"""
 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 unittest
from argparse import Namespace

from generator import generate, generator

from extensions.back.MatMulNormalizer import SmartReshape_HC_Reshape_MatMul
from extensions.ops.MatMul import MatMul
from mo.front.common.partial_infer.utils import int64_array
from mo.ops.reshape import Reshape
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, regular_op_with_shaped_data, valued_const_with_data, \
    result, connect
from mo.utils.unittest.graph import regular_op_with_empty_data as op_with_empty_data


@generator
class SmartReshape_HC_Reshape_MatMulTest(unittest.TestCase):
    @generate(
        *[
            ([1, 20, 30], [30, 40], [20, -1], False, False, [-1, 30]),
            ([1, 20, 30], [40, 30], [20, -1], False, True, [-1, 30]),
            ([1, 30, 20], [30, 40], [-1, 20], True, False, [30, -1]),
            ([1, 30, 20], [40, 30], [-1, 20], True, True, [30, -1]),
        ]
    )
    def test_reshape_on_the_A_input(self,
                                    in1_shape, in2_shape, reshape_pattern, transpose_a, transpose_b, updated_pattern):
        nodes = {
            **regular_op_with_shaped_data('in_1', in1_shape, dict(type='Parameter', op='Parameter')),
            **regular_op_with_shaped_data('in_2', in2_shape, dict(type='Parameter', op='Parameter')),
            **valued_const_with_data('dim', int64_array(reshape_pattern)),
            **op_with_empty_data('reshape',
                                 dict(type='Reshape', op='Reshape', infer=Reshape.infer, need_shape_inference=True)),
            **op_with_empty_data('matmul',
                                 dict(type='MatMul', op='MatMul', infer=MatMul.infer, need_shape_inference=True,
                                      transpose_a=transpose_a, transpose_b=transpose_b, dim_attrs={})),
            **result(),
        }
        edges = [
            *connect('in_1:0', '0:reshape'),
            *connect('dim:0', '1:reshape'),
            *connect('reshape:0', '0:matmul'),
            *connect('in_2:0', '1:matmul'),
            *connect('matmul:0', 'output'),
        ]
        graph = build_graph(nodes_attrs=nodes, edges=edges, cli=Namespace(static_shape=True))
        graph.clean_up()
        SmartReshape_HC_Reshape_MatMul().find_and_replace_pattern(graph)
        graph.clean_up()

        graph_ref = build_graph(nodes_attrs=nodes, edges=edges, update_attributes={
            'dim': {'value': int64_array(updated_pattern)}, 'dim_d': {'value': int64_array(updated_pattern)}})
        graph_ref.clean_up()

        (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
        self.assertTrue(flag, resp)

    @generate(*[
        ([20, 30], [1, 30, 40], [-1, 40], False, False, [30, -1]),
        ([20, 30], [1, 40, 30], [40, -1], False, True, [-1, 30]),
        ([30, 20], [1, 30, 40], [-1, 40], True, False, [30, -1]),
        ([30, 20], [1, 40, 30], [40, -1], True, True, [-1, 30]),
    ])
    def test_reshape_on_the_B_input(self,
                                    in1_shape, in2_shape, reshape_pattern, transpose_a, transpose_b, updated_pattern):
        nodes = {
            **regular_op_with_shaped_data('in_1', in1_shape, dict(type='Parameter', op='Parameter')),
            **regular_op_with_shaped_data('in_2', in2_shape, dict(type='Parameter', op='Parameter')),
            **valued_const_with_data('dim', int64_array(reshape_pattern)),
            **op_with_empty_data('reshape',
                                 dict(type='Reshape', op='Reshape', infer=Reshape.infer, need_shape_inference=True)),
            **op_with_empty_data('matmul',
                                 dict(type='MatMul', op='MatMul', infer=MatMul.infer, need_shape_inference=True,
                                      transpose_a=transpose_a, transpose_b=transpose_b, dim_attrs={})),
            **result(),
        }
        edges = [
            *connect('in_1:0', '0:matmul'),
            *connect('in_2:0', '0:reshape'),
            *connect('dim:0', '1:reshape'),
            *connect('reshape:0', '1:matmul'),
            *connect('matmul:0', 'output'),
        ]
        graph = build_graph(nodes_attrs=nodes, edges=edges, cli=Namespace(static_shape=True))
        graph.clean_up()
        SmartReshape_HC_Reshape_MatMul().find_and_replace_pattern(graph)
        graph.clean_up()

        graph_ref = build_graph(nodes_attrs=nodes, edges=edges, update_attributes={
            'dim': {'value': int64_array(updated_pattern)}, 'dim_d': {'value': int64_array(updated_pattern)}})
        graph_ref.clean_up()

        (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
        self.assertTrue(flag, resp)
