import torch
import torch.nn.functional as F
import torch.nn as nn


class SigmoidFocalLoss(nn.Module):

    def __init__(self, alpha: 'float'=-1, gamma: 'float'=2, reduction:
        'str'='none'):
        super(SigmoidFocalLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.reduction = reduction

    def forward(self, input, target):
        p = torch.sigmoid(input)
        ce_loss = F.binary_cross_entropy_with_logits(input, target,
            reduction='none')
        p_t = p * target + (1 - p) * (1 - target)
        loss = ce_loss * (1 - p_t) ** self.gamma
        if self.alpha >= 0:
            alpha_t = self.alpha * target + (1 - self.alpha) * (1 - target)
            loss = alpha_t * loss
        if self.reduction == 'mean':
            loss = loss.mean()
        elif self.reduction == 'sum':
            loss = loss.sum()
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

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_add_binary_cross_entropy_with_logits_mul_pow_rsub_sigmoid_0(
    in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask)
    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 = tl.sigmoid(tmp3)
    tmp14 = tmp13 * tmp0
    tmp15 = tmp1 - tmp13
    tmp16 = tmp15 * tmp2
    tmp17 = tmp14 + tmp16
    tmp18 = tmp1 - tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tmp12 * tmp19
    tl.store(out_ptr0 + x0, tmp20, 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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_binary_cross_entropy_with_logits_mul_pow_rsub_sigmoid_0[
            grid(256)](arg1_1, arg0_1, buf0, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class SigmoidFocalLossNew(nn.Module):

    def __init__(self, alpha: 'float'=-1, gamma: 'float'=2, reduction:
        'str'='none'):
        super(SigmoidFocalLossNew, self).__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]
