# Copyright (c) 2020 Huawei Technologies Co., Ltd
# All rights reserved.
#
# 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 torch
import torch_npu

from torch_npu.testing.testcase import TestCase, run_tests
from torch_npu.testing.common_utils import create_common_tensor
from torch_npu.contrib.module import LabelSmoothingCrossEntropy

class TestCrossentropy(TestCase):
    
    def test_npu_crossentropy_1(self):
        x = torch.randn(2, 10)
        y = torch.randint(0, 10, size=(2,))

        x = x.npu()
        y = y.npu()
        x.requires_grad = True
        m = LabelSmoothingCrossEntropy(10)
        npu_output = m(x, y)
        npu_output.backward()
        expedt_cpu_xgrad = torch.tensor([[ 0.0465,  0.0317,  0.0612,  0.0215,  0.0695,  
                                           0.0849,  0.0354,  0.0255,  -0.4017,  0.0255],
                                         [ 0.0133,  0.0225,  0.0104,  0.0787,  0.0202, 
                                           0.1322, -0.4969,  0.1719, 0.0331,  0.0145]], dtype=torch.float32)
        self.assertTrue(3.3496, npu_output.detach().cpu())
        self.assertRtolEqual(expedt_cpu_xgrad, x.grad.cpu())

    def test_npu_crossentropy_2(self):
        x = torch.randn(2, 10)
        y = torch.randint(0, 10, size=(2,))

        x = x.npu()
        y = y.npu()
        x.requires_grad = True
        m = LabelSmoothingCrossEntropy(10, 0.1)
        npu_output = m(x, y)
        npu_output.backward()
        expedt_cpu_xgrad = torch.tensor([[ 0.0410,  0.0261,  0.0557,  0.0160,  0.0639,  
                                           0.0793,  0.0298,  0.0200, -0.3517,  0.0199],
                                        [  0.0077,  0.0170,  0.0049,  0.0732,  0.0146,  
                                           0.1267, -0.4469,  0.1663, 0.0275,  0.0090]], dtype=torch.float32)
        self.assertTrue(3.2760, npu_output.cpu())
        self.assertRtolEqual(expedt_cpu_xgrad, x.grad.cpu())

if __name__ == "__main__":
    run_tests()