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_clamp_log_mean_mul_neg_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)
    tmp7 = tl.load(in_ptr1 + r0, None)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = 0.05
    tmp5 = tmp3 + tmp4
    tmp6 = triton_helpers.minimum(tmp5, tmp2)
    tmp8 = tmp2 - tmp7
    tmp9 = tmp6 * tmp8
    tmp10 = tmp1 * tmp7
    tmp11 = tmp9 + tmp10
    tmp12 = 1e-08
    tmp13 = triton_helpers.maximum(tmp11, tmp12)
    tmp14 = tl_math.log(tmp13)
    tmp15 = -tmp14
    tmp16 = tmp2 - tmp11
    tmp17 = 0.0
    tmp18 = tmp7 * tmp17
    tmp19 = 4.0
    tmp20 = tmp8 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = libdevice.pow(tmp16, tmp21)
    tmp23 = tmp15 * 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 * tmp2
    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_add_clamp_log_mean_mul_neg_pow_rsub_sigmoid_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): 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 asymmetric_loss(pred, target, weight=None, gamma_pos=1.0, gamma_neg=4.0,
    clip=0.05, reduction='mean', avg_factor=None, use_sigmoid=True, eps=1e-08):
    """asymmetric loss.

    Please refer to the `paper <https://arxiv.org/abs/2009.14119>`__ for
    details.

    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_pos (float): positive focusing parameter. Defaults to 0.0.
        gamma_neg (float): Negative focusing parameter. We usually set
            gamma_neg > gamma_pos. Defaults to 4.0.
        clip (float, optional): Probability margin. Defaults to 0.05.
        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.
        use_sigmoid (bool): Whether the prediction uses sigmoid instead
            of softmax. Defaults to True.
        eps (float): The minimum value of the argument of logarithm. Defaults
            to 1e-8.

    Returns:
        torch.Tensor: Loss.
    """
    assert pred.shape == target.shape, 'pred and target should be in the same shape.'
    if use_sigmoid:
        pred_sigmoid = pred.sigmoid()
    else:
        pred_sigmoid = nn.functional.softmax(pred, dim=-1)
    target = target.type_as(pred)
    if clip and clip > 0:
        pt = (1 - pred_sigmoid + clip).clamp(max=1) * (1 - target
            ) + pred_sigmoid * target
    else:
        pt = (1 - pred_sigmoid) * (1 - target) + pred_sigmoid * target
    asymmetric_weight = (1 - pt).pow(gamma_pos * target + gamma_neg * (1 -
        target))
    loss = -torch.log(pt.clamp(min=eps)) * asymmetric_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


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 = F.one_hot(targets.long().squeeze(-1), num_classes=classes
        )
    return one_hot_targets


class AsymmetricLossNew(nn.Module):
    """asymmetric loss.

    Args:
        gamma_pos (float): positive focusing parameter.
            Defaults to 0.0.
        gamma_neg (float): Negative focusing parameter. We
            usually set gamma_neg > gamma_pos. Defaults to 4.0.
        clip (float, optional): Probability margin. Defaults to 0.05.
        reduction (str): The method used to reduce the loss into
            a scalar.
        loss_weight (float): Weight of loss. Defaults to 1.0.
        use_sigmoid (bool): Whether the prediction uses sigmoid instead
            of softmax. Defaults to True.
        eps (float): The minimum value of the argument of logarithm. Defaults
            to 1e-8.
    """

    def __init__(self, gamma_pos=0.0, gamma_neg=4.0, clip=0.05, reduction=
        'mean', loss_weight=1.0, use_sigmoid=True, eps=1e-08):
        super(AsymmetricLossNew, self).__init__()
        self.gamma_pos = gamma_pos
        self.gamma_neg = gamma_neg
        self.clip = clip
        self.reduction = reduction
        self.loss_weight = loss_weight
        self.use_sigmoid = use_sigmoid
        self.eps = eps

    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: 0.015471