# Copyright (C) 2018-2022 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import unittest

import numpy as np

from openvino.tools.mo.ops.sparse_segment_mean import SparseSegmentMean
from openvino.tools.mo.front.common.partial_infer.utils import int64_array
from openvino.tools.mo.graph.graph import Node
from unit_tests.utils.graph import build_graph

# graph 1
nodes_attributes1 = {'input_data': {'shape': None, 'value': None, 'kind': 'data'},
                    'input_indices': {'shape': None, 'value': None, 'kind': 'data'},
                    'input_segment_ids': {'shape': None, 'value': None, 'kind': 'data'},
                    'sparse_segment_mean_node': {'op': 'SparseSegmentMean', 'kind': 'op'},
                    'output_segments': {'shape': None, 'value': None, 'kind': 'data'},
                    }

edges1 = [('input_data', 'sparse_segment_mean_node', {'in': 0}),
          ('input_indices', 'sparse_segment_mean_node', {'in': 1}),
          ('input_segment_ids', 'sparse_segment_mean_node', {'in': 2}),
          ('sparse_segment_mean_node', 'output_segments', {'out': 0})]

inputs1 = {'input_data': {'shape': int64_array([20, 4, 5]), 'value': None},
           'input_indices': {'shape': int64_array([40]), 'value': None},
           'input_segment_ids': {'shape': int64_array([40]), 'value': None}}

# graph 2 with constant input, mean
nodes_attributes2 = {'input_data': {'shape': None, 'value': None, 'kind': 'data'},
                    'input_indices': {'shape': None, 'value': None, 'kind': 'data'},
                    'input_segment_ids': {'shape': None, 'value': None, 'kind': 'data'},
                    'sparse_segment_mean_node': {'op': 'SparseSegmentMean', 'kind': 'op'},
                    'output_segments': {'shape': None, 'value': None, 'kind': 'data'},
                    }

edges2 = [('input_data', 'sparse_segment_mean_node', {'in': 0}),
          ('input_indices', 'sparse_segment_mean_node', {'in': 1}),
          ('input_segment_ids', 'sparse_segment_mean_node', {'in': 2}),
          ('sparse_segment_mean_node', 'output_segments', {'out': 0})]

inputs2 = {'input_data': {'shape': int64_array([3, 4]), 'value': np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float)},
           'input_indices': {'shape': int64_array([3]), 'value': np.array([0, 2, 1, 1, 2], dtype=np.float)},
           'input_segment_ids': {'shape': int64_array([3]), 'value': np.array([0, 0, 1, 2, 2], dtype=np.float)}}

class TestSparseSegmentMean(unittest.TestCase):
    def test_partial_infer(self):
        graph = build_graph(nodes_attributes1, edges1, inputs1)

        sparse_segment_mean_node = Node(graph, 'sparse_segment_mean_node')
        SparseSegmentMean.infer(sparse_segment_mean_node)

        # prepare reference results
        ref_output_segments_shape = int64_array([40, 4, 5])

        # get resulted shapes
        res_output_segments_shape = graph.node['output_segments']['shape']

        self.assertTrue(np.array_equal(ref_output_segments_shape, res_output_segments_shape),
                        'Shapes do not match expected: {} and given: {}'.format(ref_output_segments_shape, res_output_segments_shape))

    def test_incorrect_shapes(self):
        inputs = {'input_data': {'shape': int64_array([20, 4, 5]), 'value': None},
                   'input_indices': {'shape': int64_array([39]), 'value': None},
                   'input_segment_ids': {'shape': int64_array([40]), 'value': None}}
        graph = build_graph(nodes_attributes1, edges1, inputs)
        sparse_segment_mean_node = Node(graph, 'sparse_segment_mean_node')
        self.assertRaises(AssertionError, SparseSegmentMean.infer, sparse_segment_mean_node)

    def test_infer_constant_input_mean(self):
        graph = build_graph(nodes_attributes2, edges2, inputs2)

        sparse_segment_mean_node = Node(graph, 'sparse_segment_mean_node')
        SparseSegmentMean.infer(sparse_segment_mean_node)

        # prepare reference results
        ref_output_segments_shape = int64_array([3, 4])
        ref_output_segments_value = np.array([[3, 4, 5, 6], [-1, -2, -3, -4], [2, 2, 2, 2]], dtype=np.float)

        # get resulted shapes
        res_output_segments_shape = graph.node['output_segments']['shape']
        res_output_segments_value = graph.node['output_segments']['value']

        self.assertTrue(np.array_equal(ref_output_segments_shape, res_output_segments_shape),
                        'Shapes do not match expected: {} and given: {}'.format(ref_output_segments_shape, res_output_segments_shape))
        self.assertTrue(np.array_equal(ref_output_segments_value, res_output_segments_value),
                        'Shapes do not match expected: {} and given: {}'.format(ref_output_segments_value, res_output_segments_value))
