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
from typing import Any
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_rsub_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 = 1.0
    tmp5 = tmp4 - tmp0
    tmp6 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp8 = triton_helpers.promote_to_tensor(tl.sum(tmp6, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp3, None)
    tl.store(out_ptr1 + tl.full([1], 0, tl.int32), tmp8, None)


@triton.jit
def triton_per_fused_add_binary_cross_entropy_with_logits_div_mean_mul_1(
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    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 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp6 = tl.load(in_ptr1 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp11 = tl.load(in_ptr2 + (r1 + 64 * x0), xmask, other=0.0)
    tmp13 = tl.load(in_ptr3 + (r1 + 64 * x0), xmask, other=0.0)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 1e-10
    tmp5 = tmp3 + tmp4
    tmp8 = tmp7 * tmp2
    tmp9 = tmp5 + tmp8
    tmp10 = tmp5 / tmp9
    tmp12 = tmp2 - tmp11
    tmp14 = tmp12 * tmp13
    tmp15 = tmp8 / tmp5
    tmp16 = tmp15 - tmp2
    tmp17 = tmp16 * tmp11
    tmp18 = tmp17 + tmp2
    tmp19 = 0.0
    tmp20 = triton_helpers.minimum(tmp19, tmp13)
    tmp21 = tl_math.abs(tmp13)
    tmp22 = -tmp21
    tmp23 = tl_math.exp(tmp22)
    tmp24 = libdevice.log1p(tmp23)
    tmp25 = tmp20 - tmp24
    tmp26 = tmp18 * tmp25
    tmp27 = tmp14 - tmp26
    tmp28 = tmp10 * tmp27
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
    tmp31 = tl.where(xmask, tmp29, 0)
    tmp32 = tl.sum(tmp31, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp32, xmask)


@triton.jit
def triton_per_fused_add_binary_cross_entropy_with_logits_div_mean_mul_2(
    in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = 64.0
    tmp2 = tmp0 / tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.sum(tmp3, 1)[:, None]
    tmp6 = 4.0
    tmp7 = tmp5 / tmp6
    tmp8 = 1.0
    tmp9 = tmp7 * tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp9, 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((), (), torch.float32)
        buf1 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_rsub_sum_0[grid(1)](arg1_1, buf0, buf1, 1, 256,
            num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_add_binary_cross_entropy_with_logits_div_mean_mul_1[
            grid(4)](buf0, buf1, arg1_1, arg0_1, buf2, 4, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del buf0
        buf3 = buf1
        del buf1
        buf4 = buf3
        del buf3
        triton_per_fused_add_binary_cross_entropy_with_logits_div_mean_mul_2[
            grid(1)](buf4, buf2, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf2
    return buf4,


class BalancedBinaryCrossEntropyWithLogitsNew(nn.Module):
    """二分类加权交叉熵"""

    def __init__(self, reduction: 'str'='mean', class_weight: 'Any'=None,
        loss_weight: 'float'=1.0, activation: 'bool'=False, eposion:
        'float'=1e-10) ->None:
        super(BalancedBinaryCrossEntropyWithLogitsNew, self).__init__()
        self.reduction = reduction
        self.class_weight = class_weight
        self.loss_weight = loss_weight
        self.eps = eposion

    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: 0.032005