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_sum_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_per_fused_add_binary_cross_entropy_with_logits_div_mul_rsub_sum_1(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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)
    r2 = rindex
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp3 = tl.load(in_ptr1 + r2, None)
    tmp14 = tl.load(in_ptr2 + r1, None, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp2 * tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.minimum(tmp5, tmp3)
    tmp7 = tl_math.abs(tmp3)
    tmp8 = -tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = libdevice.log1p(tmp9)
    tmp11 = tmp6 - tmp10
    tmp12 = tmp4 - tmp11
    tmp13 = tmp0 * tmp12
    tmp15 = 0.0625
    tmp16 = tmp14 * tmp15
    tmp17 = tmp1 - tmp16
    tmp18 = tmp13 * tmp17
    tmp19 = tmp2 * tmp12
    tmp20 = tmp19 * tmp16
    tmp21 = tmp18 + tmp20
    tmp22 = tl.broadcast_to(tmp21, [RBLOCK])
    tmp24 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
    tmp25 = 0.015625
    tmp26 = tmp24 * tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp26, 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)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        get_raw_stream(0)
        triton_per_fused_sum_0[grid(16)](arg1_1, buf0, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused_add_binary_cross_entropy_with_logits_div_mul_rsub_sum_1[
            grid(1)](buf2, arg1_1, arg0_1, buf0, 1, 256, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        del buf0
    return buf2,


class MultiLabelSoftBinaryCrossEntropyNew(nn.Module):

    def __init__(self, smooth_factor: 'float'=0, weighted: 'bool'=True, mcb:
        'bool'=False, hp_lambda: 'int'=10, epsilon: 'float'=0.1, logits=
        True, first_class_bg=False):
        super(MultiLabelSoftBinaryCrossEntropyNew, self).__init__()
        self.smooth_factor = smooth_factor
        self.logits = logits
        if logits:
            self.criterion = nn.BCEWithLogitsLoss(reduction='none' if
                weighted else 'mean')
        else:
            self.criterion = nn.BCELoss(reduction='none' if weighted else
                'mean')
        self.weighted = weighted
        self.hp_lambda = hp_lambda
        self.MCB = mcb
        self.epsilon = epsilon
        self.first_class_bg = first_class_bg

    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: 0.027908