# coding: utf-8

import torch
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 TestLogsigmoidForward(TestCase):

    def cpu_op_exec(self, input1):
        m = torch.nn.LogSigmoid()
        output = m.forward(input1)
        return output.numpy()

    def npu_op_exec(self, input1):
        m = torch.nn.LogSigmoid().to("npu")
        output = m.forward(input1)
        output = output.to("cpu")
        return output.numpy()

    def test_sigmoid_forward_shape_format(self):
        shape_format1 = [
            [[np.float32, 0, (6, 4)]],
            [[np.float32, 3, (2, 4, 5)]],
            [[np.float32, 4, (1, 2, 3, 3)]],
            [[np.float32, 29, (1, 2, 3, 3)]]
        ]
        for item in shape_format1:
            cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 100)
            cpu_output1 = self.cpu_op_exec(cpu_input1)
            npu_output1 = self.npu_op_exec(npu_input1)
            self.assertRtolEqual(cpu_output1, npu_output1)

    def test_sigmoid_forward_fp16_shape_format(self):
        shape_format = [
            [[np.float16, 0, (6, 4)]],
            [[np.float16, 3, (2, 4, 5)]],
            [[np.float16, 4, (1, 2, 3, 3)]],
            [[np.float16, 29, (1, 2, 3, 3)]]
        ]

        def cpu_op_fp16_exec(input1):
            input1 = input1.to(torch.float32)
            m = torch.nn.LogSigmoid()
            output = m.forward(input1)
            return output.numpy().astype(np.float16)

        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item[0], 1, 100)
            cpu_output = cpu_op_fp16_exec(cpu_input)
            npu_output = self.npu_op_exec(npu_input)
            self.assertRtolEqual(cpu_output, npu_output)


if __name__ == "__main__":
    run_tests()