# 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 numpy as np
import torch
import torch.nn.functional as F

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 Mish, SiLU

class TestActivations(TestCase):

    def cpu_mish(self, input1):
        """
        Official implementation based on PyTorch link:
        https://github.com/digantamisra98/Mish/blob/master/Mish/Torch/mish.py

        Applies the mish function element-wise:
        mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x)))
        See additional documentation for mish class.
        """
        input1.requires_grad = True
        res = input1 * torch.tanh(F.softplus(input1))
        l = res.sum()
        l.backward()
        return res.detach(), input1.grad

    def npu_mish(self, input1):
        input1.requires_grad = True
        model = Mish()
        res = model(input1)
        l = res.sum()
        l.backward()
        return res.detach().cpu(), input1.grad.cpu()

    def test_mish(self):
        dtype_list = [np.float16, np.float32]
        format_list = [-1, 0, 2]
        shape_list = [
            [4],
            [2, 3],
            [6, 5, 8, 10],
            [1, 2, 3, 6, 6],
            [2, 5, 6, 8, 9, 2],
            [2, 5, 6, 8, 9, 2, 2],
        ]
        shape_format = [
            [i, j, k] for i in dtype_list for j in format_list for k in shape_list
        ]

        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item, 1, 10)
            if cpu_input.dtype == torch.float16:
                cpu_input = cpu_input.float()
                cpu_output, cpu_inputgrad = self.cpu_mish(cpu_input)
                cpu_output = cpu_output.half()
                cpu_inputgrad = cpu_inputgrad.half()
            else:
                cpu_output, cpu_inputgrad = self.cpu_mish(cpu_input)

            npu_output, npu_inputgrad = self.npu_mish(npu_input)
        
            self.assertRtolEqual(cpu_output, npu_output)
            self.assertRtolEqual(cpu_inputgrad, npu_inputgrad)

    def cpu_silu(self, input1):
        """
        Official implementation based on PyTorch link:
        https://github.com/digantamisra98/Mish/blob/master/Mish/Torch/mish.py

        Applies the mish function element-wise:
        mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x)))
        See additional documentation for mish class.
        """
        input1.requires_grad = True
        res = input1 * torch.sigmoid(input1)
        l = res.sum()
        l.backward()
        return res.detach(), input1.grad

    def npu_silu(self, input1):
        input1.requires_grad = True
        model = SiLU()
        res = model(input1)
        l = res.sum()
        l.backward()
        return res.detach().cpu(), input1.grad.cpu()

    def test_silu(self):
        dtype_list = [np.float32, np.float16]
        format_list = [-1, 0, 2]
        shape_list = [
            [5],
            [2, 3],
            [6, 5, 2, 10],
            [1, 2, 4, 6, 6],
            [2, 5, 6, 2, 9, 2],
            [2, 5, 6, 3, 9, 2, 2],
        ]
        
        shape_format = [
            [i, j, k] for i in dtype_list for j in format_list for k in shape_list
        ]

        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item, 1, 10)
            if cpu_input.dtype == torch.float16:
                cpu_input = cpu_input.float()
                cpu_output, cpu_inputgrad = self.cpu_silu(cpu_input)
                cpu_output = cpu_output.half()
                cpu_inputgrad = cpu_inputgrad.half()
            else:
                cpu_output, cpu_inputgrad = self.cpu_silu(cpu_input)

            npu_output, npu_inputgrad = self.npu_silu(npu_input)
        
            self.assertRtolEqual(cpu_output, npu_output)
            self.assertRtolEqual(cpu_inputgrad, npu_inputgrad)
    
if __name__ == "__main__":
    run_tests()