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, math as tl_math
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_per_fused_gt_sum_0(in_ptr0, out_ptr0, out_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
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0))
    tmp4 = 0.0
    tmp5 = tmp0 > tmp4
    tl.store(out_ptr1 + tl.broadcast_to(r0, [RBLOCK]), tmp5, None)
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp3, None)


@triton.jit
def triton_poi_fused_abs_add_div_mul_sqrt_sub_1(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, 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 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = 0.0004
    tmp5 = tmp3 + tmp4
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = 0.02
    tmp8 = tmp6 - tmp7
    tmp9 = tmp2 / tmp6
    tmp10 = tl_math.abs(tmp9)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused_zeros_like_2(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 = 0.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


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((), (), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_per_fused_gt_sum_0[grid(1)](arg2_1, buf0, buf4, 1, 256,
            num_warps=2, num_stages=1)
        del arg2_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_abs_add_div_mul_sqrt_sub_1[grid(256)](arg0_1,
            arg1_1, buf1, buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_zeros_like_2[grid(256)](buf3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
    return buf0, buf1, buf2, buf3, buf4


class GHMRNew(nn.Module):
    """GHM Regression Loss.

    Details of the theorem can be viewed in the paper
    "Gradient Harmonized Single-stage Detector"
    https://arxiv.org/abs/1811.05181

    Args:
        mu (float): The parameter for the Authentic Smooth L1 loss.
        bins (int): Number of the unit regions for distribution calculation.
        momentum (float): The parameter for moving average.
        loss_weight (float): The weight of the total GHM-R loss.
    """

    def __init__(self, mu=0.02, bins=10, momentum=0, loss_weight=1.0):
        super(GHMRNew, self).__init__()
        self.mu = mu
        self.bins = bins
        self.edges = torch.arange(bins + 1).float() / bins
        self.edges[-1] = 1000.0
        self.momentum = momentum
        if momentum > 0:
            self.acc_sum = torch.zeros(bins)
        self.loss_weight = loss_weight

    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: 0.01481