import torch
from torch._inductor.select_algorithm import extern_kernels
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 libdevice
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_div_mean_mul_pow_rsub_sqrt_sum_0(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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_out_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    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)
    tmp14 = tmp13 * tmp13
    tmp16 = tmp15 * tmp15
    tmp17 = tmp14 + tmp16
    tmp19 = tmp18 * tmp18
    tmp20 = tmp17 + tmp19
    tmp22 = tmp21 * tmp21
    tmp23 = tmp20 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = tmp12 * tmp24
    tmp26 = tmp0 / tmp25
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.sum(tmp27, 1)[:, None]
    tmp30 = 16.0
    tmp31 = tmp29 / tmp30
    tmp32 = 1.0
    tmp33 = tmp32 - tmp31
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp33, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg0_1, (16, 1, 4), (4, 4, 1),
            0), reinterpret_tensor(arg1_1, (16, 4, 1), (4, 1, 1), 0), out=buf0)
        buf1 = reinterpret_tensor(buf0, (4, 4), (4, 1), 0)
        del buf0
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        get_raw_stream(0)
        triton_per_fused_div_mean_mul_pow_rsub_sqrt_sum_0[grid(1)](buf1,
            buf3, arg0_1, arg1_1, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del buf1
    return buf3,


class NormalLossNew(nn.Module):

    def __init__(self):
        super(NormalLossNew, 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]


Measured latency_ms: 0.023578