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 import triton_helpers
import torch.utils.data
from torch import 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_poi_fused_mean_pow_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp10 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp14 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp21 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
    tmp22 = tl.load(in_ptr1 + (4 + x0 + 64 * x1), xmask)
    tmp25 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
    tmp26 = tl.load(in_ptr1 + (20 + x0 + 64 * x1), xmask)
    tmp30 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
    tmp31 = tl.load(in_ptr1 + (36 + x0 + 64 * x1), xmask)
    tmp35 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
    tmp36 = tl.load(in_ptr1 + (52 + x0 + 64 * x1), xmask)
    tmp42 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask)
    tmp43 = tl.load(in_ptr1 + (8 + x0 + 64 * x1), xmask)
    tmp46 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask)
    tmp47 = tl.load(in_ptr1 + (24 + x0 + 64 * x1), xmask)
    tmp51 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask)
    tmp52 = tl.load(in_ptr1 + (40 + x0 + 64 * x1), xmask)
    tmp56 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask)
    tmp57 = tl.load(in_ptr1 + (56 + x0 + 64 * x1), xmask)
    tmp63 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask)
    tmp64 = tl.load(in_ptr1 + (12 + x0 + 64 * x1), xmask)
    tmp67 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask)
    tmp68 = tl.load(in_ptr1 + (28 + x0 + 64 * x1), xmask)
    tmp72 = tl.load(in_ptr0 + (44 + x0 + 64 * x1), xmask)
    tmp73 = tl.load(in_ptr1 + (44 + x0 + 64 * x1), xmask)
    tmp77 = tl.load(in_ptr0 + (60 + x0 + 64 * x1), xmask)
    tmp78 = tl.load(in_ptr1 + (60 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = 4.0
    tmp20 = tmp18 / tmp19
    tmp23 = tmp21 - tmp22
    tmp24 = tmp23 * tmp23
    tmp27 = tmp25 - tmp26
    tmp28 = tmp27 * tmp27
    tmp29 = tmp24 + tmp28
    tmp32 = tmp30 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tmp29 + tmp33
    tmp37 = tmp35 - tmp36
    tmp38 = tmp37 * tmp37
    tmp39 = tmp34 + tmp38
    tmp40 = tmp39 / tmp19
    tmp41 = tmp20 + tmp40
    tmp44 = tmp42 - tmp43
    tmp45 = tmp44 * tmp44
    tmp48 = tmp46 - tmp47
    tmp49 = tmp48 * tmp48
    tmp50 = tmp45 + tmp49
    tmp53 = tmp51 - tmp52
    tmp54 = tmp53 * tmp53
    tmp55 = tmp50 + tmp54
    tmp58 = tmp56 - tmp57
    tmp59 = tmp58 * tmp58
    tmp60 = tmp55 + tmp59
    tmp61 = tmp60 / tmp19
    tmp62 = tmp41 + tmp61
    tmp65 = tmp63 - tmp64
    tmp66 = tmp65 * tmp65
    tmp69 = tmp67 - tmp68
    tmp70 = tmp69 * tmp69
    tmp71 = tmp66 + tmp70
    tmp74 = tmp72 - tmp73
    tmp75 = tmp74 * tmp74
    tmp76 = tmp71 + tmp75
    tmp79 = tmp77 - tmp78
    tmp80 = tmp79 * tmp79
    tmp81 = tmp76 + tmp80
    tmp82 = tmp81 / tmp19
    tmp83 = tmp62 + tmp82
    tl.store(out_ptr0 + x2, tmp83, xmask)


@triton.jit
def triton_per_fused_div_mean_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + r2, None)
    tmp2 = tmp0 / tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = 256.0
    tmp7 = tmp5 / tmp6
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp7, None)


def call(args):
    arg0_1, arg1_1, arg2_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_pow_sub_sum_0[grid(16)](arg0_1, arg1_1, buf0,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused_div_mean_1[grid(1)](buf2, buf0, arg2_1, 1, 256,
            num_warps=2, num_stages=1)
        del arg2_1
        del buf0
    return buf2,


class MaskedMSELossNew(nn.Module):

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

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


Measured latency_ms: 0.028104