# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import numpy as np

from mo.graph.graph import Node, Graph
from mo.ops.op import Op
from mo.utils.utils import symm_match_shapes


class TensorArrayGather(Op):
    op = "TensorArrayGatherV3"

    def __init__(self, graph: Graph, attrs: dict):
        mandatory_props = {
            'type': None,
            'op': __class__.op,
            'infer': TensorArrayGather.array_infer,
        }
        super().__init__(graph, mandatory_props, attrs)

    @staticmethod
    def array_infer(node: Node):
        assert len(node.in_nodes()) == 3

        handle = node.in_node(0)
        indices = node.in_node(1)
        flow_in = node.in_node(2)

        ta_node = Node(node.graph, str(handle.value))

        if ta_node.has_valid('element_shape') and ta_node.element_shape is not None and len(ta_node.element_shape) > 0:
            assert symm_match_shapes(ta_node['element_shape'], node.element_shape)
        else:
            ta_node['element_shape'] = node.element_shape
        data_shape = ta_node['element_shape']
        assert -1 not in data_shape or data_shape.size == 2 and data_shape[0] == -1 and data_shape[1] != -1

        assert ta_node.has_valid('size')
        size = ta_node['size']

        assert size > 0

        output_shape = [size] + [data_shape[i] for i in range(len(data_shape))]
        output_value = None

        for _, out_node in node.graph.out_edges(node.id):
            node.graph.node[out_node]['shape'] = np.array(output_shape)
            node.graph.node[out_node]['value'] = None if output_value is None else np.array(output_value)