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 TestPrelu(TestCase):

    def cpu_op_exec(self, input1, input2):
        output = input1.prelu(input2)
        return output.numpy()

    def npu_op_exec(self, input1, input2):
        output = input1.prelu(input2)
        output = output.to("cpu")
        if output.dtype != torch.float32:
            output = output.to(torch.float32)
        return output.numpy()

    def test_prelu_shape_format(self):
        shape_format = [
            [[np.float32, 0, [1, 1]], [np.float32, 0, 1]],
            [[np.float32, 0, [2, 2]], [np.float32, 0, 1]],
            [[np.float16, 0, [1, 1]], [np.float16, 0, 1]],
            [[np.float16, 0, [2, 2]], [np.float16, 0, 1]]
        ]
        for item in shape_format:
            cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 10)
            cpu_input2, npu_input2 = create_common_tensor(item[1], 0, 10)
            if cpu_input1.dtype == torch.float16:
                cpu_input1 = cpu_input1.to(torch.float32)
            if cpu_input2.dtype == torch.float16:
                cpu_input2 = cpu_input2.to(torch.float32)
            cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2)
            npu_output = self.npu_op_exec(npu_input1, npu_input2)
            cpu_output = cpu_output.astype(npu_output.dtype)
            self.assertRtolEqual(cpu_output, npu_output)


if __name__ == "__main__":
    run_tests()