import torch
import numpy as np
import torch.nn as nn
import torch_npu

from torch_npu.testing.testcase import TestCase, run_tests
from torch_npu.testing.common_utils import create_common_tensor


class TestConvDepthwise2d(TestCase):
    weight_grad = []
    input_grad = []

    def get_weight_grad(self, grad):
        self.weight_grad.append(grad.to("cpu"))

    def get_input_grad(self, grad):
        self.input_grad.append(grad.to("cpu"))

    # pylint:disable=huawei-too-many-arguments
    def op_exec_cpu(self, x, weight, in_channels, out_channels, kernel_size,
                    padding=0, stride=1, dilation=1, bias=True):
        input1 = x
        weight1 = weight
        input1.requires_grad = True
        input1.register_hook(lambda grad: self.get_input_grad(grad))

        m1 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias,
                       groups=in_channels)
        m1.weight.data = weight1
        m1.weight.register_hook(lambda grad: self.get_weight_grad(grad))
        cpuOutput = m1(input1)
        tmp = torch.ones_like(cpuOutput)
        cpuOutput.backward(tmp)

        return cpuOutput

    # pylint:disable=huawei-too-many-arguments
    def op_exec_npu(self, x, weight, in_channels, out_channels, kernel_size,
                    padding=0, stride=1, dilation=1, bias=True):
        input1 = x
        weight1 = weight
        input1.requires_grad = True
        input1.register_hook(lambda grad: self.get_input_grad(grad))

        m1 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias,
                       groups=in_channels)
        m1.weight.data = weight1
        m1.weight.register_hook(lambda grad: self.get_weight_grad(grad))
        m1 = m1.to("npu")
        npuOutput = m1(input1)
        npuOutput = npuOutput.to("cpu")
        tmp = torch.ones_like(npuOutput)
        npuOutput.backward(tmp)

        return npuOutput

    def conv_depthwise2d_backward_result(self, shape_format):
        for item in shape_format:
            self.weight_grad.clear()
            self.input_grad.clear()
            input_cpu, input_npu = create_common_tensor(item[0], -1, 1)
            if input_cpu.dtype == torch.float16:
                input_cpu = input_cpu.to(torch.float32)
            weight_cpu, weight_npu = create_common_tensor(item[1], -1, 1)
            if weight_cpu.dtype == torch.float16:
                weight_cpu = weight_cpu.to(torch.float32)
            kernel_size = (item[1][2][2], item[1][2][3])
            cpu_output = self.op_exec_cpu(input_cpu, weight_cpu, item[0][2][1], item[1][2][0], kernel_size=kernel_size,
                                          padding=item[2], stride=item[3], dilation=item[4], bias=item[5])
            weight_npu = weight_npu.to("cpu")
            npu_output = self.op_exec_npu(input_npu, weight_npu, item[0][2][1], item[1][2][0], kernel_size=kernel_size,
                                          padding=item[2], stride=item[3], dilation=item[4], bias=item[5])
            npu_output = npu_output.to(torch.float16)
            cpu_output = cpu_output.to(torch.float16)
            self.input_grad[0] = self.input_grad[0].to(torch.float16)
            self.input_grad[1] = self.input_grad[1].to(torch.float16)

            self.weight_grad[0] = self.weight_grad[0].to(self.weight_grad[1].dtype)

            self.assertRtolEqual(cpu_output.detach().numpy(), npu_output.detach().numpy())
            self.assertRtolEqual(self.input_grad[0].numpy(), self.input_grad[1].numpy())
            self.assertRtolEqual(self.weight_grad[0].numpy(), self.weight_grad[1].numpy())

    def test_conv_depthwise2d_backward_shape_format_fp16(self, device="npu"):
        shape_format = [
            [[np.float16, 0, [1024, 116, 28, 28]], [np.float16, 0, [116, 1, 3, 3]], 1, 2, 1, 0],
            [[np.float16, 3, [1024, 116, 14, 14]], [np.float16, 0, [116, 1, 3, 3]], 1, 1, 1, 0],
        ]
        self.conv_depthwise2d_backward_result(shape_format)

    def test_conv_depthwise2d_backward_shape_format_fp32(self, device="npu"):
        shape_format = [
            [[np.float32, 3, [256, 32, 112, 112]], [np.float32, 0, [32, 1, 3, 3]], 1, 1, 1, None],
            [[np.float32, 3, [256, 96, 112, 112]], [np.float32, 0, [96, 1, 3, 3]], 1, 2, 1, None],
        ]
        # conv类算子不支持fp32数据的精度要求


if __name__ == "__main__":
    run_tests()