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 TestEqual(TestCase):
    def cpu_op_exec(self, input1, input2):
        output = torch.eq(input1, input2)
        output = output.numpy()
        return output

    def npu_op_exec(self, input1, input2):
        output = torch.eq(input1, input2)
        output = output.to("cpu")
        output = output.numpy()
        return output

    def npu_op_exec_out(self, input1, input2):
        input3 = torch.empty(0).bool().npu()
        torch.eq(input1, input2, out=input3)
        output = input3.to("cpu")
        output = output.numpy()
        return output

    def test_equal_shape_format_fp32(self):
        dtype_list = [np.float32]
        format_list = [0, 3]
        shape_list = [[1024], [8, 128], [2, 8, 128], [2, 8, 128, 512]]
        # pylint:disable=complicate-comprehension
        shape_format = [
            [d, i, j] for d in dtype_list for i in format_list for j in shape_list
        ]
        for item in shape_format:
            cpu_input1, npu_input1 = create_common_tensor(item, 1, 100)
            cpu_input2, npu_input2 = create_common_tensor(item, 1, 100)
            cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2)
            npu_output = self.npu_op_exec(npu_input1, npu_input2)
            self.assertEqual(cpu_output, npu_output)

    def test_equal_shape_format_fp16(self):
        dtype_list = [np.float16]
        format_list = [0, 3]
        # pylint:disable=complicate-comprehension
        shape_list = [[1024], [8, 128], [2, 8, 128], [2, 8, 128, 512]]
        shape_format = [
            [d, i, j] for d in dtype_list for i in format_list for j in shape_list
        ]
        for item in shape_format:
            cpu_input1, npu_input1 = create_common_tensor(item, 1, 100)
            cpu_input2, npu_input2 = create_common_tensor(item, 1, 100)
            if cpu_input1.dtype == torch.float16:
                cpu_input1 = cpu_input1.to(torch.float32)
            if cpu_input1.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(np.float16)
            self.assertEqual(cpu_output, npu_output)

    def test_equal_out_shape_format_fp32(self):
        dtype_list = [np.float32]
        format_list = [0]
        shape_list = [[1024], [8, 128], [2, 8, 128], [2, 8, 128, 512]]
        # pylint:disable=complicate-comprehension
        shape_format = [
            [[d, i, j]] for d in dtype_list for i in format_list for j in shape_list
        ]
        for item in shape_format:
            cpu_input1, npu_input1 = create_common_tensor(item[0], -10, 10)
            cpu_input2, npu_input2 = create_common_tensor(item[0], -10, 10)
            npu_output_out = self.npu_op_exec_out(npu_input1, npu_input2)
            npu_output = self.npu_op_exec(npu_input1, npu_input2)
            self.assertEqual(npu_output_out, npu_output)

    def test_equal_scalar_out_shape_format_fp32(self):
        dtype_list = [np.float32]
        format_list = [0]
        shape_list = [[1024], [8, 128], [2, 8, 128], [2, 8, 128, 512]]
        # pylint:disable=complicate-comprehension
        shape_format = [
            [[d, i, j]] for d in dtype_list for i in format_list for j in shape_list
        ]
        for item in shape_format:
            cpu_input1, npu_input1 = create_common_tensor(item[0], -10, 10)
            npu_output_out = self.npu_op_exec_out(npu_input1, 5)
            npu_output = self.npu_op_exec(npu_input1, 5)
            self.assertEqual(npu_output_out, npu_output)

    def test_equal_mix_dtype(self):
        npu_input1, npu_input2 = create_common_tensor([np.float16, 0, (2, 3)], 1, 100)
        npu_input3, npu_input4 = create_common_tensor([np.float32, 0, (2, 3)], 1, 100)
        cpu_output = self.cpu_op_exec(npu_input1, npu_input3)
        npu_output = self.npu_op_exec(npu_input2, npu_input4)
        self.assertRtolEqual(cpu_output, npu_output)

    def test_equal_diff_device(self):
        input1 = cmp1 = torch.randn(5, 5)
        input2 = cmp2 = torch.tensor(1)
        diff_device_out = torch.eq(input1.npu(), input2)
        diff_device_cmp = torch.eq(cmp1, cmp2)
        self.assertRtolEqual(diff_device_out, diff_device_cmp)

        input1 = cmp1 = torch.tensor(1)
        input2 = cmp2 = torch.tensor(2)
        diff_device_out = torch.eq(input1, input2.npu())
        diff_device_cmp = torch.eq(cmp1, cmp2)
        self.assertRtolEqual(diff_device_out, diff_device_cmp)


if __name__ == "__main__":
    run_tests()