import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data


class L1NormLoss(nn.Module):

    def __init__(self, loss_weight=0.0005, average=True):
        super(L1NormLoss, self).__init__()
        self.loss_weight = loss_weight
        self.average = average

    def forward(self, x1, x2, x3, length):
        loss_norm = (x1 + x2 + x3) / 3
        if self.average:
            loss_norm = loss_norm / length
        return self.loss_weight * loss_norm


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
        [4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_div_mul_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp3 = tl.load(in_ptr2 + x0, xmask)
    tmp7 = tl.load(in_ptr3 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.3333333333333333
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 / tmp7
    tmp9 = 0.0005
    tmp10 = tmp8 * tmp9
    tl.store(out_ptr0 + x0, tmp10, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_mul_0[grid(256)](arg0_1, arg1_1, arg2_1,
            arg3_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return buf0,


class L1NormLossNew(nn.Module):

    def __init__(self, loss_weight=0.0005, average=True):
        super(L1NormLossNew, self).__init__()
        self.loss_weight = loss_weight
        self.average = average

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]
