

        node.out_port(0).data.set_shape(np.concatenate((offsets_shape[:1], weights_shape[1:])))


class EmbeddingBagPackedSum(EmbeddingBagBase):
    op = op_type = 'EmbeddingBagPackedSum'
    version = 'opset3'
    in_ports_count = 3

    @staticmethod
    def infer(node: Node):
        name = node.soft_get('name', node.id)

        connected_in_ports = {idx: port for idx, port in node.in_ports().items() if not port.disconnected()}
        assert len(connected_in_ports) >= 2 and all(p in connected_in_ports for p in [0, 1]), \
            "EmbeddingBagPackedSum should have at least 2 connected input port, but it doesn't for node: `{}`. " \
            "Ports: {}".format(name, connected_in_ports)

        weights_shape = node.in_port(0).data.get_shape()
        assert len(weights_shape) >= 2, \
            "EmbeddingBagPackedSum should have at least 2D weights for node: `{}`".format(name)
        input_shape = node.in_port(1).data.get_shape()

        node.out_port(0).data.set_shape(np.concatenate((input_shape[:1], weights_shape[1:])))


class EmbeddingSegmentsSum(EmbeddingBagBase):
    op = op_type = 'EmbeddingSegmentsSum'
    version = 'opset3'
    in_ports_count = 6

    @staticmethod
    def infer(node: Node):
        name = node.soft_get('name', node.id)

        connected_in_ports = {idx: port for idx, port in node.in_ports().items() if not port.disconnected()}
        assert len(connected_in_ports) >= 4 and all(p in connected_in_ports for p in [0, 1, 2, 3]), \
            "EmbeddingSegmentsSum should have at least 4 connected input port, but it doesn't for node: `{}`. " \
            "Ports: {}".format(name, connected_in_ports)

        weights_shape = node.in_port(0).data.get_shape()
        assert len(weights_shape) >= 2,\
            "EmbeddingSegmentsSum should have at least 2D weights for node: `{}`".format(name)
        indices_shape = node.in_port(1).data.get_shape()
        segment_ids = node.in_port(2).data.get_shape()
        assert len(indices_shape) == 1 and len(segment_ids) == 1 and indices_shape == segment_ids,\
            "Both indices and segment_ids should have the same shape for node: `{}`".format(name)
        num_segments = node.in_port(3).data.get_value()
        assert num_segments is not None, "EmbeddingSegmentsSum should have a constant num_segments provided, but it " \
                                         "doesn't for node: `{}`.".format(name)
        output_shape = np.concatenate(([num_segments], weights_shape[1:]))
        node.out_port(0).data.set_shape(output_shape)