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_poi_fused_abs_add_exp_log1p_mul_neg_relu_sub_sum_0(in_ptr0,
    in_ptr1, out_ptr0, 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')
    tmp3 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp24 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp33 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp35 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = tmp0 * tmp3
    tmp5 = tmp2 - tmp4
    tmp6 = tl_math.abs(tmp0)
    tmp7 = -tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp9 = libdevice.log1p(tmp8)
    tmp10 = tmp5 + tmp9
    tmp12 = triton_helpers.maximum(tmp1, tmp11)
    tmp14 = tmp11 * tmp13
    tmp15 = tmp12 - tmp14
    tmp16 = tl_math.abs(tmp11)
    tmp17 = -tmp16
    tmp18 = tl_math.exp(tmp17)
    tmp19 = libdevice.log1p(tmp18)
    tmp20 = tmp15 + tmp19
    tmp21 = tmp10 + tmp20
    tmp23 = triton_helpers.maximum(tmp1, tmp22)
    tmp25 = tmp22 * tmp24
    tmp26 = tmp23 - tmp25
    tmp27 = tl_math.abs(tmp22)
    tmp28 = -tmp27
    tmp29 = tl_math.exp(tmp28)
    tmp30 = libdevice.log1p(tmp29)
    tmp31 = tmp26 + tmp30
    tmp32 = tmp21 + tmp31
    tmp34 = triton_helpers.maximum(tmp1, tmp33)
    tmp36 = tmp33 * tmp35
    tmp37 = tmp34 - tmp36
    tmp38 = tl_math.abs(tmp33)
    tmp39 = -tmp38
    tmp40 = tl_math.exp(tmp39)
    tmp41 = libdevice.log1p(tmp40)
    tmp42 = tmp37 + tmp41
    tmp43 = tmp32 + tmp42
    tl.store(out_ptr0 + x0, tmp43, xmask)


@triton.jit
def triton_poi_fused_mean_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + x0, tmp8, 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)
        get_raw_stream(0)
        triton_poi_fused_abs_add_exp_log1p_mul_neg_relu_sub_sum_0[grid(64)](
            arg0_1, arg1_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_mean_1[grid(16)](buf0, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf0
    return buf1,


class TFBCELossNew(nn.Module):

    def __init__(self, pos_weight):
        super().__init__()
        self.pos_weight = pos_weight

    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: 0.027692