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
import torch.nn.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_binary_cross_entropy_with_logits_mean_mul_pow_rsub_sigmoid_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 = 0.25
    tmp14 = tmp0 * tmp13
    tmp15 = 0.75
    tmp16 = tmp2 * tmp15
    tmp17 = tmp14 + tmp16
    tmp18 = tl.sigmoid(tmp3)
    tmp19 = tmp1 - tmp18
    tmp20 = tmp19 * tmp0
    tmp21 = tmp18 * tmp2
    tmp22 = tmp20 + tmp21
    tmp23 = tmp22 * tmp22
    tmp24 = tmp17 * tmp23
    tmp25 = tmp12 * tmp24
    tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
    tmp28 = triton_helpers.promote_to_tensor(tl.sum(tmp26, 0))
    tmp29 = 256.0
    tmp30 = tmp28 / tmp29
    tmp31 = tmp30 * tmp1
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp31, 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_binary_cross_entropy_with_logits_mean_mul_pow_rsub_sigmoid_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 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): Average 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 convert_to_one_hot(targets: 'torch.Tensor', classes) ->torch.Tensor:
    """This function converts target class indices to one-hot vectors, given
    the number of classes.

    Args:
        targets (Tensor): The ground truth label of the prediction
                with shape (N, 1)
        classes (int): the number of classes.

    Returns:
        Tensor: Processed loss values.
    """
    assert torch.max(targets).item(
        ) < classes, 'Class Index must be less than number of classes'
    one_hot_targets = torch.zeros((targets.shape[0], classes), dtype=torch.
        long, device=targets.device)
    one_hot_targets.scatter_(1, targets.long(), 1)
    return one_hot_targets


def sigmoid_focal_loss(pred, target, weight=None, gamma=2.0, alpha=0.25,
    reduction='mean', avg_factor=None):
    """Sigmoid focal loss.

    Args:
        pred (torch.Tensor): The prediction with shape (N, \\*).
        target (torch.Tensor): The ground truth label of the prediction with
            shape (N, \\*).
        weight (torch.Tensor, optional): Sample-wise loss weight with shape
            (N, ). Defaults to None.
        gamma (float): The gamma for calculating the modulating factor.
            Defaults to 2.0.
        alpha (float): A balanced form for Focal Loss. Defaults to 0.25.
        reduction (str): The method used to reduce the loss.
            Options are "none", "mean" and "sum". If reduction is 'none' ,
            loss is same shape as pred and label. Defaults to 'mean'.
        avg_factor (int, optional): Average factor that is used to average
            the loss. Defaults to None.

    Returns:
        torch.Tensor: Loss.
    """
    assert pred.shape == target.shape, 'pred and target should be in the same shape.'
    pred_sigmoid = pred.sigmoid()
    target = target.type_as(pred)
    pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
    focal_weight = (alpha * target + (1 - alpha) * (1 - target)) * pt.pow(gamma
        )
    loss = F.binary_cross_entropy_with_logits(pred, target, reduction='none'
        ) * focal_weight
    if weight is not None:
        assert weight.dim() == 1
        weight = weight.float()
        if pred.dim() > 1:
            weight = weight.reshape(-1, 1)
    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
    return loss


class FocalLossNew(nn.Module):
    """Focal loss.

    Args:
        gamma (float): Focusing parameter in focal loss.
            Defaults to 2.0.
        alpha (float): The parameter in balanced form of focal
            loss. Defaults to 0.25.
        reduction (str): The method used to reduce the loss into
            a scalar. Options are "none" and "mean". Defaults to 'mean'.
        loss_weight (float): Weight of loss. Defaults to 1.0.
    """

    def __init__(self, gamma=2.0, alpha=0.25, reduction='mean', loss_weight=1.0
        ):
        super(FocalLossNew, self).__init__()
        self.gamma = gamma
        self.alpha = alpha
        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: 0.014948