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 libdevice, math as tl_math
import numpy as np
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_poi_fused_abs_div_isnan_lt_mul_pow_sub_where_0(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = libdevice.isnan(tmp1).to(tl.int1)
    tmp3 = tl.where(tmp2, tmp0, tmp1)
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.abs(tmp4)
    tmp6 = 0.1111111111111111
    tmp7 = tmp5 < tmp6
    tmp8 = tmp5 * tmp5
    tmp9 = 0.5
    tmp10 = tmp8 * tmp9
    tmp11 = 9.0
    tmp12 = tmp10 * tmp11
    tmp13 = 0.05555555555555555
    tmp14 = tmp5 - tmp13
    tmp15 = tl.where(tmp7, tmp12, tmp14)
    tl.store(out_ptr0 + x0, tmp15, 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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_abs_div_isnan_lt_mul_pow_sub_where_0[grid(256)](arg1_1
            , arg0_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class WeightedSmoothL1LossNew(nn.Module):
    """
    Code-wise Weighted Smooth L1 Loss modified based on fvcore.nn.smooth_l1_loss
    https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/smooth_l1_loss.py
                  | 0.5 * x ** 2 / beta   if abs(x) < beta
    smoothl1(x) = |
                  | abs(x) - 0.5 * beta   otherwise,
    where x = input - target.
    """

    def __init__(self, beta: 'float'=1.0 / 9.0, code_weights: 'list'=None):
        """
        Args:
            beta: Scalar float.
                L1 to L2 change point.
                For beta values < 1e-5, L1 loss is computed.
            code_weights: (#codes) float list if not None.
                Code-wise weights.
        """
        super(WeightedSmoothL1LossNew, self).__init__()
        self.beta = beta
        if code_weights is not None:
            self.code_weights = np.array(code_weights, dtype=np.float32)
            self.code_weights = torch.from_numpy(self.code_weights)
        else:
            self.code_weights = None

    @staticmethod
    def smooth_l1_loss(diff, beta):
        if beta < 1e-05:
            loss = torch.abs(diff)
        else:
            n = torch.abs(diff)
            loss = torch.where(n < beta, 0.5 * n ** 2 / beta, n - 0.5 * beta)
        return loss

    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: 0.015325