# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the BSD 3-Clause License  (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# 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 itertools
import torch
import numpy as np

from torch_npu.testing.testcase import TestCase, run_tests
from torch_npu.testing.common_utils import create_dtype_tensor
from torch_npu.testing.decorator import Dtypes, Formats, instantiate_tests


# For testing TestCase methods and torch_npu.testing functions
@instantiate_tests
class TestTesting(TestCase):

    # Ensure that assertTensorSlowEqual handles npu arrays properly
    @Dtypes(torch.int32, torch.bool, torch.half, torch.float)
    @Formats(0, 3, 4)
    def test_assert_tensor_slow_equal(self, device, dtype, npu_format):
        test_sizes = [
            (),
            (0,),
            (5,),
            (5, 5),
            (0, 5),
            (5, 0),
        ]
        for test_size in test_sizes:
            a_cpu, a_npu = create_dtype_tensor(test_size, dtype, npu_format, device=device)
            msg = f'Device: {device} Size: {test_size} Dtype: {dtype} Npu_format: {npu_format}'
            self.assertTensorsSlowEqual(a_cpu, a_npu, prec=1e-3, message=msg)
            self.assertTensorsSlowEqual(a_npu, a_cpu, prec=1e-3, message=msg)
            self.assertTensorsSlowEqual(a_cpu, a_cpu, prec=1e-3, message=msg)

    # Ensure that assertRtolEqual handles npu arrays properly
    @Dtypes(torch.int32, torch.bool, torch.half, torch.float)
    @Formats(0, 3, 4)
    def test_assert_rtol_equal(self, device, dtype, npu_format):
        test_sizes = [
            (),
            (0,),
            (6,),
            (6, 6),
            (0, 6),
            (6, 0),
        ]
        for test_size in test_sizes:
            a_cpu, a_npu = create_dtype_tensor(test_size, dtype, npu_format, device=device)
            msg = f'Device: {device} Size: {test_size} Dtype: {dtype} Npu_format: {npu_format}'
            self.assertRtolEqual(a_cpu, a_npu.cpu())
            self.assertRtolEqual(a_npu.cpu(), a_cpu)
            self.assertRtolEqual(a_cpu, a_cpu)

    # Ensure that assertEqual handles npu arrays properly
    @Dtypes(torch.int32, torch.bool, torch.half, torch.float)
    @Formats(0, 3, 4)
    def test_assert_equal(self, device, dtype, npu_format):
        test_sizes = [
            (),
            (0,),
            (7,),
            (7, 7),
            (0, 7),
            (7, 0),
        ]
        for test_size in test_sizes:
            a_cpu, a_npu = create_dtype_tensor(test_size, dtype, npu_format, device=device)
            msg = f'Device: {device} Size: {test_size} Dtype: {dtype} Npu_format: {npu_format}'
            self.assertEqual(a_cpu, a_npu, message=msg)
            self.assertEqual(a_npu, a_cpu, message=msg)
            self.assertEqual(a_cpu, a_cpu, message=msg)

    # Ensure that assertAlmostEqual handles npu arrays properly
    @Dtypes(torch.int32, torch.bool, torch.half, torch.float)
    @Formats(0, 3, 4)
    def test_assert_almost_equal(self, device, dtype, npu_format):
        test_sizes = [
            (),
            (0,),
            (8,),
            (8, 8),
            (0, 8),
            (8, 0),
        ]
        for test_size in test_sizes:
            a_cpu, a_npu = create_dtype_tensor(test_size, dtype, npu_format, device=device)
            msg = f'Device: {device} Size: {test_size} Dtype: {dtype} Npu_format: {npu_format}'
            self.assertAlmostEqual(a_cpu, a_npu, msg=msg)
            self.assertAlmostEqual(a_npu, a_cpu, msg=msg)
            self.assertAlmostEqual(a_cpu, a_cpu, msg=msg)

    # Ensure that assertNotEqual handles npu arrays properly
    @Dtypes(torch.int32, torch.bool, torch.float)
    @Formats(0, 3, 4)
    def test_assert_not_equal(self, device, dtype, npu_format):
        test_sizes = [
            (),
            (9,),
            (9, 9),
        ]
        for test_size in test_sizes:
            if dtype == torch.bool:
                a_cpu = torch.from_numpy(np.zeros(test_size, bool))
                a_npu = a_cpu.to(device)
                b_cpu = torch.from_numpy(np.ones(test_size, bool))
                b_npu = b_cpu.to(device)
            else:
                a_cpu, a_npu = create_dtype_tensor(test_size, dtype, npu_format,
                                                   min_value=5, max_value=10, device=device)
                b_cpu, b_npu = create_dtype_tensor(test_size, dtype, npu_format,
                                                   min_value=-10, max_value=-5, device=device)
            msg = f'Device: {device} Size: {test_size} Dtype: {dtype} Npu_format: {npu_format}'
            self.assertNotEqual(a_cpu, b_cpu, message=msg)
            self.assertNotEqual(a_cpu, b_npu, message=msg)
            self.assertNotEqual(a_npu, b_cpu, message=msg)
            self.assertNotEqual(a_npu, b_npu, message=msg)


if __name__ == '__main__':
    run_tests()