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.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_abs_lt_mul_pow_sub_sum_where_0(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp31 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp32 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = 1.0
    tmp5 = tmp3 < tmp4
    tmp6 = tmp2 * tmp2
    tmp7 = 0.5
    tmp8 = tmp6 * tmp7
    tmp9 = tmp3 - tmp7
    tmp10 = tl.where(tmp5, tmp8, tmp9)
    tmp13 = tmp11 - tmp12
    tmp14 = tl_math.abs(tmp13)
    tmp15 = tmp14 < tmp4
    tmp16 = tmp13 * tmp13
    tmp17 = tmp16 * tmp7
    tmp18 = tmp14 - tmp7
    tmp19 = tl.where(tmp15, tmp17, tmp18)
    tmp20 = tmp10 + tmp19
    tmp23 = tmp21 - tmp22
    tmp24 = tl_math.abs(tmp23)
    tmp25 = tmp24 < tmp4
    tmp26 = tmp23 * tmp23
    tmp27 = tmp26 * tmp7
    tmp28 = tmp24 - tmp7
    tmp29 = tl.where(tmp25, tmp27, tmp28)
    tmp30 = tmp20 + tmp29
    tmp33 = tmp31 - tmp32
    tmp34 = tl_math.abs(tmp33)
    tmp35 = tmp34 < tmp4
    tmp36 = tmp33 * tmp33
    tmp37 = tmp36 * tmp7
    tmp38 = tmp34 - tmp7
    tmp39 = tl.where(tmp35, tmp37, tmp38)
    tmp40 = tmp30 + tmp39
    tmp41 = 0.1111111111111111
    tmp42 = tmp40 * tmp41
    tl.store(in_out_ptr0 + x0, tmp42, xmask)


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, 4, 4), (16, 4, 1), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_abs_lt_mul_pow_sub_sum_where_0[grid(64)](buf1,
            arg1_1, arg0_1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


def smooth_l1_loss(pred, target, weight, beta):
    val = target - pred
    abs_val = val.abs()
    smooth_mask = abs_val < beta
    return weight * torch.where(smooth_mask, 0.5 / beta * val ** 2, abs_val -
        0.5 * beta).sum(dim=-1)


class SmoothL1LossNew(torch.nn.Module):

    def __init__(self, beta=1.0 / 9):
        super(SmoothL1LossNew, self).__init__()
        self.beta = beta

    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: 0.015361