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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
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_red_fused_div_mse_loss_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp34 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        r1 = rindex // 4
        tmp0 = tl.load(in_ptr0 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp1 = tl.load(in_ptr0 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp3 = tl.load(in_ptr0 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp6 = tl.load(in_ptr0 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp9 = tl.load(in_ptr0 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp16 = tl.load(in_ptr1 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp17 = tl.load(in_ptr1 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp19 = tl.load(in_ptr1 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp22 = tl.load(in_ptr1 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp25 = tl.load(in_ptr1 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp2 = tmp1 * tmp1
        tmp4 = tmp3 * tmp3
        tmp5 = tmp2 + tmp4
        tmp7 = tmp6 * tmp6
        tmp8 = tmp5 + tmp7
        tmp10 = tmp9 * tmp9
        tmp11 = tmp8 + tmp10
        tmp12 = libdevice.sqrt(tmp11)
        tmp13 = 1e-12
        tmp14 = triton_helpers.maximum(tmp12, tmp13)
        tmp15 = tmp0 / tmp14
        tmp18 = tmp17 * tmp17
        tmp20 = tmp19 * tmp19
        tmp21 = tmp18 + tmp20
        tmp23 = tmp22 * tmp22
        tmp24 = tmp21 + tmp23
        tmp26 = tmp25 * tmp25
        tmp27 = tmp24 + tmp26
        tmp28 = libdevice.sqrt(tmp27)
        tmp29 = triton_helpers.maximum(tmp28, tmp13)
        tmp30 = tmp16 / tmp29
        tmp31 = tmp15 - tmp30
        tmp32 = tmp31 * tmp31
        tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
        tmp35 = _tmp34 + tmp33
        _tmp34 = tl.where(rmask, tmp35, _tmp34)
    tmp34 = tl.sum(_tmp34, 1)[:, None]
    tmp36 = 256.0
    tmp37 = tmp34 / tmp36
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp37, 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_red_fused_div_mse_loss_0[grid(1)](buf2, arg0_1, arg1_1, 1, 
            256, XBLOCK=1, RBLOCK=256, num_warps=8, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class NormedMSENew(nn.MSELoss):

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


Measured latency_ms: 0.01559