import torch
import torch.nn as nn


class MAE(nn.Module):

    def __init__(self):
        super(MAE, self).__init__()

    def forward(self, outputs, target, *args):
        val_pixels = (target > 0).float() * (outputs > 0).float()
        err = torch.abs(target * val_pixels - outputs * val_pixels)
        loss = torch.sum(err.view(err.size(0), 1, -1), -1, keepdim=True)
        cnt = torch.sum(val_pixels.view(val_pixels.size(0), 1, -1), -1,
            keepdim=True)
        return torch.mean(loss / cnt) * 1000


def get_inputs():
    return [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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp4 = tl.load(in_ptr1 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp4 > tmp1
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp3 * tmp6
    tmp8 = tmp0 * tmp7
    tmp9 = tmp4 * tmp7
    tmp10 = tmp8 - tmp9
    tmp11 = tl_math.abs(tmp10)
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tmp16 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp18 = tl.where(xmask, tmp16, 0)
    tmp19 = tl.sum(tmp18, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp15, xmask)
    tl.store(out_ptr1 + x0, tmp19, xmask)


@triton.jit
def triton_per_fused_div_mean_mul_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 / tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.sum(tmp3, 1)[:, None]
    tmp6 = 4.0
    tmp7 = tmp5 / tmp6
    tmp8 = 1000.0
    tmp9 = tmp7 * tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp9, None)


def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        buf1 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        get_raw_stream(0)
        triton_per_fused_sum_0[grid(4)](arg0_1, arg1_1, buf0, buf1, 4, 64,
            XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused_div_mean_mul_1[grid(1)](buf3, buf0, buf1, 1, 4,
            XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
    return buf3,


class MAENew(nn.Module):

    def __init__(self):
        super(MAENew, self).__init__()

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