"""
 Copyright (C) 2018-2021 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

import numpy as np
from generator import generator, generate

from extensions.middle.L2NormFusing import L2NormToNorm
from mo.front.common.partial_infer.utils import int64_array
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph_with_attrs

# A list with nodes attributes used to build various graphs.
nodes = [
    ('l2_normalize_mul', dict(kind='op', op='Mul', name='l2_norm_name')),
    ('l2_normalize_mul_data', dict(kind='data')),
    ('maximum', dict(kind='op', op='Maximum')),
    ('maximum_data', dict(kind='data')),
    ('maximum_y_const', dict(kind='op', op='Const', value=np.array(12.e-13, dtype=np.float32))),
    ('maximum_y_data', dict(kind='data', value=np.array(12.e-13, dtype=np.float32))),
    ('rsqrt_pow', dict(kind='data', value=-0.5)),
    ('rsqrt', dict(kind='op', op='Pow')),
    ('rsqrt_data', dict(kind='data')),
    ('square_pow', dict(kind='op', op='Const', value=2.)),
    ('square_pow_data', dict(kind='data', value=2.)),
    ('square', dict(kind='op', op='Pow')),
    ('sum', dict(kind='op', op='ReduceSum')),
    ('sum_data', dict(kind='data')),
    ('sum_axes', dict(kind='op', op='Const')),
    # nodes added after replacement
    ('normalize_node', dict(kind='op', op='NormalizeL2')),
    ('weights_node', dict(kind='op', op='Const')),
    ('result', dict(kind='op', op='Result'))
]

edges = [
    ('input', 'input_data', {'out': 0}),
    ('input_data', 'square', {'in': 0}),
    ('square_pow', 'square_pow_data', {'out': 0}),
    ('square_pow_data', 'square', {'in': 1}),
    ('square', 'square_data'),
    ('square_data', 'sum'),
    ('sum_axes', 'sum_axes_data'),
    ('sum_axes_data', 'sum'),
    ('sum', 'sum_data'),
    ('maximum_y_const', 'maximum_y_data'),
    ('maximum_y_data', 'maximum'),
    ('sum_data', 'maximum'),
    ('maximum', 'maximum_data'),
    ('maximum_data', 'rsqrt', {'in': 0}),
    ('rsqrt_pow', 'rsqrt', {'in': 1}),
    ('rsqrt', 'rsqrt_data'),
    ('rsqrt_data', 'l2_normalize_mul'),
    ('input_data', 'l2_normalize_mul'),
    ('l2_normalize_mul', 'l2_normalize_mul_data'),
    ('l2_normalize_mul_data', 'result'),
]

edges_after_replacement = [
    ('input', 'input_data', {'out': 0}),
    ('input_data', 'normalize_node'),
    ('weights_node', 'weights_node_data'),
    ('weights_node_data', 'normalize_node'),
    ('normalize_node', 'l2_normalize_mul_data'),
    ('l2_normalize_mul_data', 'result'),
]


@generator
class L2NormToNormTest(unittest.TestCase):
    @generate(*[(int64_array([2, 3]), int64_array([1]), 'NCHW'),  # NC layout, normalize C dimension
                (int64_array([2, 3]), int64_array([1]), 'NHWC'),  # NC layout, normalize C dimension
                (int64_array([2, 3, 5]), int64_array([1]), 'NCHW'),  # NCH layout, normalize C dimension
                (int64_array([2, 3, 5]), int64_array([1]), 'NHWC'),  # NCH layout, normalize C dimension
                (int64_array([2, 3, 5]), int64_array([-1, -2]), 'NHWC'),  # NCH layout, normalize CH dimensions
                (int64_array([2, 3, 5]), int64_array([-1, -2]), 'NCHW'),  # NCH layout, normalize CH dimensions
                (int64_array([2, 3, 5]), int64_array([1, 2]), 'NCHW'),  # NCH layout, normalize CH dimensions
                (int64_array([2, 3, 5]), int64_array([1, 2]), 'NHWC'),  # NCH layout, normalize CH dimensions
                (int64_array([2, 3, 5, 7]), int64_array([1]), 'NCHW'),  # NCHW layout, normalize C dimension
                (int64_array([2, 3, 5, 7]), int64_array([-1]), 'NHWC'),  # NHWC layout, normalize C dimension
                (int64_array([2, 3, 5, 7]), int64_array([3]), 'NHWC'),  # NCHW layout, normalize C dimension
                (int64_array([2, 3, 5, 7]), int64_array([-1, 1, 2]), 'NCHW'),  # NCHW layout, normalize CHW dimensions
                (int64_array([2, 3, 5, 7]), int64_array([-3, -2, -1]), 'NHWC'),  # NCHW layout, normalize HWC dimensions
                ])
    def test_positive(self, input_shape, axes, layout):
        graph = build_graph_with_attrs(nodes + [
            ('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
            ('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
            ('square_data', dict(kind='data', shape=input_shape)),
            ('sum_axes_data', dict(kind='data', value=axes, shape=None)),
        ], edges, nodes_with_edges_only=True)
        graph.stage = 'middle'
        graph.graph['layout'] = layout

