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.functional as F
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_per_fused__to_copy_abs_add_binary_cross_entropy_with_logits_gt_le_mean_mul_pow_sigmoid_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)
    tmp3 = tl.load(in_ptr1 + r0, None)
    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 = tmp0 > tmp5
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp0 * tmp14
    tmp16 = tl.sigmoid(tmp3)
    tmp17 = tmp16 - tmp0
    tmp18 = tl_math.abs(tmp17)
    tmp19 = tmp18 * tmp18
    tmp20 = 0.75
    tmp21 = tmp19 * tmp20
    tmp22 = tmp0 <= tmp5
    tmp23 = tmp22.to(tl.float32)
    tmp24 = tmp21 * tmp23
    tmp25 = tmp15 + tmp24
    tmp26 = tmp12 * tmp25
    tmp27 = tl.broadcast_to(tmp26, [RBLOCK])
    tmp29 = triton_helpers.promote_to_tensor(tl.sum(tmp27, 0))
    tmp30 = 256.0
    tmp31 = tmp29 / tmp30
    tmp32 = tmp31 * tmp1
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp32, 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__to_copy_abs_add_binary_cross_entropy_with_logits_gt_le_mean_mul_pow_sigmoid_sub_0[
            grid(1)](buf1, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


def reduce_loss(loss, reduction):
    """Reduce loss as specified.

    Args:
        loss (Tensor): Elementwise loss tensor.
        reduction (str): Options are "none", "mean" and "sum".

    Return:
        Tensor: Reduced loss tensor.
    """
    reduction_enum = F._Reduction.get_enum(reduction)
    if reduction_enum == 0:
        return loss
    elif reduction_enum == 1:
        return loss.mean()
    elif reduction_enum == 2:
        return loss.sum()


def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
    """Apply element-wise weight and reduce loss.

    Args:
        loss (Tensor): Element-wise loss.
        weight (Tensor): Element-wise weights.
        reduction (str): Same as built-in losses of PyTorch.
        avg_factor (float): Avarage factor when computing the mean of losses.

    Returns:
        Tensor: Processed loss values.
    """
    if weight is not None:
        loss = loss * weight
    if avg_factor is None:
        loss = reduce_loss(loss, reduction)
    elif reduction == 'mean':
        loss = loss.sum() / avg_factor
    elif reduction != 'none':
        raise ValueError('avg_factor can not be used with reduction="sum"')
    return loss


def varifocal_loss(pred, target, weight=None, alpha=0.75, gamma=2.0,
    iou_weighted=True, reduction='mean', avg_factor=None):
    """`Varifocal Loss <https://arxiv.org/abs/2008.13367>`_

    Args:
        pred (torch.Tensor): The prediction with shape (N, C), C is the
            number of classes
        target (torch.Tensor): The learning target of the iou-aware
            classification score with shape (N, C), C is the number of classes.
        weight (torch.Tensor, optional): The weight of loss for each
            prediction. Defaults to None.
        alpha (float, optional): A balance factor for the negative part of
            Varifocal Loss, which is different from the alpha of Focal Loss.
            Defaults to 0.75.
        gamma (float, optional): The gamma for calculating the modulating
            factor. Defaults to 2.0.
        iou_weighted (bool, optional): Whether to weight the loss of the
            positive example with the iou target. Defaults to True.
        reduction (str, optional): The method used to reduce the loss into
            a scalar. Defaults to 'mean'. Options are "none", "mean" and
            "sum".
        avg_factor (int, optional): Average factor that is used to average
            the loss. Defaults to None.
    """
    assert pred.size() == target.size()
    pred_sigmoid = pred.sigmoid()
    target = target.type_as(pred)
    if iou_weighted:
        focal_weight = target * (target > 0.0).float() + alpha * (pred_sigmoid
             - target).abs().pow(gamma) * (target <= 0.0).float()
    else:
        focal_weight = (target > 0.0).float() + alpha * (pred_sigmoid - target
            ).abs().pow(gamma) * (target <= 0.0).float()
    loss = F.binary_cross_entropy_with_logits(pred, target, reduction='none'
        ) * focal_weight
    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
    return loss


class VarifocalLossNew(nn.Module):

    def __init__(self, use_sigmoid=True, alpha=0.75, gamma=2.0,
        iou_weighted=True, reduction='mean', loss_weight=1.0):
        """`Varifocal Loss <https://arxiv.org/abs/2008.13367>`_

        Args:
            use_sigmoid (bool, optional): Whether the prediction is
                used for sigmoid or softmax. Defaults to True.
            alpha (float, optional): A balance factor for the negative part of
                Varifocal Loss, which is different from the alpha of Focal
                Loss. Defaults to 0.75.
            gamma (float, optional): The gamma for calculating the modulating
                factor. Defaults to 2.0.
            iou_weighted (bool, optional): Whether to weight the loss of the
                positive examples with the iou target. Defaults to True.
            reduction (str, optional): The method used to reduce the loss into
                a scalar. Defaults to 'mean'. Options are "none", "mean" and
                "sum".
            loss_weight (float, optional): Weight of loss. Defaults to 1.0.
        """
        super(VarifocalLossNew, self).__init__()
        assert use_sigmoid is True, 'Only sigmoid varifocal loss supported now.'
        assert alpha >= 0.0
        self.use_sigmoid = use_sigmoid
        self.alpha = alpha
        self.gamma = gamma
        self.iou_weighted = iou_weighted
        self.reduction = reduction
        self.loss_weight = loss_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: 0.015041