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

from torch_npu.testing.testcase import TestCase, run_tests
from torch_npu.testing.common_utils import create_common_tensor


class TestMish(TestCase):
    def npu_op_exec(self, input1):
        output = torch_npu.npu_mish(input1)
        output = output.cpu().numpy()
        return output

    def cpu_op_exec(self, input1):
        output = input1 * (torch.tanh(F.softplus(input1)))
        output = output.numpy()
        return output

    def test_mish_fp32(self):
        shape_format = [
            [[np.float32, -1, [10, 30, 10]]],
            [[np.float32, -1, [20, 30, 20]]],
        ]
        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item[0], 0, 100)
            cpu_output = self.cpu_op_exec(cpu_input)
            npu_output = self.npu_op_exec(npu_input)
            self.assertRtolEqual(cpu_output, npu_output)

    def test_mish_fp16(self):
        shape_format = [
            [[np.float16, -1, [10, 30, 10]]],
            [[np.float16, -1, [20, 30, 20]]],
        ]
        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item[0], 0, 100)
            cpu_output = self.cpu_op_exec(cpu_input.float()).astype(np.float16)
            npu_output = self.npu_op_exec(npu_input)
            self.assertRtolEqual(cpu_output, npu_output)


if __name__ == "__main__":
    run_tests()