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 TestExpm1(TestCase):

    def get_shapeFormat1(self):
        shape_format = [
            [np.float32, -1, (4, 3)],
            [np.float32, -1, (2, 4, 3)],
            [np.float32, 3, (20, 13)],
            [np.float32, 4, (20, 13)],
            [np.float32, 29, (20, 13)]
        ]
        return shape_format

    def get_shapeFormat2(self):
        shape_format = [
            [np.float32, -1, (4, 3)],
            [np.float32, 0, (4, 3)],
            [np.float32, -1, (2, 4, 3)],
            [np.float32, 3, (20, 13)],
            [np.float32, 4, (20, 13)],
            [np.float32, 29, (20, 13)]
        ]
        return shape_format

    def get_shapeFormat3(self):
        shape_format = [
            [np.float16, -1, (4, 3)],
            [np.float16, 0, (4, 3)],
            [np.float16, -1, (2, 4, 3)],
            [np.float16, -1, (100, 20, 10)],
            [np.float16, 3, (20, 13)],
            [np.float16, 4, (20, 13)],
            [np.float16, 29, (20, 13)]
        ]
        return shape_format

    def cpu_op_exec(self, input1):
        output = torch.expm1(input1)
        output = output.numpy()
        return output

    def npu_op_exec(self, input1):
        output = torch.expm1(input1)
        output = output.to("cpu").numpy()
        return output

    def cpu_op_exec_(self, input1):
        torch.expm1_(input1)
        output = input1.numpy()
        return output

    def npu_op_exec_(self, input1):
        torch.expm1_(input1)
        output = input1.to("cpu").numpy()
        return output

    def npu_op_out_exec(self, input1, output):
        torch.expm1(input1, out=output)
        output = output.to("cpu").numpy()
        return output

    def test_expm1_float32_common_shape_format(self):
        shape_format = self.get_shapeFormat1()
        for item in shape_format:
            cpu_input1, npu_input1 = create_common_tensor(item, 1, 10)
            cpu_output = self.cpu_op_exec(cpu_input1)
            npu_output = self.npu_op_exec(npu_input1)
            self.assertRtolEqual(cpu_output, npu_output)

    def test_expm1_float321_common_shape_format(self):
        shape_format = self.get_shapeFormat1()
        for item in shape_format:
            cpu_input1, npu_input1 = create_common_tensor(item, 1, 10)
            cpu_output = self.cpu_op_exec_(cpu_input1)
            npu_output = self.npu_op_exec_(npu_input1)
            self.assertRtolEqual(cpu_output, npu_output)

    def test_expm1_out_float32_common_shape_format(self):
        shape_format = [
            [[np.float32, -1, (4, 3)], [np.float32, -1, (3, 4)]],
            [[np.float32, 0, (4, 3)], [np.float32, 0, (3, 4)]],
            [[np.float32, -1, (2, 4, 3)], [np.float32, -1, (2, 3, 4)]],
            [[np.float32, 3, (20, 13)], [np.float32, 3, (13, 20)]],
            [[np.float32, 4, (20, 13)], [np.float32, 4, (20, 13)]],
            [[np.float32, 29, (20, 13)], [np.float32, 29, (20, 13)]]
        ]
        for item in shape_format:
            cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 10)
            _, npu_out = create_common_tensor(item[1], 1, 10)
            cpu_output = self.cpu_op_exec(cpu_input1)
            npu_output = self.npu_op_out_exec(npu_input1, npu_out)
            self.assertEqual(cpu_output.shape, npu_output.shape)
            self.assertRtolEqual(cpu_output, npu_output)

    def test_expm1_float16_common_shape_format(self):
        shape_format = self.get_shapeFormat2()
        for item in shape_format:
            cpu_input1, npu_input1 = create_common_tensor(item, 1, 10)
            if item[0] == np.float16:
                cpu_input1 = cpu_input1.to(torch.float32)
            cpu_output = self.cpu_op_exec(cpu_input1)
            npu_output = self.npu_op_exec(npu_input1)
            if item[0] == np.float16:
                cpu_output = cpu_output.astype(np.float16)
            self.assertRtolEqual(cpu_output, npu_output)

    def test_expm1_float16__common_shape_format(self):
        shape_format = self.get_shapeFormat3()
        for item in shape_format:
            cpu_input1, npu_input1 = create_common_tensor(item, 1, 10)
            if item[0] == np.float16:
                cpu_input1 = cpu_input1.to(torch.float32)
            cpu_output = self.cpu_op_exec_(cpu_input1)
            npu_output = self.npu_op_exec_(npu_input1)
            if item[0] == np.float16:
                cpu_output = cpu_output.astype(np.float16)
            self.assertRtolEqual(cpu_output, npu_output)

    def test_expm1_out_float16_common_shape_format(self):
        shape_format = [
            [[np.float16, -1, (4, 3)], [np.float16, -1, (3, 4)]],
            [[np.float16, 0, (4, 3)], [np.float16, 0, (3, 4)]],
            [[np.float16, -1, (100, 20, 10)], [np.float16, -1, (10, 20, 100)]],
            [[np.float16, 3, (20, 13)], [np.float16, 3, (13, 20)]],
            [[np.float16, 4, (20, 13)], [np.float16, 4, (20, 13)]],
            [[np.float16, 29, (20, 13)], [np.float16, 29, (18, 13)]]]
        for item in shape_format:
            cpu_input1, npu_input1 = create_common_tensor(item[0], 1, 10)
            _, npu_out = create_common_tensor(item[1], 1, 10)
            if item[0][0] == np.float16:
                cpu_input1 = cpu_input1.to(torch.float32)
            cpu_output = self.cpu_op_exec(cpu_input1)
            npu_output = self.npu_op_out_exec(npu_input1, npu_out)
            if item[0][0] == np.float16:
                cpu_output = cpu_output.astype(np.float16)
            self.assertRtolEqual(cpu_output, npu_output)


if __name__ == "__main__":
    run_tests()