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
from torch import Tensor
from typing import List
from typing import Optional
from typing import Union
from torch import 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_binary_cross_entropy_with_logits_mean_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, 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 + 4 * r0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr1 + (3 + 4 * r0), 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
    tmp14 = tmp1 - tmp13
    tmp16 = tmp14 * tmp15
    tmp17 = triton_helpers.minimum(tmp5, tmp15)
    tmp18 = tl_math.abs(tmp15)
    tmp19 = -tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp21 = libdevice.log1p(tmp20)
    tmp22 = tmp17 - tmp21
    tmp23 = tmp16 - tmp22
    tmp24 = tmp12 + tmp23
    tmp26 = tmp1 - tmp25
    tmp28 = tmp26 * tmp27
    tmp29 = triton_helpers.minimum(tmp5, tmp27)
    tmp30 = tl_math.abs(tmp27)
    tmp31 = -tmp30
    tmp32 = tl_math.exp(tmp31)
    tmp33 = libdevice.log1p(tmp32)
    tmp34 = tmp29 - tmp33
    tmp35 = tmp28 - tmp34
    tmp36 = tmp24 + tmp35
    tmp38 = tmp1 - tmp37
    tmp40 = tmp38 * tmp39
    tmp41 = triton_helpers.minimum(tmp5, tmp39)
    tmp42 = tl_math.abs(tmp39)
    tmp43 = -tmp42
    tmp44 = tl_math.exp(tmp43)
    tmp45 = libdevice.log1p(tmp44)
    tmp46 = tmp41 - tmp45
    tmp47 = tmp40 - tmp46
    tmp48 = tmp36 + tmp47
    tmp49 = tl.broadcast_to(tmp48, [XBLOCK, RBLOCK])
    tmp51 = tl.sum(tmp49, 1)[:, None]
    tmp52 = 4.0
    tmp53 = tmp51 / tmp52
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp53, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_binary_cross_entropy_with_logits_mean_sum_0[grid(1)](
            buf2, arg1_1, arg0_1, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class LogitsInputsMixin:

    @classmethod
    def get_loss_inputs(cls):
        """Maps loss to the desired predicted input type."""
        return LOGITS


class SigmoidCrossEntropyLossNew(nn.Module, LogitsInputsMixin):

    def __init__(self, class_weights: 'Optional[Union[Tensor, List]]'=None,
        **kwargs):
        """
        Params:
            class_weights: List or 1D tensor of length equal to number of classes.
        """
        super().__init__()
        if class_weights:
            self.loss_fn = nn.BCEWithLogitsLoss(reduction='none',
                pos_weight=torch.Tensor(class_weights))
        else:
            self.loss_fn = nn.BCEWithLogitsLoss(reduction='none')

    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: 0.015367