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 math as tl_math
import functools
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
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_eq_log_mean_mul_neg_pow_rsub_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)
    tmp9 = tl.load(in_ptr1 + r0, None)
    tmp1 = 1e-12
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.log(tmp2)
    tmp4 = -tmp3
    tmp5 = 1.0
    tmp6 = tmp5 - tmp0
    tmp7 = tmp6 * tmp6
    tmp8 = tmp4 * tmp7
    tmp10 = tmp9 == tmp5
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp8 * tmp11
    tmp13 = tmp6 + tmp1
    tmp14 = tl_math.log(tmp13)
    tmp15 = -tmp14
    tmp16 = tmp0 * tmp0
    tmp17 = tmp15 * tmp16
    tmp18 = tmp5 - tmp9
    tmp19 = tmp18 * tmp18
    tmp20 = tmp19 * tmp19
    tmp21 = tmp17 * tmp20
    tmp22 = tmp12 + tmp21
    tmp23 = tl.broadcast_to(tmp22, [RBLOCK])
    tmp25 = triton_helpers.promote_to_tensor(tl.sum(tmp23, 0))
    tmp26 = 256.0
    tmp27 = tmp25 / tmp26
    tmp28 = tmp27 * tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp28, 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_eq_log_mean_mul_neg_pow_rsub_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): 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 weighted_loss(loss_func):
    """Create a weighted version of a given loss function.

    To use this decorator, the loss function must have the signature like
    `loss_func(pred, target, **kwargs)`. The function only needs to compute
    element-wise loss without any reduction. This decorator will add weight
    and reduction arguments to the function. The decorated function will have
    the signature like `loss_func(pred, target, weight=None, reduction='mean',
    avg_factor=None, **kwargs)`.

    :Example:

    >>> import torch
    >>> @weighted_loss
    >>> def l1_loss(pred, target):
    >>>     return (pred - target).abs()

    >>> pred = torch.Tensor([0, 2, 3])
    >>> target = torch.Tensor([1, 1, 1])
    >>> weight = torch.Tensor([1, 0, 1])

    >>> l1_loss(pred, target)
    tensor(1.3333)
    >>> l1_loss(pred, target, weight)
    tensor(1.)
    >>> l1_loss(pred, target, reduction='none')
    tensor([1., 1., 2.])
    >>> l1_loss(pred, target, weight, avg_factor=2)
    tensor(1.5000)
    """

    @functools.wraps(loss_func)
    def wrapper(pred, target, weight=None, reduction='mean', avg_factor=
        None, **kwargs):
        loss = loss_func(pred, target, **kwargs)
        loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
        return loss
    return wrapper


@weighted_loss
def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0):
    """`Focal Loss <https://arxiv.org/abs/1708.02002>`_ for targets in gaussian
    distribution.

    Args:
        pred (torch.Tensor): The prediction.
        gaussian_target (torch.Tensor): The learning target of the prediction
            in gaussian distribution.
        alpha (float, optional): A balanced form for Focal Loss.
            Defaults to 2.0.
        gamma (float, optional): The gamma for calculating the modulating
            factor. Defaults to 4.0.
    """
    eps = 1e-12
    pos_weights = gaussian_target.eq(1)
    neg_weights = (1 - gaussian_target).pow(gamma)
    pos_loss = -(pred + eps).log() * (1 - pred).pow(alpha) * pos_weights
    neg_loss = -(1 - pred + eps).log() * pred.pow(alpha) * neg_weights
    return pos_loss + neg_loss


class GaussianFocalLossNew(nn.Module):
    """GaussianFocalLoss is a variant of focal loss.

    More details can be found in the `paper
    <https://arxiv.org/abs/1808.01244>`_
    Code is modified from `kp_utils.py
    <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/kp_utils.py#L152>`_  # noqa: E501
    Please notice that the target in GaussianFocalLoss is a gaussian heatmap,
    not 0/1 binary target.

    Args:
        alpha (float): Power of prediction.
        gamma (float): Power of target for negtive samples.
        reduction (str): Options are "none", "mean" and "sum".
        loss_weight (float): Loss weight of current loss.
    """

    def __init__(self, alpha=2.0, gamma=4.0, reduction='mean', loss_weight=1.0
        ):
        super(GaussianFocalLossNew, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        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: 0.014994