        L2NormToNorm().find_and_replace_pattern(graph)

        graph_ref = build_graph_with_attrs(nodes + [
            ('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
            ('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
            ('weights_node_data', dict(kind='data', value=axes.sort())),
        ], edges_after_replacement, nodes_with_edges_only=True)

        (flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
        self.assertTrue(graph.node[graph.get_nodes_with_attributes(type='NormalizeL2')[0]]['name'] == 'l2_norm_name')
        self.assertTrue(flag, resp)

    @generate(*[(int64_array([2]), int64_array([0]), 'NCHW'),
                (int64_array([2, 3]), int64_array([0]), 'NCHW'),
                (int64_array([2, 3]), int64_array([0]), 'NHWC'),
                (int64_array([2, 3]), int64_array([0, 1]), 'NCHW'),
                (int64_array([2, 3]), int64_array([0, 1]), 'NHWC'),
                (int64_array([2, 3, 5]), int64_array([0]), 'NCHW'),
                (int64_array([2, 3, 5]), int64_array([0]), 'NHWC'),
                (int64_array([2, 3, 5]), int64_array([-1]), 'NCHW'),
                (int64_array([2, 3, 5]), int64_array([-1]), 'NHWC'),
                (int64_array([2, 3, 5]), int64_array([0, 1]), 'NCHW'),
                (int64_array([2, 3, 5]), int64_array([0, 1]), 'NHWC'),
                (int64_array([2, 3, 5]), int64_array([0, 2]), 'NCHW'),
                (int64_array([2, 3, 5]), int64_array([0, 2]), 'NHWC'),
                (int64_array([2, 3, 5, 7]), int64_array([0]), 'NCHW'),
                (int64_array([2, 3, 5, 7]), int64_array([0]), 'NHWC'),
                (int64_array([2, 3, 5, 7]), int64_array([2]), 'NCHW'),
                (int64_array([2, 3, 5, 7]), int64_array([2]), 'NHWC'),
                (int64_array([2, 3, 5, 7]), int64_array([3]), 'NCHW'),
                (int64_array([2, 3, 5, 7]), int64_array([1]), 'NHWC'),
                (int64_array([2, 3, 5, 7]), int64_array([1, 2]), 'NCHW'),
                (int64_array([2, 3, 5, 7]), int64_array([1, -1]), 'NHWC'),
                (int64_array([2, 3, 5, 7]), int64_array([1, -1]), 'NCHW'),
                (int64_array([2, 3, 5, 7]), int64_array([-2, -1]), 'NHWC'),
                (int64_array([2, 3, 5, 7]), int64_array([1, 3]), 'NCHW'),
                (int64_array([2, 3, 5, 7]), int64_array([2, 3]), 'NHWC'),
                (int64_array([2, 3, 5, 7]), int64_array([0, 1, 2]), 'NCHW'),
                (int64_array([2, 3, 5, 7]), int64_array([0, 1, 2]), 'NHWC'),
                (int64_array([2, 3, 5, 7]), int64_array([0, 2, 3]), 'NCHW'),
                (int64_array([2, 3, 5, 7]), int64_array([0, 2, 3]), 'NHWC'),
                (int64_array([2, 3, 5, 7]), int64_array([0, 1, 2, 3]), 'NCHW'),
                (int64_array([2, 3, 5, 7]), int64_array([0, 1, 2, 3]), 'NHWC'),
                (int64_array([2, 3, 5, 7, 9]), int64_array([1]), 'NCHW'),
                (int64_array([2, 3, 5, 7, 9]), int64_array([-1]), 'NHWC'),
                (int64_array([2, 3, 5, 7, 9]), int64_array([1, 2, 3, 4]), 'NCHW'),
                (int64_array([2, 3, 5, 7, 9]), int64_array([-1, -2, -3, -4]), 'NHWC'),
                ])
    def test_negative(self, input_shape, axes, layout):
        graph = build_graph_with_attrs(nodes + [
            ('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
            ('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
            ('square_data', dict(kind='data', shape=input_shape)),
            ('sum_axes_data', dict(kind='data', value=axes, shape=None)),
        ], edges, nodes_with_edges_only=True)
        graph.stage = 'middle'
        graph.graph['layout'] = layout

        L2NormToNorm().find_and_replace_pattern(graph)

        graph_ref = build_graph_with_attrs(nodes + [
            ('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
            ('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
            ('square_data', dict(kind='data', shape=input_shape)),
            ('sum_axes_data', dict(kind='data', value=axes, shape=None)),
        ], edges, nodes_with_edges_only=True)

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