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 numpy as np
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_abs_add_div_log_lt_mean_mul_sub_where_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 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = 1.0
    tmp5 = tmp3 < tmp4
    tmp6 = 19.085536923187664
    tmp7 = tmp3 * tmp6
    tmp8 = tmp7 + tmp4
    tmp9 = 0.02619784824562798
    tmp10 = tmp8 * tmp9
    tmp11 = tmp7 * tmp4
    tmp12 = tmp11 + tmp4
    tmp13 = tl_math.log(tmp12)
    tmp14 = tmp10 * tmp13
    tmp15 = 0.5
    tmp16 = tmp3 * tmp15
    tmp17 = tmp14 - tmp16
    tmp18 = 1.5
    tmp19 = tmp3 * tmp18
    tmp20 = 0.07859354473688394
    tmp21 = tmp19 + tmp20
    tmp22 = tmp21 - tmp15
    tmp23 = tl.where(tmp5, tmp17, tmp22)
    tmp24 = tl.broadcast_to(tmp23, [RBLOCK])
    tmp26 = triton_helpers.promote_to_tensor(tl.sum(tmp24, 0))
    tmp27 = 256.0
    tmp28 = tmp26 / tmp27
    tmp29 = tmp28 * tmp4
    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_abs_add_div_log_lt_mean_mul_sub_where_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 balanced_l1_loss(pred, target, beta=1.0, alpha=0.5, gamma=1.5,
    reduction='mean'):
    """Calculate balanced L1 loss.

    Please see the `Libra R-CNN <https://arxiv.org/pdf/1904.02701.pdf>`_

    Args:
        pred (torch.Tensor): The prediction with shape (N, 4).
        target (torch.Tensor): The learning target of the prediction with
            shape (N, 4).
        beta (float): The loss is a piecewise function of prediction and target
            and ``beta`` serves as a threshold for the difference between the
            prediction and target. Defaults to 1.0.
        alpha (float): The denominator ``alpha`` in the balanced L1 loss.
            Defaults to 0.5.
        gamma (float): The ``gamma`` in the balanced L1 loss.
            Defaults to 1.5.
        reduction (str, optional): The method that reduces the loss to a
            scalar. Options are "none", "mean" and "sum".

    Returns:
        torch.Tensor: The calculated loss
    """
    assert beta > 0
    assert pred.size() == target.size() and target.numel() > 0
    diff = torch.abs(pred - target)
    b = np.e ** (gamma / alpha) - 1
    loss = torch.where(diff < beta, alpha / b * (b * diff + 1) * torch.log(
        b * diff / beta + 1) - alpha * diff, gamma * diff + gamma / b - 
        alpha * beta)
    return loss


class BalancedL1LossNew(nn.Module):
    """Balanced L1 Loss.

    arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019)

    Args:
        alpha (float): The denominator ``alpha`` in the balanced L1 loss.
            Defaults to 0.5.
        gamma (float): The ``gamma`` in the balanced L1 loss. Defaults to 1.5.
        beta (float, optional): The loss is a piecewise function of prediction
            and target. ``beta`` serves as a threshold for the difference
            between the prediction and target. Defaults to 1.0.
        reduction (str, optional): The method that reduces the loss to a
            scalar. Options are "none", "mean" and "sum".
        loss_weight (float, optional): The weight of the loss. Defaults to 1.0
    """

    def __init__(self, alpha=0.5, gamma=1.5, beta=1.0, reduction='mean',
        loss_weight=1.0):
        super(BalancedL1LossNew, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.beta = beta
        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: 0.01493