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 torch.nn import functional as F
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_add_div_log_sigmoid_forward_mean_mul_neg_rsub_sub_0(
    in_out_ptr0, in_ptr0, in_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.load(in_ptr1 + r0, None)
    tmp2 = 2.0
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp3 - tmp4
    tmp6 = tmp0 * tmp5
    tmp7 = tmp6 * tmp2
    tmp8 = 0.0
    tmp9 = triton_helpers.minimum(tmp8, tmp7)
    tmp10 = tl_math.abs(tmp7)
    tmp11 = -tmp10
    tmp12 = tl_math.exp(tmp11)
    tmp13 = libdevice.log1p(tmp12)
    tmp14 = tmp9 - tmp13
    tmp15 = -tmp14
    tmp16 = 0.5
    tmp17 = tmp15 * tmp16
    tmp18 = 0.25
    tmp19 = tmp1 * tmp18
    tmp20 = tmp4 - tmp1
    tmp21 = 0.75
    tmp22 = tmp20 * tmp21
    tmp23 = tmp19 + tmp22
    tmp24 = tmp17 * tmp23
    tmp25 = tl.broadcast_to(tmp24, [RBLOCK])
    tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0))
    tmp28 = 256.0
    tmp29 = tmp27 / tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp29, 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 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_div_log_sigmoid_forward_mean_mul_neg_rsub_sub_0[
            grid(1)](buf1, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


def sigmoid_focal_loss_star(inputs: 'torch.Tensor', targets: 'torch.Tensor',
    alpha: 'float'=-1, gamma: 'float'=1, reduction: 'str'='none'
    ) ->torch.Tensor:
    """
    FL* described in RetinaNet paper Appendix: https://arxiv.org/abs/1708.02002.
    Args:
        inputs: A float tensor of arbitrary shape.
                The predictions for each example.
        targets: A float tensor with the same shape as inputs. Stores the binary
                 classification label for each element in inputs
                (0 for the negative class and 1 for the positive class).
        alpha: (optional) Weighting factor in range (0,1) to balance
                positive vs negative examples. Default = -1 (no weighting).
        gamma: Gamma parameter described in FL*. Default = 1 (no weighting).
        reduction: 'none' | 'mean' | 'sum'
                 'none': No reduction will be applied to the output.
                 'mean': The output will be averaged.
                 'sum': The output will be summed.
    Returns:
        Loss tensor with the reduction option applied.
    """
    shifted_inputs = gamma * (inputs * (2 * targets - 1))
    loss = -F.logsigmoid(shifted_inputs) / gamma
    if alpha >= 0:
        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
        loss *= alpha_t
    if reduction == 'mean':
        loss = loss.mean()
    elif reduction == 'sum':
        loss = loss.sum()
    return loss


class SigmoidFocalLossStarNew(nn.Module):

    def __init__(self, alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
        'str'='mean'):
        super().__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.reduction = reduction

    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: 0.015008