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 TestArgmax(TestCase):
    def cpu_op_exec(self, input1):
        output = torch.argmax(input1)
        output = output.numpy()
        return output

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

    def test_argmax_shape_format_fp16(self, device="npu"):
        format_list = [0]
        shape_list = [[5], [2, 4], [2, 2, 4], [2, 3, 3, 4]]
        shape_format = [[np.float16, i, j] for i in format_list for j in shape_list]
        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item, -10, 10)
            cpu_input = cpu_input.to(torch.float32)
            cpu_output = self.cpu_op_exec(cpu_input)
            npu_output = self.npu_op_exec(npu_input)
            cpu_output = cpu_output.astype(npu_output.dtype)
            self.assertRtolEqual(cpu_output, npu_output)

    def test_argmax_shape_format_fp32(self, device="npu"):
        format_list = [0]
        shape_list = [[5], [2, 4], [2, 2, 4], [2, 3, 3, 4]]
        shape_format = [[np.float32, i, j] for i in format_list for j in shape_list]
        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item, -10, 10)
            cpu_output = self.cpu_op_exec(cpu_input)
            npu_output = self.npu_op_exec(npu_input)
            cpu_output = cpu_output.astype(npu_output.dtype)
            self.assertRtolEqual(cpu_output, npu_output)

    def cpu_op_exec1(self, input1, dim):
        output = torch.argmax(input1, dim)
        output = output.numpy()
        return output

    def npu_op_exec1(self, input1, dim):
        output = torch.argmax(input1, dim)
        output = output.to("cpu")
        output = output.numpy()
        return output

    def test_argmaxd_shape_format_fp16(self, device="npu"):
        format_list = [0]
        shape_list = [[5], [2, 4], [2, 2, 4], [2, 3, 3, 4]]
        shape_format = [[np.float16, i, j] for i in format_list for j in shape_list]
        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item, -10, 10)
            cpu_input = cpu_input.to(torch.float32)
            cpu_output = self.cpu_op_exec1(cpu_input, -1)
            npu_output = self.npu_op_exec1(npu_input, -1)
            cpu_output = cpu_output.astype(npu_output.dtype)
            self.assertRtolEqual(cpu_output, npu_output)

    def test_argmaxd_shape_format_fp32(self, device="npu"):
        format_list = [0]
        shape_list = [[5], [2, 4], [2, 2, 4], [2, 3, 3, 4]]
        shape_format = [[np.float32, i, j] for i in format_list for j in shape_list]
        for item in shape_format:
            cpu_input, npu_input = create_common_tensor(item, -10, 10)
            cpu_output = self.cpu_op_exec1(cpu_input, -1)
            npu_output = self.npu_op_exec1(npu_input, -1)
            cpu_output = cpu_output.astype(npu_output.dtype)
            self.assertRtolEqual(cpu_output, npu_output)


if __name__ == "__main__":
    run_tests()